Abstracts 3 rd Biennial Conference on Resting State Brain Connectivity September 5–7,2012 Magdeburg,Germany

1 University of Pittsburgh, Psychiatry, Pittsburgh, United States 2 University of Pittsburgh, Psychology, Pittsburgh, United States 0001 The Nuisance of Nuisance Regression: Spectral Misspecification Obscures Functional Connectivity Estimates

Question: Recent resting state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates. This study explored optimal approaches for removing nuisance signals such as motion estimates from RS-fcMRI data.

Methods: Participants were 117 individuals, ages 10-26, who completed a five-minute resting state fMRI scan. We used frequency domain analyses and linear mixed models to assess the effects of nuisance regression (motion parameters and non-neural noise sources) on connectivity estimates among 244 brain regions when regression was performed before, after, or concomitant with, bandpass filtering.

Results: The conventional approach to seed-based correlational analysis of RS-fcMRI data, where nuisance regression is performed after bandpass filtering, is a misspecified form of band-spectrum regression that resulted in poor removal of low-frequency nuisance variation and the systematic induction of high-frequency nuisance-related fluctuation into RS-fcMRI time series (Fig 1). Relative to a simultaneous bandpass filter and nuisance regression transformation, the conventional bandpass-regress approach consistently overestimated functional connectivity across the brain, typically on the order of r=.10-.35 (Fig 2). Particularly affected were connections involving regions near the center of the brain, such as the thalamus and posterior cingulate cortex (Fig 3), and the parcellation of the brain into functional networks differed when proper signal processing was applied. Inflated correlations under the bandpass-regress approach reflected motion- and cardiac-related variability reintroduced outside of the filter passband.

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FIG. 1.

Cross-spectral power between fMRI BOLD time series and 18 nuisance regressors, estimated separately for each bandpass filtering and nuisance regression sequence.

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FIG. 2.

Frequency histograms of connectivity estimate changes between the Bandpass-Regress and Simultaneous sequences by connection and subject.

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FIG. 3.

The spatial distribution of correlation changes between the Bandpass-Regress and Simultaneous sequences.

Conclusions: The conventional RS-fcMRI signal processing approach under-corrects nuisance variation in the frequencies of interest (.009-.08 Hz) and reintroduces nuisance-related variability at other frequencies. A simple fix, simultaneous bandpass filtering and nuisance regression, corrects the misspecified model and improves estimates of connectivity. We hope that the marked changes in functional connectivity estimates shown here will stimulate the RS-fcMRI field, particularly seed-based correlation research, to re-examine its findings.

1 University of Liège, Coma Science Group, Cyclotron Research Center & Neurology Department, Liège, Belgium 2 University of Liège, CHU Radiology department, Liège, Belgium 3 University of Liège, CHU Anesthesiology department, Liège, Belgium 0003 Global breakdown of fMRI resting state network connectivity in patients with disorders of consciousness

Question: fMRI functional connectivity studies in resting conditions (i.e., eyes closed, no task performance) do not require sophisticated experimental setup and surpass the need for subjects' active participation. Therefore, the resting state paradigm is a suitable means to study residual brain function in non-communicative patients with disorders of consciousness, such as coma, “vegetative state”/unresponsive wakefulness syndrome and minimally conscious state. We here aimed to assess fMRI connectivity in multiple cerebral networks in resting conditions, including the default mode and its anticorrelated network, the left and right frontoparietal, salience, sensorimotor, auditory and visual networks. As the issue of pain in patients with disorders of consciousness raise medical and ethical concerns, we further aimed to regress clinical “pain” scales scores (i.e., Nociception Coma Scale) with the functional integrity of the salience network.

Methods: Three hundred fMRI scans were obtained in 22 healthy volunteers, 2 locked-in, 11 minimally conscious, 12 “vegetative”/unresponsive and 5 comatose patients (11 women; mean age: 52±17 years; 15 of non-traumatic, 7 of anoxic etiology). Functional connectivity was investigated with a seed region correlational approach on a priori coordinates for each network.

Results: Between-group comparisons showed both intra- and inter-network consciousness-level dependent decreases in functional connectivity, ranging from healthy controls and locked-in syndrome to minimally conscious, “vegetative”/unresponsive and coma patients. A disruption in crossmodal interaction between visual and auditory cortices as a function of the level of consciousness was further observed (Fig 1). “Vegetative”/unresponsive and minimally conscious patients' Nociception Coma Scale scores showed a positive correlation with the salience network functional connectivity (Fig 2).

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FIG. 1.

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FIG. 2.

Conclusions: Our results demonstrate a global breakdown in cortico-cortical connectivity in sensory and sensorimotor networks as well as “higher-order” networks, possibly accounting for patients' limited capacities for conscious cognition. The observed positive correlation between the Nociception Coma Scale scores and the salience network connectivity potentially reflects nociception-related processes in these patients measured in the absence of an external stimulus. Our results point to the utility of resting state analyses in clinical settings where short and simple setups are preferable to activation protocols with auditory visual or somatosensory stimulation devices.

1 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany 0004 Non-negative matrix factorization of fMRI data using spectral coherence

Introduction: Factorization into independent components (ICA) has become a standard procedure in fMRI data analysis. Here we present an alternative factorization using non-negative matrix factorization (NMF). NMF can be seen as a form of blind source separation with non-negativity constraints. In contrast to ICA, the components obtained by NMF are not designed to be independent and may potentially overlap which may provide greater realism. It also allows various metrics for defining similarity. NM factorizations are generated by an iterative process and the resulting factorizations are not necessarily unique. Here, we apply NMF to matrices containing pairwise similarities between fMRI time courses with similarity defined by spectral coherence. We hypothesized that different spectral bands may lead to distinct decompositions. To test this hypothesis we applied NMF using a range of different starting values. Non-robust results would falsify this hypothesis.

Methods and Results: Functional resting state MRI/EPI data were acquired of 22 normal volunteers on a 3T MRI scanner (Siemens Trio) using TR=2.3 sec, TE=30ms, 3×3 mm² in-plane resolution, 3 mm slice thickness, 1mm gap between slices. Data were acquired for 6.5 minutes during which subjects were asked to fixate on a fixation point. All data sets were initially registered to an AC/CP coordinate system where the data were resampled to an isotropic voxel grid with a resolution of (3 mm)³. We manually defined a mask containing about 40,000 voxels covering the entire cerebrum. We then computed a similarity matrix V containing spectral coherence at 0.08 Hz and 0.04 Hz between fMRI time series computed pairwise within this mask and averaged across the 22 subjects. These two matrices were factorized using NMF so that V=W H+e, where W is a matrix of 6 basis vectors, H is a matrix of weights and e residual errors. We used the ALS algorithm with 15 different starting values. We analyzed the variation across different starting values and differences between the spectral bands. Results are shown in the figures.

Conclusion: In contrast to ICA, NMF allows investigation of a range of metrics defining components and allows component overlap. NMF may thus prove to be a valuable alternative. As hypothesized, we found striking differences between spectral bands. The standard error across repetitions was small, indicating that consistent results can be obtained.

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FIG. 1.

All 6 NMF components averaged over 15 starting values using spectral coherence at 0.08 Hz.

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FIG. 2.

Component C with 1.96 times the standard error added and subtracted.

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FIG. 3.

Analogous to figure 2 with spectral coherence at 0.04 Hz.

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FIG. 4.

Difference of the means of component C at 0.04Hz versus 0.08 Hz with maximum in putamen. Similar results were also found in other components.

1 VU medical center, Nuclear Medicine & PET Research, Amsterdam, Netherlands 2 CITA-Alzheimer Foundation, Radiology, San Sebastian, Spain 3 VU medical center, Radiology, Amsterdam, Netherlands 4 VU medical center, Neurology, Amsterdam, Netherlands 5 Leiden University, Institute for Brain and Cognition, Leiden, Netherlands 6 Leiden University, Institute of Psychology, Leiden, Netherlands, ⁷LUMC, Radiology, Leiden, Netherlands 0006 Amyloid and its association with default network integrity in Alzheimer's disease

The purpose of this study was to investigate the association between functional connectivity and amyloid depositions in the default mode network (DMN) in Alzheimer's disease (AD), patients with Mild Cognitive Impairment (MCI) and healthy elderly.

Dynamic, 90 minutes [¹¹C]PIB scans, and resting-state fMRI scans were obtained in twenty-six AD patients, twelve MCI patients and eighteen healthy controls. For [¹¹C]PIB, parametric images of binding potential (BP_ND) were generated using a basis function method implementation of the simplified reference tissue model. To identify the DMN, Independent Component Analysis (ICA) was performed. Next, dual-regression (back-projection) analysis was done in order to yield individual maps corresponding to the DMN.

A negative association was found between functional connectivity in the DMN and amyloid deposition within the DMN across all subjects, but not within diagnostic groups. MCI patients that converted to AD after one year follow-up showed DMN functional connectivity as well as amyloid burden in the range of AD patients at baseline.

No direct association was found between functional connectivity of the DMN and amyloid depositions within diagnostic groups. Longitudinal studies are needed to examine if amyloid depositions precede aberrant functional connectivity in the DMN.

1 NIH, Bethesda, United States 0007 EEG correlates of non-stationary BOLD functional connectivity

Recent work has shown that BOLD functional connectivity (FC) may undergo substantial changes across a several-minute resting-state scan. Yet, the origin and relevance of these more rapid shifts in network connectivity is unclear; while it may reflect neuronal dynamics and brain state, it may to some degree result from physiological noise or random fluctuation. Here, we examine the electrical correlates of within-scan FC variations during eyes-closed rest. Using a sliding window analysis of simultaneous EEG-fMRI data, we ask whether temporal variations in coupling between three major networks (default mode; DMN, dorsal attention; DAN, and salience network; SN) are associated with temporal variations in the amplitude of posterior alpha and frontal theta oscillations. We thus regard EEG power as a state variable with which to query the cognitive and electrical dependence of nonstationary functional connectivity.

Ten subjects underwent EEG-fMRI at 3T (eyes-closed rest, durations from 9.75-12.24 min). Network regions were defined from a functional atlas[1], and an aggregate measure of pairwise correlation between networks was computed in temporal sliding windows (width 40s, overlap 50%) across each scan. Time courses of EEG power in posterior alpha and frontal theta bands were computed in identical sliding windows, and were regressed against the sliding-window FC between each network pair, accounting for temporal autocorrelation.

Alpha power was inversely related to connectivity between DMN and DAN (t(8)=−6.37, p=0.0002, p<0.001 Bonferroni); the theta term did not reach significance, but tended to show an opposite effect (Figs 1, 2). The alpha term remained significant at p<0.05 corrected over window sizes of 30-60s. Network-level SN findings were not significant, but the thalamus and anterior insula nodes of SN showed alpha-dependent increased coupling (p<0.007 unc). We also formed an index of spatial anticorrelation (% negative values in the matrix of voxelwise correlations between networks), observing that alpha power also predicted increased anticorrelation between DMN-DAN (p=0.009), while theta was inversely related (p=0.049). Results suggest an electrical signature of the time-varying FC between the DAN and DMN, with potential contributions from neural and state-dependent variations.

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FIG. 1.

Beta weights (mean±SE across subjects, N=10) of the multiple regression of EEG alpha and theta band power against the functional connectivity between nodes of the default mode, dorsal attention, and salience networks.

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FIG. 2.

Time course of DMN-DAN functional connectivity predicted from a linear combination of EEG alpha and theta bands (red), superimposed on the actual time course of DMN-DAN functional connectivity, for each subject. Window size = 40s, overlap 50%.

1 Child Mind Institute, New York, United States 2 Nathan Kline Institute for Psychiatric Research, Orangeburg, United States 3 Virginia Tech, School of Biomedical Engineering and Sciences, Blacksburg, United States 4 Virginia Tech Carilion Research Institute, Roanoke, United States 0010 Bibliometric Analysis of Resting State Literature

Introduction: The CMI Librarian resting state (RS) database is an invaluable tool for assimilating RS research. We used the database to perform a quantitative analysis of patterns within RS literature (bibliometric analysis) to glean publication trends, common experimental methods, and popular research topics.

Methods: The CMI Librarian provides a curated database of 1,150 RS publications. PubMed IDs were used to retrieve records for each paper [1]. Article title, abstract, and keywords were analyzed to identify trends in the literature. Word frequency analysis was performed on CMI Librarian tags, as well as terms appearing in the title and abstract from neuroimaging methods, cognitive ontology [2, 3], and the PubBrain lexicon [4].

Results: Growth of RS literature is piecewise exponential with 33% growth prior to 2005 and 47% after (Fig 1 bottom). In comparison, growth of fMRI literature was 29% before 2005 and 18% after (Fig 1 top). 27% of RS literature was published in Neuroimage and HBM (Fig 2). The most frequent imaging modality is fMRI (Fig 3A). Similar volumes of literature are dedicated to basic neuroscience (434) and clinical applications (422) (Fig 3C). The PFC, PCC, and ACC are the most discussed brain regions (Fig 3D). The most investigated cognitive domain is attention (Fig 3B). 26% of RS publications are open access, compared to 22% in all of fMRI.

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FIG. 1.

fMRI (top) and RS literature (bottom) paper volume by year with exponential fits overlaid. Growth rates differ before (maroon) and after (orange) 2005.

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FIG. 2.

Top 20 publication outlets for RS.

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FIG. 3.

Word cloud analyses: font size corresponds to frequency of neuroimaging (A), Cognitive Atlas (B), CMI Librarian tags (C) and PubBrain (D) terms.

Conclusion: Our bibliometric analysis of RS literature lends valuable insight into the current state of the field, demonstrating its strength, areas of focus, and future potential. The growth of RS literature is currently faster than fMRI, the most common imaging modality in RS research. The analysis identified a focus on PFC, responsible for executive function, as well as the PCC and ACC, which are central nodes of the DMN. Attention is the most discussed cognitive domain, reflecting a current research trend. Analysis of open access showed that it is not universal in resting state or fMRI, but has a strong foothold.

1 Otto von Guericke University, Institue of Medical Psychology, Magdeburg, Germany 2 Otto von Guericke University, Department of Computer Science, Magdeburg, Germany 3 Carl von Ossietzky University, Department of Experimental Psychology, Oldenburg, Germany 0012 Functional connectivity alterations after pre-chiasmatic visual system lesion

Introduction: Damage occurring along the visual pathway results in visual field (VF) defect in the area processed by the damaged tissue, while other VF parts are considered intact. However, as perceptual deficits were proven to exist in the presumably “intact” VF (Rizzo & Robin, 1996; Paramei & Sabel, 2008; Schadow et al., 2009) we hypothesize that these are mediated by network effects, namely lesion-induced changes in regions remote from the lesion site. Therefore, to study if such alterations are present, we investigated functional connectivity in patients with visual system damage.

Methods: Patients with visual field loss due to pre-chiasmatic damage (optic nerve; n=18) and age-matched healthy controls (n=14) took part in the study. Visual field of patients was tested with perimetry, to determine location of the scotoma (Fig.1). Next, all subjects performed simple shape discrimination task, where stimuli were presented in patients' intact field and in respective parts of VF of healthy controls. During the task 32 channel EEG was recorded. Data were epoched and time locked to the stimulus. Multivariate autoregressive model (MVAR) was fitted to short (140ms), stationary (ensemble normalization) time windows and functional connectivity (spectral coherence) was estimated from the model. Coherence values were baseline corrected.

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FIG. 1.

Results: Processing of the stimulus in the intact field of controls evoked increase of coherence in the parietal region at two time points - shortly after the stimulus (0.2-0.3 sec, 3-13Hz; Fig 2) and later around the response time (0.6-0.8 sec, 1-8Hz). Both coherence peaks were significantly weaker in the patients group (p<0.05). Further, we found a shift in coherence peak frequency in the occipital region. While in the control group coherence peaked at 5Hz, in the patients group the highest values were found at 11Hz (Fig 3).

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FIG. 2.

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FIG. 3.

Conclusions: Our results suggest that in patients with pre-chiasmtic visual system damage cortical functional connectivity is altered, even when the stimulus is processed in the presumably “intact” visual field. Therefore, local visual system lesions affect physiological functioning in areas much larger than the lesion site and this physiological dysfunction may explain processing deficits in non-damaged regions of the visual system.

1 Child Mind Institute, Center for the Developing Brain, New York, United States 2 Nathan Kline Institute for Psychiatric Research, Orangeburg, United States 3 Virginia Tech Carilion Research Institute, Roanoke, United States, ⁴Johns Hopkins University, Department of Applied Mathematics & Statistics, Baltimore, United States 0013 Reproducibility: the impact of preprocessing resting state fMRI

Objective: Prior research has found a significant correspondence of the global mean signal (GM) with the first principal component scores (PC1) in resting state (RS) fMRI scans (He and Liu 2011, Carbonell et al. 2009). Intuitively, this correspondence is expected as the GM represents a time series that is spatially present throughout much the brain (Scholvinck et al. 2010). A variety of methods to remove or mitigate the effects of such a signal have been simultaneously proposed and critiqued (Murphy et al. 2009). We find that this correspondence is heavily dependent upon the preprocessing steps used to analyze RS fMRI data and can change significantly for a given subject between consecutive preprocessing steps. Furthermore, we find that similar issues still occur when analysis is restricted to grey matter voxels.

