The Distinct Disrupted Plasticity in Structural and Functional Network in Mild Stroke With Basal Ganglia Region Infarcts

doi:10.21203/rs.3.rs-664225/v1

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The Distinct Disrupted Plasticity in Structural and Functional Network in Mild Stroke With Basal Ganglia Region Infarcts

https://doi.org/10.21203/rs.3.rs-664225/v1

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Objective

To characterize brain structural and functional networks in post-stroke patients with or without cognitive impairment.

Methods

Graph theory analysis was applied to diffusion-weighted imaging (DWI) data and resting-state functional MRI (fMRI) data from 23 post-stroke patients with cognitive impairment (PSCI), 17 post-stroke patients without cognitive impairment (NPSCI), and 29 healthy controls (HC). Structural and functional connectivity between 90 cortical and subcortical brain regions was estimated and thresholded to construct a set of undirected graphs. Network-based statistics (NBS) was used to characterize altered connectivity patterns among the three groups.

Results

Compared to HC, the PSCI group demonstrated substantial reductions in all three types of connections - rich club, feeder, and local - in structural and functional networks. Specifically, in structural network analysis, reduced connections were observed within basal ganglia and basal ganglia-frontal networks, whereas in the functional network analysis, reduced connections were observed in fronto-parietal network (FPN) and cingulo-opercular networks (CON). Meanwhile, compared to HC, the NPSCI group demonstrated reductions in both feeder and local connections only within occipital area and occipital-temporal structural networks.

Conclusions

The findings of reduced structural connectivity in regions stemming from a basal ganglia core and reduced functional connectivity in FPN and CON may indicate a bottom-up cognitive impairment induced by stroke. Graph analysis and connectomics may aid clinical diagnosis and serve as potential imaging biomarkers for post-stroke patients with cognitive impairment.

Structural Biology

graph theory

DTI

resting-state fMRI

basal ganglia

stroke

cognitive impairment

Basal ganglia infarction is known to induce stroke, which may, in turn, impair cognitive function(Allan, Rowan et al. 2011, Campbell, De Silva et al. 2019) .Through high spatial resolution imaging techniques (such as fMRI and DTI), it is possible to explore the neurophysiological plasticity characteristics of patients with cognitive impairment after stroke with basal ganglia infarction(Dacosta‐Aguayo, Grana et al. 2015). This experimental pursuit is of great importance, and may contribute to the development of novel therapeutic approaches targeting restoration of normal brain function.

The human brain is a highly complex system where multiple brain areas work collaboratively(Bressler and Menon 2010, Schedlbauer, Copara et al. 2014). The emergence of network science in the neuroscience field(Bassett and Sporns 2017) provides a new perspective to model and topologically characterize structural and functional integrative bases supporting brain activity(Griffis, Metcalf et al. 2019). Previous studies have reported that post-stroke patients with basal ganglia infarcts may be resilient against cognitive impairment(Zuo, Li et al. 2018). However, how brain structural and functional networks are impaired in patients with post-stroke cognitive impairment remains unclear.

The current research objective is to investigate changes in brain structural and functional networks associated with cognitive impairment in post-stroke patients with basal ganglia infarcts. Post-stroke patients with (PSCI) or without cognitive impairment (NPSCI) and healthy people were recruited. Neurobiological data was recorded with DWI and fMRI, and analyzed using graph theory to construct structural and functional networks. For structural networks, stroke with basal ganglia infarcts may damage cognitive function by impairing physical connections between regions(Stilley, Ryan et al. 2004). Thus, we hypothesized that the PSCI group would exhibit impaired basal ganglia-related structural networks. Regarding functional networks, previous studies found that damage in the fronto-parietal network (FPN) and cingulo-opercular network (CON) contributes to cognitive impairment in disease states such as schizophrenia and psychotic disorder(Sheffield, Repovs et al. 2015, Sheffield, Rogers et al. 2019), not including stoke. For the current study, we hypothesized that FPN and CON aberrations would be present in stroke as well.

Participants

We recruited 23 first-time mild stroke patients with cognitive impairment (PSCI), 17 first-time stroke patients without cognitive impairment (NPSCI), and 29 healthy controls (HC) matched for age, sex, and education. All mild patients were recruited consecutively from one stroke ward in the Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, from December 1, 2014 to May 31, 2016. This study was approved by the Beijing Tiantan Hospital Ethics Review Board. Informed written consent was obtained from all participants. This study met the guidelines of Capital Medical University, which abides by the Helsinki Declaration on ethical principles for medical research involving human participants.

The inclusion criteria for patients were being aged between 35 and 65 years, having experienced a mild stroke occurring in the basal ganglia, internal capsule, and/or thalamus, without other cortical or subcortical cerebrovascular abnormality, and having no previous history of stroke or transient ischemic attack (TIA). Acute ischemic stroke was diagnosed by neurologists using World Health Organization criteria and confirmed by brain computed tomography (CT) or MRI. The exclusion criteria were presence of stroke mimics (i.e., seizures, migraine), illiteracy, neurological (except stroke) or psychiatric diseases, acute stroke in cortex or other regions, or obvious demyelination on fluid-attenuated inversion recovery (FLAIR) image.

The inclusion of healthy control individuals was based on the absence of both a previous history of neurological or psychiatric diseases and obvious myelination and lacunar infarction.

Image acquisition and preprocessing

MR images were obtained using a Siemens Trio Tim 3T MR scanner. Resting-state fMRI, DWI and 3D high resolution structural brain images were acquired using a 12-channel phased-array head coil with implementation of the parallel imaging scheme GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions).

DWI data were acquired using a single-shot twice-refocused spin-echo diffusion echo-planar imaging (EPI) sequence. The sequence parameters were repetition time (TR)=8000ms, echo time (TE)=60ms, 64 non-linear diffusion directions with b=1000 s/mm² and an additional volume with b=0 s/mm², data matrix=128x128, field of view (FOV)=256mmx256mm, 2mm slice thickness, isotropic voxel size (2mm³), and 75 transverse slices with no gap, covering the whole brain. The acquisition time was approximately 12 min for each DWI scan.

