Reduced Segregation of Brain Networks in Spatial Neglect After Stroke

Abstract

Background/Purpose:

To investigate the association between the degree of spatial neglect and the changes of brain system segregation (SyS; i.e., the ratio of the extent to which brain networks interact internally and with each other) after stroke.

Methods:

A cohort of 20 patients with right hemisphere lesion was submitted to neuropsychological assessment as well as to resting-state functional magnetic resonance imaging session at acute stage after stroke. The severity of spatial neglect was quantified using the Center of Cancellation (CoC) scores of the Bells cancellation test. For each patient, resting-state functional connectivity (FC) matrices were assessed by implementing a brain parcellation of nine networks that included the visual network, dorsal attention network (DAN), ventral attention network (VAN), sensorimotor network (SMN), auditory network, cingulo-opercular network, language network, frontoparietal network, and default mode network (DMN). For each patient and each network, we then computed the SyS derived by subtracting the between-network FC from the within-network FC (normalized by the within-network FC). Finally, for each network, the CoC scores were correlated with the SyS.

Results:

The correlational analyses indicated a negative association between CoC and SyS in the DAN, VAN, SMN, and DMN (q < 0.05 false discovery rate [FDR]-corrected). Patients with more severe spatial neglect exhibited lower SyS and vice versa.

Conclusion:

The loss of segregation in multiple and specific networks provides a functional framework for the deficits in spatial and nonspatial attention and motor/exploratory ability observed in neglect patients.

Impact statement

In a graph-theoretic framework, we identify a loss of system segregation associative and sensorimotor networks in neglect patients who had suffered from right hemisphere stroke. From a theoretical standpoint, our findings corroborate the working hypothesis that the efficient segregation among brain systems is relevant for executing higher functions such as spatial attention. Clinically, the set of networks that exhibit loss of segregation offers a therapeutic opportunity and can be targets of neuromodulation protocols for neglect rehabilitation.

Introduction

Based on the seminal work by von Monakow (1911; Finger et al., 2004), a novel type of diaschisis (i.e., “connectomal” diaschisis) has been recently proposed. The model implies widespread changes in structural and functional connections as well as the reorganization of brain networks that occur in distant and structurally spared areas after focal brain injury (Carrera and Tononi, 2014). In healthy brains, the activity of large-scale brain networks can be assessed in terms of functional connectivity (FC) variations by using resting-state functional magnetic resonance imaging (rs-fMRI), the method based on the evaluation of temporal correlations of blood-oxygenated-level-dependent signals occurring between brain regions in the absence of a task (Biswal et al., 1995; Fox and Raichle, 2007). These resting-state networks show coherent intrinsic activity and exhibit a functional profile that mirrors the one observed in active conditions (Gordon et al., 2016; Power et al., 2011; Yeo et al., 2011).

A considerable amount of neuroimaging studies indicates that stroke generates widespread changes of FC both within and between resting-state networks. These FC alterations are associated with neuropsychological deficits in several domains (Baldassarre et al., 2016b; Siegel et al., 2016), including visuospatial attention (Baldassarre et al., 2014, 2016a; Carter et al., 2010; de Pasquale et al., 2021; He et al., 2007), motor (Baldassarre et al., 2016a; Bauer et al., 2014; Carter et al., 2010; Rehme et al., 2015; Wang et al., 2010), language (Baldassarre et al., 2019; Klingbeil et al., 2019), praxis (Watson et al., 2019), and constructional abilities (Sebastiani et al., 2021).

Connectome diaschisis has been described in neglect, the neuropsychological syndrome characterized by the failure of patients to orient, respond, and process stimuli in the contralesional part of the space and body after brain lesion (a condition more frequently occurring after a stroke in the right hemisphere) (Corbetta and Shulman, 2011; Halligan et al., 1989; Heilman and Watson, 1977).

Previous studies have demonstrated that the severity of visuospatial neglect is related to a reduction of the interhemispheric FC within dorsal attention network (DAN)/sensorimotor network (SMN) as well as a loss of negative FC between these networks and the default mode network (DMN) (Baldassarre et al., 2014). More recently, implementing a graph-theoretic framework, we have shown that visuospatial neglect is characterized by widespread changes of functional topology in multiple brain systems (de Pasquale et al., 2021). By assessing the betweenness centrality metric [i.e., the number of the shortest paths passing through a given node (Rubinov and Sporns, 2010)], we detected two sets of neglect-relevant hubs, that is, nodes that exhibit a crucial role in neglect-driven altered network communication.