Method: We examine the impact of preprocessing steps commonly applied to RS data on the correspondence between GM and PC1. These steps include realignment (RA), skull stripping (SS), global mean scaling (GMS), linear nuisance regression (LNR), temporal filtering (TF) and spatial smoothing (SM). The LNR model linearly removes linear trend and head motion parameter effects. Two processing pipelines are compared (A (Fox et al. 2005) and B) using a publicly available dataset containing 25 subjects, each with 3 scan sessions (Shehzad et al. 2009). The differences between pipelines is the order of LNR with respect to TF and SM. For each pipeline, temporal correlation is calculated between the full brain PC1 and GM after each processing step. Furthermore, Spearman's rank correlation coefficient is calculated between consecutive steps to determine if the relative ordering of correlation values for the 25 subjects persists between steps.

Results: As shown in Fig 1 and 2, the distribution of correlation values changes substantially between each step. As shown in Fig 3 the ordering is particularly affected between GMS and LNR in pipeline B. Fig 4 also shows a significant impact between GMS and TF in pipeline A. Fig. 2 illustrates a noticeable convergence of correlation values towards unity for a majority of subjects after SM step in both pipelines.

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FIG. 1.

Global signal and first principal component scores temporal correlation of pipeline B (linear nuisance regression precede temporal filtering and spatial smoothing). Each line represents the correlation values of a single subject during each stage of the pipeline.

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FIG. 2.

Global signal and first principal component scores temporal correlation of pipeline A (temporal filtering and spatial smoothing precede linear detrending and linear regression). Each line represents the correlation values of a single subject during each stage of the pipeline.

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FIG. 3.

Spearman′s rank correlation coefficient calculated from the sampled correlation value of each subject between consecutive preprocessing steps steps of pipeline B.

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FIG. 4.

Spearman′s rank correlation coefficient calculated from the sampled correlation value of each subject between consecutive preprocessing steps steps of pipeline A.

Conclusion: Preprocessing steps can have drastic effects on the data. Care must be taken when making subsequent inferences on preprocessed data.

1 Max Planck Institute for Human Cognitive and Brain Sciences, Neurology, Leipzig, Germany 0015 Functional dissociation of cerebello-frontal and cerebello-parietal networks on the individual and group level using intrinsic functional connectivity.

Question: Fronto-parieto-cerebellar fMRI activations were found in various cognitive tasks. Recent intrinsic functional connectivity fMRI (iFC-fMRI) studies differentiated between fronto-parietal and sensorimotor-related networks. However, animal studies propose: Superior cerebellar regions (sCb) are connected to prefrontal cortex (PFC) and inferior cerebellar regions (iCb) to posterior parietal cortex (PPC). Clinical data also suggest a functional dissociation between sCb (ataxia) and iCb (adaptive impairment). We hypothesize that sCb shows stronger iFC to prefrontal regions and iCb shows stronger iFC to parietal regions.

Methods: In 4 sessions functional and anatomical MRI data of 13 subjects were acquired and preprocessed using standard procedures. Further analysis is shown in Fig 1. Cerebellar regions which show iFC to both PFC and PPC were analyzed (color-coded lines in Fig 2). A regression between signals in PFC and PPC revealed residuals which were correlated with signals in cerebellar voxels (Fig 1, step 14). A cortico-cortical index (CCI, Fig 1, step 15) distinguished cerebellar voxels based on their iFC to PFC and PPC (Fig 3 and 4).

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FIG. 1.

A novel seed selection process and iFC map generation.

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FIG. 2.

iFC maps (top) of the 4 cerebellar lobules (bottom). For simplicity, left cerebral hemisphere shows iFC of right cerebellar hemisphere and vice versa.

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FIG. 3.

Plots of group CCI over cerebellar voxels (in lobules of sCb) show dissected cerebro-cerebellar networks. Vertical line indicates the transition from one cerebral region (CCI0). Significantly stronger iFCs are indicated by red circles (p

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FIG. 4.

Plots of CCI for lobules in iCb (see Fig 3).

Results: sCb (crus I and II) and iCb (HVIIb, HIX) showed fronto-parietal iFC (Fig 2). CCI plots show a significantly stronger iFC of sCb to PFC than to PPC. This was found in 12 individuals (except left crus I). iCb showed iFC to PFC and PPC. Stronger iFC to PPC was only found in right HVIIb.

Conclusion: iFC-fMRI is able to functionally dissociate fronto-parieto-cerebellar networks. Whereas sCb is connected to PFC, iCb is connected to both PFC/PPC. A functional dissociation between sCb and PFC, and iCb and PPC was only found for the right cerebellar hemisphere.

1 MIT, Electrical Engineering & Computer Science, Cambridge, United States 2 Harvard Medical School, Psychiatry Neuroimaging Laboratory, Boston, United States 0019 From Brain Connectivity Models to Identifying Foci of a Neurological Disorder

Research Question: Aberrations in functional connectivity are often correlated with neuropsychiatric disorders. We propose a unified probabilistic framework that aggregates population differences in connectivity to isolate foci of a neurological disorder. We use neural anatomy as a substrate for modeling functional connectivity. Since neural communication in the brain is constrained by white matter fibers, we hypothesize that the strongest effects of a disorder will occur along direct anatomical connections. Hence, Diffusion Weighted Imaging (DWI) tractography is used to estimate the underlying white matter fibers in the brain. The anatomical connectivity inferred from these fibers constrains the graph of abnormal functional connections.

Methods: We assume that impairments of the disorder localize to a small subset of brain regions, which we call foci, and affect the neural signaling along pathways associated with these regions. Our model consists of a latent structure, which represents the underlying organization of the brain, and the observed fMRI and DWI measurements. Fig 1 presents a network diagram of our latent graph. The nodes in Fig. 1 correspond to regions in the brain. The green nodes are healthy, and the red nodes are diseased. The edges denote neural connections, which are captured by latent anatomical connectivity A_ij. Specifically, the presence or absence of edge (i,j) in the network is governed by the binary value of A_ij. The anatomical network structure is shared between the control and clinical populations.

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FIG. 1.

A network model of connectivity. The nodes correspond to regions in the brain, and the lines denote anatomical connections between them. The green nodes and edges correspond to the normal regions and connections, respectively. The red nodes are foci of the disease, and the red edges specify pathways of abnormal functional connectivity. The solid lines are deterministic given the region labels; the dashed lines are probabilistic. A_ij represents the latent anatomical connectivity between regions i and j. F_ij denotes the corresponding latent functional connectivity.

Based on the region assignments, aberrant functional connectivity along anatomical pathways is defined using a simple set of rules: (1) a connection between two diseased regions is always abnormal (solid red lines in Fig. 1), (2) a connection between two healthy regions is never abnormal (solid green lines), and (3) a connection between a healthy and a diseased region is abnormal with probability η (dashed lines). We use latent functional connectivity variables F_ij and barF_ij to model the neural synchrony between two regions in the control and clinical populations, respectively. Ideally, barF_ij≠F_ij for abnormal connections and barF_ij=F_ij for healthy connections. However, due to noise, we assume that the latent templates can deviate from the above rules with some probabiliy. The observed DWI values and fMRI correlations are noisy measurements of the latent network structure.

We employ a maximum likelihood (ML) framework to fit the model to the data. The region assignments induces a complex coupling between pairwise connections. Therefore, we use a variational approximation for the posterior probability distribution when deriving the ML solution.

Results: We demonstrate our model on a study of 19 male patients with chronic schizophrenia and 19 male healthy controls. The control participants were group matched to the patients on demographics and clinical indicators. For each subject, an anatomical scan (SPGR, TR=7.4s, TE=3ms, FOV=26cm², res=1mm³), a diffusion-weighted scan (EPI, TR=17s, TE=78ms, FOV=24cm, res=1.66×× 1.66×1.7mm, 51 gradient directions with b=900s/mm², 8 baseline scans with b=0s/mm²) and a resting-state functional scan (EPI-BOLD, TR=3s, TE=30ms, FOV=24cm, res=1.875×1.875×3mm) were acquired using a 3T GE Echospeed system.

We segmented the anatomical images into 77 cortical and sub-cortical regions using FreeSurfer. The DWI data was corrected for eddy-current distortions, and two-tensor tractography was used to estimate the white matter fibers. We compute the DWI connectivity in each subject by averaging FA along all detected fibers between pairs of regions. The DWI value is set to zero if no tracts are found.

We discarded the first five fMRI time points and performed motion correction by rigid body alignment and slice timing correction using FSL. The data was spatially smoothed using a Gaussian filter, temporally low-pass filtered with 0.08Hz cutoff, and motion corrected via linear regression. We also removed global contributions from the white matter, ventricles and the whole brain. We compute the fMRI connectivity in each subject as the Pearson correlation coefficient between the mean time courses of two regions.

Our method identifies three disease foci, as displayed in Fig 2. Our results implicate the right posterior cingulate (p<0.004), the right superior temporal gyrus (p<0.014), and the left superior temporal gyrus (p<0.044). Prior studies have found abnormalities in the superior temporal gyri in schizophrenia. These impairments correlate with clinical measures of auditory hallucination and attentional deficits. The default network has been implicated in resting-state fMRI studies. Reducted connectivity in the posterior cingulate correlate with both positive and negative symptoms of schizophrenia.

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FIG. 2.

Significant regions based on permutation tests (uncorrected p<0.044). The colorbar corresponds to the negative log p-value. We present the lateral and medial viewpoints for each hemisphere. The highlighted regions are the posterior cingulate (R PCC) and the superior temporal gyrus (L STG & R STG).

In Fig 3, we observe that functional abnormalities originating in the posterior cingulate project to the midbrain and frontal lobe, whereas abnormalities stemming from the right and left superior temporal gyri tend to span their respective hemispheres. Overall, schizophrenia patients exhibit reduced functional connectivity. Of notable exception are connections to the frontal lobe. This phenomenon has been reported in prior studies of schizophrenia and is believed to interfere with perception by misdirecting attentional resources.

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FIG. 3.

Estimated graph of functional connectivity differences. The red nodes indicate the disease foci. Blue lines indicate reduced functional connectivity and yellow lines indicate increased functional connectivity in the schizophrenia population.

Fig 4 illustrates the results of varying a parameter β of our model that specifies the expected number of diseased regions. Empirically, we observe that sets of regions affected by the disease form a nested substructure as β increases. We color each of the selected regions according to the smallest value of β that implicates it as a disease foci. The yellow regions are always identified as foci, whereas the orange/red regions are selected for larger parameter values. The nesting property is a highly desirable feature of our model. It suggests an initial set of disease foci, identical to the significant regions in Fig. 4. We can then tune a single scalar to progressively include regions that exhibit some functional abnormalities but are not as strongly implicated by the data.

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FIG. 4.

Evolution of the disease foci when varying the parameter β. The highlighted regions are the posterior cingulate (L PCC & R PCC), the superior temporal gyrus (L STG & R STG), the postcentral gyrus (R PC), the frontal pole (L FP), the caudal middle frontal gyrus (R CMF), the transverse temporal gyrus (L TTG), the pars orbitalis (L pOrb), the entorhinal cortex (R Ent) and the lateral occipital cortex (R LOcc).

Conclusion: We proposed a novel probabilistic framework for multimodal analysis of fMRI and DWI data that integrates population differences in connectivity to isolate foci of a neurological disorder. This is achieved by defining a network of abnormal connectivity emanating from the affected regions. We demonstrate that our method identifies a stable set of schizophrenia foci consisting of the right posterior cingulate and the right and left superior temporal gyri. Prior clinical studies have linked these regions to the effects of schizophrenia. Moreover, we uncover additional regions by adjusting the prior on the number of disease foci. These results establish the promise of our approach for aggregating connectivity information to localize region effects.

1 Medical University Graz, Neurology, Graz, Austria 2 University of Oxford, Nuffield Department of Clinical Neuroscience, Oxford, United Kingdom 0021 An exploratory study on the effect of Natalizumab on resting state networks in Multiple Sclerosis patients

Cognitive impairment is frequent in multiple sclerosis (MS), occurs in early stages and affects everyday life. Whereas functional reorganization in the context of cognition is increasingly studied using conventional task-related functional MRI, equivalent research examining changes in resting state networks (RSN) due to disease progression is still scarce, and hithertho is largely based on extrapolation from cross-sectional observations. Changes in RSN bear potential to be used as a sensitive means to capture treatment effects on brain function in the context of CNS diseases, circumventing limitations of task-related fMRI.

In the presented exploratory, longitudinal study we thus attempted to assess the effects of a highly effective disease modifying drug (Natalizumab) used for escalation therapy in aggressive forms of MS on cognitive function and RSN function in patients with relapsing-remitting MS (RRMS).We longitudinally investigated seven RRMS-patients, using resting state fMRI and comprehensive neuropsychological testing (e.g. verbal and spatial memory, attention, processing speed, verbal fluency).

All patients were tested at baseline, three months and twelve months after the initiation of pharmalogical therapy (Natalizumab). We only included patients who participated in all fMRI and neuropsychological sessions, resulting in a final sample of five patients (mean age=33 years; SD=10; 3 male). Results of analyses aiming to identify changes in activity in six RSN (visual RSN, auditory RSN, somatosensory RSN, default mode network, fronto-parietal “attentional RSN and “executive control & salience” RSN) will be presented at the congress and set in relation to status of and changes in cognitive performance.

1 National Institute of Mental Health, Section on Functional Imaging Methods, Bethesda, United States 2 National Institute of Mental Health, Functional MRI Facility, Bethesda, United States 0022 A non-neural explanation for some fMRI resting connectivity dynamics

Introduction: Neuronal interactions between brain regions obviously vary across time. There is also a growing literature showing dynamic changes in fMRI correlation magnitudes between brain regions. Given the many sources of noise and signal changes in fMRI, it is unlikely that all correlation fluctuations represent changes in neuronal interactions. We examine scenario that could create connectivity fluctations without corresponding fluctuations in neural interactions.

Methods: Data were collected from 12 healthy adults in a 3T GE HDx MRI (TR=2 sec, 10 min of data per rest run). Preprocessing included RETROICOR (Glover, Li et. al. 2000) and RVT (Birn, Diamond, et. al. 2006) corrections for cardiac and respiratory fluctuations, and regression of motion parameters and their first derivatives. Correlations were calculated from a posterior cingulate cortex (PCC) seed to the rest of the brain. Sliding window correlations were used to calculate correlation changes over time from the PCC. Figure 1 shows maps correlation changes over time along with a demonstration of how the time a pair of voxel time series is used to calculate correlations with different window sizes.

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FIG. 1.

Power spectra for each voxel's correlation time series were calculated. Time series from spatially distinct voxels that showed periodic fluctuations were repeatedly phase randomized. The phase randomized time series were used to for sliding window correlations and power spectrum estimations.

Results: Figure 2 shows maps of relative power at 4 power spectrum peaks from one volunteer. Even though these correlations are based on a single seed, distinct brain regions have high power at different frequencies. Figure 3 shows that the frequency peak magnitudes from actual data almost always falls within 99.9% of the distribution of phase randomized data.

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FIG. 2.

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FIG. 3.

Discussion: We show periodic fluctuations of correlations that follow cortical structures. Periodic fluctuations of these magnitudes remain after phase randomization. Since phase randomization removes the temporal relationship between signal changes across time series, such results do not represent neuronal interactions. The observed correlation changes often have lower fluctuation changes than the phase randomized question. While we don't show any specific regional interaction is non-neural, this work highlights a challenge for distinguishing neural from non-neural connectivity dynamics using correlation.

1 University of Liège, Coma Science Group, Cyclotron Research Center & Neurology Department, Liège, Belgium 2 University of Liège, CHU Pain Clinic, Liège, Belgium 0023 Functional connectivity changes in hypnotic state measured by fMRI

Question: We here employed functional MRI to better characterize hypnosis-related functional connectivity changes in large-scale cerebral networks.

Methods: Twelve subjects were scanned in three conditions: (1) normal eyes-closed wakefulness, (2) during mental imagery of pleasant autobiographical memories (i.e., control condition), and (3) during hypnotic state (reviving pleasant autobiographical memories). Seven seed regions were used to identify functional connectivity patterns of the default mode, left and right frontoparietal, salience sensorimotor, auditory, and visual networks. Behavioral data concerning body sense modification, partial amnesia, and time sense modifications were collected at the end of eac fMRI session.

Results: Behaviorally, more subjects under hypnosis (as compared to the control condition) reported a modified sense of body and time as well as partial amnesia. Compared to the control condition of autobiographical mental imagery, we identified increased within-network functional connectivity for the default mode, left and right frontoparietal, salience, sensorimotor, and auditory networks; an enhanced cross-modal interaction between auditory and visual cortices was further observed (Fig 1). The visual network only showed decreases in functional connectivity in both within and between-network areas (i.e., hippocampus).

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FIG. 1.

Conclusions: Hypnosis, compared to a control condition of revivification of pleasant autobiographical memories, leads to increases in functional connectivity in the default mode, left and right frontoparietal, salience, sensorimotor, and auditory networks, potentially reflecting lack of inhibitory cortico-cortical mechanisms. Additionally, hypnosis-related decreases in visual network functional connectivity and increases in cross-modal interaction between auditory and visual networks could be identified, hypothesized to reflect a revivification of hypnotic suggestions and not merely cognitively guided memory retrieval.