We also acquired 3D high resolution brain structural images (voxel size = 1mm³, isotropic) using a T1-weighted MP-RAGE sequence for each participant. The sequence parameters were TR/TE=2300 ms/2.3 ms, inversion time (TI) =900ms, flip angle=8^o, FOV=256mm x 256mm, slice thickness=1mm, and 176 sagittal slices covering the whole brain. All participants were scanned using the same MR scanner. For each participant, resting-state fMRI was acquired using an EPI sequence with TR=2500ms, TE=30ms, flip angle=90^o, voxel size=2.86x2.86x3 mm³, image matrix =70x70x43, 200 volumes.

For data preprocessing, both the DWI data and T1-weighted data were visually inspected for apparent artifacts arising from participant motion or instrument malfunction. Distortions in the diffusion tensor images caused by eddy currents and simple head motions were corrected by applying affine alignment. Particularly, EPI distortion induced by the presence of geometrical and intensity distortions along the phase-encode direction caused by field inhomogeneities and concomitant fields were corrected by registering the first B0 image in each DWI set to its corresponding undistorted T1-weighted image, with a cubic B-spline transformation of knot grid size 10×10×10 mm³, partitioning the image space into 2×2×1.65 cm³. After correction, 3D maps of the diffusion tensors and the FA were calculated²³. Correction and calculation were performed using FMRIB’s Diffusion Toolbox. Resting-state functional MRI data was slice-time corrected, re-aligned, co-registered to the corresponding structural images, normalized to atlas space and resampled to 3mm cubic voxel resolution using a combination of linear transformations and non-linear warps. Confounds related to head motion, global signal fluctuations, and non-gray matter signal compartments were removed from the data by regression of the six head motion parameters obtained from rigid body correction, along with global signal, the cerebrospinal fluid (CSF) and white matter (WM) signal. The resting-state functional MRI data preprocessing was performed using the DPABI software(Yan, Wang et al. 2016).

Network construction

For structural network construction, we used the automated anatomical labeling (AAL) template(Tzourio-Mazoyer, Landeau et al. 2002), containing 90 brain regions defined as network nodes(Gong, He et al. 2009). FA described the degree of directional preference (anisotropy) of water diffusion values along the midpoint of the tracked fiber and was calculated from the eigenvalues of the diffusion tensor in three dimensions. Large FA values represent confined diffusion in one direction. Fiber tracking stopped when either consecutive voxels in tracking showed FA less than 0.2 or the angle between the eigenvectors of two consecutive voxels was larger than 35º. Therefore, we obtained the matrix of an anatomically connected network formed by weighted connections (the value of FA) in each participant. Data preprocessing and network construction were performed using the Pipeline for Analyzing Brain Diffusion Images (PANDA) toolbox(Cui, Zhong et al. 2013).

For functional network construction, we also used the automated anatomical labeling (AAL) template to define network nodes. For each participant, the time series of all voxels in each ROI were extracted and averaged to obtain a mean time series. The spurious sources of variance, six head motion parameters, were removed from the meantime series using a multiple regression model. The time series were also band-pass filtered (0.01-0.08HZ) to reduce low-frequency drift and high-frequency respiratory and cardiac noise. Pearson’s correlation analysis was performed between the processed time series of every pair of ROIs to produce a 90x90 temporal symmetric correlation matrix for each participant. Functional network were constructed using the MATLAB-based Graph Theoretical Network Analysis (GRETNA) toolbox(Wang, Wang et al. 2015).

A correlation analysis was used to test the coupling between structural and functional connections among the three experimental groups.

Global network parameters

We selected some global parameters to analysis the overall organization characteristics in structural and functional networks. Global parameters included (1) weighted clustering coefficient (C_p), computed as the average likelihood that the neighbors of a node are interconnected; (2) average path length (L_p), computed as the average of the shortest path length for all possible connections among the nodes in the network(Latora and Marchiori 2001); (3) global efficiency (E_glob), computed as the average inverse shortest path length(Watts and Strogatz 1998); and (4) small-worldness (σ), computed as the ratio of C_p to L_p with normalization to a null random network. All global parameters were computed using the GRETNA toolbox.

Statistical analysis

The differences in global parameters (C_p, L_p, E_glob, σ) of networks were tested by one-way ANOVA and corrected by permutation test(Zhang, Liao et al. 2011). A null distribution of differences for global parameters was generated after 5000 permutations repeat. Finally, we assigned a p-value to the differences after computing the proportion of the differences that exceeded the null distribution values. The p < 0.05 was used as a significant threshold to correct for multiple comparisons when testing for statistically significant differences among all global parameters.

Rich club analysis

A rich-club analysis was used for determining the hubs and nonhubs in constructed brain networks. The rich-club coefficient (Φ^w(k)) was used to determine the degree (k) threshold for a hub in the network. Nodes with degree larger than k belong to a hubs. Accordingly, the nodes were classified into hubs (with nodal degree greater than k) and nonhubs. Thus, connections in the network were classified as rich club (linking the rich club nodes), feeder (linking rich club nodes to non-rich club nodes) and local (linking non-rich club nodes).

Network-based statistics analysis

A network-based statistic (NBS) analysis was used to explore the differences in network components (rich club, feeder or local) in brain network among the three groups. We used the correction for family-wise error rate to control the power of the analysis (Zalesky, Fornito et al. 2010). A nonparametric two-tail sign test was performed to compare eachstructural and functional network connections between groups. Connections with a test-statistic t > 2.66 (corresponding to p < 0.01) were marked 1, and 0 otherwise. A binary matrix could be generated to represent the changed connections of network between groups. The largest matrix with all possibly connected brain areas was identified from the network and the size of the matrix was stored. Random assignment with 5,000 permutations of corresponding voxels between two groups was performed to investigate the effect of different components. A t-test was used to examine whether the initial matrix was different from the new matrix generated by the permutation. P < 0.05 indicated altered connectivity between groups. Three different components (rich, feeder and local) were tested separately.

