Abstract

Poster Session 1: Thursday, 11 September 2014
Theme 1: Technical Advances and Methodological Issues
Subthalamic Nucleus in the Stop Network: Evidence from resting state functional connectivity MRI
A.A. Martinos Imaging Center at McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, USA
Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Science, Houston TX
Size matters: the influence of the size of the posterior cingulate cortex seed on the correlation values of the voxels of the medial prefrontal cortex
Neurophysics Group - Unicamp, Campinas, São Paulo, Brazil
The Mind Research Network, Albuquerque, NM, USA
Vanderbilt University Institute of Imaging Science, Nashville, TN, USA
Resting state spatio-temporal correlation tensor

Maps of spatio-temporal correlation and diffusion tensors, and reconstructed functional pathways.
Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
Resting state frequency signatures across regions
Section on Functional Imaging Methods, NIMH, NIH, Bethesda, MD, USA
DMN=Default mode network (Laird component 13), MV1&2=motor/visuospatial (Laird components 6&7) SPP=Speed production network (Laird component 17), VS1&3=visual (Lair components 10&12). * marks frequency bins where power for 11 subjects are greater in one region.
Craddock et. al. (2012) Hum Brain Mapp 33, pp1914–1928; Jo et. al.(2010) Neuroimage 52 pp571–582.; Laird, et. al. (2011). J Cogn Neurosci 23, pp4022–4037.
MR Center of Excellence, Center for Med. Physics and Biomed. Engrg,, Med. University of Vienna, Vienna, Austria
University of Pennsylvania, CBICA, Philadelphia PA, USA

Left, one parcellation. Middle adjusted Rand measures (for all the resolutions together, but each hemisphere separately). Right, p-value obtained at each resolution (approximate number of parcels).
TUM-Neuroimaging Center, Technische Universität München, Munich, Bayern, Germany
The Number of Revealed Independent Components, and Subject Averages of IQR (mean±std), and the Normalized Frequency (Hz) of the Peak of PSD (mean±std) Over all Consistently Detected Networks
Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
[1]
Pharmacog is funded by the EU-FP7 for the Innovative Medicine Initiative (grant n°115009).
Dynamic mapping of resting-state network coherence at multiple frequencies
Medical Center Freiburg, Freiburg, Baden-Württemberg, Germany
(Left)
*This work was supported by European Research Council Advanced Grant agreement 232908 “OVOC” and DFG Cluster of Excellence EXC-1086 “Brain Links-Brain Tools”.
Boston College, Chestnut Hill, MA, USA
Rutgers University, Newark, NJ, USA
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under grant number DGE-1313667.
Geisinger Health Systems, Danville, PA, USA
McLean Hospital & Harvard University, Belmont, MA, USA

Amplitude effects: A) within-network: voxels in ROI (red) have greater TC amp than voxels in green, and B) network-wide (Group B amp > Group A amp). Shape differences: C) Basal ganglia connected to ECN in Group A and D) LFPN in Group B.

Only SSMs with SSTC normalization accurately identify within-network amplitude group differences. All other SSMs show group differences outside of the ROI. A) SSMs with SSTC normalization, B) raw SSMs without SSTC normalization, C) Z(t), and D) Z(r) statistic maps (red-yellow: Group B > Group A, blue-light blue: Group A > Group B, p < 0.05).
Wellcome Trust Centre for Neuroimaging, UCL, London, U. K

Stochastic DCM

Spectral DCM
Laboratory for Advanced Medical Image Processing
Boston University, Boston, MA, USA
1. Kramer MA, Eden UT, Cash SS, Kolaczyk ED. Phys Rev E, 79:061916, 2009.
2. Pascual-Marqui RD, Michel CM, Lehmann D. IEEE Trans Biomed Eng, 42:658, 1995.
Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Actual correlation vs. sliding window correlations for different window lengths

State distribution of 3 simulated networks.
Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
Hippocampal connectivity to posterior cingulate cortex reliably predicts memory across sessions and age groups
Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
NeuroImage (2014) (available online: DOI:10.1016/j.neuroimage.2014.04.039)
Detecting Intrinsic Brain Activity Using Whole Brain 3D-VASO Imaging
State Key Lab of Brain and Cognitive Science, CAS, Beijing, China

Brain networks detected by the independent component analysis of the VASO (a) and BOLD (b) data.
New Jersey Institute of Technology, Newark, USA
Theme 2: Structural Brain Connectivity/Multi-modal Approaches/Animal Models
Bernstein Center Freiburg, Germany

Hubs specific to the KO.

Hubs specific to the WT.

Significant group differences (CTRL vs KO) in the FC of the CPu and amygdala.
Neurobiology Research Unit, Copenhagen, Denmark
Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
Program in Neurobiology and Behavior, University of Washington, Seattle, WA, USA
Singapore Bioimaging Consortium, A*STAR, Singapore
Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts
A forward solution and the constrained minimum norm imaging kernel were computed with Brainstorm. For each source time-series we determined the strongest phase-amplitude coupling (PAC) between 2–30 Hz and 80–150 Hz. At the troughs and peaks of the low-frequency phase the gamma-amplitude was extracted and a new time-series generated. Based on these time series the resting state networks were obtained with singular value decomposition.
Injury alters the intrinsic functional connectivity of spinal cord grey matter in monkeys
Vanderbilt University Institute of Imaging Science, Nashville, TN, USA
Texas Tech University, Lubbock, TX, USA
Oulu University Hospital, Oulu, Finland
Departments of Neurosurgery, Houston Methodist, Houston, TX, USA

Department of Biomedical Engineering and The Huck Institutes of Life Sciences, The Pennsylvania State University, University Park, PA 16802

Distinct spatial CAPs of IL. Left (a-d), awake. Right (e-h), anesthetized. a,e, average d map. b-d, f-h, CAPs. Colorbar in standard deviation (SD).
Department of Psychology, Georgetown University, Washington, DC
Developmental Biopsychiatry Research Program, McLean Hospital, Belmont, MA
J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, USA
A fundamental hypothesis in neuroscience is that connectivity mirrors function at a fine spatial grain across the cortex. Previous research supports this hypothesis for the human brain, by demonstrating that the degree of voxelwise face-selectivity in the fusiform gyrus of individual subjects can be predicted from that voxel's connections to the rest of the brain (its unique connectivity fingerprint), measured through diffusion-weighted imaging (DWI; Saygin et al. 2012). Here we asked whether resting-state functional connectivity (fcMRI) can also predict face-selectivity in the fusiform gyrus, and whether structural or functional connectivity fingerprints also predict other visual selectivities in multiple extrastriate cortices. We found that both fcMRI and DWI connectivity predicted face selectivity in the fusiform more accurately than did a group analysis of face selectivity from other subjects. Prediction accuracies from DWI connectivity were slightly but significantly better than predictions from fcMRI connectivity for the fusiform gyrus. A direct comparison of the subset of connections that best predicted face-selectivity revealed that DWI and fcMRI connectivity fingerprints for function were generally quite similar, especially for the top predictors, although differences existed among weaker predictors. We performed similar comparisons of DWI and fcMRI connectivity fingerprints for other extrastriate regions and for body, object, and scene perception. These data provide converging evidence from both DWI and fcMRI that i) connectivity and function are tightly linked at a voxelwise scale in extrastriate cortex in humans, and ii) functionally-selective voxels can be predicted from either diffusion or resting functional data alone. These results also raise the possibility that connectivity fingerprints direct the functional specialization of cortex in development. Finally, this work has practical relevance for researchers and clinicians, by providing a method to infer functional brain maps from structural images alone in individuals who cannot be functionally scanned (e.g. comatose subjects, or sleeping infants).
Automatic Annotation of 3D Axoplasmic Reticula for Neuron Segmentation
Department of Computer Science, The Johns Hopkins University, Baltimore, MD, USA
Inserm, U836, F-38000 Grenoble, France
This work is supported by a grant from the Rhône-Alpes Région.
School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, United States