One cohort of neglect hubs was observed in higher order associative systems such as the DAN, ventral attention network (VAN), frontoparietal network (FPN) executive-control, and DMN. These networks and associated cortical regions showed a decrease of centrality and an increase of shortest paths length (i.e., less efficient) associated with severe neglect. By contrast, an opposite pattern was detected in a second group of neglect hubs in lower level sensory-processing systems such as the visual network (VN) and motor network. These hubs exhibited a dysfunctional increase of centrality as well as a pathological decrease (i.e., more efficient) of shortest paths length related to neglect severity. Our findings thus suggest that spatial neglect can be considered as a network-level deficit regarding the functional architecture of brain systems.

In nonpathological conditions, the functional architecture of brain systems is characterized by the balance between functional specialization and dynamic integration of distinct and segregated (sub)networks (Tononi et al., 1994; Wig, 2017). An increase of between-networks interactions, that is, reduction of segregation, can negatively affect the functioning of the whole connectome as observed in Alzheimer's disease (Brier et al., 2014) and schizophrenia (Yang et al., 2016) or in physiological aging (Chan et al., 2014). Therefore, the topological dysfunctions associated with visuospatial neglect can be an indirect index of altered system segregation (SyS) and the balance loss of within- and between-system functional interactions.

In this study, to assess this hypothesis, we investigated the relationship between degrees of rightward spatial deficit and changes of SyS, indexed by the ratio among the difference of within- versus between-network connectivity and within-network connectivity, in multiple resting-state networks. Neglect is a multicomponent syndrome affecting different functions (e.g., spatial/nonspatial attention and motor/exploratory). Given the widespread alterations associated with these deficits, changes in multiple networks, including associative-control (e.g., DAN, DMN) and sensory (e.g., motor) systems, were closely investigated.

Materials and Methods

Stroke patients and assessment of neglect

A cohort of 20 patients who had suffered from a stroke in the right hemisphere (mean age 65.1 years, standard deviation = 12.3 years) was recruited within 2 weeks after first-time stroke onset at the Department of Neurology of IRCCS NEUROMED, Pozzilli, Italy. The enrollment in the study was based on the following inclusion criteria: (1) diagnosis of stroke (ischemic or hemorrhagic) in the right hemisphere at hospital discharge; (2) presence of stroke symptom(s); (3) wakeful condition to carry out the experiment; and (4) age older than 18 years. Exclusion criteria are as follows: (1) presence of psychiatric or neurological illness; (2) claustrophobia; and (3) presence of metal bodies. The demographic and clinical information of the cohort is reported in Table 1. The degree of neglect was evaluated employing the Center of Cancellation (CoC) (Binder et al., 1992) on the scores of the Bells cancellation test (Rorden and Karnath, 2010).

Table 1.

Demographic and Clinical Characteristics of the Stroke Patients

ID	Age at stroke (years)	Sex	Education	Lesion type	Lesion site
3	84	F	8	I	BS
4	69	F	8	I	Cau; Pal; Put; STG
6	73	M	13	H	PHG; LG
7	41	M	13	I	SPL; PreCun; AG; SLF
8	92	F	5	I	Cau; Pal; Put; Ins; IntCap; ExtCap
9	67	M	13	I	Cau; Pal; Put;
11	65	F	8	H	Pal; Put; Ins; ExtCap; STG
14	60	M	13	I	Tha
16	62	M	13	I	Tha
20	65	M	13	I	LOG; FFG; PHG
21	73	F	5	I	Put; IntCap
22	53	M	8	H	Pal; Put
23	77	F	5	I	MFG; PrCG; SPL
24	74	F	5	I	Put; Ins; Cau; CorRad; IntCap
26	73	M	5	I	CorRad
30	62	F	8	I	SLF
31	56	M	13	I	Tha
32	73	F	5	I	IFG; Ins; Put; ExtCap
33	76	F	5	I	Put; Ins; STG; IFG; CorRad; IntCap
34	51	M	8	I	Put; Cau; CorRad; IntCap; SLF
Total		10 F
Mean	65.1		9
SD	12.6		3.5