1 Leiden University Medical Center, Radiology, Leiden, Netherlands 2 Leiden University, Institute of Psychology, Leiden, Netherlands 3 Leiden University, Leiden Institute for Brain and Cognition, Leiden, Netherlands 4 Leiden University Medical Center, Department of Gerontology and Geriatrics, Leiden, Netherlands 5 Leiden University Medical Center, Neurology, Leiden, Netherlands 0024 Elderly with subjective memory complaints show smaller brain structures and higher functional brain connectivity

Background: Subjective memory complaints (SMC) are common among elderly. Although subtle changes in memory functioning can hardly be determined using neuropsychological evaluation, neuroimaging studies indicate regionally smaller brain structures in elderly with SMC. Imaging of resting state functional connectivity is sensitive to detect early changes in neurodegenerative diseases, but is currently underexplored in SMC. Here we investigate brain structure and resting state functional connectivity in elderly with SMC. Methods: We analyzed MRI data of 25 elderly with SMC (14 male) and 29 control elderly (17 male), both with a mean age of 71 years. Voxel-based morphometry and volume measurements of subcortical structures were employed on the structural scans using FSL. The dual regression method was used to analyze voxel-wise functional connectivity in relation to eight well-characterized resting state networks, taking regional volume differences in gray matter into account. Two-sample t-tests were used to obtain group differences (p<0.05, corrected).

Results: Additionally to gray matter volume reductions (hippocampus, anterior cingulate cortex, medial prefrontal cortex, cuneus, precuneus, and precentral gyrus), elderly with SMC showed increased functional connectivity in the default mode network (fig. 1A; hippocampus, thalamus, posterior cingulate cortex, cuneus, precuneus, and superior temporal gyrus), the medial visual network (fig. 1B; anterior and posterior cingulate cortex, cuneus, and precuneus), and the executive control network (fig. 1C; medial prefrontal cortex). Conclusion: This study is the first to demonstrate that, additionally to smaller regional brain volumes, increases in functional connectivity are present in elderly with SMC. This suggests that self-reported SMCs are a reflection of objective alternations in brain function and might be a subclinical form of functional neurodegeneration. Furthermore, our results indicate that functional imaging, in addition to structural imaging, can be a useful tool to objectively determine a difference in brain integrity in SMC.

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FIG. 1.

Increased functional connectivity in elderly with SMCThree resting state networks in healthy elderly (HC) and in elderly with SMC. Increased functional connectivity in elderly with SMC (SMC>HC).

1 Baylor College of Medicine, Pediatric Neurology, Houston, United States 0027 Whole Brain Resting State Sedated Functional MRI by Probabilistic ICA for Pediatric Epilepsy Pre-surgical Evaluation

Question: In planning epilepsy surgery, it is important to assess the likelihood of loss of abilities secondary to removal of active brain tissue against improved quality of life from seizure reduction or cure. In this study, whole brain resting-state sedated functional MRI (rs-fMRI) of a developmentally delayed child was performed to determine optimum depth of left peri-rolandic and parietal located lesion resection for seizure freedom, avoiding right homonymous hemianopsia.

Methods: Whole brain rs-fMRI analyzed by probabilistic independent component analysis (PICA), and comparative passive activation (pa-fMRI) with checkerboard visual stimulation paradigm were collected on a sedated four year old child with intractable localization related epilepsy before surgery. Prior magneto encephalography demonstrated seizure foci localized to the left lateral peri-rolandic area.

Results: Both pa-fMRI and PICA methods were able to detect spatial distribution of bilateral visual network in relation to the lesion. But, PICA was also able to determine the atypical motor network location, anterior to lesion, as well as nearby left temporal receptive language.

Conclusions: This case report suggests that whole brain rs-fMRI analyzed by PICA may be more informative or complementary to standard approaches in epilepsy surgery, especially in patients who are not able to fully cooperate with standard testing, such as Wada or certain clinical exams such as visual perimetry, or may have altered anatomical distribution of cortical networks. This method yields the anatomical location of involved and neighboring neuronal networks with implications of network-associated functional ability to be considered by the surgical team and family.

1. Primary Vision Network by PICA

2. Receptive Language Network by PICA

3. Deactivation of Vision Network by GLM

4. Bilateral Motor Network by PICA

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1 NIH, NINDS, LFMI, AMRI, Bethesda, United States 0028 Multiple Activity Patterns Uncovered by Temporal Decomposition May Explain Non-Stationary Network Dynamics

Objectives: Resting-State functional connectivity measured through blood-oxygenation-level-dependent (BOLD) signal correlations has been intensively investigated recently with attempt to understand dynamic organization of the brain. The functional connectivity has been shown to be non-stationary and vary significantly over time, even over the course of a typical scan session (∼ 10-15 minutes). However, such non-stationary variation in BOLD correlation may reflect solely the variation in the relative levels of signals and noise. In this study, we apply a novel technique to temporally decompose RSN patterns into multiple resting-state patterns (RSPs) with the purpose to understand the temporal dynamics of RSNs.

Methods: The resting-state data from 247 participants were selected from the “1000 functional connectomes project” (FCP). The RSN patterns obtained with conventional seed-based correlation maps were first replicated using a simplified point process analysis (PPA). It was then decomposed into multiple RSPs by applying classification analysis in the time domain. Each RSP is associated with a distinct set of time points.

Results: The group correlation map with respect to the posterior cingulate cortex (PCC, [0, −53, 26] in MNI coordinates) can be closely replicated by using our simplified PPA (Fig 1). This default mode network (DMN) pattern was then temporally decomposed into 8 PCC-RSPs (Fig. 2A), which demonstrate distinct spatial patterns (Fig. 2B). With similar processing, the dorsal attention network (DAN) pattern in the correlation map with respect to the intra-parietal cortex (IPS, [22, −58, 54]) was also divided to 12 IPS-RSPs covering distinct brain regions (Fig 3). In comparison, the group independent component analysis (ICA) on the same data set could not replicate most of the RSPs (Fig 4).

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FIG. 1.

The DMN pattern shown in the group correlation map (the upper row) derived from a PCC seed region (green square) can be closely replicated with the simplified PPA method (the lower row). The spatial correlation between two maps is 0.995. The color scale for displaying was automatically adjusted for each map according to their map values.

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FIG. 2.

The DMN pattern is decomposed into 8 PCC-RSPs associated with different sets of time points (A). Four PCC-RSPs strongly involving the PCC seed region (green square) are overlaid over each other to highlight their differences (B).

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FIG. 3.

The DAN pattern extracted derived from a seed region in the right IPS (green square) is decomposed into 12 IPS-RSPs. The first 6 IPS-RSPs show strong activity at the bilateral IPS regions, suggesting their functional relevance to this brain region.

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FIG. 4.

Six (out of 30) independent components (ICs) showing significant values (Z>3) at the posterior cingulate cortex (PCC) or left intra-parietal sulcus (IPS) seed regions. Four of them (IC 13, 16, 27, 29) show patterns significantly involving the PCC or IPS regions. Among them, the IC 16 and 29 display the DMN and DAN patterns, and the IC 27 covers parts of the visual cortex and the posterior IPS region.

Conclusion: The RSN pattern found with the conventional seed-based correlation map appears to be a summation of multiple distinct RSPs appearing at different time. These RSPs could not be identified using the ICA approach because their spatial patterns are not distinct from each other enough to satisfy its strict “independence” constraints. Switching between these RSPs over time may account for, at least partially, the non-stationary characteristic of the resting-state functional connectivity.

1 Universitat Jaume I, Basic and Clinical Psychology and Psychobiology, Castellón, Spain 2 Universitat Pompeu Fabra, Department of Information and Communication Technologies, Barcelona, Spain 3 Universitat Pompeu Fabra, Departament of Technology, Barcelona, Spain 0032 Resting State fMRI as an index of learning and ability: The case of Phonetic Training

Objectives: Our aim was to investigate the effect of phonetic training on the connectivity of neural networks in the brain. Data of an fMRI task and resting state fMRI (rs-fMRI) was obtained before and after a 2-week training session on a phonetic Hindi dental-retroflex non-native contrast. We hypothesized that this training session would result in changes in functional connectivity (FC) during rs-fMRI.

Methods: Nineteen right-handed participants (mean age 23.74±2.54, 9 males) completed the rs-fMRI and fMRI task on a 1.5T Siemens scanner before and after training (Figure 1). During the blocked-designed fMRI task, participants had to identify a non-native Hindi contrast (dental /da/ and retroflex /da/ sound) and a native contrast (/da/ and / ta/ dental sounds). Brain activation changes obtained from the comparison of pre and post training fMRI task were used as seed regions for rs-fMRI. We used REST toolkit to calculate FC. A pairwise linear correlations between seeds was computed obtaining the z-values for each subject. Furthermore, we performed independent component analysis (ICA) using GIFT software to obtain the FC networks before and after learning. A paired t-test was computed to evaluate changes in FC.

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FIG. 1.

Schematic timing representation of experimental procedure.

Results: The following seed regions were derived from the fMRI non-native contrast analyses: Left Frontal Inferior Operculum/anterior Insula (LFO/aI), left Supramarginal Gyrus (LSMG) and left Superior Parietal Lobe (LSPL). On the other hand, the native contrast resulted in a seed region in the left middle temporal gyrus (LMTG) (Figure 2). The FC analysis showed a significant connectivity decrease between LFO/aI and LSPL after training (Figure 3). Additionally we performed ICA, which showed that there were two networks anchored by the LFO/aI and LSPL: the Left Fronto-Parietal Network (L FPN) and the Salience Network. The comparison post vs pre training showed a significant increase in FC of LFO/aI within the Salience Network and a significant decrease within the L FPN (Figure 4).

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FIG. 2.

ROIs selected as seeds for the functional connectivity analysis. These ROIs were derived from an ANOVA (z-value >3) by identifying (a) brain areas involved in native contrast processing (in blue), and (b) brain areas involved in non-native contrast processing (in red).

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FIG. 3.

Comparison of FC between rs-fMRI periods, pre-training (RESTpre) in light blue, and post-training (RESTpost) in dark blue. We only observed a significant decrease in correlation of LFO/aI and LSP [t(18)=3.27 p<0.004].

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FIG. 4.

Comparison of FC of the LFO/aI seed within the Salience Network and L FPN, and of the LSPL seed within L FPN between rs-fMRI periods. After training, the LFO/aI showed a significant decrease of FC within L FPN and a significant increase within Salience network, while no change were observed for LSPL.

Conclusion: Phonemic learning was associated with functional changes in LFO/aI, LSPL and LSMG. Importantly, rs-fMRI revealed post-training changes involving a reduction of FC between LFO/aI and LSPL. This reduction was associated with an increased activity of LFO/aI within the Salience network after training, that decouples its activity from LSPL within the L FPN.

1 Georgetown University, Washington DC, United States 0035 Working memory-related changes in functional connectivity persist beyond task disengagement

Objectives: Research has indicated that resting-state functional connectivity is not stable. Compared to a pre-task resting state, connectivity measured after sensorimotor and language tasks was altered within and between task-relevant networks. However, it is not known whether 1) such post-task effects on connectivity can also be observed after a cognitively effortful task such as working memory (WM); and 2) whether post-task connectivity alterations affect connectivity-behavior relationships observable during the “baseline” resting state.

Method: 50 healthy young adults were scanned using fMRI during a baseline resting state, during an Nback WM task, and during a post-task resting state. Default mode network (DMN) and task-positive network (TPN) nodes were identified respectively as regions positively and negatively correlated with a posterior cingulate cortex (PCC) seed during the baseline resting state. Within-DMN, DMN-TPN and within-TPN connectivities were calculated for each run. StateXNode ANOVAs were conducted to identify effects of State (baseline rest, Nback, post-task rest). Finally, in each run, PCC connectivity was correlated against a behavioral measure of Inattentiveness.

Results: Effects of State were observed in within-TPN and DMN-TPN connections, but not in within-DMN connections. Compared to the pre-task resting state, within-TPN connectivity was reduced across all nodes during WM and remained reduced in the post-task resting state. Similarly, DMN-TPN connectivity was less negative in all nodes during WM and remained less negative after the task. Correlations between PCC connectivity and Inattentiveness scores were observed in DMN regions before and during WM, but were eliminated during the post-task resting state.

Conclusions: Performance of an effortful WM task results in a less efficient network configuration (reduced within-TPN and less negative DMN-TPN connectivity), and this persists after task conclusion, possibly reflecting continued cognitive resource depletion. Further, connectivity-behavior relationships are obscured in this altered resting state. These findings have implications for models of brain recovery following transient effortful cognition, as well as for study designs investigating connectivity relationships with behavior.

1 University Ulm, Neurology, Ulm, Germany 0036 Analysis of Oculomotor Networks in Parkinson's Disease Comparing Resting-State fMRI Functional Connectivity and Videooculographic Results

Objective: It ist known that oculomotor networks comprising of midbrain, cerebellar and basal ganglial structures are affected in patients with Parkinson's Disease (PD). In addition, there is an increasing evidence of the involvement of non-motor symptoms in oculomotor control that is functionally associated with the frontal cortex which appears to be contributed in executive control. Oculomotor performance and the functional connectivity between brain regions of interest (ROI) associated with the pathology of PD was investigated. Therefore videooculography (VOG) and resting-state fMRI (RS-fMRI) were performed to assess a group analysis between PD patients and controls.

Methods: Twenty-five patients with PD and 9 controls underwent oculomotor examination and RS-fMRI. VOG was performed by means of Eye-Link System I. The data were analyzed by an in-house developed software. Reactive saccades and smooth pursuit eye movement as well as visually triggered delayed saccades and alternating voluntary gaze shifts were examined. The RS-MRI protocol (1.5 T scanner) consisted of 120 volumes, in-plane resolution 3.27×3.27 mm2, slice thickness 3mm (TR=3080 ms, TE=28 ms). RS-fMRI functional connectivity analysis was calculated by the REST-software utilizing 9 distinct ROIs: frontal cortex, parietal cortex, thalamus, cerebellum, basal ganglia, striatum, midbrain, pons and medulla oblongata.

Results: Executive control of oculomotor performance as well as functional connectivity within the oculomotor networks in the brain stem, basal ganglia and thalamus significantly differed between PD and the control group. Projections from the basal ganglia into the parietal cortex and into the cerebellum were affected. RS-fMRI results did not propose an altered connectivity of the frontal cortex.

Conclusion: RS-fMRI showed a disturbed functional connectivity of basal ganglia, brain stem, and thalamic circuits that play a crucial role in oculomotor function. The close relation of these findings to oculomotor performance in PD patients gives evidence for electrophysiologic and neuropathological findings by the in vivo imaging method of RS-fMRI. Nevertheless, evidence for a possible role of the frontal cortex in the executive control of oculomotor function could not be elicited and needs further investigation.

1 VUmc, Physics and Medical Technology, Amsterdam, Netherlands 2 VU University Amsterdam, Mathematics, Amsterdam, Netherlands 0037 fMRI correlates of micro states within the alpha band

Simultaneous recording of EEG and fMRI is a way to provide new insights into the generators of EEG phenomena. EEG/fMRI is clinically used to find the underlying sources of inter-ictal epileptic discharges in patients that are candidates for brain surgery. The generators of the alpha band have been studied by correlating the time variations of occipital alpha amplitudes to the fMRI time series. A limitation of these simple EEG-informed analyses is that the available EEG information is used only partly to construct regressors for the correlation analysis. As a first step towards a more complete integration of EEG and fMRI we here propose to construct regressors of interest by clustering the (normalized) spatial maps within the alpha band.

Simultaneous EEG and fMRI were recorded from 16 healthy subjects in eyes-closed resting-state condition during 30 min. A TR of 3 s was used on a 1.5 T Siemens Sonata scanner. EEG data were subdivided into 3 s epochs, corresponding to the fMRI volumes. Gradient and heart beat artifacts were removed from the EEG using in-house developed software, as were outlying channels and epochs. For each electrode and epoch the data power was computed in the frequency band ranging from 8.6 to 11.6 Hz. Normalized power maps were subjected to hierarchical clustering analysis using four to five clusters (fig 1). Cluster memberships (fig 2) were convolved with standard HRF and used as regressor of interest in the general linear model applied to all fMRI voxel time series.

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FIG. 2.

It was found that the clusters generally could be interpreted as low, intermediate or high frequency or non-specific parts of the alpha band (fig 3). In a pilot study we found that for one of the subjects, the voxels that correlated significantly with the cluster memberships were very similar to those found in the occipital alpha power method (fig 4). For the other subjects, a systematic comparison is ongoing.

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FIG. 3.

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FIG. 4.

The proposed cluster analysis of EEG maps is expected to give a physiologically more plausible interpretation than the alpha power method, because it is more directly connected to the concept of EEG micro-states. Furthermore, it is based on all electrodes rather than on a small subset. The preliminary finding that the fMRI correlation patterns are nevertheless very similar is quite unexpected because the ways the regressors were constructed are complementary in many respects.

1 VU University, Integrative Neurophysiology, Amsterdam, Netherlands 2 VU University, Biological Psychology, Amsterdam, Netherlands 3 NIN, Sleep & cognition, Amsterdam, Netherlands 0038 Resting-state cognitive processing decreases hemodynamic low-frequency oscillations within functional brain networks

Humans tend to devote consciousness to rational and emotional reflection on past or imagined experiences when they rest or engage in low-demand tasks. Given the importance of these non-sensory, endogenous cognitive processes to the human condition and their (putative) impairment in brain disorders, it is imperative to understand their neural correlates. Interestingly, whereas neuroimaging of brain activity during rest has advanced rapidly over recent years, remarkably few studies have tested whether cognition can be related to brain activity during resting-state neuroimaging.

In the present study, resting-state functional magnetic resonance imaging (RS-fMRI) data were acquired from 68 healthy adults in two sessions. Each session was immediately followed by completion of a Resting-State Questionnaire (RSQ) with 50 items for rating thoughts and feelings on a five-point Likert scale. We here report on within-subject variation in two dimensions of RS cognition derived from the RSQ, sleepiness and somatic awareness, and how this variation related to the changes in RS-fMRI functional connectivity (FC) within ten commonly described functional brain networks.