From left to right are shown the raw ECoG spectrum, the separated spectra of rhythmic and arrhythmic signals, their corresponding correlation matrices and patterns.
Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea
Theme 3: Applications in Neurological and Psychiatric Diseases
Purdue University, West Lafayette, IN, USA

The number of significant DMN connections for each session, using all possible permutation of subjects, choosing 7 at a time.
Massachusetts Eye and Ear Infirmary, Boston, MA, USA

HARDI reconstruction of a representative sighted control (top) and CVI subject (bottom). Notice the significant lack of long-range projections in the CVI subject.

Connection strength and node degree of occipital seeds to the whole brain for cortical visual impairment
Brooklyn College of The City University of New York (CUNY), Brooklyn, NY, USA
NICHE Lab, Department of Psychiatry, Rudolf Magnus Institute for Neurosciences, University Medical Center Utrecht, The Netherlands
Columbia University, College of Physicians and Surgeons, Department of Psychiatry and the New York State Psychiatric Institute, New York, New York, USA
Emory University, Atlanta, GA, USA

Necessary Pathways.

Prospective targeting.

Probabilistic SC pattern.
Dept. Psychiatry, Univ. of Minnesota (UMN) Medical Sch., Minneapolis, MN, USA
San Diego State University, San Diego, CA
We tested for (a) altered functional and structural connectivity within a network associated with imitation, a social cognition domain putatively impaired in ASD; (b) links between clinical symptoms, functional connectivity of this imitation network, and white matter microstructure within pathways connecting network nodes.
Department of Nuclear Medicine, Christian Medical College, Vellore, India

Epileptic focus identified in subject ID 19001.
Comparison of FC Analysis Results with iEEG diagnosis
Effects of cannabis and THC on reward circuitry in patients with schizophrenia and cannabis use disorder
Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH
Massachusetts Institute of Technology, USA
Can individuals who have been blind for the first several years of life acquire the ability to visually distinguish faces from non-faces after sight restoring surgery? What kinds of neural changes underlie the very initial stages of such development? Is there any possibility to improve the neural activity across face-selective cortical regions late in life, and if so, how rapidly does it emerge? Is it spatially and topographically similar in organization to that found in the normally developed brain?
We report here results from an unusual opportunity to address these questions by studying congenitally blind individuals whose sight we were able to restore post-adolescence. Our behavioral tests reveal the development of face/non-face discrimination skills over the span of a few months after sight onset. We complemented these assessments with fMRI studies. In order to examine the development functional connection across face selective cortical network, we selected the right fusiform face area (rFFA) seed as the region of interest and apply the seed based correlations across brain regions in the resting state fMRI blood oxygenation level-dependent time series signals. The experiment was conducted as close to the surgery date as possible and repeated several times in the subsequent months, depending on each subject's availability.
In this report, we are including results of two congenital cataract blind children (12 years and 18 years, male), who had only light perception before surgery. We followed up these two kids two months and 18 months after sight onset. In order to show the contrast with age matched control subjects, we have included one fifteen-year male subject. In this resting state functional connectivity analysis, we have used the rFFA seed (the rFFA seed is generated from face localizer study) from the last time point of experimental subject's data in three longitudinal time point of observation. We find strong evidence of brain plasticity in these subjects. There is a rapid emergence of spatially localized, functionally specific responses in higher visual cortical areas after surgery. Taken together, these findings have important implications for our understanding of brain plasticity as well as the development of object representations in the brain.
Center for MRI Research, Peking University, Beijing, China

The network for intelligible speech. The significant links (P < 0.05) are represented and the dDTF-value of the connections are idicated by the width of the arrows. pSTG was the major node with the maximum eccentricity (shown as dark oval). aSTG was predominantly information driven (shown with dark circle) while pSTG was a strong driver.
Cluster-in and Cluster-out for All ROIs
Center for Translational Research and Systems Neuroscience and Psychiatry, University Medical Center, Goettingen, Germany
VA San Diego Healthcare System, San Diego, CA, USA
US Dept. of Veterans Affairs, Northern Healthcare System, Martinez, CA, USA
Dept. Psychology, The University of New Mexico (UNM), Albuquerque (ABQ), NM, USA
TUM-Neuroimaging Center, Technische Universität München, Munich, Bayern, Germany
Subject Averages of Standard Deviation of Correlation Coefficients (mean±std) for Significant InterFC Dynamics Changes after Single-ECT from ANCOVA
Departments of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, INDIA
Harvard Medical School, Boston, MA, USA
Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA, USA
UCSD Department of Cognitive Science
The course and expression of bipolar disorder (BD) clearly differs between women and men. Women more often have a seasonal pattern of mood disturbance, and are more likely to experience rapid cycling than men. Men are more likely to have a comorbid substance use disorder, while women more frequently have comorbid anxiety disorders. Previous studies have observed sex effects in connectivity of the default mode network (DMN) of healthy individuals, but more research is needed to see whether a similar effects holds among BD patients. This study investigates 1)how sex differences in DMN activity among BD I patients compared to individuals without BD, and 2)how sex differences may relate to clinical or cognitive differences in BD. We compared 27 euthymic patients with bipolar I disorder to 28 age and gender comparable healthy participants using functional magnetic resonance image during a period of eyes open rest. Averaged functional activity between the nodes of the DMN (medial prefrontal cortex, posterior cingulate, and bilateral angular gyrus) revealed that BD females tend to have greater co-activity within the default mode network than male BDs, contrasting with the pattern of greater connectivity among male healthy participants compared to females (p=.01). Negative psychotic symptoms were more pronounced in male than female bipolar participants (p=.07). There was a significant negative correlation of average DMN connectivity with negative symptoms (r(28)=−.44, p=.05). These results suggest subtle sex differences in the inter-relationship of resting brain activity within the DMN that may relate to clinical differences between men and women with bipolar disorder, including severity of negative psychotic symptoms.
Cleveland Clinic, Cleveland, OH, USA
The University of Texas at Dallas, Richardson, Texas, USA
Cyclotron Research Center, University of Liège, Belgium
Investigating local changes of EEG and fMRI spectral power in multiple sleep stages
Graduate Institute of Biomedical Engineering, National Central University, Taoyuan, Taiwan

Spectrum of fMRI (top) and EEG (bottom) across 3 NREM sleep stages. The red regions in MR image denote the selected ROI (M1 & mPFC), matching EEG channels from central and frontal regions.
Developmental Biopsychiatry Research Program, McLean Hospital, Belmont, MA, USA
Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A.
Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI
University of Queensland, Queensland Brain Institute, Brisbane, Queensland, Australia
Massachusetts General Hospital, Boston, MA, USA
NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences (INMAS), Delhi, India

Reduced connectivity in regions of (a) Default-mode network, (b) Medial visual network, (c) Auditory network and (d) Temporo-parietal-frontal network in high anxious subjects (FWE corrected p<0.05)
Behavioural and Clinical Neuroscience Institute, Cambridge, UK
Dept. Neuroimaging, National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
CHU de Caen, Service de Psychiatrie Adultes, Centre Esquirol Caen, F-14000, France
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Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, USA
Data preprocessing included: RETROICOR physiological noise removal, slice timing correction, motion correction, normalization to MNI space, and spatial smoothing (4 mm FWHM). Nuisance regressors (first principal component of head motion, and the global mean) were removed prior to low-pass filtering (<0.08 Hz). verage AAL template ROI timecourses were extracted and the correlation between every pair of ROI timecourses was calculated and z-scored. Univariate t-tests were used to examine significant differences versus time and group. Partial Least Squares (PLS) was used to find the combination of group and time saliences (i.e., contrast weights) that explain the most covariance in the data. Permutation testing was used for reliability.