Inter- and intra-module alterations determination

We have tried to analyze and located the connections to find out which components had the greatest influence on the global efficiency of network after NBS-analysis. We divided the whole brain network into eight modules: central, frontal, insula, limbic, occipital, parietal, subcortical and temporal modules(Cheng, Rolls et al. 2016). We subsequently quantified the proportion of intra- and inter-modular connections using the sum of connections located inter- and intra-modules divided by all altered connections.

Data availability statement

The dataset used and analyzed are available to other researchers subject to review of the request by the Scientific Committee of the study and ethical approval.

We constructed brain networks for the three participant groups based on the weighted FA values obtained from DTI and correlation values obtained from resting-state fMRI. Correlation analysis between structural and functional networks revealed that connectivity strength in the structural brain network was strongly positively correlated with connectivity strength in the functional brain network within the three groups (in HC, r = 0.4422, p < 0.0001; in NPSCI, r = 0.4822, p < 0.0001; in PSCI, r = 0.4295, p < 0.0001) (Fig 1). Correlation analysis between structural and functional connectivity strength indicated that stronger connectivity in the structural network was closely related to stronger connectivity in the functional network.

Rich club organization

For the structural network, the three groups had the same k = 20 threshold for determination of hub nodes. We found 14 hub nodes in HC, 10 hub nodes in NPSCI, and 16 hub nodes in PSCI (Fig 3A). Most hubs in the HC structural network were in basal ganglia (PUT, CAU), occipital cortex (CAL, CUN, LING), and parietal cortex (SPG, PCUN). Most hubs in the NPSCI structural network were in parietal cortex (PCUN), basal ganglia (PUT, CAU), and occipital cortex (CAL, LING). Most hubs in the PSCI structural network were in basal ganglia (PUT, CAU), parietal cortex (PCUN, SPG), and frontal cortex (ORBsup, ORBinf, SFGmed). For the functional network, the three groups had the same k = 46 threshold for determination of hub nodes (Fig 3B). Most hubs in the HC functional network were in frontal cortex (IFG, SFG, MFG, REC, SMA, OLF), central cortex (PoCG, ROL, PreCG), and temporal cortex (MTG, HES, STG, ITG). Most hubs in the NPSCI functional network were in frontal cortex (IFG, SFG, REC), temporal cortex (MTG, STG), and central cortex (ROL, PoCG, PreCG). Most hubs in the PSCI functional network were in frontal cortex (IFG, REC, SMA), temporal cortex (STG, MTG, HES), limbic system (DCG, ACG), and central gyrus (ROL, PoCG).

PSCI relative to HC

Relative to HC, the PSCI group showed severe global structural network alterations (lower clustering coefficient, global efficiency, and longer shortest path length) (Fig 2, Table 1, and Table 2). At the regional connectivity level (NBS), in structural network analyses, the PSCI group exhibited a subnetwork with reduced connectivity comprising 86 connections that connected 69 brain regions, compared to HC. The connections were composed of 3 rich club, 35 feeder, and 48 local connections (Fig 5A). The widespread structural subnetwork reductions in pathways linked basal ganglia and orbitofrontal regions (15.7%), and within basal ganglia (11.2%) after qualifying the proportion of the intra- and inter-modular connectivity (Fig 5A). In functional network analyses, compared to HC, the PSCI group showed a reduced functional subnetwork comprising 88 connections that connected 64 brain regions (Fig 5B). The pathways were composed of 25 rich club, 42 feeder, and 21 local connections which involved intra-frontal (19.3%), frontal-limbic (13.6%), and frontal-parietal (10.2%) networks (Fig 6). Specifically, the reduced pathways in intra-frontal and frontal-parietal networks linked lateral and medial frontal, prefrontal and parietal regions (IFGoperc, IFGtriang, ORBinf, PoCG, ORBsupmed, ROL, SMA, PreCG, SFGmed, SFGdor, REC, etc.). Meanwhile, reduced pathways in the frontal-limbic network linked anterior insula/frontal operculum and dorsal anterior cingulate cortex/medial superior frontal regions (INS, ACG, DCG, HIP, SFGdor, SFGmed, ORBsupmed, etc.). Together, the reduced connectivity grouped the regions into two distinct networks, the fronto-parietal network (FPN) and the cingulo-opercular network (CON).

NPSCI relative to HC

NPSCI showed no global network abnormalities relative to HC in structural and functional network (Fig 2, Table 1, and Table 2). At the regional connectivity level, in the structural network, relative to HC, NPSCI were characterized by 26 reduced connections composed of 10 feeder and 16 local connections (Fig 4C, Table 5) while there were no altered connections in the functional network. After qualifying the proportion of intra- and inter-modular connectivity, the disrupted connections of the structural subnetwork were mainly located in intra-occipital (26.9%) and occipital-temporal (19.2%) networks (Fig 5C).

Using graph analysis in comparison with HC, PSCI patients exhibited structural connectivity breakdown in basal-frontal and intra-frontal pathways, and functional connectivity breakdown in both FPN and CON, which may indicate a bottom-up pathological mechanism of stroke with cognitive impairment. However, NPSCI showed reduced connectivity in intra-occipital and occipital-temporal networks. In addition, the global topologic organization of the PSCI structural network was significantly disrupted, whilst that of NPSCI was relatively preserved. In terms of connection components, PSCI showed a large number of reduced connections in all three types of connections while NPSCI only showed a small number of reduced connections in feeder connections, as well as local connections, in structural network.

Global graph theoretical parameters were altered in the PSCI structural network. Patients with cognitive impairment had lower clustering coefficients, global efficiency, and higher characteristic path length compared with HC, indicating a decreased capacity for information transmissions throughout the brain(Sporns 2011). Our findings are in line with other brain studies, using network analysis, which showed a decreased global efficiency of structural networks after stroke^,(Crofts, Higham et al. 2011). In contrast to the finding of structural connectivity network abnormalities, no differences were observed in the functional connectivity networks. Similarly, inconsistent topological characteristics of structural and functional networks were observed in the NBS results. Only 26 connections in the structural brain network were significantly reduced and no edge with significantly reduced connection strength in functional networks was found in NPSCI. In general, the functional connectivity network show more flexible characteristics, whereas the structural connectivity topology is relatively stable(Bullmore and Sporns 2009). Global properties of functional connectivity network in PSCI might get back to normal levels in a relatively short time period through compensatory connections in intact regions. Thus, structural connectivity may be more sensitive to stroke-induced cerebral lesions than functional connectivity network across the brain network. Our findings expand the understanding of network reorganization in patients with brain injury after stroke at the system level.