The RSQ data revealed intra-individual changes in sleepiness and somatic awareness experienced by the participants over the two sessions. To test whether changes in these two factors were associated with FC changes, we performed within-subject testing using the so-called dual-regression approach (Filippini et al., PNAS 2009) with predefined spatial masks derived from an ICA analysis of BrainMap data (Smith et al., PNAS 2009). Results show that increased sleepiness is associated with increased FC within sensorimotor and visual resting-state networks. In contrast, increased somatic awareness is associated with decreased FC within visual, auditory and executive resting-state networks.

We propose that the observed associations between resting-state cognition and the expression of intrinsic connectivity networks can parsimoniously be explained by a framework in which cognitive processing leads to more differentiated and spatially confined hemodynamic responses, whereas disengagement gives rise to widespread non-specific (idling-like) low-frequency hemodynamic oscillations within functional brain networks.

1 University of Electronic Science and Technology of China, School of Life Science and Technology of China, Chengdu, China 2 Indiana University, Department of Psychological and Brain Sciences, Bloomington, United States 3 West China Hospital of Sichuan University, Department of Neurology, Chengdu, China 4 Hangzhou Normal University, Center for Cognition and Brain Disorders, Hangzhou, China 0039 Decreased Coupling between Functional and Structural Networks in Psychogenic Non-epilepsy Seizures

Introduction: Psychogenic non-epileptic seizures (PNES) resemble epileptic seizures, but lack epileptiform discharges (1). Recent evidence from resting-state functional MRI (fMRI) (2) and EEG synchronization (3) indicate abnormal functional connectivity in cognitive-emotional attention system (4). Functional connectivity and structural connectivity are complementary and the relationship between them is disrupted in disease states (5,6). However, whether the relationship of functional-structural connectivity is altered in PNES is unknown.

Methods: Nineteen PNES patients (6 males, 19.63±7.76 years) and 20 healthy controls (8 males, 21.85±1.70 years) underwent resting-state fMRI and diffusion tensor imaging scanning on a 3T siemens Trio system (Erlangen, German) in West China Hospital of Sichuan University. Using 90 regions defined by AAL template, we constructed weighted functional networks using Pearson's correlation, and structural networks via Fiber Assignment by Continuous Tracking (FACT) algorithm, following a definition of weighted edges similar to our previous study (6). The coupling analysis was constrained by the edges with non-zero structural connectivity, similar to our previous study (6). Then, coupling of functional-structural connectivity was compared by using permutation tests (5000 permutations). Finally, we investigated the relationship between coupling strength and duration of disease.

Results: The coupling strength of functional-structural connectivity were significantly decreased (r=−3.8098, p=0.0004) in PNES patients (0.2636±0.0410) compared with healthy controls (0.3213±0.0525). Furthermore, the decreased coupling strength of functional-structural connectivity was negatively correlated with duration of disease (r=−0.4801, p=0.0375).

Conclusions: Decreased coupling strength suggests loss of coalescence of functional and structural connectomes in PNES patients. Furthermore, the negative correlation between the coupling strength and durations of disease indicates that the decoupling of functional-structural connectivity might be related to the progress of long-term impairment in patients, which could improve our understanding of the disorder's pathophysiological mechanisms.

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Coupling of functional-structural connectivity in PNES patients and healthy controls.

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FIG. 2.

Correlation between coupling strength of functionalstructural connectivity and duration of disease in PNES.

1 Humboldt University, Berlin School of Mind and Brain, Mind and Brain Institute, Berlin, Germany 2 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany 3 Humboldt University, Institute of Psychology, Berlin, Germany 4 Charité University, Center for Stroke Research, Berlin, Germany 0042 Dynamics of functional connectivity following lacunar stroke

Background: Stroke has been shown to alter functional connectivity even in areas that are structurally intact and far from the lesion site. This phenomenon has been demonstrated in several networks, such as the motor and attention networks. However, in order to address this effect on a general stroke population, characterized by heterogeneous lesions, a more global, whole brain approach is needed.

Objective: Our aim was to test the longitudinal effect of lacunar stroke on functional networks in heterogeneous stroke population. Specifically, we hypothesized that lesions located in a specific network will cause a functional change restricted to the affected network.

Methods: 16 patients (age 65.75±11.7 years) following lacunar ischemic strokes were included in the analysis. Longitudinal resting-state fMRI scans were acquired at three consecutive time points (day 1, 7, and 90 post stroke). Lesion location was determined based on diffusion weighted imaging and/or FLAIR. Standard resting-state preprocessing was applied including global signal regression and band-pass filtering (0.01–0.1Hz). Lesions were mapped into affected/unaffected networks based on individual drawing of the lesions area and a template of eight independent networks previously computed in healthy controls (N=10) by Beckmann et al., 2005. In order to assess the stability of the functional networks over time for each individual, we used dual regression based on the same templates. Correlation concordance coefficient was used to determine spatial similarity over the time points.

Results: We found that networks containing lesions demonstrated decreased concordance over time as compared to unaffected networks (p<0.05). A change in the spatial patterns of the network was reflected in lower concordance over time. The change in the patterns of functional connectivity was significantly related to networks containing lesions, thus reflecting the fact that the longitudinal change is more pronounced in affected networks.Conclusions: We suggest that these processes are related to the clinical well-known phenomenon of diaschisis and are limited by the functional architecture of spatially independent networks in the brain.

1 Radboud University Nijmegen - Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands 2 University of Twente - MIRA Institute for Biomedical Technology and Technical Medicine, Twente, Netherlands 3 University Duisburg-Essen - Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Netherlands 0043 Correlated electrophysiological frequency patterns of resting state networks in concurrent EEG-fMRI.

In this study we investigated the relationship of RSNs and their Electrophysiological Correlation Patterns (ECPs) using a long resting state scan per subject. This allowed us to study ECPs on group as well as on single subject level, and to examine the temporal stability of ECPs within each subject.

Resting state data (34 minutes, eyes open) of 12 healthy subjects were acquired on a 3T Siemens scanner (32 channel head coil using a multi-echo EPI sequence: TR=2000 ms, five echoes, 3.5 mm isotropic voxels). Simultaneously EEG data were recorded with a 32 channel cap (ANT), using a BrainAmp amplifier. After standard preprocessing, group independent component analysis (ICA) was performed on the fMRI data to obtain 30 ICs. A dual regression approach was used to derive subject specific RSN maps and time courses. The EEG signal (preprocessed using Analyzer2) was split into 2s segments corresponding to the TR used. For every segment the mean frequency power over all channels for four frequency bands (δ: (2–4)Hz, θ: (4–7)Hz, α: (8–12)Hz, β: (12–30)Hz) was calculated, resulting in one time series for each frequency band. These were convolved with a hemodynamic response function (HRF) and correlated with the RSN time courses taking into account common variance (partial correlation). ECPs were calculated for each subject and averaged for group analysis (Fig 1). Additionally, each data set was split in to five subsets and corresponding ECPs were calculated (Fig 3).

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FIG. 1.

RSNs at group level shows 11 group fMRI RSNs as maximum intensity projection on the central slices and their groups ECPs, representing the average Z scores (12 subjects) for the four EEG frequency bands. Only clusters larger than 15 voxels were plotted. The large standard errors indicate the large variability of the subject-specific ECPs.

We found reproducible RSNs across subjects and significant correlations with EEG in four of the twelve analysed subjects, three of them showed negative alpha correlation with visual RSNs which is in good agreement with previous findings (Fig 2). However, we also observed large inter-subject variability in the ECPs. Besides a clear inter-individual difference in EEG patterns, we found temporal variability of the ECPs within a subject, which could be caused by the temporal dynamics of the RSNs. Due to volume conduction several RSNs contribute to the actual EEG on scalp level so that fluctuations of RSNs result in temporally unstable ECPs. An alternative explanation would be that the different RSNs do not have a specific ECP, but that different states of one RSN lead to different ECPs. In any case it explains a part of the observed inter-subject variability in the ECPs.

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FIG. 2.

RSN2: Occipial pole component at subject level depicts RSN 2 (one of three occipital components) after dual regression on single subject level as maximum intensity projection on the central slices and the subject specific ECPs for all 12 subjects, showing the high inter-subject variability of the ECPs but also significant negative alpha correlation in subject 1, subject 4 and subject 8.

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FIG. 3.

RSN2: split into 5 parts for every subject shows the ECPs of RSN 2 (occipital pole component) for all five parts of the split datasets for all 12 subjects. The ECPs show higher Z scores at these shorter time intervals and the pattern change over time.

1 Universidad Nacional Autónoma de México, Instituto de Neurobiología, Querétaro, Mexico 0045 Hemispheric asymmetries of the Default Mode Network in children

Resting state networks (RSN) are brain regions presenting strong temporal coherence in low frequency fluctuations of the BOLD signal, and have been widely described. One of these networks is the Default Mode Network (DMN) (Raichle et al. 2001), which includes the prefrontal (PFG), anterior (ACG), and posterior cingulate (PCG), the lateral parietal (LPG), and inferior/middle temporal gyri (MTG), the precuneus, thalamic nuclei, and regions extending to the medial temporal lobe (Boly et al. 2008), nevertheless little is known about hemispheric asymmetry. Recent studies in adult subjects have shown lateralization (Saenger et al. 2012). We investigated such hemispheric asymmetries in children's DMN.

50 healthy children (28 female, age range 7–9) participated after their parents informed signed consent. Imaging was done in a G.E. Discovery MR750 3.0T with a 32 channel head coil, using an EPI sequence with TR/TE=2000/40 ms, in 4×4×4 mm³ spatial resolution. The data were analyzed using FSL's MELODIC module (V 4.1.3, Smith et al. 2004). Standart preprocessing was done with registration to an age-appropriate atlas (Fonov et al. 2011). Data was temporally concatenated in a single 4D data set and decomposed into resting state patterns of functional connectivity using ICA. The DMN was identified from previous studies. Each DMN map was flipped in the L-R direction, to determine functional laterality a paired two-sample t-tests with randomized permutation methods between the flipped and un-flipped was made (Saenger et al. 2012).

The medial frontal gyrus (medFG), PCG, cingulate gyrus (CG), angular gyrus (AG) as well as the precuneus, LPG, and MTG were identified as part of the children's DMN. Greater functional connectivity in the left hemisphere (leftward asymmetries) was observed and comprise the PCG, CG, and precuneus. These results suggest that functional connectivity in children's DMN is not equally distributed between hemispheres.

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FIG. 1.

The Default Mode Network for children is shown in blue. Common hemispheric asymmetries of functional connectivity are shown in red-yellow, greater leftward connectivity is in the left hemisphere.

1 Centre for Biomedical Technology, Madrid, Spain 2 Universidad Complutense, Madrid, Spain 3 Ayuntamiento, Madrid, Spain 4 Hospital Clínico San Carlos, Madrid, Spain 0050 The connectivity patterns of the Default Mode Network. A MEG study in Mild Cognitive Impairment

The default mode network (DMN) has received growing attention in recent years because it seems to be involved in the neuropathology of psychiatric and neurodegenerative disorders such as autism, schizophrenia and Alzheimer Disease. It has been defined as a task negative network, because the activity of all its brain regions is increased during the resting state and suspended during external or goal directed tasks.

The DMN is particularly relevant for aging and dementia since its structures are vulnerable to atrophy, deposition of the amyloid protein and show reduced glucose metabolism. Several functional studies have found that the task-induced deactivation pattern and its functional connectivity are progressively decreased along the continuum from normal aging to Mild Cognitive Impairment (MCI) and to clinical Alzheimer Disease (AD). In order to provide more information about the connectivity changes in the DMN in normal and pathological aging, we have used the magnetoencephalography (MEG) with 40 healthy people and 70 mild cognitive impairment (MCI) subjects (35 with amnestic MCI, 35 with multidomain MCI) comparing a resting state condition with eyes closed with a mental arithmetic task with two levels of difficulty. Additionally an extensive neuropsychological battery has been applied to all subjects in order to assess their cognitive state. To analyze the connectivity we have applied different methods as Mutual Information (MI), Phase locking value (PLV) and Phase locking index (PLI). Our results are in line with previous studies, finding differences in connectivity and task- induced deactivation, depending on the difficulty of the task, although with differences between the MCI and the control group, suggesting that the alteration of the DMN could be considered as an early marker of AD pathology.

1 University Hospital Lübeck, Neurology, Lübeck, Germany 2 International Neuroscience Institute (INI), Hannover, Germany 0051 Abnormal Brain Networks in Parkinson's Disease

We used a graph theoretical approach to investigate properties of human brain networks in individuals with idiopathic Parkinson's disease (PD; n=40) compared to age- and gender-matched healthy controls (n=21). A second, independent data set with 15 PD patients and 14 healthy controls was used to replicate results found in the main data set. Brain networks were derived from resting-state fMRI functional connectivity between 90 cortical and subcortical regions. The clustering coefficient was computed to characterize the brain network on a local level, whereas the mean clustering coefficient and the characteristic path length were used to characterize the brain network on a global level. The clustering coefficient C_i of a node i is defined as the fraction of a node's neighbors which are neighbors of each other. The characteristic path length L is defined as the average shortest path length in the network. While the mean clustering coefficient is a measure for the segregation of the network, the characteristic path length is a measure for the global integration and thus the efficiency of the network. On a global level, we found a significantly higher mean clustering and a significantly higher characteristic path length for patients compared to controls. This indicates a less efficiently organized brain network for individuals suffering from PD. The higher mean clustering coefficient mainly originated from higher clustering coefficients found in sub-cortical structures, including the putamen and thalamus, and in the occipital lobe. We replicated the significantly higher local clustering in the occipital lobe with the second independent sample. The increased clustering coefficient observed for the right thalamus was found to result from increased functional connectivity between brain regions involved in motor control, i.e. thalamus, pallidum, dorsal cingulate cortex and the SMA. The results suggest that graph metrics can be used to characterize altered functional brain networks in Parkinson's disease. More research is needed to better understand functional and clinical correlates of the identified network topology changes.

1 University Medical Center, Hamburg-Eppendorf, Systems Neuroscience, Hamburg, Germany 0052 Long-Range Temporal Pattern Repetition of the BOLD Signal During Resting State

Question: Examining the temporal characteristics of the BOLD signal may elucidate the dynamic correlation structure of spontaneous fluctuations. The objective of this investigation was to assess the complexity of the BOLD signal during resting state using sample entropy. Sample entropy quantifies the complexity of a physiological time series by computing the inverse log probability of observing repeating segments (m) of similar states constrained by a tolerance threshold (r).

Method: High temporal resolution resting state fMRI data (TR=645 ms) were acquired from the NKI Enhanced Rockland test-retest database. Data from 17 healthy (4 female) participants (21–57 years) were included. EPI data were aligned and smoothed (6 mm FWHM). Nuisance variance associated with white matter and CSF signals, and head movement was removed with linear regression. Time series were bandpass filtered (.008–.2 Hz) and the signal was standardized. Sample entropy was computed with parameters: m=2 and r=.15.

Results: Sample entropy values ranged from .74 to 1.83 within and across participants. There was a mean global sample entropy value of 1.34 (SD=.17). Global sample entropy values were not significantly correlated with age. The spatial distribution of group averaged sample entropy for the cortical surface shows that sensory-motor systems (posterior) have greater regularity and that complementary cognitive systems (anterior) show greater complexity. A similar spatial pattern was observed for all participants. Sample entropy was strongly inversely correlated with the intrinsic BOLD AR1 process. However, for all participants, sample entropy of surrogate time series matched for AR1 coefficients was significantly greater than actual complexity. This suggests that pattern repetition induces long-range order in the resting BOLD signal that cannot be explained by short-range autocorrelation. Sample entropy computed at higher bandwidths of the BOLD signal was significantly greater and near uniformly distributed at an asymptotic value (approximately 2.4), and had weak AR1 coefficients, resembling a random signal.

Consulusion: Spontaneous brain networks are comprised of voxels whose signals are non-random and yet vary in the complexity of long-range structure and pattern repetition, which may play a role in the dynamic reconfiguration of network connectivity.

1 Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience., Madrid, Spain 2 Servicio de Neurología. HUSC, Madrid, Spain 3 Servicio de Geriatría. HUSC, Madrid, Spain 4 Universidad Complutense, Madrid, Spain 0053 Resting state functional connectivity patterns. A MEG study in Amnesic Mild Cognitive Impairment and Multidomain Mild Cognitive Impairment.

Alteration of brain communication due to abnormal patterns of synchronization is nowadays one of the most suitable mechanisms for having a better understanding of brain pathologies. Very recently, it has been proved that abnormal changes in both local and long range functional interactions underlie the cognitive deficits associated with different brain disorders. Mild cognitive impairment (MCI) is a state characterized for cognitive dysfunction, such as the memory. The study of the spatial and dynamic alterations in MCI subjects' functional networks could provide important evidences of the brain mechanisms responsible for such impairment.

Here we use magnetoencephalography (MEG) to record resting state activity of healthy elderly people and patients with MCI, with both eyes closed and eyes open. Additionally, the subjects had a neuropsychological test done to determine their MCI subtype. Our database consists in 30 healthy elderly people and 60 MCI patients (30 with amnestic type and 30 with multidomain type). In order to provide a functional connectivity pattern, we calculate the Mutual Information and Phase Locking Value of the MEG time series. The analysis is done for the classical frequency bands, via a statistical test to search for differences between groups with different diagnosis.

Our result shows an increased connectivity in DCL when compared with Control and an increased connectivity in DCLa when compared with MCIm.