Center for Neurorestoration and Neurotechnology, Providence VA Medical Center; Alpert Medical School of Brown University, Providence RI USA
Massachusettes Institute of Technology, Cambridge, MA, USA
Rutgers Univesity Center of Alcohol Studies, Piscataway, NJ, USA
Neuroscience Graduate Program, University of Iowa, IC, IA
Department of Neuropsychology & Psychopharmacology, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, NL
the University of Auckland, Auckland, New Zealand
Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
McLean Hospital, Belmont, MA, USA
We used FSL v5.0.6 for image analyses. After discarding the first 4 volumes, images were slice-time and motion corrected, smoothed with a 6 mm Gaussian kernel, band-pass filtered (0.009 Hz<f<0.08 Hz), and affine registered to standard MNI space.
We used the 17 network (N) cortical parcellation map of Yeo et al. (2011) as the basis for our FC seeds. We excluded the Visual Peripheral (N1) and Visual Central (N2) networks due to potential confounding by signal from visual cortical regions, located immediately adjacent to cerebellum. Consistent with Baker et al. (2014), we also combined N9 (temporal pole) and N10 (orbitofrontal cortex) into a single Limbic network. Thus we analyzed 14 total networks. With the exception of the already small Control C (N11) and Default Mode C (N15) networks, we eroded each of the network maps by one voxel layer using a 3D kernel. The BOLD time course from each of the 14 network seeds was extracted and entered into GLM, with signal from white matter, CSF, and motion correction parameters regressed. Data from first-level analyses were entered into a mixed-effects group analysis. Given our goal to investigate between-group FC differences in the cerebellum, we restricted our analysis to the cerebellum, using the cerebellum atlas in FSL as a pre-threshold mask. We set our voxel threshold to p<0.01, correcting for multiple comparisons with a p<0.05 cluster threshold.
Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA
NORMENT, KG Jebsen Center for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
University Hospital Münster, Department of Clinical Radiology, Münster, NRW, Germany
Child Study Center, Department of Psychology, Penn State University, University Park, PA
Department of Neurology, Massachusetts General Hospital, Boston, MA
Department of Psychiatry, University of Cambridge, Cambridge, UK

Underlay is high-resolution MNI template
Neuroimaging Laboratory, Department of Neurology, University of Campinas (Unicamp), Brazil
Leiden Institute for Brain and Cognition, Leiden, The Netherlands
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Department of Biomedical Engineering, Hanyang University, Seoul, south Korea
Department of Psychology, Georgetown University, Washington, DC
Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519
Neuroimaging Laboratory - IRCCS Santa Lucia, Roma, Italy
1) Mastropasqua et al. (2014); 2) Liston et al. (2014); 3) Gratton et al. (2013); 4) Bonni et al. (2014); 5) van den Heuvel et al. (2009); 6) Chan et al. (2001).
Poster Session 2: Friday, 12 September 2014
Theme 1: Technical Advances and Methodological Issues
For the denoising procedure we followed Smith, PNAS 2012. Individual datasets were fed into MELODIC ICA before spatial normalization and without spatial smoothing. The ICs were classified as artifacts by temporal frequency power >0.2 Hz, excessive involvement outside the gray matter and excessive slice dependency. The thresholds were chosen to minimize false-positive in artifact detection. The seed-based correlation mapping was done before and after the ICA denoising, following a classic method (Fox, PNAS 2005). The posterior cingulate was used as the seed region and the signals from the deep white matter and CSF were used as regressor of no interest.
Resting state functional connectivity in the human spinal cord at 7 Tesla
Vanderbilt University Institute of Imaging Science, Nashville, TN, USA

Group-level functional connectivity between regions of spinal gray matter (GM) and surrounding white matter (WM) within slices. (
Emory University, Graduate Division of Biological and Biomedical Sciences – Program in Neuroscience, GA, USA
Here C and
Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
Left and right visual related thalamus (LT and RT) were defined using the 6th region of the Oxford thalamic connectivity atlas (4) (Fig A). Left and right V1 (LV1 and RV1) were defined as the calcarine sulcus of the AAL template (5). The first eigen-variable of the four ROIs were extracted, and PPI were calculated between the LT and LV1, and between the RT and RV1, respectively. GLM of PPI effects were built for each subject and each condition, including two time series of the two ROIs, their PPI effect, two regressors representing the WM and CSF signals, and 24 regressors of head motion parameters. One sample t-test and paired t-test were performed for group level inference. Results were thresholded at p<0.01, and cluster level FDR corrected at p<0.05.
Hub crosslinks in resting state fMRI
Department of Nuclear Medicine and Radiology, and Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, KOREA
To investigate the synchronized dynamics of brain networks, the mathematical framework for hypergraph theory has been utilized. Hyperedges (or crosslinks) are synchronized patterns of interactions among brain regions, which co-vary in time.
In this study, we carried out identification and analysis of hub edges in resting state fMRI, which can be regarded as important communication pathways in the brain network.
Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
Graduate Program in Electrical Engineering, PUCRS, Porto Alegre, Rio Grande do Sul, Brazil
[1] Shrier et al. 2012 Cer Cortex; [2] Gonzalez-Castillo et al. OHBM 2013; [3] Craddock et al. 2012 Hum. Brain Mapp.; [4] Rubinov et al. 2010 NeuroImage. [5] Hubert et al. 1989 J. Classification.
The Virtues of Exploration: A Philosophical Framework for Resting State Connectivity and Data-Driven Connectomics Approaches
Sensitivity of the default-mode network at 7T
MR Center of Excellence, Center for Med. Physics and Biomed. Engrg, Med. University of Vienna, Vienna, Austria
All subjects were healthy volunteers. The datasets underwent the same pre-processing, including slice-timing correction, realignment, normalization, smoothing (9 mm FWHM), nuisance signal regression, bandpass-filtering (0.009 Hz-0.08 Hz), motion-scrubbing and seed-voxel correlation with the posterior cingulate cortex (PCC, x/y/z=0/-52/30 mm). For any number N ranging from 3 to 40, connectivity maps of N subjects were randomly chosen from either the 297 3T or the 40 7T datasets and subjected to one-sample t-tests. This procedure was then repeated 300 times to obtain robust estimates of sensitivity. Finally, the mean p-values from all repetitions were compared between 3T and 7T.
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Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taiwan
Validity and Reliability of a Proposed New Standard for Resting fMRI Data
Center for Neurodevelopmental Disorders, NYU Child Study Center, New York, NY, USA
MR Centre of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
INESC-ID/ Instituto Superior Técnico – Universidade de Lisboa, Portugal
Advanced MRI, LFMI, NINDS, National Institutes of Health

CAPs in “task-negative”

Visual
It was also found that CAP 23, covering a part of sensorimotor cortex, occurs much more frequently (p<0.01, Bonferroni corrected) in males than in females (Fig. 3), indicating a means to derive quantitative information from data-driven RSN analysis.