Interestingly, we found that the PSCI group had the most structural network hubs and the second most functional network hubs across the three groups. Specifically, the new hubs in the PSCI structural network were the superior orbital frontal gyrus and superior medial frontal gyrus. Generally, abnormal hub reduction was found in brain networks after acute brain injury. Nevertheless, the observation of more new hubs in the PSCI structural network may indicate that the increased importance of normal nonhubs could balance the decreased hubs. Such hub alteration have been found previoulsy in Alzheimer’s disease(Supekar, Menon et al. 2008, Yao, Zhang et al. 2010), stroke(Desmurget, Bonnetblanc et al. 2007) and schizophrenia(Lynall, Bassett et al. 2010). As we show here, the reorganized location of network hubs radically in PSCI, implying that a specific topological-spatial pattern is critical for network topology underlying cognitive processing.

In the NBS results, brain structural and functional networks in the PSCI group showed significantly reduced connection strength compared to NPSCI networks. In the structural network, the connection strength of nearly 90 connections was significantly reduced in PSCI. These connections included all three types of connections (rich club, feeder and local). However, reduced connections in NPSCI structural networks were feeder and local connections. Due to the importance of rich clubs in the network(Van Den Heuvel and Sporns 2011), alterations of rich club parameters will cause massive network paralysis(van den Heuvel, Sporns et al. 2013, Collin, Scholtens et al. 2017). Meanwhile, feeder connections maintain the balance between efficiency and cost of brain network information transmission(van den Heuvel and Sporns 2019, Zhu, Pi et al. 2019). Combined with current results, it is possible that rich club and feeder damage may be the basis of structural network breakdown involved in cognitive impairment following stroke.

Notably, structural connectivity breakdown in PSCI is concentrated in basal ganglia-frontal, as well as in intra-frontal pathways. This may explain the core position of the basal ganglia in the cognitive processing network. As part of the cortical-striatal-thalamic-cortical loops, cognitive functions such as working memory(Schroll, Vitay et al. 2012), rule-guided behavior(Badre and Frank 2012), cognitive skill learning(Hélie, Waldschmidt et al. 2010) are mediated by frontal-basal ganglia network. However, functional network disruptions are mainly distributed in the FPN and CON. FPN and CON are extremely important in the support of cognitive control(Marek and Dosenbach 2018) and execution function(Engelhardt, Harden et al. 2019). Studies have found that FPN and CON damage not only causes dyskinesia, but also cognitive dysfunction in domains including decision-making, executive control(Bhandari and Badre 2020), and verbal processing in disorders such as stroke(Siegel, Seitzman et al. 2018), major depressive disorder(Wu, Lin et al. 2016), Alzheimer’s disease(Tumati, Marsman et al. 2020), and ADHD(Cai, Griffiths et al. 2019). Our results are consistent with the point of view that a major role of the basal ganglia is to train cortico-cortical connection(Helie, Ell et al. 2015). In this study, we speculate that low structural connectivity in the basal ganglia-frontal circuit may be associated with the low cortical functional connectivity and cognitive impairment in stroke patients. The different changes in structural and functional networks may just reflect this bottom-up mechanism of cognitive impairment in the brain networks.

As compared with HC, NPSCI showed reduced structural connectivity in intra-occipital and occipital-temporal networks. Especially, the areas in intra-occipital included the calcarine area, cuneus, and lingual gyrus. Collectively, the peripheral striatum are made up with these areas which are critical for visual information process and multiple visual scene representations(Saint‐Cyr, Ungerleider et al. 1990). Moreover, the occipital-temporal pathway is in charge of identifying and recognizing objects(Milner and Goodale 2008). The findings align with previous statements that visual field loss after stroke has largely been attributed to damaged visual pathway in cortical stroke (François and Neetens 1954), and occipital and temporal infarct especially is responsible for visual field loss related stroke(Pambakian and Kennard 1997). A previous study reported that post-stroke patients with visual field loss had blurred vision, perceptual difficulties, eye movement deficits and visual perceptual difficulties(Rowe, Wright et al. 2013). Taken together, the findings highlight the potential risk of visual field loss to NPSCI. Furthermore, the relationship between visual field loss and cognitive impairment needs to be investigated in future studies.

Our study was not without limitations. First, the participant sample size was relatively small. Further studies with larger sample size are required to replicate our current findings. Second, the AAL90 atlas were used to parcellate the brain regions for node definition. However, the choice of atlas is somewhat arbitrary across studies, either based on anatomic or functional parcellations(Wig, Schlaggar et al. 2011). It is likely that more refined brain atlases (e.g., power264) would have produced different network parameter estimates. Third, we only used FA values to construct the structural weighted network. Alternative weighted networks, including measures such as fiber number and distance between ROIs, are needed to further verify current findings.

Our study showed that the approach of characterizing the brain as a network using multimodal MRI and graph theoretical analysis can provide new insights into how cognitive impairment affects brain structural and functional topological organization in first-time mild stroke patients with or without cognitive impairment. Future studies that integrate different clinical symptoms and brain network models will be helpful to clarify the mechanisms underlying cognitive impairment in stroke patients at the network level.

AAL = automated anatomical labeling template; CON = cingulo-opercular network; CT = computed tomography; DTI = diffusion tensor imaging; DWI = diffusion-weighted imaging; FA = fractional anisotropy; FLAIR = fluid-attenuated inversion recovery; FPN = fronto-parietal network; HC = healthy controls; MRI = magnetic resonance imaging; NBS = network-based statistics; NPSCI = post-stroke patients without cognitive impairment; PSCI = post-stroke patients with cognitive impairment; TIA = transient ischemic attack;

Acknowledgment

This research received support from Beijing Natural Science Foundation (Grant No. Z200016), the Natural Science Foundation of China (Grant No. 81871434, 61971017 and 92046016), the Ministry of Science and Technology of the People’s Republic of China (National Key R&D Programme of China, 2018YFC1311700, 2018YFC1311706 and 2017YFC1310901), Beijing Municipal Commission of Health and Family Planning (No.2016-1-2041, SML20150502), CAMS Innovation Fund for Medical Sciences (Grant No. 2019-I2M-5-029), and Beijing Municipal Committee of Science and Technology (Grant No. D151100002015003, Z201100005620010).