1 RWTH University, Neurology, Aachen, Germany 2 JARA Juelich Research Alliance, Juelich, Germany 3 Research Centre Juelich, Institute of Neuroscience and Medicine INM-4, Juelich, Germany 4 University Muenster, Neurology, Muenster, Germany 0054 Disturbed fronto-striato-thalamic resting state connectivity in Huntington's Disease

Questions: There has been a significant surge in resting state functional magnetic resonance imaging (rs-fMRI) in clinical settings, recently. In particular, notable changes in resting state functional connectivity (rs-fc) in neurological and psychiatric disorders have been identified (Fox et al., Front Sys Neurosci 2010). However, there are no data on rs-fc in symptomatic Huntington's Disease (HD), a lethal, but until now untreatable neuropsychiatric disorder.

Methods: We examined 17 genetically confirmed symptomatic HD patients (age 45+/−10y, 7m) and 19 age- and sex-matched controls (age 47+/−10y, 8m) in an rs-fMRI paradigm at 3 Tesla using a Siemens Trio MRI (TR 2.2s, 270 volumes). Data were analysed by temporal concatenation ICA followed by Dual Regression, corrected for age and atrophy by including VBM data as a confound (Beckmann et al., Phil Trans Royal Soc 2005, Zuo XN et al., Neuroimage 2010).

Results: 30 common rs-fMRI components were identified by ICA. Of these, 25 were affected by the disease. In particular, caudate nucleus, thalamus and prefrontal/frontal/motor areas were altered significantly (p=0.001 TFCE corrected). Parietal maps were affected to a lesser degree (0.01

Conclusions: Our data demonstrate a severe disturbance of rs-fc in HD, independently of atrophy-related changes with an emphasis on fronto-striato-thalamic networks. Further studies will focus on prognostic value and correlations with functional markers.

1 Nathan S. Kline Institute for Psychiatric Research, Orangeburg, United States 2 Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York, United States 3 Center For the Developing Brain, Child Mind Institute, New York, United States 4 McGovern Institute for Brain Research, Cambridge, United States 5 Johns Hopkins University, Applied Mathematics and Statistics, Baltimore, United States 6 Johns Hopkins University, Department of Computer Science, Baltimore, United States 7 Virginia Tech Carilion Research Institute, Roanoke, United States 8 Donders Institute for Brain, Cognition and Behaviour Functional Brain Imaging, nijmegen, Netherlands 0055 Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC)

Once a distant goal, discovery science for the human connectome is now a reality. Researchers who previously struggled to obtain neuroimaging data from 20–30 participants are now exploring the functional connectome using data acquired from thousands of participants, made publicly available through the 1000 Functional Connectomes Project and its International Neuroimaging Data-sharing Initiative (INDI). Beyond access to data, scientists need access to appropriate tools to facilitate data exploration - particularly those who are inexperienced with the nuances of fMRI image analysis, or lack the programming support necessary for handling and analyzing large-scale datasets.

Here, we announce the creation of the Configurable Pipeline for the Analysis of Connectomes (C-PAC) - a configurable, open-source, Nipype-based, automated processing pipeline for resting state fMRI (R-fMRI) data, for use by both novices and experts. C-PAC brings the power, flexibility and elegance of Nipype to users in a plug-and-play fashion - without any programming. Using an easy to read, text-editable configuration file, C-PAC users can rapidly orchestrate automated procedures central to R-fMRI analyses, including:

• quality assurance measurements

C-PAC makes it possible to use a single configuration file to launch a product set of pipelines that differ with respect to specific parameters in each set (e.g., spatial/temporal filter setting, global correction strategies, motion correction strategies) though conserve computational and storage resources. Additionally, C-PAC can handle any systematic directory organization and distributed processing via Nipype. C-PAC maintains key Nipype strengths, including the ability to (i)interface with different software packages (e.g., FSL, AFNI), (ii)protect against redundant computation and/or storage. The C-PAC beta-release will be distributed via INDI in the summer 2012. Future updates will include a graphical user interface, advanced analytic features (e.g. support vector machines, cluster analysis) and diffusion tensor imaging.

1 Dalhousie University, Department of Psychology and Neuroscience, Halifax, Canada 2 Dalhousie University, Psychiatry, Halifax, Canada 0056 Resting State Connectivity in the Default Mode Network is related to Performance Variability in Multiple Sclerosis

Patients with Multiple Sclerosis (MS) demonstrate slower and more variable performance on attention and information processing speed tasks. High within-subject variability in performance is associated with poorer cognitive functioning and neurologic status in a variety of neurodegenerative populations. Within-subject variability has also been associated with alterations in default mode connectivity in healthy young and older adults. This study investigated potential changes in default mode connectivity associated with performance variability in MS.Methods: Relapsing-remitting MS patients and matched healthy controls completed test of information processing speed and resting-state fMRI scans. Within-subject variability in reaction time performance was calculated from tests of simple reaction time (SRT), choice reaction time (CRT), and semantic search reaction time (SSRT). Functional connectivity in the default mode network (DMN) was investigated with seed-based analyses using anterior (i.e. anterior cingulate/medial frontal gyri) and posterior seeds (i.e. posterior cingulate gyrus). The effects of within-subject variability on connectivity in the DMN were also evaluated.Results:MS patients demonstrated greater within-subject variability on the SRT and SSRT cognitive tasks compared to healthy controls (SRT:F(1, 32)=4.22,p=.048; SSRT:F(1, 32)=4.25,p=.047). The DMNs were similar for the anterior and posterior seeds (Figures 1 and 2). For MS patients, lower within-subject variability (i.e. greater stability) on the SSRT task was associated with greater resting state connectivity in the frontal pole when the anterior seed was used (Figure 3). This relationship was not found using the posterior seed. These findings suggest that among patients with MS, greater frontal pole connectivity at rest may be associated with greater performance stability on complex speed-dependent information processing tasks.

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FIG. 1.

Functional connectivity in all subjects associated with anterior seed.

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FIG. 2.

Functional connectivity in all subjects associated with posterior seed.

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FIG. 3.

In MS patients, greater functional connectivity between the anterior seed and the frontal pole was associated with lower within-subject variability on a semantic search reaction time task.

1 University of Bonn, Psychology, Bonn, Germany 2 University of Bonn, Center for Economics and Neuorscience, Bonn, Germany 3 University of Bonn, Life and Brain Center, Bonn, Germany 4 University Clinics Bonn, Epileptology, Bonn, Germany 0059 Assessing the function of the fronto-parietal attention network: Insights from resting state fMRI and the attentional network test.

The analysis of fMRI time series has revealed that brain regions that are routinely deactivated and those that are usually activated in demanding task conditions form two distinct networks in the resting brain. The former network is labeled default mode (DMN) or task-negative network while the latter is called fronto-parietal (FPN) or task positive network. In the absence of any task, both networks still show coherent BOLD fluctuations. While the DMN has received considerable attention in the past years, the FPN is less well explored. The functional role of the FPN is subject of the here presented research.

Question: The FPN has been labeled ‘attention network’. The evidence for this claim, however, is primarily based on the activation of the FPN in attention demanding tasks. A relationship between properties of the FPN at rest and individual attentional skills has not been addressed yet.

Methods: With the present study we seek to assess the assumed attentional function of the FPN at rest by means of an individual differences approach. N=23 participants underwent an fMRI scan at rest. Outside of the fMRI environment all participants performed the attentional network test (ANT). The ANT is a behavioral protocol that quantifies an individual's attentional capacity in three different domains: alerting, orienting and executive control.

Results: We modeled the FPN as a graph based on a functional connectivity analysis of time-series from sixteen regions of interest that were derived from the literature. Three different centrality measures (node degree, eigenvector and betweenness centrality) were computed for all sixteen regions. In a next step, we used stepwise multiple regression analysis to examine whether any of the centrality measures relate to attentional task-performance in any of the attentional domains. We found strong multiple correlations with the FPN and the alerting and executive control scores. The results regarding the orienting score did not survive a correction for multiple testing.

Conclusion: In sum, individual differences in functional connectivity within the FPN covary with an individual's attentional capacity. This findings indicate that the FPN in the resting brain can indeed be labeled an attentional network.

1 Institute of Cognitive Neuroscience, UCL, London, United Kingdom 2 Weizmann Institute of science, Neurobilogy, Rehovot, Israel 3 University of Wisconsin - Madison School of Medicine, Psychiatry, Madison, United States 4 Hebrew University of Jerusalem, Psychology, Jerusalem, Israel 0060 Resting state functional connectivity reflects abnormal response patterns in human visual cortex

Even in the absence of specific tasks the cerebral cortex shows an incessant pattern of ultra slow fluctuations termed resting-state activity. The resting state coherent (also termed functionally connected) patterns are not random but appear to be similar, in a number of systems, to the large scale organization of task-induced functional networks. However, it is not clear to what extent this similarity occurs in cases of abnormal functionality. We have previously found that LG, a sighted individual suffering from developmental object agnosia, has no apparent cortical structural abnormality, yet upon visual stimulation shows an abnormal response pattern along the hierarchy of visual areas. Specifically, LG's intermediate visual areas (V2, V3) show a paradoxical inactivation upon visual stimulation. Here, examination of LG's resting state functional connectivity revealed the same abnormality- including a strong atypical decorrelation of areas V2–V3. Thus, our results demonstrate that the resting-state connectivity also reflects functional abnormalities in task-induced cortical activation. These results suggest that resting state connectivity could provide a powerful tool for detecting abnormalities in cortical activations during task performance.

1 BCCN, Berlin, Germany 2 Technical University, Berlin, Germany 3 Northeastern University, Boston, Germany 0061 Resting state functional connectivity of the human cortex: large-scale neural model

Question: Here, we aim to address questions of functional connectivity (FC) between anatomically unconnected areas of the human cortex, studying topology and dynamics of empirically derived resting state networks (RSNs) from fMRI data.

Methods: Our RSNs are comprised of 64 functionally connected network nodes/regions of interest (ROIs), as adapted from the study of Kiviniemi et al. (2009). Correlation matrices, characterizing FC between these regions, are used for exploration of the network topology and dynamics. The network topologies are characterized by graph theory methods and its dynamics is modelled as system of 64 neural oscillators, describing individual network node, and coupled by time-delayed interaction terms.

Results: Thresholded correlation matrices are used to create graphs for the cortex functional networks. We investigate how network topologies change for different levels of correlation and found that they diverse from comparable random networks. Similarly, networks dynamics simulated for different spatiotemporal networks, which are created after thresholding correlation matrices, enabled us to characterize parameters important for the emergence of the coherent fluctuations in the network. From a stability analysis of the networks dynamics we demonstrate that time-delay play significant role in emergence of different networks.

Conclusion: Combination of the networks topology and different time-delays could account for the presence of the so-called functional connectivity between anatomically unconnected regions in human cortex.

1 Charité – Universitätsmedizin Berlin, Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Berlin, Germany 2 University of Heidelberg, Central Institute of Mental Health, Mannheim, Germany 3 University Hospital of Magdeburg, Department of Neurology, Magdeburg, Germany 4 Charité – Universitätsmedizin Berlin, Division of Neuroimaging Research, Department of Psychiatry and Psychotherapy, Berlin, Germany 0062 Personality relates to differences in small-world properties of intrinsic functional brain connectivity networks

Introduction: Personality is an established research area but little is known about the neural correlates. To address this we used an integrative neuroimaging approach (task fMRI and resting state fMRI (rs-fMRI)). Graph theory provides a powerful way to describe the topological organization of brain connectivity for rs-fMRI data. Changes in statistical properties such as small-worldness and the clustering coefficient (ClustCoef) have been associated to neuropsychiatric diseases (Basset et al., 2009;Curr Opin Neurol), but research is lacking on whether differences in personality may also relate to altered small-word properties.

Methods: (1) 280 healthy subjects completed an emotional faces task during fMRI that was analyzed using whole-brain regression models with NEO-FFI measures serving as covariates to determine potential ROIs associated with each personality trait. For a sub-sample (n=235), network metrics (characteristic path length and ClustCoef) were computed from a 90-node undirected graph derived from resting state fMRI. (2) Correlations between whole-brain network metrics and NEO-FFI measures were computed to later on (3) examine local small-world properties of fMRI derived ROIs for traits that showed an association in this analysis.

Results: (1) fMRI results showed a positive association between the personality factor Conscientiousness (C) and activation in the right Superior Frontal Gyrus (SFG), left Postcentral Gyrus (PCG), and left Middle Temporal Gyrus (MTG) among others. (2) rs-fMRI results indicated that among NEO-FFI measures only C showed an association to whole-brain network metrics in particular to the ClustCoef (r=0.17; p=0.009). (3) The local ClustCoef of the right SFG (r=0.16; p=0.016), left PCG (r=0.18; p=0.005), and left MTG (r=0.22; p=0.001) showed associations to this personality trait as well.

Implications: These preliminary presented data suggest that differences in personality do relate to altered small-word properties, specifically trait C and ClustCoef. As C has been implicated as a risk factor for Alzheimer disease (Wilson et al.,2007; Arch Gen Psychiatry) the reduced ClustCoef in the SFG and MTG for subjects scoring low in C partly overlap with findings of altered small-world properties in this disease (He et al.,2009;Neuroscientist) since SFG and MTG dysfunctions have been reported in Alzheimer's disease.

1 Friedrich Schiller Univerität Jena, Neurology, Jena, Germany 2 Friedrich Schiller University Jena, Otolaryngology, Jena, Germany 0063 The time course of cortical plasticity after facial nerve palsy.

How does the brain reorganize in order to support functional recovery from transiently decreased motor control? An understanding of the underlying processes requires detailed knowledge of the time course of cerebral reorganization. Here, we longitudinally studied voxel-based morphometry and resting state functional magnetic resonance imaging (MRI) 10 times in one patient during the course of Bell's palsy (idiopathic facial nerve palsy) up to complete clinical recovery. Morphometric analysis revealed a significant alteration in the face area of the primary motor cortex (MI) contralateral to the paretic face, with an initial increase in gray matter concentration. Functional connectivity analysis between the MI and other parts of the facial motor network revealed acutely disrupted intrahemispheric connectivity but unaltered interhemispheric connectivity. The disrupted connectivity was most pronounced on the day of the onset of symptoms, with a subsequent increase during the course of recovery. This time course was found to differ between the selected parts of the facial motor network. However, the increase in connectivity strength preceded clinical recovery in all areas and reached a stable level before the patient fully recovered. These results demonstrate that recovery from facial nerve palsy is complemented by cortical reorganization, with pronounced changes of connectivity that precede clinical recovery.

1 Maastricht University, Neuropsychology and Psychopharmacology, Maastricht, Netherlands 2 VU University Medical Center, Anatomy and Neuroscience, Amsterdam, Netherlands 0064 Intrinsic functional connectivity parcellation and task activation in human lateral prefrontal cortex

Questions: fMRI studies during cognitive task performance show that prefrontal cortex (PFC) activity is functionally segregated in distributed foci. This is in line with cyto-architectonic studies showing that the cortex is divided in discrete functional areas. The relationship between task activation and functional areas is not clear because the latter can only be studied in vitro, whereas task activation is studied in vivo. The current aim was to study how lateral PFC functional modules delineated with an fMRI based parcellation method (Goulas et al., submitted) relate to brain activity during a cognitive task.

Method: Thirty-two participants underwent a rest-task-rest sequence, 10 minutes each, in a 3.0 T scanner. After preprocessing, detrending and 0.01–0.1 Hz filtering was applied. Task effects (2nd phase) were regressed out. Grey matter voxels overlapping with a lateral PFC mask were used to create an undirected dichotomized graph matrix, which was entered in theLouvainmodule detection algorithm (Blondel et al., 2008).

Results: First, we established 0.0.2 to be the best density at which to dichotomize the correlation matrix, yielding an optimal trade-off between high modularity (>0.75), across data set reproducibility (nMI=0.885 (0.037), and module number (12.27 (1.53)). Next we assigned individual modules obtained from the second rest period to functional areas based on similarity in functional connectivity profile and spatial location with the group data in Goulas et al. (submitted). We quantified per area the average BOLD response during execution of the cognitive task. This learned that the most prominent areas of the task positive network were areas 44, 8Av, FEF and to a lesser extend 46. Task deactivated areas are 8Ad, 9/46d, and to a lesser extend 45 were deactivated by the task.

Conclusion: These results show that it is possible to directly study the relationship between task actiavtons and specific functional areas in the prefrontal cortex.

1 Charité – Universitätsmedizin Berlin, Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Berlin, Germany 2 Charité – Universitätsmedizin Berlin, Division of Neuroimaging Research, Department of Psychiatry and Psychotherapy, Berlin, Germany 3 University of Heidelberg, Central Institute of Mental Health, Mannheim, Germany 0065 Personality as reflected in activation patterns during an active reward task does not reflect regional intensities of spontaneous brain activity

Introduction: Recent research suggested that inter-individual differences in the Big Five personality dimensions (Costa&McCrae) are reflected in intensities of spontaneous low-frequency fluctuations during rest (Kunisato et al., 2011;Neurosci Lett.). However, it remains unclear whether differences in personality as reflected in activation patterns during task fMRI may also reflect differences in intensities of regional spontaneous brain activity during rest in specific task associated regions. Therefore, we used rs-fMRI to explore whether regional low-frequency fluctuations during rest may relate to differences in the Big Five in regions activated by task fMRI.