AG, angular gyrus; BS, brainstem; Cau, caudate; CorRad, corona radiata; ExtCap, external capsule; F, female; FFG, fusiform gyrus; H, hemorrhagic; I, ischemic; IFG, inferior frontal gyrus; Ins, insula; IntCap, internal capsule; LG, lingual gyrus; LOG, lateral occipital gyrus; M, male; MFG, middle frontal gyrus; Pal, pallidum; PHG, parahippocampal gyrus; PrCG, precentral gyrus; PreCun, precuneus; Put, putamen; SD, standard deviation; SLF, superior longitudinal fasciculus; SPL, superior parietal lobule; STG, superior temporal gyrus; Tha, thalamus.

fMRI acquisition

The (f)MRI session was carried out on a GE Signa HDxt 3T installed at NEUROMED within 24 h of the neuropsychological evaluation. Structural scans were collected using: (1) an axial T1-weighted three-dimensional spoiled gradient recalled echo (repetition time [TR] = 1644 msec, time to echo [TE] = 2.856 msec, flip angle = 13°, voxel size = 1.0 × 1.0 × 1.0 mm) and (2) an axial T2-weighted turbo spin-echo (TR = 2.856 msec, TE = 127.712 msec, slice thickness 3 mm, matrix size: 512 × 512). Resting-state functional scans were acquired with a gradient echo EPI sequence with TR = 1714 msec, TE = 30 msec, 34 contiguous 3.6 mm slices, during which participants were instructed to keep open eyes in a low luminance environment. Patients performed three resting-state scans of 7.5 min.

fMRI data preprocessing

The entire preprocessing pipeline of resting-state fMRI data is described in our previous works (Baldassarre et al., 2014; de Pasquale et al., 2021). Specifically, the following steps were conducted in the AFNI software (https://afni.nimh.nih.gov): (1) compensation for asynchronous slice acquisition using sinc interpolation; (2) elimination of odd/even slice intensity differences; (3) normalization of whole-brain intensity to reach a mode value of 1000; (4) spatial realignment of fMRI runs; and (5) resampling to 3 mm³ voxels in atlas space including realignment and atlas transformation in one resampling step.

Moreover, data underwent further preprocessing steps for FC analysis (Fox et al., 2009): (1) isotropic spatial smoothing using a 6 mm full width at half maximum Gaussian blur in all directions; (2) temporal low-pass filtering with a cutoff frequency of 0.1 Hz; (3) removal of the following sources of spurious variance through linear regression: (i) six parameters obtained by rigid body head motion correction, (ii) signal averaged over fixed region in atlas space, (iii) signal averaged over a ventricular region of interest (ROI), and (iv) signal from a region centered in the white matter.

Before the FC analyses, motion affected volumes were detected by means of the root mean square change of the temporally differentiated fMRI signals averaged over the brain (Power et al., 2012).

Lesion segmentation

Brain lesions were manually segmented and drawn from the T1-weighted and T2-weighted images, simultaneously displayed in the atlas space, by means of the MRIcron software. A trained radiologist (Giovanni Grillea in de Pasquale et al., 2021) reviewed all the segmentations (see Fig. 1A for the density map of the lesions).

FIG. 1.

Lesion topography and behavioral results. (A) Spatial distribution of lesion locations in the sample of patients (n = 20) for right hemisphere lesions. The color bar indicates the number of patients with a lesion in that location. (B) Bar plot of the CoC scores of the Bells cancellation test for each patient. The horizontal dashed line indicates the CoC cutoff; patients with scores above this value are classified as neglect patients. CoC, Center of Cancellation.

Resting-state networks

In the present study, we used a cohort of 153 ROIs, corresponding to known resting state networks (RSNs) derived from an fMRI resting-state analysis (de Pasquale et al., 2021; Hacker et al., 2013). The nine RSNs composed of associative systems including DAN, VAN, cingulo-opercular network (CON), language network (LN), FPN, and DMN as well as sensory-motor/processing systems, namely VN, auditory network (AN), and SMN.

Computation of SyS

For each patient, we estimated the resting-state FC by calculating the Pearson correlation coefficient (r) among fMRI signals between all the possible pairs of 153 nodes of the above-mentioned parcellation. Next, r scores were Fisher-transformed into z-scores to obtain normally distributed values (de Pasquale et al., 2021). Each node was labeled in accordance with the independent and previously defined brain parcellation (de Pasquale et al., 2021; Hacker et al., 2013). For each network, the SyS was estimated as follows (Chan et al., 2014): for each patient and network, the within-network functional connectivity (WNF) and the between-network functional connectivity (BNF) were computed.