Funding: Not applicable

Availability of data and material: The dataset used and analyzed are available to other researchers subject to review of the request by the Scientific Committee of the study and ethical approval.

Code availability: Not applicable

Conflicts of interest: The authors report no disclosures relevant to the manuscript.

Authors’ contributions:

Name	Contribution
Hua Zhu, BA	Drafting and revision of the manuscript for content, analysis and interpretation of data
Lijun Zuo, PhD	Revision of the manuscript for content, study concept and design, interpretation of data
Wanlin Zhu, PhD	Discussion of study, revision of manuscript for intellectual content
Jing Jing, PhD	Analysis of data, discussion of study
Zhe Zhang, PhD	Analysis of data, discussion of study
Lingling Ding, PhD	Revision of manuscript for intellectual content
Fengjuan Wang, MA	Revision of manuscript for intellectual content
Jian Cheng, PhD	Acquisition and interpretation of data， revision of manuscript for intellectual content
Yongjun Wang, PhD	Acquisition and interpretation of data, revision of manuscript for intellectual content
Tao Liu, PhD	Interpretation of data, critical revision of manuscript for intellectual content
Zixiao Li, PhD	Interpretation of data, critical revision of manuscript for intellectual content

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Table 1. Hub nodes in brain structural network in HC, NPSCI and PSCI groups.

H			NPSCI			PSCI
Nodes	K	Module	Nodes	K	Module	Nodes	K	Module
PUT.R	33	Subcortical	PCUN.R	32	Parietal	PCUN.R	31	Parietal
PCUN.R	32	Parietal	PCUN.L	29	Parietal	PUT.R	30	Subcortical
PCUN.L	32	Parietal	PUT.L	27	Subcortical	PCUN.L	29	Parietal
PUT.L	28	Subcortical	PUT.R	24	Subcortical	PUT.L	27	Subcortical
CAL.R	26	Occipital	CAL.L	23	Occipital	CAU.L	24	Subcortical
CAL.L	26	Occipital	CAL.R	22	Occipital	CAL.R	23	Occipital
LING.L	23	Occipital	CAU.R	22	Subcortical	CAL.L	22	Occipital
TPOsup.R	21	Limbic	LING.L	21	Occipital	ORBsup.L	22	Frontal
CAU.L	21	Subcortical	ORBsup.L	20	Frontal	HIP.R	22	Limbic
SPG.R	21	Parietal	TPOsup.R	20	Limbic	CAU.R	21	Subcortical
CUN.R	21	Occipital				ORBinf.R	21	Frontal
HIP.R	21	Limbic				ITG.R	20	Temporal
LING.R	20	Occipital				ORBsup.R	20	Frontal
ORBinf.R	20	Frontal				SFGmed.L	20	Frontal
						SPG.R	20	Parietal

Note: The cells of hubs in structural network in three groups.

Abbreviation: CAL = calcarine; CAU = caudate nucleus; CUN = cuneus; HIP = hippocampus; ITG = inferior temporal gyrus; LING = lingual gyrus; ORBinf = Inferior frontal gyrus, orbital part; ORBsup = superior frontal gyrus, orbital part; PCUN = precuneus; PUT = putamen; SFGmed = superior frontal gyrus, medial; SPG = superior parietal gyrus; TPOsup = temporal pole, superior temporal gyrus.

Table 2. Hub nodes in brain functional network in HC, NPSCI and PSCI groups.

NPSCI

PSCI

Nodes

Module

Nodes

Module

Nodes

Module

ACG.L

Limbic

MTG.L

Temporal

STG.R

Temporal

DCG.R

Limbic

MTG.R

Temporal

ROL.R

Central

DCG.L

Limbic

PreCG.R

Central

IFGoperc.R

Frontal

PreCG.L

Central

STG.R

Temporal

IFGtriang.R

Frontal

ROL.L

Central

IFGoperc.L

Frontal

ROL.L

Central

INS.L

Insula

IFGtriang.R

Frontal

DCG.L

Limbic

ACG.R

Limbic

ORBinf.R

Frontal

STG.L

Temporal

PreCG.R

Central

ORBsupmed.L

Frontal

MTG.R

Temporal

ORBinf.R

Frontal

INS.R

Insula

IFGoperc.L

Frontal

ORBmid.R

Frontal

ORBinf.L

Frontal

REC.L

Frontal

IFGtriang.R

Frontal

IFGoperc.R

Frontal

ORBinf.L

Frontal

INS.R

Insula

IFGtriang.L

Frontal

REC.R

Frontal

MTG.R

Temporal

SFGmed.L

Frontal

INS.R

Insula

ORBinf.L

Frontal

ORBsupmed.R

Frontal

DCG.R

Limbic

ORBsupmed.R

Frontal

REC.L

Frontal

PUT.L

Subcortical

SFGdor.R

Frontal

PoCG.L

Central

IFGtriang.L

Frontal

ORBsupmed.L

Frontal

ROL.L

Central

ORBsupmed.R

Frontal

HES.L

Temporal

INS.L

Insula

MTG.L

Temporal

ORBmid.L

Frontal

DCG.R

Limbic

INS.L

Insula

IFGoperc.R

Frontal

SOG.R

Occipital

PoCG.R

Central

IFGtriang.L

Frontal

SMG.L

Parietal

ORBinf.R

Frontal

SFGmed.L

Frontal

PUT.R

Subcortical

ACG.L

Limbic

AMYG.R

Subcortical

STG.L

Temporal

ACG.R

Limbic

PoCG.R

Central

PoCG.R

Central

PoCG.L

Central

STG.R

Temporal

ANG.R

Parietal

THA.L

Subcortical

SFGdor.L

Frontal

ORBmid.L

Frontal

MFG.R

Frontal

SMA.L

Frontal

SFGmed.R

Frontal

ORBsupmed.L

Frontal

FFG.L

Occipital

HES.R

Temporal

SPG.R

Parietal

CAU.L

Subcortical

MTG.L

Temporal

ROL.R

Central

REC.L

Frontal

PoCG.L

Central

PCL.L

Frontal

STG.L

Temporal

ITG.R

Temporal

IFGoperc.L

Frontal

SMA.L

Frontal

OLF.L

Frontal

SMG.R

Parietal

PUT.R

Subcortical

Note: The cells of hubs in structural network in three groups.