Methods: (1) 281 healthy subjects completed a reward-anticipation task during fMRI that was analyzed using whole-brain regression models with NEO-FFI measures serving as covariates to determine potential regions associated with each trait. (2) For a sub-sample (n=200), rs-fMRI derived ALFF (Song et al., 2011; PLoS ONE) was then used to obtain measures of the intensity of regional brain baseline activity during rest for the regions derived from the findings in the reward fMRI task. Using correlational analyses we determined the association of NEO-FFI measures and ALFF values.

Results: (1) Reward fMRI results revealed a negative association between the personality factor Conscientiousness and activations in the right Caudatus Nucleus and the right Putamen. Agreeableness was positively associated with activity in the left Precentral Gyrus (PCG) and the right Supramarginal Gyrus. Openness was positively associated to activity in the left PCG. Extraversion and Neuroticism did not show any associations. (2) No significant associations between ALFF values and NEO-FFI related ROIs obtained in the reward task were found for any personality dimension.

Implications: It was shown that several of the Big 5 dimensions were associated with activity in different brain regions in a functional reward task. However, amplitudes of regional low frequency fluctuations in these brain regions were not associated to the personality traits. Thus, this study suggests that inter-individual differences in the Big 5 reflected in activation patterns during an active reward task do not reflect regional intensities of spontaneous brain activity.

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1 University of North Carolina, Biomedical Research Imaging Center, Chapel Hill, United States 2 UNAM, Mexico City, Mexico 3 Universidad Veracruzana, Instituto de Neuroetologia, Xalapa, Mexico 0066 Resting state connectivity in Spinocerebellar Ataxia type 7: an fMRI study.

Introduction: We recently documented gray and white matter changes in Spinocerebellar ataxia type 7 (SCA 7), which is a progressive neurodegenerative disorder characterized by cerebellar ataxia and visual loss. Currently there are no reports on the neurophysiological impact of SCA7. The aim of this study was to measure differences in “resting state networks” (RSNs) using fMRI in SCA7 genetically confirmed patients relative to normal controls.

Methods: Nine patients with genetically confirmed SCA7 and nine healthy volunteers participated in this IRB approved protocol. Genetic and clinical data were described in Alcauter et al. (2011). Images were acquired with a 3T Philips Achieva MR scanner. Whole brain standard functional images were collected by using an Echo Planar Imaging Single Shot sequence (TR=2000 ms, TE=35 ms). Structural imaging included a T1 Fast Field-Echo sequence (1×1×1 mm3). Subjects kept their eyes closed but remained awake.

Image processing: After standard preprocessing, functional images were co-registered to MNI template registered structural images. Nine regions of interest (ROI) were defined in MNI template space as spheres with ∼8 mm diameter, to define the Dorsal Attention, Default Mode, Fronto Parietal Control, Sensory Motor, and Visual networks, and two spheres centered in the Cerebellum. ROIs where warped back to functional space to extract the mean time series within them, correlation values between these and each voxel time series were computed voxel-wise for whole brain for each subject. To compare between groups, a two tailed t test was performed voxel-wise for each network (FDR, P

Results: We found SCA7 affecting all networks. The disease led to larger correlation values in some areas, as well as smaller correlation values in others. With some exceptions (i.e. posterior cingulate), we found a correlation increase near the selected ROIs, while a correlation decrease for distant areas.

Conclusion: These results suggest that neurodegeneration in SCA7 patients lead to increases in short distance functional connectivity of the remaining tissue. Analyses including 20 more patients already identified will allow us to make correlations with other data measured from the participants.

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FIG. 1.

DMN greater coactivation in SCA7 patients (red), and controls (blue).

1 University of Bergen, Biological and medical psychology, Bergen, Norway 0068 Sex differences in resting state activity within the inferior frontal gyrus is only evident when women are in the follicular phase

Introduction: Menstrual cycle hormones have been suggested to modulate functional hemispheric asymmetry through the reduction of interhemispheric inhibition. In a recent fMRI study, it was demonstrated that the right inferior frontal gyrus (IFG) was less inhibited by the left during the follicular phase, when participants performed a verbal decision task. The current study aims to investigate whether this hormone-dependent reduction in inter-hemispheric inhibition within the IFG is also present in resting state activity.

Method: 16 women and 15 men were tested three times whereby the women were tested in their menstrual, follicular, and luteal phase. The participants were asked to relax and keep their eyes closed during the session. The data were collected with a 3T GE-Signa MRI scanner. EPI; TR 2.8 s.; 96×96 matrix. SPM8: Realign and unwarp, normalization, and smoothing. GIFT: independent component analysis (ICA) with 40 components which were spatially sorted after activation in IFG triangularis. SPM8: ANOVA was done with the variables sex and phase explored with an uncorrected threshold of p<0.001. The ANOVA was followed up with t-contrasts explored with threshold of FDR p<0.05.

Results: The ANOVA resulted in a main effect of sex with an activation pattern in left IFG, superior parietal gyrus, paracentral lobule, and precuneus driven by stronger activation for the men. Also an activation in the right middle frontal gyrus was detected which was stronger for women. Additionally, the ANOVA resulted in an interaction between sex and phase within the right IFG, driven by a stronger activation for women in the follicular phase compared to men.

Conclusion: The results suggest that women in the follicular phase have a stronger right sided frontal involvement during rest as compared to men. Whether the activation reflects an especially low inhibition from the left hemisphere onto the right, as previously found, needs to be further explored with a connectivity analysis.

1 UCSF Memory & Aging Center, Department of Neurology, San Francisco, United States 2 Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, United States 3 Dementia Research Centre, UCL Institute of Neurology, London, United States 4 Massachusetts General Hospital, Harvard Medical School, Harvard, United States 0069 Functional connectivity networks in healthy subjects predict regional atrophy patterns in different variants of Alzheimer's disease

Question: Neurodegenerative diseases such as Alzheimer's disease (AD) target large-scale neural networks, however, little is known about networks specifically affected in different AD variants. We aimed to assess functional networks in healthy subjects by seeding regions that are specifically atrophied in 3 common AD syndromes: early-onset AD (EOAD), language variant AD, and visual variant AD.

Methods: 112 healthy controls (age 66.6 (3.5) years, 36% male) underwent task-free functional magnetic resonance imaging. Regions specifically atrophied in each AD variant were: right middle frontal gyrus in EOAD, left superior temporal sulcus in AD-language, and right middle occipital gyrus in AD-visual. 4mm spheres were drawn around the peak atrophy voxels (Figure 1). The average time series in these seeds were used as covariates of interest in a whole-brain regression analysis to determine correlations with each voxel's spontaneous BOLD signal time series.

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FIG. 1.

4mm seeds used for the seed-based functional connectivity analysis.

Results: Each seed produced specific functional network maps that greatly overlap with AD syndrome-specific regional atrophy patterns (Figure 2). Activity in the EOAD seed correlated with activity in bilateral (right more than left) frontal, insula, anterior cingulate, right precuneus and lateral parietal regions, greatly overlapping with the executive control network. The network map of the AD-language seed involved mainly regions of the language network, including bilateral (left more than right) middle and superior temporal, and parietal lobe regions, with some involvement of left inferior and middle frontal lobe regions as well. Finally, activity in the AD-visual seed correlated with regions involved in primary and higher visual networks, including bilateral lateral and medial occipital lobe regions, extending anteriorly into the fusiform gyrus and superior parietal lobe regions.

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FIG. 2.

Task-free functional connectivity networks in healthy individuals produced by seeding 3 different regions. Shown are statistical p maps after correction for multiple comparisons (FWE p<0.05).

Conclusion: Our data show that syndrome-specific atrophy patterns in AD mirror functional networks in the healthy brain. We hypothesize that AD syndromes are associated with degeneration of specific functional networks.

1 The Open University, Milton Keynes, United Kingdom 0071 Neurophysiological differences between people with Obsessive Compulsive Disorder and controls.

Obsessive compulsive disorder (OCD) is an anxiety disorder characterised by recurrent intrusive obsessions and compulsions. This study investigates EEG power spectra in people with OCD compared to controls. Neurophysiological studies have identified an aberrant positive feedback loop in the orbito-striato-thalamic network in people with OCD. It is thought that this causes hyperactivity in the orbito frontal and anterior cingulate cortex, which may underlie the behavioural symptoms in OCD. 21 people with OCD (14 female; mean age 46, range 21 to 74), and 22 controls without a mental health disorder (12 females; mean age 37, range 18 to 64) were recruited. The Yale Brown Obsessive Compulsive Scale (Y-BOCS) and the Minnesota Multiphasic Personality Inventory 2 Restructured Form (MMPI-II-RF) were administered. EEG was digitally recorded at 75 Hz during five minutes eyes closed and five minutes eyes open and analysed quantitatively. Data was analysed (0.3Hz to 50Hz with power line notch filter at 50Hz,) on a common average montage.

The OCD and the control group differed significantly on the clinical scales of the MMPI-II-RF (e.g. self doubt, stress, anxiety and social avoidance all p<0.01). Power spectra during the eyes-closed condition revealed a decrease in alpha amplitude (8–10Hz) in temporal electrodes and a widespread increase in amplitude in the beta range (21–25 Hz) in the OCD group (both p<0.05). During the eyes-open condition, we observed widespread decreased amplitudes in the alpha range (9–11Hz) and increases in beta frequencies (22–26 Hz) in parietal electrodes, (all p<0.05). As alpha is generally considered to indicate visual disengagement, a decrease in this frequency range might be a sign of hyperactivity as described by the fronto-striatal model of OCD. Additionally, beta is generally considered to indicate mental focus, the observed increases in this frequency range could reflect a heightened state of alertness in people with OCD, also in line with the models predictions. Our interpretation will be further evaluated by correlating the behavioural and neurophysiological data and by examining the results of complementary evoked response experiments.

1 Mount Sinai School of Medicine, Neurology, Radiology, Neuroscience, New York, United States 0072 Functional connectivity changes in Parkinson's disease: A graph theory analysis of resting-state fMRI data

The degeneration of midbrain dopaminergic neurons in Parkinson's disease (PD) is accompanied by impairment of motor, cognitive and emotional functions. We should thus expect that PD presents with abnormalities of functional connectivity extending outside motor areas. In this study, we characterized the whole brain functional connectivity in PD patients compared to healthy controls (HC) using graph theory analysis of resting-state (RS) fMRI data.

RS-fMRI was acquired on 10 patients with PD (6M/4F, 62.4±7.77 yrs, UPDRS motor score 16.9±6.8, H&Y staging 1.8±0.4) and 10 HC (6M/4F, 63.5±8.84 yrs) using standard acquisition protocol. The whole brain was parcellated in 206 bilateral cortical, subcortical and cerebellum regions based on cytoarchitectonic maximum probability map and macrolabels. The connectivity matrices were constructed using voxelwise Pearson's correlation coefficients between averaged time series from all brain regions. Using Fisher's Z transformation on correlation matrices, we ensured variance stabilization. Both global [clustering coefficient (gamma), path length (lambda), global efficiency (E-glob)] and local [nodal betweenness centrality (BC), degree (D), local efficiency (E-loc)] network metrics were computed using Brain Connectivity Toolbox. The correlation matrices were threshold over a wide range of sparsity (10% ≤S≤65% with 5% interval).

Both controls and patients showed small-world network properties with normalized clustering (gamma=1.2 HC and 1.3 PD) and path length (lambda=1.39 HC and 1.13 PD). However, clustering coefficient was significantly lower (PD=0.34 HC=0.41 p=0.001) and path length higher (PD=1.98, HC=1.69,p=0.017) in patients compared to controls. On further investigation of local network properties, we found statistically significant decreased E-loc in right supramarginal and middle occipital gyri and in left thalamus in PD as compared to HC (p<= 0.004). The number of hubs was lower in PD (12) than in HC (33) based on degree. In comparison to HC, PD patients showed statistically higher BC in right amygdala and superior temporal gyrus, in left thalamus and bilateral insula (p<= 0.004).

Our results show that network topological properties are altered in PD not only in motor areas but also in brain areas involved with cognitive and emotional functions.

1 University of Sheffield, Academic Clinical Psychiatry, Sheffield, United Kingdom 2 University of Oxford, FMRIB Centre, Nuffield Department of Clinical Neurosciences, Oxford, United Kingdom 0073 Time-frequency investigation of dynamic coherence and phase in fMRI resting state functional connectivity networks

Functional connectivity analysis of fMRI data obtained during awake rest has revealed that the brain is intrinsically organized into distinct networks including the default mode network (DMN) and the anti-correlated network (ACN). This study aims to investigate the temporal dynamics and potential non-stationarities that are present in pre-defined resting state networks using wavelet coherence analysis.

Resting state functional imaging data were acquired from 9 subjects (3T, 400 volumes, TR=1050ms). Pre-processing included motion correction, physiological noise removal (RETROICOR, Glover, Li, & Ress, 2000), slice timing correction, normalization into standard space and spatial smoothing (5mm FWHM). Three regions of interest (ROIs) for both the DMN and ACN were defined based on second level group results (p<.001, uncorrected) of z-transformed whole-brain Pearson correlation coefficients with two a priori ROIs in the posterior cingulate cortex and intra-parietal sulcus (Toro, Fox, & Paus, 2008). Implementation of the wavelet transform coherence was based on available mathematical processing scripts (http://www.pol.ac.uk/home/research/waveletcoherence/, Grinsted, Moore, & Jevrejeva, 2004). Time-averaged coherence and phase variability of intra-network (DMN and ACN) and inter-network connectivity were compared against a null distribution derived from between-subject results and against simulation data.

Results indicate that coherence was significantly lower in the DMN compared with the ACN and that resting state coherence was found across the full frequency spectrum. Hence, information regarding resting state connectivity may be contained in higher frequencies than the commonly explored ‘low frequency range’ (<0.1 Hz). Furthermore, our results show that phase relationships between resting state networks became increasingly unstable at higher frequencies and that phase relationships were significantly more stable within the DMN compared with the ACN. In conclusion, resting state networks are characterized by frequency-specific dynamic fluctuations in coherence and phase that are overlooked by conventional functional connectivity approaches.

1 University of Oxford, FMRIB Centre, Nuffield Department of Clinical Neurosciences, Oxford, United States 2 UC Berkeley, Department of Psychology and Helen Wills Neuroscience Institute, Berkeley, United States 0074 Dual clustering analysis of individual differences in resting state connectivity: beyond anxiety

Previous research on influences of individual differences in anxiety or depression upon resting state connectivity has typically related seed-based correlation coefficient maps to a single measure of trait affect. We propose a dual-clustering approach that allows parallel investigation of resting state networks and multiple dimensions of affect.

Two runs of resting state fMRI data were acquired from 20 subjects (150 volumes, TR=2100ms). Subjects completed standardized measures of anxiety, depression, worry and neuroticism. Following standard FSL-based pre-processing, single-subject independent component analysis was used to remove components linked to movement and physiological noise. Variance associated with outside brain, white matter and movement parameters was also removed.Mean signal was extracted from 27 regions of interest (ROIs) previously implicated in emotion-related processing.

Hierarchical clustering of extracted time courses replicated previously established networks. Clustering of questionnaire data revealed a primary distinction between depression and anxiety. Depression further sub-clustered into the presence of depressed mood and anhedonia. Anxiety sub-clusters dissociated worry and neuroticism from anxious arousal.

Non-parametric permutation testing (thesholded based on cross-run reliability) revealed cross-measure vulnerability to negative affect to be associated with increased subcortical (hippocampus-amygdala) connectivity and decreased frontal (Dorsolateral Prefrontal Cortex (DLPFC) - Orbitofrontal Cortex (OFC)) connectivity. The two depression dimensions were characterized by distinctive differences in OFC - amygdala - hippocampal connectivity. The two anxiety dimensions were characterized by changes in insula connectivity; altered connectivity with other frontal regions primarily being observed for the worry/neuroticism dimension.

We present a novel dual-clustering analysis that allows a shift from exploratory towards targeted hypothesis-driven research. Our findings point to a generalized vulnerability to negative affect (altered amygdala and frontal connectivity), which may manifest as depression (disrupted OFC connectivity), or as anxiety (disrupted insula connectivity).

1 CEDIMAT, Dep. of Radiology, Santo Domingo, Dominican Republic 2 University Medical School, Dep. of Neurology, Hannover, Germany 3 CEDIMAT, Department of Scientific Investigations, Santo Domingo, Dominican Republic 4 CEDIMAT, Dep. of Neurology, Santo Domingo, Dominican Republic 0075 The degree of dystonia in patients with Pantothenate Kinase Associated Neurodegeneration (PKAN) predicts components in resting state fMRI

Objective: In a group of 7 patients with secondary dystonia due to Pantothenate Kinase Associated Neurodegeneration (PKAN), we saw a “hyperactivation” in the posterior cingulum during an event-related motor activation study, present only 4 and 3 sec before the actual event (Fig. 1). For further exploration, we conducted functional Magnetic Resonance Imaging (fMRI) during true resting state conditions in 5 of them.

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FIG. 1.

Patients and Methods: Included were 5 individuals from the south-west of the Dominican Republic suffering from an identical mutation of the PANK2-gene, and 6 healthy controls.

FMRI was performed by the Philips 3T system “Achieva”: Echo Planar Imaging (EPI) gradient echo sequence (TR 2 s, 34 slices, voxel size 2.4×2.4×3 mm) over 10 min (where subjects were instructed to lie quietly with eyes closed) and 3D T1-weighted images for co-registration. Data processing by Statistical Parametric Mapping (SPM8) involved slice timing, realignment, spatial normalization to a standard EPI template and smoothing (8 mm Gaussian kernel). Independent Component Analysis (ICA) was performed by GroupICATv2.0 (http://icat.sourceforge.net) using Infomax algorithm. 3 components were correlated to the degree of dystonia (Burke Fahn Marsden scale) using SPM 2^nd level multiple regression analysis and family wise error correction.