Specifically, the WNF was derived as the mean correlation, among all possible pairs of regions within that network, whereas the BNF as the averaged correlation among regions of a given network and all other nodes of rest of the brain connectome. The computation of WNF and BNF scores was carried out on each participant's Z-matrix where the main diagonal was replaced with NaNs (Pedersen et al., 2021), and negative FC values were replaced with zero, as reported in previous studies (Chan et al., 2014; Ewers et al., 2021; Malagurski et al., 2020). Next, the SyS was obtained as SyS = (WNF − BNF)/WNF (i.e., by subtracting the BNF from the WNF normalized by this last one).

Accordingly, higher values indicate that a given network exhibits relatively lower between-system FC in relation to within-system FC (i.e., robust segregation of the system), whereas lower scores suggest that such network shows relatively higher between-system FC compared with within-system FC (i.e., weaker segregation of the system). Next, for each of the nine networks, the Pearson correlation was computed between SyS and CoC scores across the entire sample of patients. Finally, the nine p-values were corrected by means of the Benjamini and Hochberg false discovery rate (FDR) (Benjamini and Hochberg, 1995) obtaining the corresponded q-values.

SyS and lesion size

To rule out for the potential impact of lesion size on the association between neglect severity and SyS, we performed the following analyses: (1) correlation between lesion size and CoC scores; (2) correlation between lesion size and SyS values; and (3) partial correlation between CoC and SyS, regressing out the volume of lesion.

SyS and FC patterns

Finally, we investigated whether the changes of SyS were related to the patterns of FC previously associated with spatial neglect, such as decreased interhemispheric connectivity of the DAN (Carter et al., 2010; He et al., 2007), reduction of FC within the VAN (Barrett et al., 2019), as well as loss of anti-correlation between DAN and DMN (Baldassarre et al., 2014). To this aim, for each patient, we computed: (1) the DAN interhemispheric FC, by calculating the average of the FC between each node of the DAN in one hemisphere with all nodes of the DAN in the other hemisphere; (2) the VAN intra-network FC, by computing the average of the FC between all pairs of nodes of the VAN; (3) the intra-hemispheric DAN-DMN FC, by averaging the FC between all pairs of DAN and DMN nodes in each hemisphere (i.e., avoiding interhemispheric connections). Finally, such metrics were correlated with the SyS scores of the DAN, VAN, and DMN, respectively.

Standard protocol approvals and patient consents

The study was conducted in accordance with the ethical standards of the Declaration of Helsinki and was approved by the institutional review board (IRB) of IRCCS NEUROMED. All patients participated in the study after providing written informed consent.

Results

As reported in our earlier work (de Pasquale et al., 2021), 11 patients (55%) were classified as having neglect since they had a score greater than the CoC cutoff of 0.081 (Rorden and Karnath, 2010). Furthermore, within the group of neglect patients, some of them also showed impairments in general cognitive efficiency (60%), executive functions (57%), constructional abilities (37%), and verbal memory (66%). Finally, as previously reported, the topography of lesion location revealed the highest frequency of damage to the thalamus and putamen regions.

Loss of SyS in multiple networks associated with neglect

As described above, for each network, we correlated the scores of the CoC of the Bells test (see Fig. 1B for scores distribution) with the magnitude of the SyS. The correlational analyses revealed a negative relationship between CoC and SyS in four of the nine investigated networks, namely the DAN, VAN, DMN, and SMN (FDR-corrected q = 0.0267; q = 0.0267; q = 0.027: q = 0.0267, respectively) (Fig. 2). Patients with more severe spatial neglect exhibited a significantly lower SyS, indicating a loss of segregation across multiple networks (Fig. 3).

FIG. 2.

Neglect and SyS of all resting-state networks. The bar graph displays the magnitude of the Pearson correlation between the CoC and the SyS of the nine resting state networks. FDR-corrected *q < 0.05. FDR, false discovery rate; SyS, system segregation.

FIG. 3.

Neglect and SyS of the four resting-state networks. Each scatterplot displays the Pearson correlation between the CoC (x-axis) and the SyS (y-axis; FDR-corrected q) of the DAN (upper left panel), motor network (upper right panel), VAN (lower left panel), and DMN (lower right panel). Each dot represents a patient. DAN, dorsal attention network; DMN, default mode network; VAN, ventral attention network.