Abbreviation: ACG = anterior cingulate and paracingulate gyri; AMYG = amygdala; ANG = angular gyrus; CAU = caudate nucleus; DCG = median cingulate and paracingulate gyri; FFG = fusiform gyrus; HES = heschl gyrus; IFGoperc = inferior frontal gyrus, opercular part; IFGtriang = inferior frontal gyrus, triangular part; INS = insula; ITG = inferior temporal gyrus; MTG = middle temporal gyrus; OLF = olfactory cortex; ORBinf = Inferior frontal gyrus, orbital part; ORBmid = middle frontal gyrus, orbital part; ORBsupmed = superior frontal gyrus, medial orbital; PCL = paracentral lobule; PreCG = precentral gyrus; PoCG = postcentral gyrus; PUT = putamen; REC = gyrus rectus; ROL = rolandic operculum; SFGdor = superior frontal gyrus, dorsolateral; SMA = supplementary motor area; SMG = supramarginal gyrus; SOG = superior occipital gyrus; SPG = superior parietal gyrus; STG = superior temporal gyrus; THA = thalamic.

Table 3. Global graph analysis properties of brain structural network among healthy controls (HC), post-stroke patients without cognitive impairment (NPSCI), and post-stroke patients with cognitive impairment (PSCI).

	HC	NPSCI	PSCI	F	p-value	NPSCI vs HC	P: PSCI vs HC	P: NPSCI vs PSCI
C_P	0.2451±0.0148	0.2419±0.0192	0.2302±0.0168	5.418	0.0066	P > 0.05	P < 0.01**	P > 0.05
L_P	5.196 ± 0.2268	5.377 ± 0.4169	5.593 ± 0.4319	8.033	0.0008	P > 0.05	P < 0.001***	P > 0.05
E_global	0.1922±0.0084	0.1869±0.0136	0.1782 ± 0.0143	9.864	0.0002	P > 0.05	P < 0.001***	P > 0.05
σ	2.768 ± 0.1949	2.746 ± 0.3864	2.822±0.3565	0.3343	0.717	P > 0.05	P > 0.05	P > 0.05

Note: Values are number or mean ± SD. F and P Values refer to one-way ANOVA, followed by post hoc pairwise comparisons (permutation-correct for multiple comparisons). C_p means clustering coefficient; L_p denotes path length; E_global denotes global efficiency; σ denotes small-worldness. ** donates p < 0.01. *** donates p < 0.001.

Table 4. Global graph analysis properties of brain functional network among healthy controls (HC), post-stroke patients without cognitive impairment (NPSCI), and post-stroke patients with cognitive impairment (PSCI).

	HC	NPSCI	PSCI	F	p-value	P: NPSCI vs HC	P: PSCI vs HC	P: NPSCI vs PSCI
C_P	0.3727±0.0183	0.3324±0.0167	0.3588± 0.0191	1.927	0.1537	P > 0.05	P > 0.05	P > 0.05
L_P	4.87 ± 0.1407	4.833 ± 0.1286	4.626± 0.1473	1.942	0.1515	P > 0.05	P > 0.05	P > 0.05
E_global	0.2071± 0.0064	0.2092±0.0058	0.218 ± 0.0067	1.835	0.1677	P > 0.05	P > 0.05	P > 0.05
σ	2.945 ± 0.1282	2.865 ± 0.1171	2.838± 0.1342	0.4529	0.6378	P > 0.05	P > 0.05	P > 0.05

Note: Values are number or mean ± SD. F and P Values refer to one-way ANOVA, followed by post hoc pairwise comparisons (Bonferroni-corrected for multiple comparisons). C_p means clustering coefficient; L_p denotes path length; E_global denotes global efficiency; σ denotes small-worldness.

Table 5. Result of NBS-analysis in structural network between PSCI and HC.

