Matter Structural Networks Research Articles

Impact of sleep disruptions on gray matter structural covariance networks across the Alzheimer's disease continuum.

This study explores the impact of sleep disturbances on gray matter structural covariance networks (SCNs) across the Alzheimer's disease (AD) continuum. Amyloid-negative participants served as controls, whereas amyloid positive (A+) individuals were categorized into six groups based on cognitive status and sleep quality. SCNsfor the default mode network (DMN), salience network (SN), and executive control network (ECN) were derived from T1-weighted magnetic resonance images. In the DMN, increased structural associations were observed in cognitive unimpaired (CU) A+ and mild cognitive impairment (MCI) groups regardless of sleep quality, whereas AD with poor sleep (PS) showed a decrease and AD with normal sleep (NS) an increase. For the ECN, AD-NS showed increased and AD-PS showed reduced associations. In the SN, reduced associations were observed in CU A+ NS and MCI-NS, whereas AD-NS displayed increased associations; only AD-PS had decreased associations. Distinct SCN damage patterns between normal and poor sleepers provide insights into sleep disturbances in AD. We delineated distinct patterns of structural covariance networks (SCN) impairment across the Alzheimer's disease (AD) continuum, uncovering significant disparities between individuals with normal sleep architecture and those afflicted by sleep disturbances.These observations underscore the pivotal importance of addressing sleep disruptions in AD therapeutics, providing a refined understanding of their detrimental impact on brain networks implicated in the disease.Our investigation epitomizes methodological precision by constructing an AD continuum using amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers to minimize diagnostic heterogeneity, further enhanced by a substantial cohort size that bolsters the robustness and generalizability of our findings.

Whole-brain functional connectivity and structural network properties in stroke patients with hemiplegia.

This study explored structural and functional alterations in the whole brain of stroke patients with hemiplegia. We collected multimodal magnetic resonance images of 24 patients with ischaemic stroke and 16 age-matched controls. Resting-state functional connectivity (FC) for all brain regions was evaluated. Diffusion tensor imaging was used to construct white matter structural networks, and the graph properties of the structural network were analysed using graph theory to determine group differences. The ipsilesional posterior parietal cortex (PPC) in the frontoparietal network accounts for more than half of the 25 brain regions with altered FC in stroke patients. The nodal efficiency of multiple ipsilesional frontal lobes and cerebellar regions, such as the ipsilateral cerebellum 8, was reduced. The contralesional cerebellum 8 showed elevated FC with the lingual gyrus and the visual network. Our results suggest that the PPC and cerebellum 8 are regions worthy of in-depth study. The cerebellum 8 may supplement deficits in motor balance function by enhancing functional congruence with the visual area. This study identified key brain regions and characteristics that exhibit structural and functional changes following stroke injury.

Altered gray matter structural covariance networks in young adults with obesity.

Overwhelming evidence showed that obesity was associated with abnormal brain functional networks. However, the changes of structural covariance networks (SCNs) based on cortical thickness (CT) and cortical surface area (CSA) in obesity is still unclear. In this study, 243 young adults with obesity and matched 243 lean individuals were enrolled from the Human Connectome Project Release S1200 dataset. All participants underwent magnetic resonance imaging scans following clinical and neuropsychological assessments. SCNs matrices were constructed by Brain Connectivity Toolbox based on both CT and CSA. Nonparametric permutation tests were adopted to examine group differences of these matrices. Young adults with obesity exhibited lower CSA of left entorhinal cortex, but higher CT of both left rostral anterior cingulate cortex and right superior parietal lobule, as well as lower CT of left temporal pole. While in terms of global network measures, there were no significant group differences; in terms of nodal network measures, young adults with obesity exhibited alterations in widespread brain regions including left posterior cingulate cortex, bilateral superior frontal gyrus, left entorhinal cortex and right insula. Young adults with obesity exhibited abnormal nodal network measures in widespread brain regions involved in default mode network, central executive network and salience network. These findings indicate the adverse effects of obesity on young adults might be associated with the altered triple network.

Progressive structural alterations associated with negative symptoms in schizophrenia: A causal structural covariance network analysis.