Furthermore, the analyses controlling for the extension of the structural damage indicated that: (1) lesion size (mm³) was not associated with CoC scores (r = 0.17; p = 0.46); (2) correlation between lesion size and SyS values was not significant (p > 0.15 for all networks); (3) CoC was negatively correlated (partial correlation) with the SyS in the DAN and VAN as well as DMN and SMN (FDR-corrected, q = 0.032; q = 0.032; q = 0.032; q = 0.028; q = 0.032, respectively; Supplementary Fig. S1). Overall, these findings indicate that the relationship between SyS and neglect severity does not depend on the mount of structural damage.

Finally, the connectivity–segregation analyses showed that: (1) the SyS of DAN was positively correlated with the interhemispheric FC in the DAN (r = 0.656; q = 0.0051), indicating that lower SyS was associated with greater interhemispheric breakdown of the DAN (Fig. 4A); (2) the SyS of VAN was positively correlated with the intra-network FC of the VAN (r = 0.535; q = 0.0226), suggesting that lower SyS is related to lower FC (Fig. 4B); (3) the SyS of DMN was negatively correlated with the DAN-DMN intra-hemispheric FC (r = −0.444; q = 0.049), denoting that lower SyS corresponded to higher (i.e., dysfunctional) FC between DAN and DMN (Fig. 4C). Overall, such findings suggest that the modulation of SyS is related to the changes of FC (both within and between resting-state networks).

FIG. 4.

SyS and resting-state FC. Each scatterplot displays the Pearson correlation (FDR-corrected q) between the SyS (x-axis) and resting-state FC (y-axis). Specifically, DAN SyS and DAN interhemispheric FC (A); VAN SyS and VAN intra-network FC (B); DMN SyS and DAN-DMN intra-hemispheric FC (C). FC, functional connectivity.

Discussion

In this work, we assessed variations of rs-fMRI FC to investigate the modulation of the segregation in brain networks related to the degree of visuospatial neglect in a sample of patients affected by acute stroke in the right hemisphere. We observed that a group of resting-state networks such as the DAN, VAN, SMN, and DMN exhibited a reduction of SyS. These networks changes aligned with the extent of the rightward bias such as more impaired patients showed lower SyS scores and vice versa. Notably, the association between SyS and neglect severity did not depend on the amount of structural damage. The loss of SyS in multiple networks can reflect different compromised cognitive processes as indicated by a poor performance in the attention domain (spatial and nonspatial) and in the visual and motor ones (visuomotor exploration). These deficits coexist and cannot be uniquely linked to distinct brain regions' centrality. However, we speculate on different neglect components that are most likely associated with these network dysfunctions.

Correlational analyses indicated a significant association between neglect severity and the SyS reduction within the DAN, which comprises a set of frontoparietal regions acting as attentional systems (Corbetta and Shulman, 2002). Therefore, one can hypothesize that such pattern reflects the spatial component of neglect and the failure of patients to orient their attention and react to stimuli occurring in the contralesional part of the visual field. This hypothesis is consistent with previous neuroimaging studies in a cohort of neglect patients. The DANs of these patients indeed exhibited interhemispheric imbalance of evoked activity during visuospatial attention tasks (Corbetta et al., 2005) together with a marked decrease of the interhemispheric FC at rest (Baldassarre et al., 2014; Carter et al., 2010; He et al., 2007) related to the spatial impairment.

As directed tested in the current study, the reduction of SyS of the DAN was associated with its lower interhemispheric FC. Notably, such a pattern is also in line with recent observations of a neglect-driven loss of betweenness centrality of a key parietal node of the DAN and its decreased efficacy in communication within the other DAN regions in the same cohort of patients (de Pasquale et al., 2021).

A reduction of SyS was also detected within the DMN (Shulman et al., 1997), a system involved in the internal cognition, for example, in self-referential introspective states such as autobiographical (Buckner et al., 2008) and semantic memory (Croce et al., 2021; Spadone et al., 2017). This network can also contribute to the focus and sustain of the attention (Gilbert et al., 2007; Gusnard and Raichle, 2001; Hampson et al., 2006; Shulman et al., 1997) as well as to maintain the level of arousal, vigilance, and awareness (Boly et al., 2008; Vogt and Laureys, 2005). Thus, the DMN alterations we observed can be functionally linked to the impairment of nonspatial attention, reduction of arousal level, and sustained attention, which negatively affect the extent of spatial deficit (Robertson et al., 1995).