Rich club			Feeder			Local
NodeA	NodeB	t	NodeA (hub)	NodeB (nonhub)	t	NodeA	NodeB	t
CAL.L	CUN.R	2.73	CAU.L	ACG.L	2.99	AMYG.L	TPOmid.L	2.18
PCUN.L	PCUN.R	2.6	PUT.R	AMYG.R	2.16	AMYG.R	LING.R	2.07
CAL.R	PUT.R	2.13	CAL.L	SOG.R	2.78	ANG.R	MTG.L	2.66
			CAL.R	STG.R	2.19	ANG.R	MTG.R	2.03
			CUN.R	SOG.L	2.1	CUN.L	STG.L	2.04
			PCUN.L	DCG.R	2.52	CUN.L	TPOsup.L	2.65
			PUT.R	FFG.R	3.46	DCG.L	DCG.R	3.21
			PCUN.L	HIP.R	2.47	DCG.R	PCL.L	2.29
			PUT.R	IFGoperc.L	2.93	FFG.L	TPOsup.L	2.95
			PUT.R	IFGtriang.R	2.33	FFG.R	THA.R	2.92
			PUT.R	IOG.R	2.15	HIP.L	SOG.L	2.36
			PUT.R	LING.R	3.06	HIP.L	THA.L	3.21
			SPG.R	MOG.R	2.17	HIP.L	TPOsup.L	2.39
			ITG.R	MTG.R	2.71	IFGoperc.L	SMA.L	2.76
			ORBinf.R	OLF.R	2.39	IFGperc.R	SMA.R	2.64
			PUT.R	ORBinf.L	2.1	INS.L	SOG.L	2.4
			ORBsup.L	ORBmid.L	2.71	IOG.L	FFG.L	2.35
			ORBsup.L	ORBinf.L	2.47	IOG.R	FFG.R	2.38
			ORBsup.L	INS.L	3.4	IOG.R	STG.R	2.14
			ORBsup.L	SPG.L	2.14	IOG.R	TPOmid.R	2.15
			CAU.L	ORBsupmed.L	2.94	IPL.R	MTG.R	3.38
			CAU.R	ORBsupmed.L	2.96	MFG.R	IFGoperc	2.11
			CAU.L	ORBsupmed.R	3.1	MFG.R	IFGtriang	2.15
			CAL.R	PCG.R	2.34	MOG.L	SPG.L	2.16
			CAL.L	PCG.R	2.62	MOG.L	ITG.L	2.73
			PCUN.L	PCL.R	2.9	MTG.L	TPOmid.L	2.08
			LING.L	PHG.L	2.72	ORBinf.L	REC.L	2.09
			PUT.R	TPOmid.R	2.06	ORBinf.L	INS.L	2.95
			CAU.L	REC.L	2.8	ORBinf.L	PHG.L	2.35
			CAU.L	REC.R	2.55	ORBinf.L	SPG.L	2.57
			CAU.R	REC.R	2.78	ORBinf.L	PAL.L	2.3
			ORBinf.R	SFGdor.R	2.4	PCG.L	PHG.L	2.9
			SPG.R	IPL.R	2.12	PHG.L	TPOsup.L	3.23
			SPG.R	ANG.R	4.35	PHG.R	LING.r	2.45
			ITG.R	TPOmid.R	2.07	PoCG.L	PCL.L	2.1
						PreCG.L	SMA.R	2.4
						PreCG.R	IFGoperc.R	3.058
						PreCG.R	PoCG.R	2.36
						REC.L	REC.R	2.43
						ROL.L	SMA.L	2.39
						ROL.L	INS.L	2.24
						SFGdor.R	IFGoperc.R	3.35
						SMA.L	SMA.R	2.16
						SMA.R	PCL.L	3.69
						SOG.L	SOG.R	2.09
						SOG.R	MOG.R	2.01
						THA.L	TPOsup.L	2.37
						TPOmid.L	ITG.L	2.26

Note: The cells of results in NBS in structural network between PSCI and HC.

Abbreviation: ACG = anterior cingulate and paracingulate gyri; AMYG = amygdala; ANG = angular gyrus; CAL = calcarine; CAU = caudate nucleus; CUN = cuneus; DCG = median cingulate and paracingulate gyri; FFG = fusiform gyrus; HES = heschl gyrus; HIP = hippocampus; IFGoperc = inferior frontal gyrus, opercular part; IFGtriang = inferior frontal gyrus = triangular part; INS = insula; IOG = inferior occipital gyrus; IPL = inferior parietal gyrus; ITG = inferior temporal gyrus; LING = lingual gyrus; MFG = middle frontal gyrus; MOG = middle occipital gyrus; MTG = middle temporal gyrus; OLF = olfactory cortex; ORBinf = inferior frontal gyrus, orbital part; ORBmid = middle frontal gyrus, orbital part; ORBsup.L = superior frontal gyrus, orbital part; ORBsupmed = superior frontal gyrus, medial orbital; PAL = pallidum; PCG = posterior cingulate gyrus; PCL = paracentral lobule; PCUN = precuneus; PHG = parahippocampal gyrus; PoCG = postcentral gyrus; PreCG = precentral gyrus; PUT = putamen; REC = gyrus rectus; ROL = Rolandic operculum; SFGdor = superior frontal gyrus, dorsolateral; SFGmed = superior frontal gyrus, medial; SMA = supplementary motor area; SMG = supramarginal gyrus; SOG = superior occipital gyrus; SPG = superior parietal gyrus; STG = superior temporal gyrus; THA = thalamus; TPOmid = temporal pole: middle temporal gyrus; TPOsup = temporal pole: superior temporal gyrus.

Table 6. Result of NBS-analysis in functional network between PSCI and HC.