Results: In three components in resting state fMRI, which are probably relevant for executive and motor function, the outcome could be predicted by the extent of dystonia. The regression analysis highlighted 2 bilateral clusters in the posterior cingulum, the left precuneus and the right parieto-temporal cortex (Fig. 2), as well as in right ventro-lateral prefrontal cortex and the SMA (Fig. 3 and 4).

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FIG. 2.

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FIG. 3.

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FIG. 4.

Conclusion: This resting state fMRI study shows a relation between the degree of dystonia and motor-related components during resting state fMRI. Reports about resting state in movement disorders are rare and demonstrated reduced default-mode processing in ventral prefrontal and posterior cingulated cortices in Tourette's syndrome and reduced connectivity, combined with a reversed pattern of activation and deactivation in Parkinson's disease. The significance of our findings remains to be determined, but may partially be due to patients' efforts to suppress their involuntary movements.

1 University of Tehran, College of Engineering, School of ECE, Tehran, Iran, Islamic Republic of 0076 Resting State Functional Connectivity in Medial Temporal Lobe Epileptic Patients: Seed-Based Correlation Analysis in Resting State after ICA

Objective: To localize impairments of resting state functional connectivity of Default Mode Network (DMN) in patients with medial Temporal Lobe Epilepsy (mTLE).

Methods: To localize DMN impairments, we compare resting state functional connectivity in five healthy subjects and five mTLE patients. We apply probabilistic Independent Component Analysis (pICA) and Seed-based Correlation Analysis (SCA) to estimate functional connectivity in resting state. Using pICA the spatial map of DMN is estimated and used to determine a seed for the further SCA. Therefore, the seed is selected exploratory, and unlike ICA, the method generates reproducible results. Then the mean of all voxels' time series within the seed is defined as the reference function for seed-based correlation analysis. The resulted correlation maps of subjects in two groups are then analyzed via a random effect model to find the between group differences.

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FIG. 1.

Graphical display of ROI-to-seed connectivity in controls (left) and patients (right). The seed is shown as a green circle and other regions are shown as red circles.

TABLE 1. Regions with Significant Difference between Controls and Patients

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Results: For healthy subjects, we have found a functional network containing 11 regions connected to seed (DMN) whereas in the patients there are 5 regions connected to the seed. In the network obtained for patients, there is a region which is not found in the control subjects. Between groups analysis showed significant decreases in the functional connectivity in patient (vs. controls) in right orbitofrontal cortex, right inferior temporal gyrus, left angular gyrus, left orbitofrontal cortex, left dorsal anterior cingulated cortex and left anterior prefrontal cortex. No significant increase was found in connectivity of patients (vs controls).

Conclusion: In this paper, we assessed the resting state functional connectivity in patients with mTLE and healthy subjects. We used a joint ICA-SCA approach and an exploratory algorithm of ICA for seed selection. This method conceals the reproducibility issue in group comparison of functional connectivity maps. Results show that the functional connectivity was significantly decreased among the frontal and temporal regions in patients relative to controls.

1 University of Campinas, Neurology, Campinas, Brazil 0078 Learning and delayed recall impairment in Alzheimer's disease are related to functional connectivity alterations in Default Mode Network

Introduction: New methods in functional magnetic resonance imaging, especially during resting state, may help to clarify the functional organization of cognitive networks. While their alterations have been reported in AD, especially in the Default Mode Network (DMN), their relationship with cognitive symptoms remains unclear. In this study, we aimed to verify the relation between DMN connectivity and memory performance in AD patients.

Methods: We studied 20 mild to moderate AD patients. High-resolution Magnetic Resonance Imaging (MRI) was performed using a 3.0 T scanner (Philips - Achieva). All patients underwent a 10 minutes task-free functional MRI (fMRI). We used FSL's Melodic Independent Component Analysis to study fMRI images in order to select each patient's DMN. They also underwent a cognitive evaluation, including the Rey Auditory Verbal Learning Test (RAVLT). We correlated RAVLT's scores (encoding, delayed recall and recognition) with individual maps of DMN connectivity, considering age, education and dementia severity. We also performed VBM analysis to obtain grey matter maps to correct these data for atrophy.

Results: We found significant areas of correlation between DMN functional connectivity and RAVLT encoding and delayed recall with left precuneus (MNI coordinates: −13, −67, 34). We did not find any areas of correlation with recognition, after correction for atrophy.

Conclusion: We report the association of changes in DMN connectivity (atrophy corrected) with episodic amnesia in AD, especially in left precuneus. These findings confirm the importance of neurofunctional networks alterations in the origin of AD symptoms.

1 The Nathan Kline Institute for Psychiatric Research, Orangeburg, United States 2 Child Mind Institute, Center for the Developing Brain, New York, United States 3 Hangzhou Normal University, Center for Cognition and Brain Disorders and The Affiliated Hospital, Hangzhou, China 0079 DPARSF 2: an updated MATLAB toolbox for “pipeline” data analysis of resting-state fMRI

The previous Version 1.0 of the Data Processing Assistant for Resting-State fMRI (DPARSF) (Yan and Zang, 2010) (www.restfmri.net) - a “pipeline” data analysis MATLAB toolbox, has successfully addressed issues of time-consuming manual procedures when acquiring resting-state fMRI measures, e.g., functional connectivity (Biswal et al., 1995), regional homogeneity (Zang et al., 2004), amplitude of low frequency fluctuation (ALFF) (Zang et al., 2007) and fractional ALFF (Zou et al., 2008). This user-friendly toolbox has made the relatively novel technique of resting-state fMRI easier to study, and has been cited for 34 times.

However, the DPARSF 1.0 is somewhat limited in its flexibility and fixed to a certain order of process steps. As such, we continue to update DPARSF to version 2 including the following features:

 •The processing steps can be freely skipped or combined.

Similar with the operation in version 1.0 of DPARSF, users simply need to arrange the raw files, and click a few buttons to set parameters in GUI to generate the desired preprocessed data and results with the new DPARSF (Figure 1). We hope this open-source toolbox could continually contribute to our novice (by user-friendly GUI) and expert users (by efficient command line) and promote the resting-state fMRI studies.

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FIG. 1.

Graphical user interface of DPARSF.

1 DZNE, Magdeburg, Germany 2 CBBS, Magdeburg, Germany 3 Otto-von-Guericke University, Psychiatry and Psychotherapy, Magdeburg, Germany 4 Leibniz Institute for Neurobiology, Magdeburg, Germany 5 Otto- von- Guericke University, Neurology, Magdeburg, Germany, ⁶DZNE, Rostock, Germany 7 MHH, Neurology, Hannover, Germany 0081 Resting-state functional connectivity in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive disease involving neurons within the motor cortex (M1). Furthermore, alterations of major brain network have been described in ALS patients using independent component analysis (ICA; Mohammadi, 2009), but the specific functional connectivity (FC) pattern related to M1 has not been specified yet. Our study therefore aimed to test for global and local resting state behaviour of the motor cortex in patients with ALS compared to healthy controls (HC). 26 ALS-Patients and 26 HC underwent a 10min eyes-closed resting state scan (3T). Data was analyzed using DPARSF (Yan, 2010), which is based on REST (Song et al., 2011) and SPM8 (Wellcome Trust Centre for Neuroimaging, London). Whole-brain FC was calculated for left and right motor cortex separately, as defined by the AAL-templates (Tzourio-Mazoyer et al., 2002). In addition whole brain fractional amplitude of low-frequency fluctuation (fALFF) (Zang et al., 2007) was calculated voxel-wise for patients and HC. Functional connectivity of right M1 with the posterior cingulate cortex, frontal pole, lateral parietal cortex and inferior temporal cortex within the default mode network was significantly decreased in ALS. In contrast, connectivity of M1 with supplemental motor area (SMA), precentral- and postcentral gyrus was significantly increased in ALS compared to HC. No significant differences were found for FC with left M1. fALFF in right M1 was significantly altered in ALS compared to HC, with significant higher fALFF in M1 and lower fALFF in the premotor cortex. Our study shows that alterations in FC seeded from the motor cortex in ALS coincides with alterations in local resting state properties. Alterations of FC with a large scale network are in line with previous findings in ALS using ICA (Mohammadi, 2009). However, we here for the first time focused on differences in functional connectivity and fALFF. M1 tissue abnormalities, as indicated by fALFF could influence long range connectivity, thereby affecting regions involved in emotional and cognitive processes, such as memory and attention - functional abnormalities that have previously been linked to ALS. Higher functional connectivity of M1 with secondary motor areas like SMA could be a hint to a compensatory mechanism as described by Schoenfeld et al. (2005).

1 RWTh Aachen Uniklinikum, Klinik für Psychiatrie, Psychotherapie und Psychosomatik, Aachen, Germany 0082 Temporal Dynamic of Intrinsic Spontaneous Brain Fluctuations revealed by Independent Component Analysis

Introduction: Patterns of spontaneous fluctuations of the BOLD signal can be identified by their spatial coherence using a correlation or independent component (ICA) approach (Beckmann et al. 2005). Examinations across time reveal varying fluctuations within each and between networks (Raichle, 2011) reflecting spontaneous changes (Smith et al. 2012). However, the extend of those intrinsic fluctuations remains still unclear. As correlation-based approaches measure the average functional connectivity between regions, we therefore used an ICA to a) identify number of resting state networks and b) to calculate their fluctuation over a time period of 20 min.

Methods: Data acquisition: A young volunteer was examined for 20 min. with a 32 channel head coil at 3 Tesla system and an EPI sequence with 3 mm isotropic resolution (46 slices, TR 2800 ms, TE 28 ms).

Data Analysis: Preprocessing: Data was preprocessed i.e. detrend and filtered (0.001–0.02Hz) using SPM Rest toolbox (Xiao-Wei, 2011) with the removal of noise (Cong et al., 2011b). Model order selection (He et al., 2010) was used to estimate the number of sources. ICA was performed using infomaxICA (Bell & Sejnowski, 2011) through ICASSO software (Himberg et al., 2004). The component of interest were selected and projected back to the scan field to obtain the temporal course.

Results: The ICA Analysis in Fig. 1 indicates the compactness of isolated clusters. Icasso plot in Fig.1a depicts a total of 52 clusters using a frequency bandwidth ranging from 0.008 to 0.02. All cluster were examined for stability and 21 showed a stability index > 0.8 (Fig.1b). Compact and isolated clusters suggest reliable estimates (Fig.1c). The maps of three ICA components and their temporal courses are depicted across 400 scans (Fig. 2).

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FIG. 1.

ICA Analysis: a) clustering quality plot: x axis represents number of selected clusters i.e. 52. b) Estimate quality: x axis represents the stability index for ICA-estimate-clusters. y axis is the label of clusters. 21 clusters showed higher > 0.8 stability index. c) Similarity graph / cluster compactness: Compact and isolated clusters suggest reliable estimates.

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FIG. 2.

Top: Surface view of three ICA components with pos. and neg. amplitudes are colored in red-yellow and blue-light blue respectively. Bottom: Temporal characteristics of the three ICA components.

1 Trinity College Dublin, Trinity Centre for Bioengineering, Dublin, Ireland 0084 Connectivity changes in the EEG resting state network with subject's own name

Objective: To evaluate the changes in connectivity of the resting state network in EEG when the subject's own name (SON), another name (OTHER) and a word are presented in and oddball paradigm.

Methods: Resting state activity from 10 healthy controls (mean age 27 years, with no history of neurological disease or hearing impairment) was recorded using a 128-channel EEG system (Fs: 512Hz, recording time: from 2 to 4min). Next, an auditory oddball paradigm was played to the participants. The paradigm consisted of standard (80% probability of occurrence) and deviant tones (14%) and three novels (2% each). The standard sounds were complex tones of 800Hz, 1600Hz and 3200Hz with a duration of 75ms; the deviant tones had the same frequencies but with a duration of 30ms. The novels consisted on SON, OTHER and a word.

After preprocessing, the connectivity analysis was performed using regions of interest. The four lobes, the hippocampus, the medial temporal gyrus, the superior temporal sulcus and the inferior frontal gyrus were chosen. The coherence and the phase-lag index were used as weights to create a connectivity map.

Results: Within an auditory oddball paradigm, a known evoked-response potential (ERP) is expected. For healthy participants, an early P300 component (mostly around 260ms) is visible for deviant tones. This is explained by the involuntary attention that the participants may be giving to the stimuli. The results from one participant are shown in Fig. 1 and Fig. 2.

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FIG. 1.

ERP to standard and deviant tones.

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FIG. 2.

ERP to SON, other name and a word.

The resting state network will be compared to the auditory network for each type of stimuli.

Conclusions: It is expected to find a significant difference in the inferior frontal gyrus in the results of the cortical sources for the presentation of the SON over OTHER and the word, as previously reported in fMRI studies. In particular, for each novel (SON, OTHER and word) it is expected to find a different composition of the network that would show the involvement of specific brain regions for each condition.

This passive paradigm was designed to be used in comatose patients after cardiopulmonary arrest in the intensive care unit. The results from this study will be used to analyse the relevant networks in the clinical cohort and evaluate the feasibility of their use for prognosis.

1 National Institute of Mental Health, SFIM, Bethesda, United States 2 NIMH, FMRI Facility, Bethesda, United States 0088 Identification of most and least stable connections in resting state fMRI during an hour long continuous resting scans

Introduction: Although it is common to assume temporal stationarity of resting state networks for the duration of a resting scan (5−10 mins), recent studies have shown that resting state network show a dynamic behavior and that they vary their configuration at a smaller time scale. The purpose of this work is to study the temporal stability of resting state connectivity within a continuous 1 hour resting state scan. We look both at long term (>7 minutes) and short term (<5 minutes) stability.

Methods: 54 minutes resting scans were acquired on 5 subjects on a 7T scanner using a TR of 400ms. The scans include a slab of 11 slices placed to partially include the motor, visual and default mode networks. A subset of 40 spherical ROIs from Dosenbauch et al. (Science 2010) that intersect the functional field of view were used as nodes for the connectivity analysis. A running window correlation analysis with different window lengths (2–30 mins) was used to evaluate stability. Most stable and least stable connection pairs across all subjects were also identified.

Results: Figure 1 shows the connectivity matrix for representative subject for non-overlapping window durations of 54, 30, 15 and 7.5 minutes. Connectivity varies across windows even thought their duration exceeds 5 minutes. Figure 2 shows most stable connections, which concentrate on medial occipital regions. Figure 3 shows least stable connections, which seem to originate from right parietal regions.

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FIG. 1.

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FIG. 2.

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FIG. 3.

Conclusions: resting state networks vary their configuration considerably during 1 hour scans. Using durations longer than 5 minutes we can still observe within subject differences across time-slots. Occipital regions seem more stable than parietal regions in their connectivity profile.

1 NYU Child Study Center, Child Psychiatry, NY, United States 2 Child Mind Institute, Center for the Developing Brain, New York, United States 3 Donders Institute for Brain, Dept. of Cognitive Neuroscience, Nijmegen, United States 0089 Toward individually-based biomarkers of verbal proficiency in Autism

Question: Verbal proficiency in 6 year-olds with Autistic Disorder (AD) is a prognostic factor of long-term functioning. As a first step toward identifying biomarkers as early as the first identification of AD, we characterize the neuronal underpinnings of verbal proficiency in children with AD. By means of resting-state fMRI (R-fMRI), we first examined intrinsic functional connectivity (iFC) of language-based circuits in a sample of school-age children. Then, to explore the stability of the identified marker(s), we examined its relationship with verbal proficiency in an independent group of preschoolers with AD.

Methods: Two samples of children with AD included: 34 school-age kids (age 11±2yrs) completing an awake R-fMRI scan; and 20 preschoolers (age 60±10months) completing a R-fMRI scan during natural sleep. To examine iFC of language circuits we focused on the left inferior frontal gyrus (IFG): the pars triangularis (pt), pars opercularis and ventral premotor cortex. We indexed verbal proficiency with the Vineland Expressive Language (VEL) standard scores of expressive language skills. In the 34 school-age children with AD, we examined the relationship between VEL scores and inter-individual differences in iFC patterns associated with each of the IFG seeds, at the voxel-wise, whole-brain level (Z>2.3, p<0.05, Gaussian random field theory corrected). Then, we examined the relationship between iFC of circuit(s) identified in the first step with the individual VEL score in the preschoolers with AD. We plan to apply one-class support vector machine to examine whether pattern of iFC can classify verbal proficient children with AD from those with poor verbal proficiency.

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FIG. 1.

Results: Voxel-wise analyses showed a significant positive relationship between VEL scores and the iFC between left IFGpt and a cluster in the posterior aspects of the right superior temporal sulcus (STS) in the school-age kids.Guided by this finding, we correlated the iFC within this circuit with VEL scores of 20 preschoolers with AD.The iFC of this circuit explained 16% of the variance in verbal proficiency (r=0.40).

Conclusions: R-fMRI during natural sleep provides a feasible means for identifying loci of disconnection in autism that may serve to identify prognostic markers of verbal proficiency at the individual level at the time of first diagnosis.

1 Leiden University Medical Center, Radiology, Leiden, Netherlands 2 Leiden University, Institute of Psychology, Leiden, Netherlands 3 Leiden University, Leiden Institute for Brain and Cognition, Leiden, Netherlands 0090 ICA reveals age dependency of structural covariance networks in elderly

Background: Analysis of structural covariance networks (SCNs) is a powerful technique to study networks in the brain. SCNs show correspondence with resting state fMRI networks. The effect of aging and white matter lesions on SCNs is underexplored. Here we investigate the association between age, severity of white matter lesions and SCNs in healthy elderly.