Occurrence rates of CAPs in males and females.
Centre for Neuropsychopharmacology, Imperial College London, London, UK

The Mind Research Network, Albuquerque, NM, USA
Centre for Neuropsychopharmacology, Imperial College London, London, UK
In real data, the standard deviation of r was quadratically related to its mean in a network relevant to epilepsy (Fig 1 left, R2=0.34). This was not true for the standard deviation of the Fisher transformed correlation z (Fig 1 right, R2=0.01).
Mean inter-hippocampal connectivity was lower in people with epilepsy (r=0.39) than in controls (r=0.61). The standard deviation of r was higher in epilepsy (Fig 2 left, p=0.001). When this measure of dynamic connectivity was statistically adjusted for the mean r or when the Fisher transform was used (Fig 2 center and right) the difference between groups disappeared (p>0.2). This suggests that the important difference in epilepsy was actually in the baseline level of connectivity, not in its dynamic variation.
Pattern Approach for brain connectivity using K-means clustering of resting-state fMRI time series using 10-10 EEG related seeds
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Advanced Medical Image Processing Lab, Clínica Las Condes, Las Condes, Santiago, Chile
Binary and K-Means has symmetrical seeds in contralateral hemisphere (for example FP1-FP2, AF3-AF4). Other cluster intra-connections has been detected, for example Central-Parietal area connects with Central and Parietal Areas or Frontal-Parietal zone connects with Frontal zone and Parietal zone.
Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
Center for the Developing Brain, Child Mind Institute, New York, NY
NYU Langone Medical Center, New York, NY, USA

ICC maps for PCC correlation analyses.

Scatterplots for rank-ordered ROI-wise fALFF (x-axis: session1, y-axis: session2).
Brain Imaging Center, Mclean Hospital, 115 Mill Street, Belmont, MA 02478, USA
Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
Nathan Kline Institute for Psychiatric Research (NKI), Orangeburg, NY, USA
Yale University, New Haven, CT, U.S.A.
MRI Research Center and Beijing City Key Lab for Medical Physics and Engineering, Peking University, Beijing, China
Theme 2: Structural Brain Connectivity/Multi-modal Approaches/Animal
Stanford University, Stanford, CA, USA

Functional correlations are well predicted by tractography in areas LO1/2 and language regions including supramarginal gyrus, middle temporal gyrus, and Broca's area, but functional and anatomical measures diverge in visual areas such as the extrastriate body area (EBA), MT, and IPS1-4.

Functional and anatomical parcellations (top), showing that the PPA spans multiple parcels (white outline). The posterior PPA parcels are more connected to occipital regions (bottom, in red), while the anterior parcels are more connected to medial parietal regions and angular gyrus (in blue).
University of California San Francisco, USA
Dept. of Psychology, Georgetown University, Washington, DC, USA
Advanced MRI Section, NIH, Bethesda, MD, USA

Center for Neuropolicy, Emory University, Atlanta, GA, USA
Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
EEG analysis of brain activity at rest is well suited to study the fast temporal dynamics of the resting state networks, but the low SNR characterizing electrophysiological signal at rest hinders an effective analysis.
Boston College, Chestnut Hill, Massachusetts, USA
Department of Neurology, University of Ulm, Ulm, Germany
Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Neuroscience Campus Amsterdam (NCA), VU University Amsterdam, Amsterdam, Netherlands
Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, Maryland, United States
All MRI data were acquired using a Bruker Biospin 9.4T scanner. Three sessions were scanned, the second session (S2) and third session (S3) were acquired two weeks and three months later than the first session (S1), respectively. Each session contained the high-resolution anatomical images and two resting-state scans (rs-scan, TR=1,000 ms), 10 min apart.
Motion correlation, spatial smoothing, detrend and temporally band-pass filtered (0.01 – 0.1 Hz) were used in preprocessing. All rs-scans were included in ICA analysis by MELODIC and dual regression approaching. A one-sample t-test was performed to obtain a robust functional connectivity map (t>20).
We computed three ICCs for each connectivity component to investigate the reproducibility of rs-fMRI signal [6] : 1) the ICC between rs-fMRI scan 1 and 2 (ICC1, 10 min apart); 2) the ICC between S1 and S2 (ICC2, 2 weeks apart); and c) the ICC between S1 and S3 (ICC3, 3 months apart).

Functional connectivity maps (t>15) resulting from the group ICA of rs-fMRI data of 16 rats. Component maps, which appear to be anatomically meaningful (13 out of 15), are shown.
ICC Values Across Components. The Mean ICC Value was Calculated Across Components
Department of Psychology, Tel Aviv University, Tel Aviv 6997801, Israel
Advanced MRI, LFMI, NINDS, NIH, USA

A comparison between the fMRI-derived RSNs and covariation patterns of ECoG broadband power under four onditions.

Power spectra of the ECoG-derived networks under four conditions.
Medical Physics, Dep. of Radiology, University Medical Center Freiburg, Germany
Local Activity Determines Functional Connectivity in the Resting Human Brain: A Simultaneous FDG-PET/fMRI Study
Department of Neuroradiology, Klinikum Rechts der Isar der Technischen Universitaet Muenchen, 81675 Muenchen, Germany
(The Journal of Neuroscience, April 30th, 2014)
Non-human primates: We acquired resting-state data from sub-regions of SI in anesthetized squirrel monkeys and measured the dynamic behavior of functional connectivity between somatosensory cortex sub-regions 3a, 3b, 1 and a control.
For example, figure 1 shows the probability density functions (pdf) of the cross-correlations between two sub-regions of SI for fMRI data obtained from one monkey, along with the pdf of two stationary simulated signals with the same mean correlation, for window sizes 45 s and 180 s, respectively. For statistical comparison, the Kolmogorov-Smirnov (K-S) test shows that correlations between functionally related regions 3a and 3b in the monkey data are distributed similarly to the correlations derived from simulated stationary data at short window sizes (smaller ks-value indicates less difference), but depart substantially at larger window sizes. The correlations between functionally unrelated regions 3a and control are distributed similarly to the stationary data from the simulation at all window sizes (Fig 2).
Lesion to left hippocampus changes functional connectivity according to changes in structure
Data analysis department, Faculty of Psychology, Ghent University, Ghent, Belgium
Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA

Comodulogram of coupling between phase at 0–1 Hz (horizontal) and amplitude at 1–50 Hz (vertical) under iso. Hot colors are significant vs. shuffled data. The significant but non-specific coupling seen here is due to iso inducing a “burst state” of alternating activity and quiescence in the LFP. Phase-amplitude coupling under dex was either inconsistent or not statistically significant.
Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
Northeastern University, Boston, MA
Theme 3: Applications in Neurological and Psychiatric Diseases
University of North Carolina, Chapel Hill, NC, USA
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Frontoparietal network connectivity associates with executive functioning deficits in young adults at risk for depression
Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Department of Neurology, Washington University in St Louis, St. Louis, MO, USA
University of Texas at Dallas, Richardson, TX, USA
Functional connectivity—primarily in the DMN and ECN-R—was correlated with CERAD total score, and with a subset of the CERAD scores. Further modified Boston naming task and verbal fluency were the most sensitive scores of cognitive decline.
MFA analysis differentiated between the three cohorts using a combination of DMN, SN, ECN-L, ECN-R connectivity, and CDR and CERAD scores. This analysis showed that DMN is negatively correlated with SN, and ECN-R is negatively correlated with ECN-L, but CDR and CERAD were positively correlated as expected.
Association of resting state fronto-striatal network function with striatal presynaptic dopamine synthesis in humans
Development of Fronto-Occipital Connectivity in Congenitally Blind Children
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
Neurosciences and Mental Health, Hospital for Sick Children, McMaster University, Toronto, Ontario
Center for Biomedical Imaging, Boston University School of Medicine
University of Massachusetts Medical School, Worcester, MA, USA

Contrast map of amygdala connectivity between pre- and post-MBSR. Color voxels indicate significantly changed FC with amygdala seed (

Relationship between right amygdala FC and mindfulness traits. A: amygdala-dlPFC and total KIMS; B: amygdala-insula and KIMS Aware subscale.
Instituto de Neurobiología, Universidad Nacional Autónoma México (UNAM), Juriquilla, Querétaro, México
Eddy-current distortion correction of DWI was made by fsl (FMRIB, Oxford). Constrained spherical deconvolution (CSD) was performed, followed by the creation of track-density images (TDI) based on 1 million tracks seeded homogeneously throughout the white matter using MRtrix (Brain Research Institute, Melbourne Australia). Final resolution of the TDI was of 0.2×0.2×0.2 mm3; these images were crucial for the accurate visualization of the different subthalamic structures, which were manually segmented based on the Schaltenbrand- Wahren atlas. Next, we seeded the Raprl for probabilistic tractography and the resulting bundles were selected according to their targets, which were automatically parceled by freesurfer (FreeSurfer Software Suite, Harvard).
Computational Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, London, UK
We fractionated the midline prefrontal cortex into subregions based on an independent age-matched resting state dataset (ROI in Figure). Resulting subregions within SMA/dACC showed connectivity with multiple different brain networks. For example a dorsal subregion showed connectivity with left fronto-temporo-parietal network. This network showed significant activation in Speech>Count in all three groups.
Another subregion showed connectivity with the default mode network (DMN). This component showed a significant relative deactivation in Speech>Count in all three groups. Finally another subregion showed connectivity with a right fronto-parietal network that was significantly active during the Count and Decision trials reflecting attentional demands of these tasks. We explored the differential activation within these networks between the three groups.
Department of Biomedical Engineering, Gachon University, Incheon, Republic of Korea

Functional Connectivity.
Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI, USA
Human Motor Control Section, NINDS, NIH, Bethesda, MD, USA
Department of Psychology, University of Miami, Coral Gables FL
Departments of Pediatrics and Neurology, School of Medicine, Wayne State University, Detroit, Michigan, USA
Multimodal Brain Image Analysis Laboratory (MBIAL), Bangalore, Karnataka, INDIA.
Hawaii Center for AIDS, Department of Medicine, University of Hawaii, Honolulu, HI, USA
Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
Detailed Examination of First Component. (A&B) Internetwork connectivity changes. Width of arc indicates quantity of affected edges. (C) Gray matter volume map reveals increased dlPFC and ACC. (D) Scatter shows component expression versus age.
Radiology, Oulu Univ. Hospital (OUH), Finland
Figure above shows DC-potential topographical changes (30 s windows) in right internal carotid and left vertebral artery infusions on left panel. In the middle summed DC-EEG potentials over whole mannitol infusion period. Right panel shows fNIRS measurement data from forehead. Mannitol infusion marked with arrow.
Cleveland Clinic, Cleveland, OH, USA

PCC ROI (red) and regions in the DMN that showed significant differences in connectivity in preHD (green).
NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences, Delhi, India
Connectivity Table
R-Right, L- Left, IOG & MOG-Inferior and Middle Occipital gyrus, IFG- Inferior Frontal gyrus, ITG- Inferior Temporal gyrus.
Dept. of Radiology, University of Wisconsin-Madison, Madison, WI, USA
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, U.S.A
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A, 102(27), 9673–9678.
Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, Maryland, United States
Department of Neurology, University of Ulm, Ulm, Germany
Cognitive neuroscience centre, Dept of NIIR, NIMHANS, MSR-INS, Bangalore, Karnataka, India
Brigham and Women's Hospital, Harvard Medical School (HMS), Boston, MA, USA
Institute of Higher Nervous Activity and Neurophysiology RAS, Moscow, Russia

Post-hoc of z scores between RSNs. VIS-red, AUD -blue, SSM - green, FRO - orange, RFP - purple and DMN - black line. 1 - post-stroke, 2-young, 3 raw- old subjects.
1,3,4
Department of Psychiatry and Psychotherapy, Centre for Behavioral Brain Sciences (CBBS), Otto-von-Guericke University, Magdeburg, Germany
McLean Hospital, Belmont, MA
Developmental Changes in the Functional Connectivity of the Insular Cortex in Individuals with Autism Spectrum Disorder
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
2,
Dept. of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States
Table 1. Top Five Nodes Having the Highest Summed Absolute Average Weights in the Regression Model

Scaled regression weights for each ROI, on standard MNI anatomy. color corresponds to network identity (Black: Cingulo-opercular, Red: Default, Green: Occipital, Turquoise: Sensorimotor, Yellow: Fronto-parietal, Blue: Cerebellum).
Independent component analysis of passive listening reveals alterations in inter-network connectivity that correlate with emotional valence
McLean Hospital, Belmont, MA, USA

Negative inverse covariance matrix of three components during the two conditions.
University of Virginia, Charlottesville, Virginia, USA
For the lacrosse players, the total SVM classification accuracy was 51.7% (p=0.383) across all individual scans. The classifier correctly identified 16 of 30 preseason scans and 15 of 30 postseason scans; classification accuracy for the preseason scans was 53.3% (p=0.388) and for the postseason scans was 50.0% (p=0.507). The overall sensitivity was 51.6% and specificity was 51.7%. The SVM model for the lacrosse players performs no different from chance in distinguishing preseason from postseason connectivity metrics.
VA Boston Healthcare System, Boston, MA, USA