Aberrant brain structures in schizophrenia have been widely explored. However, the causal effects of negative symptoms on brain structural alterations are still unclear. This study aims to explore the synchronous and progressive alterations in gray matter volume (GMV) associated with negative symptoms. 81 Deficit schizophrenia (DS) patients, 101 non-deficit schizophrenia (NDS) patients, and 177 healthy controls (HCs) were enrolled in this study. T1-weighted images were collected, and the severity of clinical symptoms in patients was evaluated. Then voxel-based morphometry and source-based morphometry were using for gray matter segmentation and structural covariance network construction. Finally, DS patients were ranked based on the severity of negative symptoms, and a causal structural covariance network (CaSCN) was constructed using Granger causality analysis. Twenty-four independent components were identified. Among them, 20 components showed smaller GMV in patient with schizophrenia compared to HCs. Furthermore, DS exhibited decreased GMV in right inferior frontal gyrus triangular part, bilateral para-hippocampal gyrus, and bilateral anterior cerebellum compared to NDS. Both patient groups showed increased structural covariance across various brain regions compared to HCs. Additionally, DS exhibited decreased structural covariance in left middle frontal gyrus, bilateral inferior frontal gyrus and right superior frontal gyrus compared to NDS. In CaSCN, as negative symptoms worsened, the volume of bilateral caudate decreased along with the atrophy of bilateral para-hippocampal gyrus, the volume of bilateral thalamus increased along with the decline in multiple brain regions, and the decreased volume of bilateral posterior cingulate cortex resulted in increased volume of bilateral lingual gyrus and other brain regions. The present study demonstrated the specific brain structural covariance patterns in DS, providing new evidence for the causal effects of negative symptoms on progressive structural abnormalities in schizophrenia.

Disrupted topologic efficiency of white matter structural connectome in migraine: a graph-based connectomics study

ObjectiveTo delineate the structural connectome alterations in patients with chronic migraine (CM), episodic migraine (EM), and healthy controls (HCs).BackgroundThe pathogenesis of migraine chronification remains elusive, with structural brain network changes potentially playing a key role. However, there is a paucity of research employing graph theory analysis to explore changes in the whole brain structural networks in patients with CM and EM.MethodsThe individual structural brain connectome of 60 patients with CM, 34 patients with EM, and 39 healthy control participants were constructed by using deterministic diffusion-tensor tractography. Graph metrics including global efficiency, characteristic path length, local efficiency, clustering coefficient, and small-world parameters were evaluated to describe the topologic organization of the white matter structural networks. Additionally, nodal clustering coefficient and efficiency were considered to assess the regional characteristics of the brain connectome. A graph-based statistic was used to assess brain network properties across the groups.ResultsGraph theory analysis revealed significant disruptions in the structural brain networks of CM patients, characterized by reduced global efficiency, local efficiency, and increased characteristic path length compared to HCs. Additionally, CM patients exhibited significantly lower local efficiency than EM patients. Notably, the CM group demonstrated marked reductions in local clustering coefficient and nodal local efficiency in the frontal and temporal regions compared with the healthy control group and EM group. Nodal local efficiency can effectively distinguish CM from EM and HCs. Moreover, the disrupted topologic efficiency was significantly associated with attack frequency and MIDAS score in patients with migraine after Bonferroni correction.ConclusionDecreased structural connectivity in the frontal and temporal regions may serve as a neuroimaging marker for migraine chronification and disease progression, providing valuable insights into the pathophysiology of chronic migraine.

Cerebral microbleeds linked to structural network disruption and cognitive impairment in white matter hyperintensities

ObjectiveThis study aims to explore the relationship between the cerebral microbleeds (CMBs) and white matter structural network in patients with white matter hyperintensities (WMHs), and the correlation with the cognitive impairment. MethodOne hundred and fifty-eight participants with WMHs underwent 3.0 T magnetic resonance imaging scans and neuropsychological assessment. The grouping method included count grading with categories: non-CMBs (n-CMBs), CMBs < 5, and CMBs ≥ 5. Additionally, the distribution of CMBs was considered, distinguishing between strictly lobar CMBs (SL-CMBs) and deep or infratentorial CMBs (DI-CMBs). Diffusion tensor imaging was employed to construct the white matter structural network for each participant. Subsequently, we analyzed the correlations between the topological parameters of network and cognitive performance in individuals with CMBs. ResultsCompared with n-CMBs group, both CMBs ≥ 5 and DI-CMBs participants exhibited a significant decreased in global efficiency and increased in characteristic path length. Moreover, in the nodal network metrics, the CMBs ≥ 5 showed the left middle occipital gyrus (MOG.L) of nodal efficiency and nodal shortest path correlated with SCWT-A and MMSE, while in the DI-CMBs groups, the left cuneus (CUN.L) of nodal shortest path correlated with SCWT-A. However, there were no significant neuropsychological correlations observed in the SL-CMBs and CMBs < 5 groups. ConclusionIn this study, patients with a high count (≥5) of CMBs or DI-CMBs are associated with disruptions in the microstructure of the white matter structural network, partially impacting the visual network of occipital lobe and affecting the cognitive function of information processing speed and attention.