This hypothesis is sustained by the detected association between lower SyS of DMN and reduced DAN-DMN anti-correlations, which are thought to be more relevant for the nonspatial deficit after right hemisphere lesion (Baldassarre et al., 2014). Several cortical hubs have been consistently reported within the DMN, including the posterior cingulate cortex and medial prefrontal cortex (Buckner et al., 2009; de Pasquale et al., 2010, 2012; Gordon et al., 2018). Thus, a possible interpretation is that the loss of SyS within the DMN reflects broader changes in brain network topology at a large scale. These could be associated with multiple impairments in distinct cognitive domains. In fact, some neglect patients exhibited a large spectrum of deficits as described on our previous report. However, the hypothesis of the association between DMN SyS and several deficits might be the topic of future work employing larger cohorts of patients.

The deficit of nonspatial attention can also be associated with the loss of SyS of the VAN, which is recruited in cognitive processes such as reorienting, arousal, and detection of novel stimuli (Corbetta and Shulman, 2002). Lesion-mapping studies indicated that damage of the VAN in the right hemisphere concurs to generate spatial neglect (Corbetta and Shulman, 2011). In accordance with this interpretation, a reduction of SyS within the VAN likely reflects the nonspatial component of neglect. Notably, the negative associations between Bells CoC and VAN SyS as well as between VAN SyS and VAN FC are consistent with the recent neuroimaging findings showing that neglect patients exhibit a reduction of intranetwork FC of such network (Barrett et al., 2019).

Finally, a neglect-relevant reduction of SyS was detected in the SMN. This finding is consistent with recent studies reporting a loss of interhemispheric FC within this network (Baldassarre et al., 2014) as well as a pathological increase in functional interactions between the central sulcus and the visual system (de Pasquale et al., 2021) in neglect patients. Such a pattern likely reflects the inability of neglect patients to explore the contralateral visual field and reach target stimuli.

Taken together, the neglect-relevant reduction of SyS occurring in specific networks is consistent with the decrease of the overall, not network-specific, brain modularity and its restoration observed in cohorts of stroke patients recovering from neglect and aphasia (Siegel et al., 2018). Our results support converging lines of evidence indicating that the amount of SyS is crucial within functional architecture of the human brain during the life span in health and diseases (Chan et al., 2014; Ewers et al., 2021; Marek et al., 2015).

Conclusion

From a theoretical standpoint, our findings support the notion that efficient segregation among brain systems is crucial for the correct functioning of higher functions, including, in this case, spatial attention. From a clinical standpoint, the loss of SyS can represent a potential biomarker for the rehabilitation of neglect as recent studies indicate that the degree of segregation predicts the response to cognitive training in clinical cohorts (Arnemann et al., 2015) and elderly subjects (Gallen et al., 2016).

Footnotes

Authors' Contributions

S.S. and A.B. designed and conceptualized the study. S.S., F.P., P.Ca., A.D., S.L.S., G.C., and A.B. drafted the article for intellectual content. L.P. and A.B. played a major role in the acquisition of data. S.S., P.Ch., and A.B. analyzed the data. All authors contributed to the final version of the article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This study was supported by the Ministry of Health Italy.

Supplementary Material

Supplementary Figure S1

References

Arnemann

, Chen

, Novakovic-Agopian

, et al. 2015. Functional brain network modularity predicts response to cognitive training after brain injury. Neurology, 84:1568–1574.

Baldassarre

, Metcalf

, Shulman

, et al. 2019. Brain networks' functional connectivity separates aphasic deficits in stroke. Neurology, 92:e125–e135.

Baldassarre

, Ramsey

, Hacker

, et al. 2014. Large-scale changes in network interactions as a physiological signature of spatial neglect. Brain, 137:3267–3283.

Baldassarre

, Ramsey

, Rengachary

, et al. 2016a. Dissociated functional connectivity profiles for motor and attention deficits in acute right-hemisphere stroke. Brain, 139:2024–2038.

Baldassarre

, Ramsey

, Siegel

, et al. 2016b. Brain connectivity and neurological disorders after stroke. Curr Opin Neurol, 29:706–713.