Group differences (p=0.05, uncorrected) due to (Left) close-range blast exposure (n=209), and (Right) Comorbid TBI and PTSD vs PTSD only (n=112). Analyzed with (Top) and without (Bottom) inclusion of the global signal regression.
UT Southwestern Medical Center, Dallas, TX, United States
University Clinics for Child and Adolescent Psychiatry (UCCAP), University of Zurich, Zürich, Switzerland
Massachusetts General Hospital, Boston, MA, USA
1
1
1,2
3
5
1
1,2,4
The Responsive Amygdala: Treatment-induced Alterations in Functional Connectivity in Pediatric Complex Regional Pain Syndrome
P.A.I.N. Group, Department of Anesthesia, Boston Children's Hospital and Center for Pain and the Brain
Boston Children's Hospital, MA, USA
University Hospital Münster, Department of Clinical Radiology, Münster, NRW, Germany
Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
[1] Power JD, et al, Neuron. 2011, 72(4):665–78.
Université de Liège, Liège, Belgium
Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
Autism Spectrum Disorder (ASD) has been associated with a reduction in resting-state functional-connectivity (Just et al., 2004), though this assertion has recently been challenged by reports of increased connectivity in ASD (Lynch et al., 2013; Muller et al., 2011; Uddin et al., 2013). To address these contradictory findings, we examined whole-brain inter- and intra-hemispheric functional-connectivity in several resting-state datasets acquired from adults with high-functioning ASD and matched controls. In all analyzed datasets, we revealed consistent sets of regions of increased and decreased connectivity in ASD groups compared to controls. Importantly, we show that this heterogeneity stems from a novel ASD characteristic: idiosyncratic distortions of the functional-connectivity pattern relative to the typical “canonical” template. The magnitude of the individual pattern-distortion in homotopic inter-hemispheric connectivity was significantly correlated with behavioral symptoms of ASD. We propose that individualized alterations of the functional-connectivity organization is a core characteristic of high-functioning ASD. This result not only accounts for existing discrepant findings but offers a potential signature of altered functional brain organization in ASD.
Just, M.A., Cherkassky, V.L., Keller, T.A., and Minshew, N.J. (2004). Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. Brain 127, 1811–1821.
Lynch, C.J., Uddin, L.Q., Supekar, K., Khouzam, A., Phillips, J., and Menon, V. (2013). Default mode network in childhood autism: posteromedial cortex heterogeneity and relationship with social deficits. Biol Psychiatry 74, 212–219.
Muller, R.A., Shih, P., Keehn, B., Deyoe, J.R., Leyden, K.M., and Shukla, D.K. (2011). Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders. Cereb Cortex 21, 2233–2243.
Uddin, L.Q., Supekar, K., Lynch, C.J., Khouzam, A., Phillips, J., Feinstein, C., Ryali, S., and Menon, V. (2013). Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry 70, 869–879.
The University of Iowa, Department of Psychology, Iowa City, Iowa, USA
Department of Pediatric Neurology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Teicher MH, Anderson CM, Ohashi K, Polcari A. (2013) Childhood Maltreatment: Altered Network Centrality of Cingulate, Precuneus, Temporal Pole and Insula. Biol Psychiatry. pii: S0006-3223(13)00857-3.
Massachussette General Hospital Institute of Health Professions, Boston, MA, USA
Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
Departments of Neurology, Pennsylvania State University, Milton S. Hershey Medical Center, Hershey, PA 17033, USA

Component correlation changes between PD and Control group.
Fordham University, Bronx, NY, USA
Poster Session 3: Saturday, 13 September 2014
Theme 1: Technical Advances and Methodological Issues
Revealing brief meditation brain functional connectivity remnants
Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
Nathan S. Kline Institute for Psychiatric Research, Orangeburg, VA
A GLM model was defined for each subject with 48 regressors representing activations of every trial, and 48 motion regressors for the two separate runs. Trial regressor was calculated by convolving a single impulse function at the trial onset with the canonical hemodynamic function (hrf). After model estimation, 48 beta maps were obtained for each subject, which represented activations of each trial. Spatial ICA (4) was performed on the concatenated beta image series across subjects. 20 components were extracted.
Sparse Connectivity Patterns in Resting State fMRI
Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
Interdisciplinary Program in Neuroscience, Georgetown University Medical Center
New Jersey Institute of Technology, Newark, New Jersey
Departments of Radiology & Imaging Sciences, Emory University, Atlanta, GA
McLean Hospital, Belmont, MA, USA
Birn, R. M., Diamond, J. B., Smith, M. A., & Bandettini, P. A. (2006). Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage, 31(4), 1536–1548; Chang C, Cunningham JP, Glover GH. Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage 2009;44(3):857–869; Tong, Y., Hocke, L. M., & Licata, S. C. (2012). Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals. Journal of biomedical optics, 17(10), 1060041–10600410.
Graduate Institute of Biomedical Engineering, National Central University, Taoyuan, Taiwan

Sensorimotor connectivity of 4 participants in the 4 sessions: (1) Before sleep, (2) awakening 1 (+0 min), (3) awake 2 (+20 min), and (4) awake 3 (+40 min). Time in bed of each participant and response time to the PVT task (in ms) were listed below each subject/session.
Olin Neuropsychiatry Research Center, Hartford Hospital-IOL, Hartford, CT
Groupe d'Imagerie Neurofonctionnelle, UMR5296 CNRS, CEA, Univ. Bordeaux
1. Zuo, 2010; 2. Cherbuin, 2006; 3. Beckmann, 2004; 4. Naveau, 2012; 5. Seeley, 2007
This work was supported by grant ANR-13-CORD-0007 BIOMIST.
Radiology, Oulu Univ. Hospital, Oulu, Finland
Impact comparison between physiological-noise-removal and low-pass filter in resting-state functional connectivity
Graduate Institute of Biomedical Engineering, National Central University, Taoyuan, Taiwan