Cortical structural network characteristics in non-cognitive impairment end-stage renal disease.

Explore alterations in topological features of gray matter volume (GMV) and structural networks in non-cognitive impairment end-stage renal disease (Non-CI ESRD). Utilizing graph theory, we collected structural magnetic resonance imaging (sMRI) data from 38 Non-CI ESRD patients and 50 normal controls (NC). We compared, and extracted the GMV across subject groups, constructed corresponding structural covariance networks (SCNs), and investigated the alterations in SCNs feature parameters between groups. In Non-CI ESRD patients, The GMV were reduced in several brain regions, predominantly on the left side (p < 0.05, FWE correction). The small-world network characteristics of the patient group's brain networks showed a tendency toward regular. In a few densities, global network parameters, transitivity, (p < 0.05) was significantly increased in the ESRD group. Regional network measurements revealed inconsistent changes in regional efficiency across different brain areas. In the analysis of network hubs, the right temporal pole is likely a compensatory hub for Non-CI ESRD patients. The SCNs in Non-CI ESRD patients demonstrated reduced topological stability against targeted attacks. This study reveals that patients with renal failure exhibited subtle changes in brain network characteristics even before a decline in cognitive scores. These changes involve compensatory activation in certain brain regions, which enhances network transitivity to maintain the efficiency of whole-brain network information integration without significant loss. Additionally, the SCNs characteristics can serve as a neuroanatomical marker for brain alterations in Non-CI ESRD patients, offering new insights into the mechanisms of early brain injury in ESRD patients.

Disrupted white matter structural networks in patients with acute ischemic stroke in the right basal ganglia

Widespread structural changes have been observed in patients with stroke in previous diffusion tensor imaging studies. However, the topological organization of white matter structural networks after acute ischemic stroke (AIS) in the right basal ganglia (BG) remains unknown. The aim of our study is to investigate whether the topological structure of the white matter structural network is altered in patients with AIS in the right BG, and its relationship with cognition. Graph theoretical analysis was employed to investigate the topological architecture of whole-brain white matter structural networks in 40 AIS patients in the right BG and 40 healthy controls (HC), and network-based statistics (NBS) were applied to examine structural connectivity alterations. Compared to HC, AIS patients exhibited altered global network properties characterized by increased small-worldness, normalized clustering coefficient, and shortest path length, as well as decreased clustering coefficient, local efficiency, and global efficiency. The nodes with significantly decreased nodal properties in AIS patients were primarily located in the default mode network, limbic system, sensorimotor system, salience network, and central executive network. Reduced structural connectivity detected by NBS in AIS patients were primarily located in the lesional hemisphere. Furthermore, altered nodal properties were correlated with cognitive scores. Documenting the alterations in the topological patterns of white matter structural networks will help to promote the understanding of the neural mechanisms of cognitive impairment after AIS in the right BG.

Distributed associations among white matter hyperintensities and structural brain networks with fluid cognition in healthy aging

White matter hyperintensities (WMHs) are associated with age-related cognitive impairment and increased risk of Alzheimer’s disease. However, the manner by which WMHs contribute to cognitive impairment is unclear. Using a combination of predictive modeling and network neuroscience, we investigated the relationship between structural white matter connectivity and age, fluid cognition, and WMHs in 68 healthy adults (18–78 years). Consistent with previous work, WMHs were increased in older adults and exhibited a strong negative association with fluid cognition. Extending previous work, using predictive modeling, we demonstrated that age, WMHs, and fluid cognition were jointly associated with widespread alterations in structural connectivity. Subcortical-cortical connections between the thalamus/basal ganglia and frontal and parietal regions of the default mode and frontoparietal networks were most prominent. At the network level, both age and WMHs were negatively associated with network density and communicability, and positively associated with modularity. Spatially, WMHs were most prominent in arterial zones served by the middle cerebral artery and associated lenticulostriate branches that supply subcortical regions. Finally, WMHs overlapped with all major white matter tracts, most prominently in tracts that facilitate subcortical-cortical communication and are implicated in fluid cognition, including the anterior thalamic-radiations and forceps minor. Finally, results of mediation analyses suggest that whole-brain WMH load influences age-related decline in fluid cognition. Thus, across multiple levels of analysis, we showed that WMHs were increased in older adults and associated with altered structural white matter connectivity and network topology involving subcortical-cortical pathways critical for fluid cognition.