Barrett

, Boukrina

, Saleh

. 2019. Ventral attention and motor network connectivity is relevant to functional impairment in spatial neglect after right brain stroke. Brain Cogn, 129:16–24.

Bauer

, Kraft

, Wright

, et al. 2014. Optical imaging of disrupted functional connectivity following ischemic stroke in mice. Neuroimage, 99:388–401.

Benjamini

, Hochberg

. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B, 57:289–300.

Binder

, Marshall

, Lazar

, et al. 1992. Distinct syndromes of hemineglect. Arch Neurol, 49:1187–1194.

10.

Biswal

, Yetkin

, Haughton

, et al. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med, 34:537–541.

11.

Boly

, Phillips

, Tshibanda

, et al. 2008. Intrinsic brain activity in altered states of consciousness: how conscious is the default mode of brain function?. Ann N Y Acad Sci, 1129:119–129.

12.

Brier

, Thomas

, Fagan

, et al. 2014. Functional connectivity and graph theory in preclinical Alzheimer's disease. Neurobiol Aging, 35:757–768.

13.

Buckner

, Andrews-Hanna

, Schacter

. 2008. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci, 1124:1–38.

14.

Buckner

, Sepulcre

, Talukdar

, et al. 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J Neurosci, 29:1860–1873.

15.

Carrera

, Tononi

. 2014. Diaschisis: past, present, future. Brain, 137:2408–2422.

16.

Carter

, Astafiev

, Lang

, et al. 2010. Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke. Ann Neurol, 67:365–375.

17.

Chan

, Park

, Savalia

, et al. 2014. Decreased segregation of brain systems across the healthy adult lifespan. Proc Natl Acad Sci U S A, 111:E4997–E5006.

18.

Corbetta

, Kincade

, Lewis

, et al. 2005. Neural basis and recovery of spatial attention deficits in spatial neglect. Nat Neurosci, 8:1603–1610.

19.

Corbetta

, Shulman

. 2002. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci, 3:201–215.

20.

Corbetta

, Shulman

. 2011. Spatial neglect and attention networks. Annu Rev Neurosci, 34:569–599.

21.

Croce

, Spadone

, Zappasodi

, et al. 2021. rTMS affects EEG microstates dynamic during evoked activity. Cortex, 138:302–310.

22.

de Pasquale

, Chiacchiaretta

, Pavone

, et al. 2023. Brain topological reorganization associated with visual neglect after stroke. Brain Connect, 13:473–486.

23.

de Pasquale

, Della Penna

, Snyder

, et al. 2010. Temporal dynamics of spontaneous MEG activity in brain networks. Proc Natl Acad Sci U S A, 107:6040–6045.

24.

de Pasquale

, Della Penna

, Snyder

, et al. 2012. A cortical core for dynamic integration of functional networks in the resting human brain. Neuron, 74:753–764.

25.

Ewers

, Luan

, Frontzkowski

, et al. 2021. Segregation of functional networks is associated with cognitive resilience in Alzheimer's disease. Brain, 144:2176–2185.

26.

Finger

, Koehler

, Jagella

. 2004. The Monakow concept of diaschisis: origins and perspectives. Arch Neurol, 61:283–288.

27.

Fox

, Raichle

. 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci, 8:700–711.

28.

Fox

, Zhang

, Snyder

, et al. 2009. The global signal and observed anticorrelated resting state brain networks. J Neurophysiol, 101:3270–3283.

29.

Gallen

, Baniqued

, Chapman

, et al. 2016. Modular brain network organization predicts response to cognitive training in older adults. PLoS One, 11:e0169015.

30.

Gilbert

, Dumontheil

, Simons

, et al. 2007. Comment on “Wandering minds: the default network and stimulus-independent thought.”. Science, 317:43; author reply 43.

31.

Gordon

, Laumann

, Adeyemo

, et al. 2016. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb Cortex, 26:288–303.

32.

Gordon

, Lynch

, Gratton

, et al. 2018. Three distinct sets of connector hubs integrate human brain function. Cell Rep, 24:1687.e4–1695.e4.

33.

Gusnard

, Raichle

. 2001. Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci, 2:685–694.

34.

Hacker

, Laumann

, Szrama

, et al. 2013. Resting-state network estimation in individual subjects. Neuroimage, 82:616–633.

35.