Comparison of DMN connectivity patterns in both normal breathing and controlled breathing, through two types of filter and regression strategies.
University of Gent, Belgium
Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
Interdisciplinary Center for Clinical Research (IZKF), RWTH Aachen University, Aachen, Germany
Technical University of Madrid, Madrid, Spain
The different roles played by rest in functional neuroimaging and electrophysiological studies are concisely illustrated. It is shown that, in functional neuroimaging, rest is essentially equated to an energy consumption level, and used as a baseline for gauging local amplitude modulations, while in electrophysiological studies, task-independent brain activity is endowed with complex generic properties, with important implications on various operational properties of brain activity.
We first briefly illustrate various ways in which this can be done, concentrating our attention to a novel way of quantifying brain activity in terms of temperature. For equilibrium systems, the relationship between the system's relaxation after a perturbation and the correlations of the unperturbed system are expressed by temperature 2 . For non-equilibrium systems such as the brain, the FDT must be generalized, and equilibrium temperatures can be replaced by effective temperatures, which quantify the extent to which brain activity violates this theorem in its classical equilibrium form. Out-of-equilibrium systems can be at equilibrium on one scale and out of equilibrium on another, or may even be in equilibrium but show scale-dependent properties, each timescale may be associated with its own effective temperature. This allows understanding the relationship between spontaneous and stimulus-induced brain activity at various spatial and temporal scales, and the extent to which each scale of brain activity deviates from equilibrium conditions, produces entropy, etc.
1Papo D. (2013) Why should cognitive neuroscientists study the brain's resting state? Front. Hum. Neurosci.
2Papo D. (2014) Measuring brain temperature without a thermometer Front. Physiol.
Advanced Medical Image Processing Lab, Santiago, Chile
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA
We consider 11 orientation methods: R1 and R2 use the idea that the distribution of summands should be closer to Gaussian than the individual summands. R3 uses entropy to decide between x→y and y→x. R4 is based on ICA and finds cyclic models. These four use Anderson-Darling score for non-Gaussianity. We included methods by Hyvärinen and Smith (2013) that use LiNGAM likelihood to estimate the log of the likelihood ratio of models x→y and y→x. Finally we tested Smith et al. implementation of Patel's τ (Patel et al., 2006). For the 500 nodes network we estimated adjacencies with the PC algorithm (Spirtes et al., 2000) that recursively test conditional independence and orientations with the above methods except R2 and R4.
In the difficult case of non-stationary connections only R4 and Patel's τ perform accurate enough with single subject data. R3, Patel's τ and R4 can be useful for this case only with group data. R1/R4 are the only that can output feedback loops. They perform much better with group data. R4 has the highest recall with good precision.
In the 500 nodes graph, for single subject data 6% of the estimated adjacencies with PC are false positives, for group data only 2%. R1, R3, Skew, RSkew, SkewE and RSkewE excel at orientation precision with group data.
Department of Neurology, Massachusetts General Hospital, Boston, MA
Center for the Developing Brain, Child Mind Institute, New York, NY, USA
The toolbox also includes a processing stream for resting state and block-design task fMRI. The processing stream is quite flexible. For example, several options for partialing nuisance signals are available, including local and total white matter signal (Jo et al., 2013), calculation of Saad et al. (2013)'s GCOR, and the use of Chen et al. (2012) GNI method to determine whether global signal partialing is needed. In addition, Power et al. (2014)'s motion scrubbing method is available. For block-design task fMRI, the toolbox includes an option to deconvolve the signal (using SPM's method), split the resulting timeseries by condition, and calculate assortativity matrices for each condition (which can be used as a repeated factor in the GLM).
Resting connectivity of the Bed Nucleus of the Stria Terminalis
Section on the Neurobiology of Fear and Anxiety, National Institute of Mental Health, Bethesda, MD, USA
Davis, M., et al., 2010. Neuropsychopharm 35, 105–135; Fischl, B., et al., 2002. Neuron 33, 341–355; Jo, H.J., et al., 2010. NeuroImage 52, 571–582; Keuken, M.C., et al., 2014. NeuroImage 94, 40–46; Lim, I.A.L., et al., 2013. NeuroImage 82, 449–469.
University of Zurich, Institute of Pharmacology and Toxicology, Zurich, Switzerland
[1] Wang J et al., Magn Reson Med, 2003, 49:796, [2] Sämann et al., Mag Reson Mater Phy, 2010, 23:375.
Supported by a Sinergia Grant (CRSII3-136249) of the Swiss National Science Foundation.
Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
The Mind Research Network, Albuquerque, NM, USA

Flowchart of the algorithmic pipeline for the analysis. group ICA, segment ICA time courses, and calculate the correlation between any pair of (N=48) independent components (ICs) for each time-window; compute nodal connectivity strength of the weighted brain graph for each time-window; calculate the correlation of nodal connectivity strength between any pair of time-windows (W=131) across (N=48) ICs; reorder the time-windows based on the modular organization of the correlation matrix; compute the brain connectivity states by averaging the connectivity matrices of the time windows belong to the same module.

Variances of the graph metrics of time-varying brain connectivity (over 131 time-windows). The mean and bootstrapped 95% confidence interval are in red, as well as a boxplot and smoothed density histogram. HCs show higher variances (Wilcoxon and permutation tests, P<0.001 for all three metrics).
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Theme 2: Structural Brain Connectivity/Multi-modal Approaches/Animal Models
Massachusetts Eye and Ear Infirmary, Boston, MA, USA

Whole brain connectivity matrices from controls (left) and PVL subjects (right). The top row shows a fixed QA threshold of 0.055, while the bottom row shows the matrix generated with a QA threshold optimized on for each subject (0.06–0.155). The black boxes highlight examples where the number of fibres between ROIs is significantly different in the two QA threshold conditions.
Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
Inter-regional resting state MRI functional connectivity covaries with correlations between Delta band local field potentials in primary somatosensory cortex of monkeys
Vanderbilt University Institute of Imaging Science, Nashville, TN, USA
Center for Neuropolicy, Atlanta, GA, USA
This work was supported by a grant from the Office of Naval Research.
A.A. Martinos Center for Biomedical Imaging, MGH, Charlestown, MA, USA
Institute of Imaging Science, Vanderbilt University, Nashville, TN, US
Georgia Institute of Technology, Atlanta, GA, USA

Department of Psychiatry, Boston, Massachusetts, United States
*
Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
All T1-weighted MRI scan data were submitted to fully-automated CIVET pipeline. Cortical thickness was measured as the Euclidean distance between inner and outer cortical surface on 81,924 vertices. Enhanced myelin contrast (T1w/T2w ratio) was calculated after sampling MRI intensities at a depth of 50% through cortical thickness.
Average cortical thickness and myelin contrast were obtained on the 78 regions using the AAL template after regressing out the gender effect. Interregional partial correlation matrix (78×78) was obtained for each group and measure. Graph theoretical metrics (small-worldness, clustering coefficient, global efficiency and shortest pathlength) of structural brain network were calculated using cortical thickness and myelin contrast as features separately.
Boston University, Boston, MA, USA
Altered white matter integrity and structural connectivity in adults born preterm
Department of Neuroradiology, Technische Universität München, Munich, Germany
BME, Emory University/ Georgia Tech, Atlanta, GA, United State

Cross correlations between channel pairs. Significant higher correlations for bilateral hide paws or bilateral S1s than other pairs on 0.05–0.15 Hz signals (One-way ANOVA, n=25, p=0.00, *, **; no significant different between pairs of Lpaw/Rpaw and LS1/RS1). Bars indicate mean+/−SEM.
(References: [1]. Pan, W.-J., et al., Neuroimage 74,288–297, 2013; [2]. Mayhew, J.E.W. et al, Neuroimage 4, 183–193,1996)
Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Tehran, Iran
Division of Brain Sciences, Imperial College London, UK

Whole-brain functional connectivity maps for the two components: (A) temporal (pSTG); (B) parietal (vAPL), positive shown in warm colors and negative (anti-correlation) in cold colors. Sagittal (x = −44 and 44 mm), and two rows of axial slices (upper, z=43 to 15 mm, in 4 mm decrements, and lower, z=11 to −17 mm). The statistical threshold for the overlays was set at P<0.01, corrected for multiple comparisons using a correction for familywise error rate.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA

Time-averaged MEG map vs. fcMRI map. Dot near the colorbar shows the minimal size of a significant cluster. Seed is in green.

Frequentness pattern of MEG connectivity shows similarity to fcMRI along the cingulate regions.