Topological properties of white matter network in first untreated children and adolescents with obsessive-compulsive disorder

Objective: To investigate the topological properties of the white matter network in the drug-naive first-episode children and adolescent with obsessive-compulsive disorder (OCD). Methods: This study was a case-control study. First-episode OCD childhren and adolescents(OCD group) who were treated in the outpatient or inpatient department of the Second Affiliated Hospital of Xinxiang Medical University from July 2018 to October 2023 were collected as the research subjects. Healthy controls (control group)matched by gender, age and education level were used as controls. Deterministic tractography technique was used to construct the whole brain white matter structural network, and graph theory analysis method was used to analyze the topological attributes of the whole brain white matter structure network in OCD children and adolescents. A network-based statistical method was used to examine the inter-group differences in the functional connectivity strength of the whole brain network. Results: Finally, 31 cases were included in the obsessive-compulsive disorder group, including 22 males and 9 females, with an age of (13.5±1.6) years; There were 34 cases in the control group, including 22 males and 12 females, with an age of (12.7±1.4) years. The global efficiency and local efficiency of the OCD group (0.62±0.03, 0.70±0.07) were significantly higher than those of the control group (0.50±0.06, 0.54±0.21) [both P<0.01, false discovery rate(FDR)correction]; while the characteristic path length (1.77±0.08) was significantly smaller than that of the control group (2.10±0.23) (P<0.01, FDR correction).The centrality comparison of nodal betweenness centrality showed that in the OCD group, the connections were enhanced in the left lateral orbitofrontal gyrus, right dorsal agranular insula, left dorsal granular insula, right granular insula, right posterior central gyrus main area of parietal lobe, left ventral granular insula, granular insula, left ventral granulosa, left granular insula, and left dorsal agranular insula [all P<0.001, family wise error (FWE) correction], while the connection of right thalamic was weakened (P<0.001, FWE correction), There were sub-networks characterized by significantly enhanced connection strength of relevant nodes in subcortical, visual network, and default mode network (P<0.05, permutation test 5 000 times). Conclusions: The topological properties of the brain's functional network in children and adolescents with OCD exhibit abnormalities, indicating an immature state of brain functional connectivity.

Alterations in Gray Matter Structural Networks in Amnestic Mild Cognitive Impairment: A Source-Based Morphometry Study.

Amnestic mild cognitive impairment (aMCI), considered as the prodromal stage of Alzheimer's disease, is characterized by isolated memory impairment and cerebral gray matter volume (GMV) alterations. Previous structural MRI studies in aMCI have been mainly based on univariate statistics using voxel-based morphometry. We investigated structural network differences between aMCI patients and cognitively normal older adults by using source-based morphometry, a multivariate approach that considers the relationship between voxels of various parts of the brain. Ninety-one aMCI patients and 80 cognitively normal controls underwent structural MRI and neuropsychological assessment. Spatially independent components (ICs) that covaried between participants were estimated and a multivariate analysis of covariance was performed with ICs as dependent variables, diagnosis as independent variable, and age, sex, education level, and site as covariates. aMCI patients exhibited reduced GMV in the precentral, temporo-cerebellar, frontal, and temporal network, and increased GMV in the left superior parietal network compared to controls (pFWER < 0.05, Holm-Bonferroni correction). Moreover, we found that diagnosis, more specifically aMCI, moderated the positive relationship between occipital network and Mini-Mental State Examination scores (pFWER < 0.05, Holm-Bonferroni correction). Our results showed GMV alterations in temporo-fronto-parieto-cerebellar networks in aMCI, extending previous results obtained with univariate approaches.