Halligan

, Marshall

, Wade

. 1989. Visuospatial neglect: underlying factors and test sensitivity. Lancet, 2:908–911.

36.

Hampson

, Driesen

, Skudlarski

, et al. 2006. Brain connectivity related to working memory performance. J Neurosci, 26:13338–13343.

37.

, Snyder

, Vincent

, et al. 2007. Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron, 53:905–918.

38.

Heilman

, Watson

. 1977. The neglect syndrome—a unilateral defect of the orienting response. In: Harnad

, Doty

, Goldstein

, et al. (eds.) Lateralization in the Nervous System. New York, NY: Academic Press; pp. 285–302.

39.

Klingbeil

, Wawrzyniak

, Stockert

, et al. 2019. Resting-state functional connectivity: an emerging method for the study of language networks in post-stroke aphasia. Brain Cogn, 131:22–33.

40.

Malagurski

, Liem

, Oschwald

, et al. 2020. Functional dedifferentiation of associative resting state networks in older adults—a longitudinal study. Neuroimage, 214:116680.

41.

Marek

, Hwang

, Foran

, et al. 2015. The contribution of network organization and integration to the development of cognitive control. PLoS Biol, 13:e1002328.

42.

Pedersen

, Geerligs

, Andersson

, et al. 2021. When functional blurring becomes deleterious: reduced system segregation is associated with less white matter integrity and cognitive decline in aging. Neuroimage, 242:118449.

43.

Power

, Barnes

, Snyder

, et al. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59:2142–2154.

44.

Power

, Cohen

, Nelson

, et al. 2011. Functional network organization of the human brain. Neuron, 72:665–678.

45.

Rehme

, Volz

, Feis

, et al. 2015. Identifying neuroimaging markers of motor disability in acute stroke by machine learning techniques. Cereb Cortex, 25:3046–3056.

46.

Robertson

, Tegner

, Tham

, et al. 1995. Sustained attention training for unilateral neglect: theoretical and rehabilitation implications. J Clin Exp Neuropsychol, 17:416–430.

47.

Rorden

, Karnath

. 2010. A simple measure of neglect severity. Neuropsychologia, 48:2758–2763.

48.

Rubinov

, Sporns

. 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52:1059–1069.

49.

Sebastiani

, Chiacchiaretta

, Pavone

, et al. 2021. Cortical hyper-connectivity in a stroke patient with rotated drawing. Case Rep Neurol, 13:677–686.

50.

Shulman

, Fiez

, Corbetta

, et al. 1997. Common blood flow changes across visual tasks: II. decreases in cerebral cortex. J Cogn Neurosci, 9:648–663.

51.

Siegel

, Ramsey

, Snyder

, et al. 2016. Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proc Natl Acad Sci U S A, 113:E4367–E4376.

52.

Siegel

, Seitzman

, Ramsey

, et al. 2018. Re-emergence of modular brain networks in stroke recovery. Cortex, 101:44–59.

53.

Spadone

, Sestieri

, Baldassarre

, et al. 2017. Temporal dynamics of TMS interference over preparatory alpha activity during semantic decisions. Sci Rep, 7:2372.

54.

Tononi

, Sporns

, Edelman

. 1994. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc Natl Acad Sci U S A, 91:5033–5037.

55.

Vogt

, Laureys

. 2005. Posterior cingulate, precuneal and retrosplenial cortices: cytology and components of the neural network correlates of consciousness. Prog Brain Res, 150:205–217.

56.

von Monakow

. 1911. Lokalisation der Hirnfunktionen [Localization of brain functions]. J Psychol Neurol, 17:185–200.

57.

Wang

, Yu

, Chen

, et al. 2010. Dynamic functional reorganization of the motor execution network after stroke. Brain, 133:1224–1238.

58.

Watson

, Gotts

, Martin

, et al. 2019. Bilateral functional connectivity at rest predicts apraxic symptoms after left hemisphere stroke. Neuroimage Clin, 21:101526.

59.

Wig

. 2017. Segregated systems of human brain networks. Trends Cogn Sci, 21:981–996.

60.

Yang

, Murray

, Wang

, et al. 2016. Functional hierarchy underlies preferential connectivity disturbances in schizophrenia. Proc Natl Acad Sci U S A, 113:E219–E228.

61.

Yeo

, Krienen

, Sepulcre

, et al. 2011. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol, 106:1125–1165.

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