Rich club			Feeder			Local
NodeA	NodeB	t	NodeA (hub)	NodeB (nonhub)	t	NodeA	NodeB	t
IFGoperc.L	ORBinf.R	2.86	ORBmid.L	MFG.L	2.94	SFGdor.R	ORBmid.R	2.03
ROL.L	SMA.R	3.23	ORBmid.L	MFG.R	2.39	ORBsup.L	SFGmed.R.	2.02
SMA.R	INS.L	2.16	ROL.L	PreCG.R	2.84	SFGdor.L	PCG.L.	2.12
SMA.R	INS.R	2.45	ROL.R	PreCG.R	2.53	SFGdor.R	PCG.L.	2.12
ROL.L	ACG.R	3.44	ORBinf.L	SFGmed.L	3.55	SFGmed.L	PCG.L.	2.62
INS.R	DCG.L	2.6	ORBinf.R	SFGmed.L	2.11	SFGmed.R	PCG.L.	2.92
ACG.L	DCG.L	2.47	ORBinf.L	SFGmed.R	4.37	SFGmed.L	PCG.R.	2.31
ACG.R	DCG.L	2.42	ORBinf.R	SFGmed.R	3.4	SFGmed.R	PCG.R.	2.05
INS.L	DCG.R	2.86	ORBsupmed.L.	SFGdor.L	2.03	MOG.L	SPG.L.	2.66
INS.R	DCG.R	2.35	ORBsupmed.L.	SFGmed.L	2.76	MOG.R	SPG.L.	3.2
ACG.L	DCG.R	2.04	ORBsupmed.L.	SFGmed.R	2.31	SOG.R	SPG.R.	2.17
ROL.L	PoCG.L	3.54	ORBsupmed.R.	SFGdor.R	3.11	MOG.R	SPG.R.	2.12
ROL.R	PoCG.L	2.3	ORBsupmed.R.	SFGmed.L	2.57	MOG.L	IPL.L.	2.4
ROL.L	PoCG.R	2.89	ORBsupmed.R.	SFGmed.R	2.09	MFG.R	SMG.R.	2.11
ROL.R	PoCG.R	2.62	REC.L.	SFGmed.L	2.59	SPG.L	SMG.R.	2.39
PoCG.L	PoCG.R	2.3	REC.L.	SFGmed.R	2.3	SFGdor.L	PCUN.L.	2.1
IFGtriang.L	ANG.L.	2.82	ORBsupmed.R	PCG.L.	2.82	SMG.L	PUT.R.	2.6
PoCG.R	HES.R.	2.84	ACG.L	PCG.L.	3.06	SMG.R	PUT.R.	2.59
ROL.R	STG.L.	2.7	ACG.R	PCG.L.	2.14	SMG.R	TPOsup.R.	2.36
DCG.L	STG.L.	2.87	ORBinf.L	HIP.R.	2.35	SFGmed.R	TPOmid.R.	2.36
PoCG.L	STG.L.	2.85	REC.R	HIP.R.	4.24	MFG.L	ITG.L.	3.62
PoCG.R	STG.L.	2.28	REC.L	PHG.R.	2.84
PoCG.L	STG.R.	2.02	REC.R	PHG.R.	2.49
PoCG.R	STG.R.	3.25	PoCG.L	FFG.R	2.11
ORBsupmed.L	MTG.L.	3.19	PoCG.R	CAL.R	2.55
			PoCG.R	LING.L	2.44
			IFGoperc.R	IPL.L.	3.21
			IFGoperc.R	IPL.R.	2.58
			IFGtriang.R	SMG.L.	2.38
			INS.L	SMG.L.	2.8
			INS.R	SMG.R	2.32
			ANG.L.	SFGdor.L	2.56
			ANG.L.	MFG.L	2.49
			IFGoperc.R	ANG.R.	3.11
			ROL.L	PCL.L.	2.3
			ROL.R	PCL.L.	2.31
			SMA.R	PUT.R.	2.13
			STG.R.	PreCG.R	3.12
			STG.R.	PCL.L	2.95
			INS.L	TPOsup.R.	2.34
			IFGoperc.L	ITG.L.	2.12
			IFGtriang.L	ITG.L	2.16		.

Note: The cells of results in NBS in functional network between PSCI and HC.

Abbreviation: ACG = anterior cingulate and paracingulate gyri; AMYG = amygdala; ANG = angular gyrus; CAL = calcarine; CAU = caudate nucleus; CUN = cuneus; DCG = median cingulate and paracingulate gyri; FFG = fusiform gyrus; HIP = hippocampus; HES = heschl gyrus; IFGoperc = inferior frontal gyrus, opercular part; IFGtriang = inferior frontal gyrus, triangular part; INS = insula; IOG = inferior occipital gyrus; IPL = inferior parietal gyrus; ITG = inferior temporal gyrus; LING = lingual gyrus; MFG = middle frontal gyrus; MOG = middle occipital gyrus; MTG = middle temporal gyrus; OLF = olfactory cortex; ORBinf = inferior frontal gyrus, orbital part; ORBmid = middle frontal gyrus, orbital part; ORBsup.L = superior frontal gyrus, orbital part; ORBsupmed = superior frontal gyrus, medial orbital; PAL = pallidum; PCG = posterior cingulate gyrus; PCL = paracentral lobule; PCUN = precuneus; PHG = parahippocampal gyrus; PoCG = postcentral gyrus; PreCG = precentral gyrus; PUT = putamen; REC = gyrus rectus; ROL = Rolandic operculum; SFGdor = superior frontal gyrus, dorsolateral; SFGmed = superior frontal gyrus, medial; SMA = supplementary motor area; SMG = supramarginal gyrus; SOG = superior occipital gyrus; SPG = superior parietal gyrus; STG = superior temporal gyrus; THA = thalamus; TPOmid = temporal pole: middle temporal gyrus; TPOsup = temporal pole: superior temporal gyrus.

Table 7. Result of NBS-analysis in structural network between NPSCI and HC.

Feeder			Local
NodeA (hub)	NodeB (nonhub)	t	NodeA	NodeB	t
ORBinf.R	OLF.R	2.1	OLF.R	DCG.R	2.25
CAL.L	INS.L	2.7	DCG.L	DCG.R	2.34
CAL.R	INS.R	2.15	INS.L	PHG.L	2.03
CAL.R	CUN.L	2.18	OLF.R	PHG.R	2.17
HIP.R	LING.R	2.99	DCG.L	CUN.L	2.27
CAL.R	LING.R	2.1	HIP.L	CUN.L	3.13
CAL.L	FFG.L	2.08	INS.L	LING.L	2.06
ORBinf.R	FFG.R	2.49	CUN.L	LING.L	2.76
CAL.R	STG.R	2.11	CUN.L	SOG.L	2.25
ITG.R	FFG.R	2.06	IOG.L	FFG.L	2.17
			IOG.R	FFG.R	2.25
			FFG.R	THA.R	2.38
			CUN.L	TPOsup.L	2.27
			MOG.L	ITG.L	2.99
			IOG.L	ITG.L	2.62
			FFG.L	ITG.L	3.06

Note: The cells of results in NBS in structural network between NPSCI and HC.

Abbreviation: CAL = calcarine; CUN = cuneus; DCG = median cingulate and paracingulate gyri; FFG = fusiform gyrus; HIP = hippocampus; INS = insula; IOG = inferior occipital gyrus; ITG = inferior temporal gyrus; LING = lingual gyrus; MOG = middle occipital gyrus; OLF = olfactory cortex; ORBinf = inferior frontal gyrus, orbital part; TPOsup = temporal pole: superior temporal gyrus.

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Reviews received at journal
04 Aug, 2021
Reviewers invited by journal
20 Jul, 2021
Editor invited by journal
05 Jul, 2021
Editor assigned by journal
28 Jun, 2021
First submitted to journal
27 Jun, 2021

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The Distinct Disrupted Plasticity in Structural and Functional Network in Mild Stroke With Basal Ganglia Region Infarcts

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