Distinct structural brain network properties in children with familial versus non-familial attention-deficit/hyperactivity disorder (ADHD)

Attention-deficit/hyperactivity disorder (ADHD) is among the most prevalent, inheritable, and heterogeneous childhood-onset neurodevelopmental disorders. Children with a hereditary background of ADHD have heightened risk of having ADHD and persistent impairment symptoms into adulthood. These facts suggest distinct familial-specific neuropathological substrates in ADHD that may exist in anatomical components subserving attention and cognitive control processing pathways during development. The objective of this study is to investigate the topological properties of the gray matter (GM) structural brain networks in children with familial ADHD (ADHD-F), non-familial ADHD (ADHD-NF), as well as matched controls. A total of 452 participants were involved, including 132, 165 and 155 in groups of ADHD-F, ADHD-NF and typically developed children, respectively. The GM structural brain network was constructed for each group using graph theoretical techniques with cortical and subcortical structures as nodes and correlations between volume of each pair of the nodes within each group as edges, while controlled for confounding factors using regression analysis. Relative to controls, children in both ADHD-F and ADHD-NF groups showed significantly higher nodal global and nodal local efficiencies in the left caudal middle frontal gyrus. Compared to controls and ADHD-NF, children with ADHD-F showed distinct structural network topological patterns associated with right precuneus (significantly higher nodal global efficiency and significantly higher nodal strength), left paracentral gyrus (significantly higher nodal strength and trend toward significantly higher nodal local efficiency) and left putamen (significantly higher nodal global efficiency and trend toward significantly higher nodal local efficiency). Our results for the first time in the field provide evidence of familial-specific structural brain network alterations in ADHD, that may contribute to distinct clinical/behavioral symptomology and developmental trajectories in children with ADHD-F.

Gray matter morphometric biomarkers for distinguishing manganese-exposed welders from healthy adults revealed by source-based morphometry

BackgroundChronic overexposure to manganese (Mn) may result in neurotoxicity, which is characterized by motor and cognitive dysfunctions. This study aimed to utilize multivariate source-based morphometry (SBM) to explore the biomarkers for distinguishing Mn-exposed welders from healthy controls (HCs). MethodsHigh-quality 3D T1-weighted MRI scans were obtained from 45 Mn-exposed full-time welders and 33 age-matched HCs in this study. After extracting gray matter structural covariation networks by SBM, multiple classic interaction linear models were applied to investigate distinct patterns in welders compared to HCs, and Z-transformed loading coefficients were compared between the two groups. A receiver operating characteristic (ROC) curve was used to identify potential biomarkers for distinguishing Mn-exposed welders from HCs. Additionally, we assessed the relationships between clinical features and gray matter volumes in the welders group. ResultsA total of 78 subjects (45 welders, mean age 46.23±4.93 years; 33 HCs, mean age 45.55±3.40 years) were evaluated. SBM identified five components that differed between the groups. These components displayed lower loading weights in the basal ganglia, thalamus, default mode network (including the lingual gyrus and precuneus), and temporal lobe network (including the temporal pole and parahippocampus), as well as higher loading weights in the sensorimotor network (including the supplementary motor cortex). ROC analysis identified the highest classification power in the thalamic network. ConclusionsAltered brain structures might be implicated in Mn overexposure-related disturbances in motivative modulation, cognitive control and information integration. These results encourage further studies that focus on the interaction mechanisms, including the basal ganglia network, thalamic network and default mode network. Our study identified potential neurobiological markers in Mn-exposed welders and illustrated the utility of a multivariate method of gray matter analysis.

Characteristics of the White Matter Structural Network in Individuals with Subjective Cognitive Decline with and without APOEε4 Based on Graph Theory Study.

To determine whether individuals with subjective cognitive decline (SCD) have changes in whole-brain network characteristics and intracerebral node characteristics in the structural network, and whether there is a difference between SCD with and without Apolipoprotein E4 (APOEε4). This cross-sectional study included 36 individuals without SCD without APOEε4 (healthy control, HC group), 21 individuals with SCD with APOEε4 (APOEε4+ group), and 33 individuals with SCD without APOEε4 (APOEε4- group). The white matter structural network was constructed using the fractional anisotropy (FA) based deterministic fiber tracking method. Graph theory was used to analyze the whole-brain network characteristics and intracerebral node characteristics of the three groups. Regarding the whole-brain network characteristics, all three groups exhibited small-worldness in their structural networks. The clustering coefficient (Cp) and local efficiency (Eloc) in the APOEε4+ and APOEε4- groups were significantly lower than in the HC group (p < 0.05), but no significant difference in Cp or Eloc was observed between the APOEε4+ and APOEε4- groups. Regarding intracerebral node characteristics, there were significant differences in some brain regions, mainly the default mode network (DMN), the occipital lobe, the temporal lobe, and subcortical regions. The change in intracerebral node characteristics was different between the APOEε4+ group and the APOEε4- group. Individuals with SCD demonstrate changes in whole-brain network characteristics and intracerebral node characteristics in the structural network. Moreover, differences exist between APOEε4+ and APOEε4- individuals.

Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles.

Spinocerebellar ataxia type 3 (SCA3) is primarily characterized by progressive cerebellar degeneration, including gray matter atrophy and disrupted anatomical and functional connectivity. The alterations of cerebellar white matter structural network in SCA3 and the underlying neurobiological mechanism remain unknown. Using a cohort of 20 patients with SCA3 and 20 healthy controls, we constructed cerebellar structural networks from diffusion MRI and investigated alterations of topological organization. Then, we mapped the alterations with transcriptome data from the Allen Human Brain Atlas to identify possible biological mechanisms for regional selective vulnerability to white matter damage. Compared with healthy controls, SCA3 patients exhibited reduced global and nodal efficiency, along with a widespread decrease in edge strength, particularly affecting edges connected to hub regions. The strength of inter-module connections was lower in SCA3 group and negatively correlated with the Scale for the Assessment and Rating of Ataxia score, International Cooperative Ataxia Rating Scale score, and cytosine-adenine-guanine repeat number. Moreover, the transcriptome-connectome association study identified the expression of genes involved in synapse-related and metabolic biological processes. These findings suggest a mechanism of white matter vulnerability and a potential image biomarker for the disease severity, providing insights into neurodegeneration and pathogenesis in this disease.

Vascular Risk Factors and Brain Health in Aging: Insights from a Community-Based Cohort Study.

The aging population and high rates of Alzheimer's disease (AD) create significant medical burdens, prompting a need for early prevention. Targeting modifiable risk factors like vascular risk factors (VRFs), closely linked to AD, may provide a promising strategy for intervention. This study investigates how VRFs influence cognitive performance and brain structures in a community-based cohort. In this cross-sectional study, 4,667 participants over 50 years old, drawn from the Beijing Ageing Brain Rejuvenation Initiative project, were meticulously examined. Cognitive function and VRFs (diabetes mellitus, hypertension, hyperlipidemia, obesity, and smoking), were comprehensively assessed through one-to-one interviews. Additionally, a subset of participants (n = 719) underwent MRI, encompassing T1-weighted and diffusion-weighted scans, to elucidate gray matter volume and white matter structural network organization. The findings unveil diabetes as a potent detriment to memory, manifesting in atrophy within the right supramarginal gyrus and diminished nodal efficiency and degree centrality in the right inferior parietal lobe. Hypertension solely impaired memory without significant structural changes. Intriguingly, individuals with comorbid diabetes and hypertension exhibited the most pronounced deficits in both brain structure and cognitive performance. Remarkably, hyperlipidemia emerged as a factor associated with enhanced cognition, and preservation of brain structure. This study illuminates the intricate associations between VRFs and the varied patterns of cognitive and brain structural damage. Notably, the synergistic effect of diabetes and hypertension emerges as particularly deleterious. These findings underscore the imperative to tailor interventions for patients with distinct VRF comorbidities, especially when addressing cognitive decline and structural brain changes.

Altered individual gray matter structural covariance networks in early abstinence patients with alcohol dependence.

While alterations in cortical thickness have been widely observed in individuals with alcohol dependence, knowledge about cortical thickness-based structural covariance networks is limited. This study aimed to explore the topological disorganization of structural covariance networks based on cortical thickness at the single-subject level among patients with alcohol dependence. Structural imaging data were obtained from 61 patients with alcohol dependence during early abstinence and 59 healthy controls. The single-subject structural covariance networks were constructed based on cortical thickness data from 68 brain regions and were analyzed using graph theory. The relationships between network architecture and clinical characteristics were further investigated using partial correlation analysis. In the structural covariance networks, both patients with alcohol dependence and healthy controls displayed small-world topology. However, compared to controls, alcohol-dependent individuals exhibited significantly altered global network properties characterized by greater normalized shortest path length, greater shortest path length, and lower global efficiency. Patients exhibited lower degree centrality and nodal efficiency, primarily in the right precuneus. Additionally, scores on the Alcohol Use Disorder Identification Test were negatively correlated with the degree centrality and nodal efficiency of the left middle temporal gyrus. The results of this correlation analysis did not survive after multiple comparisons in the exploratory analysis. Our findings may reveal alterations in the topological organization of gray matter networks in alcoholism patients, which may contribute to understanding the mechanisms of alcohol addiction from a network perspective.

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease.

Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI). We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population. The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier. Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

Altered topological structure of the brain white matter in maltreated children through topological data analysis.

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set.

Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.