Methods: Structural MRI scans were tissue type segmented using voxel-based morphometry of FSL. Independent component analysis (ICA) was performed on the gray matter maps of 370 elderly between 45 and 85 years. This method defines structural components based on the covariation of gray matter volume among subjects. Dimensionality estimation was set on ten components. The effect of age on the amount of gray matter within each of the SCNs was analyzed with a univariate analysis of variance in SPSS (corrected for gender). ANOVA was used to study the correlation between white matter lesions and SCNs (corrected for gender and age). Results: The amount of gray matter within each of the ten SCNs is negatively correlated with age (fig. 1). Strongest correlations are found in a network with nucleus accumbens, hippocampus, thalamus, putamen, PCC, cuneus, precuneus as main regions (p<0.0001, R²=0.369, fig. 2) and in a somatosensory network (p<0.0001, R²=0.252). Weakest correlations are found in two cerebellum networks (p=0.001, R²=0.042 and p=0.006, R²=0.034). Nine of ten SCNs show a negative correlation with the severity of periventricular and subcortical white matter lesions (p<0.01). Conclusion: This study demonstrates that age-dependent covariation of gray matter is organized in networks in elderly. Five of the SCNs show a strong correlation with age, while the age-dependency of the other five SCNs is weak (fig. 1). We also demonstrate that the severity of white matter lesions is associated with gray matter networks, independent of age.

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FIG. 1.

Structural covariance networks. In all ten SCNs the amount of gray matter is negatively correlated with age. SCNs are ordered such that SCN 1 (fig. 2) shows the strongest correlation with age and SCN 10 the weakest. All SCNs, except SCN 9, show a negative correlation with white matter lesions.

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FIG. 2.

Correlation between gray matter volume and age. The strongest correlation between gray matter volume and age is found in SCN 1 (fig. 1).

1 Leiden University Medical Center, Radiology, Leiden, Netherlands 2 Leiden University, Leiden Institute for Brain and Cognition, Leiden, Netherlands 3 Leiden University, Psychology, Leiden, Netherlands 0093 Hippocampal volume is associated with functional connectivity in the salience network

Introduction: In Alzheimer's disease, RS-FMRI connectivity within the default mode network (DMN), including hippocampal regions, decreases while RS-FMRI connectivity in the so-called salience network has been shown to increase. A key feature of Alzheimer's disease is atrophy of the hippocampus, suggesting an association of hippocampal atrophy with DMN and salience network RS-FMRI connectivity. Here we study the relation between RS-FMRI networks and hippocampal volume in healthy elderly.

Methods: Resting state network connectivity of 72 healthy elderly subjects (M age: 65 years; SD: 6.9; age range: 47–80) was investigated with RS-FMRI. After preprocessing, RS-FMRI networks were extracted using a dual regression analysis with FSL based on eight standard network templates, including the DMN and the salience network. Regional linear relationships between RS-FMRI connectivity with each of these networks, and hippocampal volume were examined voxel-wise at p<0.05, corrected.

Results: RS-FMRI connectivity of parts of the prefrontal cortex and anterior cingulate cortex with the rest of the salience network showed a negative correlation with hippocampal volume (see Figure 1). Other regional RS-FMRI connections were not correlated with hippocampal volume.

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FIG. 1.

RS-FMRI connectivity between parts of the prefrontal cortex and anterior cingulate cortex (depicted in red) and the salience network showed a significant negative correlation (p<0.05, corrected) with hippocampal volume in healthy elderly

Discussion: A smaller hippocampus is associated with higher RS-FMRI connectivity in the salience network in healthy elderly. This relation corresponds to features of Alzheimer's disease, in which less hippocampal volume and an increase in salience network activity have been demonstrated.

1 CRICM, Paris, France 2 Lyon neuroscience center, Lyon, france, France 0095 Functional role of transient reduction of activity in the default-mode network (DMN) and the ventral attentional network (VAN)

Functional neuroimaging studies have shown that the default mode network (DMN) displays an elevated activity during resting state and a systematic reduction of activity during attention demanding task. The functional significance of this network deactivation remains largely debated and cannot be completely resolved without accessing its fine-scale temporal dynamics. In recent studies, intracranial and MEG recordings have been used to explore the electrophysiological underpinnings of the human DMN. Using a visual search task we recently showed that the deactivation in the DMN, but also in the ventral attentional network (VAN), is correlated with the task complexity and the individual performance and could encode the attentional engagement for external stimuli (Ossandon et al 2011).

Intracranial recordings were obtained from 11 epilepsy patients performing a high attention-demanding discrimination task (emotion discrimination) and a low attention-demanding detection task (color detection). We observe the same brain-wide pattern of high gamma power suppression which we previously reported in the visuospatial search task (figure-top), and confirm the relation-ship between power decreases and task difficulty. But, we also found that the power suppression was more pronounced for the target facial expression (fear) in the emotion discrimination task, and was only significant for the target stimuli in the color detection task (figure-bottom).

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FIG. 1.

A: Anatomical distribution of gamma band suppression. B. Example of time course of deactivation (medial prefrontal cortex) for the color detection task (green) and the emotion discrimination task (blue) In both task, target stimuli (dotted line) elicited a stronger deactivation.

These results provide novel insights into the functional role of DMN and VAN deactivation that would not reflect a global attentional engagement to external stimuli but the detection of task-relevant stimuli or the involvement in a goal-directed behavior.

1 Max Planck Institute for Human Cognitive and Brain Sciences, Research Group Neuroanatomy and Connectivity, Leipzig, Germany 2 Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany 3 Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurophysics, Leipzig, Germany 4 Max Planck Institute for Human Cognitive and Brain Sciences, MEG and Cortical Networks, Leipzig, Germany 5 Berlin School of Mind and Brain, Berlin, Germany 0097 Three-dimensional force-directed edge-bundling of functional connectivity

Question: Visualizing a full graph of functional connectivity in a single three-dimensional anatomical rendering would help for intuitive investigation of brain organization. Since drawing connections in high-resolution data as straight lines suffers from heavy overdraw, we employ the non-hierarchical edge-bundling method. This approach visually changes the shape of the connections by grouping similar edges together. In order to make this possible in anatomical space, we implemented the method for three-dimensional data.

Methods: We preprocessed and co-registered 127 resting-state fMRI datasets. For each dataset, we calculated a connectivity matrix using Pearson correlation between the average time-series of 463 ROIs from a whole brain atlas, which were generated via spatially constrained spectral clustering of the boundary surface between gray and white matter. After Fisher's r-to-z transformation, we averaged correlations across subjects, and included connections with z>0.3 and a length > 20 mm between the centers of gravity of the ROIs.

We then applied force-directed edge-bundling. After calculating a measure of similarity using several criteria such as length and direction, it iteratively subdivides the edges, and simulates electrostatic attraction between similar edges. The result is spline-like bundles of grouped connections. For the visualization, we superimposed the edge bundles on a single surface derived from a Freesurfer extraction.

Results: Figure 1 shows that while using straight edges (left) the underlying structure is hard to discern, after bundling, more structure becomes visible (right). The most obvious feature in the superimposed visualizations (Figures 2 –4) is the sensori-motor system (1). Also visible are several other bundles of long-range connectivity, such as the midline component of the default-mode network (2), as well as the visual (3) and dorsal fronto-parietal (4) networks.

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FIG. 2.

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FIG. 4.

Conclusions: Our preliminary results suggest that visualizing the full connectivity information in a single image is a valuable tool in the exploration of the human connectome. Edge-bundling offers a method from the data visualization community to help clarify the combined complexity of integrating graph information with three-dimensional space - a problem at the heart of understanding the brain.

1 University of Texas at Dallas, Behavioral and Brain Sciences, Richardson, United States 0098 Connectivity dynamics underlying age differences in processing speed

Psychometric research has revealed consistent differences among individuals in the speed with which they can perform simple and more complex cognitive tasks. This observation has led to hypotheses that the efficient use of limited sets of cognitive operations governs performance across a broad range of tasks. FMRI research suggests lateral prefrontal cortex (PFC) mediates this cognitive efficiency. Research also has revealed age-related slowing on measures of cognitive processing speed and that age-related processing speed declines account for significant age-related variability in performance on a variety of more complex cognitive tasks. These results suggest that age-related cognitive declines, in general, are due to declines in the efficient use of basic cognitive processes. The present study used FMRI to examine the neural basis for age-related differences in connectivity patterns mediating cognitive efficiency. On each trial, participants determined whether a paired symbol and number appeared in a key of nine symbol-number pairs (a rapid event-related design; 153 trials over three 6 minute runs). In previous work, faster younger adults showed less BOLD signal-change within PFC than slower younger adults, but faster older adults showed greater BOLD signal-change within PFC than slower older adults. Granger causality analysis was used to evaluate feed-forward and feed-back functional connectivity to the three identified PFC regions, controlling for the autocorrelation and influence of non-target ROIs with each regression. Feed-forward and feed-back connectivity to the different PFC regions varied with age and performance. However, Age X Performance interaction effects in feed-forward connectivity estimates also were observed in frontal, parietal, temporal, and insula regions, and interaction effects in feedback-feedforward connectivity estimates also were observed in visual association, parietal, and motor regions. The results provide evidence for age differences in PFC integration and modulation functions mediating processing speed.

1 Wake Forest School of Medicine, Radiology - Neuroradiology, Winston-Salem, United States 0099 Brain Intratumoral Graph Theory Network Metrics Can Be Computed from Resting-State BOLD Signal and May Vary by Tumor Histological Subtype

Objective: The objective of this study was to determine if graph theory network metrics could be acquired from glial cell brain tumors using RS-BOLD. We hypothesized that some network metrics might vary according to glial tumor grade.

Methods: Four patients with brain tumors were scanned for collection of structural T1- (Figure 1) and T2-weighted (Figure 2) MRI, as well as RS-BOLD data as part of routine presurgical fMRI motor and language mapping at Wake Forest School of Medicine. Pearson correlation coefficient was computed between all pairs of node time series within the brain and tumor to generate a correlation matrix (Cij), which was thresholded and dichotomized to generate a binarized adjacency matrix (Aij) for each patient's tumor at a cost of 0.3. Graph theory metrics were then computed in the standard fashion to evaluate the effects of tumor histologic subtype on functional intratumoral network connectivity.

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Results: Patients included a 69 y.o. female with right frontoparietal glioblastoma multiforme (GBM) (Grade IV), 42 y.o. female with left temporal astrocytoma (Grade II), 37 y.o. male with left frontal oligodendroglioma (Grade II), and 45 y.o. male with right frontal oligodendroglioma (Grade II). All tumors demonstrated functional network connectivity with the surrounding cerebral and cerebellar hemispheres (Figure 3), as well as dense functional intratumoral network connectivity (Figure 4). Comparison of network metrics between astrocytomas and oligodendrogliomas demonstrated similar values for clustering coefficient (p>.05), characteristic path length (p>.05), local efficiency (p>.05), and global efficiency (p>.05), with a trend towards significance for differences in small-worldness (p=0.06).

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Conclusion: Intratumoral graph theory network metrics can be computed from RS-BOLD signal, and may vary by tumor histological subtype. Higher histologic grade astrocytomas demonstrated greater small worldness than the lower grade gliomas, suggesting a less random and more organized pattern of intratumoral connectivity. Such connectivity properties may help to explain the more infiltrative and aggressive nature of astrocytomas compared to oligodendrogliomas. Further characterization of network characteristics of brain tumors could potentially lead to novel functional imaging biomarkers with which to study disease progression, guide therapy and predict patient outcomes.

1 University of Michigan, Ann Arbor, United States 0100 Support vector classification and prediction of resting-state functional connectivity over the lifespan

Objektive: Multivariate classification is an important alternative to univariate techniques in studying functional connectivity. Following recent work (Dosenbach 2010), this study extends the investigation of predicting brain age to the entire lifespan.

Methods: Resting-state functional MRI data were collected on 3T GE scanner using a spiral-in acquisition, with parameters (TR/TE/FA=2000/30/90, 40–43 slices, 3.44×3.44×3mm resolution). Anatomical T1 overlays matching the prescription of the functional data and whole-brain T1 SPGRs were also collected. 188 subjects in total were scanned (73<18 years old, 38 18–50, 77>50), with eyes open using a fixation cross during the resting state.

MR data preprocessing: All data was preprocessed using SPM8, including slice timing correction and realignment; anatomical coregistration and segmentation, normalization to MNI space, and spatial smoothing (5mm FWHM). Functional connectivity analysis : Preprocessed resting-state data were then detrended, and CSF/WM timecourses and head motion parameters regressed out. Data were then bandpass filtered (0.01–0.1 Hz). 160 ROI timecourses (defined in Dosenbach 2010) were extracted and correlated with each other for every subject. The resulting functional connectivity matrices were then used in the multivariate analysis. Support vector analysis : Support vector machine learning was performed using the 3dsvm toolbox in AFNI. Binary SVM classification was performed using a linear kernel and multistate classification, using young (<18), middle (18–50), and old age (>50) as labels. Support vector regression was performed using an epsilon width of 0.1. Leave-one-out cross-validation (LOOCV) was used to calculate accuracies and predicted values.

Results: SVM classification results in 87% in classifying young vs. old, 75% for middle vs. old, and 68% for young vs. middle. Support vector regression resulted in a predicted brain age that tracked well with chronological age (see Figure 1).

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FIG. 1.

Chronological age vs predicted brain maturity. Maturity values are normalized to predicted value at 40 years of age.

Conclusion: Resting state functional connectivity, combined with multivariate analysis, can be used to examine and predict internal maturational state across the lifespan.

1 University Hospital of Liège, University department of Neurology, Liège, Belgium 2 University of Liège, Coma Science Group, Cyclotron Research Centre, Liège, Belgium 3 University Hospital of Liège, University department of Neurology, Myelin Disorder Research Team (MYDREAM), Liège, Belgium 4 University of Liège, Cyclotron Research Centre, Liège, Belgium 5 University Hospital of Liège, University Department of Anesthesia and ICM, Liège, Belgium 6 Laboratory for Sleep and Consciousness Research, University Department of Psychology, Salzburg, Austria 0107 Relationship between spontaneous fluctuation, auditory evoked activity and consciousness: an EEG-fMRI study

Question: Recent functional MRI studies have identified coherent spontaneous activity fluctuations in large scale brain networks in the awake resting human brain (Boly et al 2007, 2008, Sadaghiani et al, 2010). These slow blood oxygen level-dependent (BOLD) fluctuations persist during anesthesia (Vincent et al, 2007). In particular, spontaneous activity within auditory cortices has been shown to remain unchanged across propofol-induced sedation stages (Boveroux et al, 2010). The functional significance of the preserved ongoing BOLD signal fluctuations during propofol-induced loss of consciousness remains poorly understood. The aim of this study is to investigate the influence of spontaneous fluctuation in the auditory resting-state network on stimulus-evoked auditory responses under propofol anesthesia, as compared to wakefulness.

Methods: Simultaneous functional MRI and EEG data were acquired in 13 healthy volunteers (6 females, mean age 23±5 y). All subjects underwent 4 scanning sessions (normal wakefulness, propofol-induced mild sedation, loss of consciousness, and recovery of consciousness) where pure tones were presented with a randomized jitter (median interstimulus interval: 2910 ms, standard deviation: 10706 ms). First, an auditory network template was obtained by assessing the response to all sounds during awake state. Second, we performed independent component analysis on fMRI data and identified (through template matching) a spontaneous auditory brain activity spatial map (which time course was not correlated to sounds presentation) in each condition for each subject. Third, sounds were classified into two classes: a tone was classified in the 'up' category if its onset occurred within the upper half spontaneous auditory map BOLD activity values, and was categorized as 'down' otherwise. Differential brain responses to 'up' versus 'down' sounds were assessed in all sessions and the presence of a correlation between the effect of spontaneous activity and the level of consciousness was investigated. An additional EEG analysis searched for an effect of spontaneous BOLD activity fluctuations on auditory event-related spectral perturbations of (fMRI-classified) 'up' and 'down' sounds across sedation stages. We also looked for a correlation between the level of consciousness and the effect of spontaneous activity on stimulus-induced oscillatory activity. Statistical analyses were all performed with SPM8 and thresholded at p<0.05 corrected for multiple comparisons using false discovery rate.

Results: During wakefulness, 'up' tones induced stronger cerebral activation than the 'down' tones, in a set of areas encompassing temporal, parietal, frontal and limbic cortices. During deep sedation, the effect of spontaneous activity was restricted to primary auditory cortices. A correlation between the influence of spontaneous BOLD signal fluctuations on the responses to sounds and the level of consciousness was found in parietal, frontal and occipital cortices. A consciousness-dependent effect of spontaneous BOLD activity on the processing of stimuli was also found for stimulus-induced beta band synchronisation at a latency of 200 ms after the presentation of sounds.

Conclusions: During wakefulness, spontaneous auditory cortices BOLD fluctuations elicits large differences in BOLD activation (encompassing frontoparietal cortices) and beta synchronization in late latencies - commonly reported as neural correlates for conscious perception of stimuli (Gaillard et al, 2009; Gross et al, 2004). In contrast, the localized effect of spontaneous activity in primary auditory cortices during deep sedation is unlikely to lead to changes in awareness of auditory stimuli. Our data suggest a graded correlation between the level of consciousness and the interplay between spontaneous and stimulus evoked activity. They shed light on the (lack of) functional significance of BOLD fluctuations observed during anesthesia-induced loss of consciousness for the processing of external stimuli.