Difference of the power spectral density (PSD) by contrasting time periods where connectivity was strong/weak.
Medical Image and Signal Processing Group, Ghent University-iMinds Medical IT department, Ghent, Belgium,

Findings of one rat in whole-brain BOLD fMRI during hippocampal DBS. The statistical map is thresholded at p<0.05. Activation occurs in hippocam-pal (HC) and thalamic (VPM/ VPL) structures.
NYU Dept. of Child & Adolescent Psychiatry, NY, NY
[1] Douaud et al., (2011) NeuroImage; [2] Ennis & Kindlmann (2006) Magn Reson Med
[3] Yendiki et al., (2014) NeuroImage; [4] Reiss et al., (2012) NeuroImage.
Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi Province, China
Theme 3: Applications in Neurological and Psychiatric Diseases
Universidad Nacional Autónoma de México, Instituto de Neurobiología, Querétaro QRO. 76230, Mexico
Contrast Images between hypnotic and alert state, positive and negative low frequency BOLD signal shows functional connectivity of DMN, ECN and SaN seeds above cero in yellow-red palette and below cero in blue-green palette.
Pain/Analgesia Imaging Neuroscience Group, Boston Children's Hospital; Waltham, MA USA
Dept. of Health Sciences, Boston University, Boston, MA, USA
[1] Behzadi, Y., et al. (2007). Neuroimage, 37(1), 90–101; [2] Biswal, B. B., et al. (2010). PNAS, 107(10), 4734–4739; [3] Friedman, J., Hastie, T., & Tibshirani, R. (2008). Biostatistics, 9(3), 432–441.
Dept. of Neurophysiology and Pathophysiology, UKE, Hamburg, Germany
Vanderbilt Psychiatric Neuroimaging Program, Nashville, TN, USA
MGH/MIT/HMS AA Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
AU MRI Research Center, Depts. of ECE and Psychology, Auburn University, Auburn, AL, USA
Coma Science Group, Cyclotron Research Center & Neurology Department, University of Liège, Belgium
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA
Defence Institute of Physiology and Allied Sciences, New Delhi
A large body of research on high altitude residents has reported the impairment in cognitive functions. Prolonged exposure to hypobaric hypoxia has devastating consequences to brain architecture and functions. Previous studies have reported the alternation of brain structure and consequently the functions just after the exposure to high altitude. The results are not very consistent across subject populations and the duration of exposure. Here, we have an unique opportunity to work with a group of seven subjects, who were born and brought up in sea level but were exposed to high altitude (12,000–15,000 feet) for a period of 24–36 months and living in sea level last three years. Both neuropsychological test (WAIS IV) and the resting state functional connectivity (RSFC) was conducted during the de-induction period on experimental as well as age matched five control subjects in India. Functional connectivity analysis was performed using seed-based approaches with MATLAB based custom software package: CONN. For seed-based analysis, sources are defined as multiple seeds corresponding to the pre-defined seed regions for: (i) Default mode network (DMN) and (ii) Executive control network (ECN). Seeds for DMN and ECN were chosen to be 10-mm spheres centered on previously published foci. The cognitive performance of experimental (high altitude exposed) subjects was lower (p<0.01) than the age matched controls in neuropsychological test. In RSFC analysis, we use DMN seeds (LLP, MPFC, PCC, RLP) and ECN seeds (Dorsal medial PFC, Right anterior PFC, Left superior parietal, Right superior parietal) as regions of interest (ROIs). The connectivity in executive control network is significantly higher (p<0.01) in controls compared to high altitude exposed subjects group. Both the behavioral and neuroimaging studies point the impaired resting state executive network in human expose to hypobaric hypoxia condition even shorter than three years. Additional studies are underway to understand the limit of plasticity in these subject groups during de-induction stages longitudinally.
Faculty of Medicine and University Hospital, Universidad Autonoma de Nuevo Leon (UANL), Monterrey, Mexico
National Institute of Mental Health and Neurosciences, Banglore, Karntaka, India.
Center for BrainHealth, The University of Texas at Dallas, Dallas, TX, USA
Departamento de Fisiología, Facultad de Medicina, UNAM, Distrito Federal, México
Center for Comparative NeuroImaging (CCNI), Department of Psychiatry, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA, USA, 01655
Alzheimer Centre Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
CHU de Caen, Service de Psychiatrie Adultes, Centre Esquirol Caen, F-14000, France
Clinical and Research Program in Pediatric Psychopharmacology, Massachusetts General Hospital, Boston, Massachusetts, USA
Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
Athinoula A. Martinos Center for Biomedical Imaging, Radiology, MGH, Charlestown, MA
Northeastern University, Boston, MA, USA
Nathan Kline Institute for Psychiatric Research (NKI), Orangeburg, NY, USA

Left IPS (fALFF & ReHo).
Institution of Brain Science, National Yang-Ming University, Taipei, Taiwan

Power spectrum of thalamus between HC (blue) and SZ (red), green highlights significant difference for p<0.05.

Between-group functional connectivity maps in distinct frequency ranges (P<0.05, FWE corrected).
Department of Neurology, Pennsylvania State University, Milton S. Hershey Medical Center, Hershey, Pennsylvania 17033, USA
McLean Hospital, Belmont, MA, USA
Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
Radiology, University of Washington, Seattle, WA

Clusters where resting state BOLD variability is significantly correlated with CSF α-Syn.
McGovern Institute for Brain Research, Cambridge, MA, USA
University of Bonn, Germany
McGovern Institute for Brain Research, Cambridge, MA, USA
Rutgers University, Newark, NJ
Vanderbilt University Institute of Imaging Science, Nashville, TN, USA
Graph theoretic characterization of functional MEG networks during interictal resting state in epilepsy
*
Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain
Dept. of Neuroimaging & Interventional Radiology, National Institute of Mental Health and Neuro Science (NIMHANS), Bangalore, India
Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Modular organizations of the global brain functional network in major depressive disorder group
*
Radiology, Seattle Children's Hospital, Seattle, WA, USA

Imaging result in a 3 y old male patient with FCD. a) MRI T1 revealed thickening cortex characterized by somewhat blurry grey-white boundary. b) FDG-PET showed increased metabolism and FCD location was consistent with EEG findings c) VMHC map shows correlation coefficient of the fcMRI signals for homotopic voxels; for this case a substantial reduction of homotopic connectivity (crosshairs) compared to surrounding areas.
Department of Neurology, Washington University, St Louis, USA
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
University of Wisconsin-Milwaukee, Milwaukee, WI, USA
Georgia Institute of Technology/Georgia State University, Atlanta, GA, USA
[1] Ressler KJ, et al. Nature 2011,470(7335):492–497 [2] Stevens JS, et al. PNAS 2014,111(8):3158–3163 [3] Jo HJ, et al. NeuroImage 2010,52(2):571–582 [4] Power JD, et al. NeuroImage 2012,59(3):2142–2154
Center for Comparative NeuroImaging, Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
VA North Texas Health Care System, Dallas, Texas
Department of Psychiatry and Behavioral Sciences, Stanford University
Harvard Medical School, Boston, MA, USA
The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
Departments of Neurology, Washington University in St Louis, St Louis, MO, USA

Within (top and middle) and between (bottom) group maps for the default mode network (using posterior cingulate seed)
FMRIB, University of Oxford, Oxford, United Kingdom
State Key Laboratory of Brain and Cognitive Sciences
Child Mind Institute, New York, NY 10022, USA
Yale University, New Haven, CT, U.S.A.
[1] Anticevic et al. (2013). “Characterizing Thalamo-Cortical Disturbances in Schizophrenia and Bipolar Illness.” Cerebral Cortex 2013.
[2] Yang et al. “Altered global brain signal in schizophrenia.” Proc Natl Acad Sci. 2014.
[3] Baker et al. “Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder.” JAMA Psychiatry 71(2): 109–118. 2014.
Laureate Institute for Brain Research, Tulsa, OK 74136, USA

BG-T (left) and OFC/MPFC (right) network showing increased functional connectivity in AD compared to HC (corrected p–value images, p<0.05).
Footnotes
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These authors contributed equally to this work
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Current affiliation: Department of Physiology, K.S. Hegde Medical Academy, Nitte University, Mangalore, Karnataka, India – 575018
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contributed equally
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equal contribution
(*equal contribution)s
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Both authors contributed equally to this work
