Structural Covariance Networks Research Articles

Abnormal topological organization of structural covariance networks in early-stage Parkinson’s disease patients with autonomic dysfunction

Temporal evolution of brain structural changes associated with anxiety sensitivity in patients with breast cancer: A causal network analysis.

BackgroundOverwhelming evidence suggests that adults with chronic pain have altered brain structure and related networks. However, little is currently known regarding changes in structural gray matter and structural covariance networks (SCNs) in children with multisite pain (MP) and their potential relationships with biopsychosocial characteristics.MethodsThis study enrolled 444 children with MP and 444 matched controls from the Adolescent Brain Cognitive Development Study. All participants underwent magnetic resonance imaging (MRI) scans following biopsychosocial assessment. Then, SCNs matrices were constructed by the Brain Connectivity Toolbox based on both cortical thickness (CT) and cortical surface area (CSA) among 415 children with MP and 404 controls. Nonparametric permutation tests were employed to examine the group differences in these matrices.ResultsCompared with controls, children with MP exhibited both lower CSA and CT in widespread regions involved in the pain matrix, including the anterior cingulate cortex (ACC), middle frontal gyrus (MFG), superior frontal gyrus (SFG), and anterior insula (aIns). While there were no significant group differences in global network measures, children with MP exhibited alterations in nodal network measures in brain regions including the ACC, MFG, and aIns. Besides, children with MP showed significant relationships between abnormal structural gray matter and biopsychosocial characteristics, including general somatic symptoms, conduct disorder symptoms, and sleep quality.ConclusionsChildren with MP exhibited abnormal structural gray matter and SCNs in brain regions involved in the pain matrix, which were further associated with biopsychosocial characteristics. These findings could suggest individualized treatments for children with MP, such as transcranial magnetic stimulation therapy, that focus on specific brain nodes within the pain matrix and improve related biopsychosocial characteristics.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1186/s10194-025-02174-1.

Abstract White matter hyperintensities (WMH), a neuroimaging marker of cerebral small vessel disease, are closely associated with cognitive decline and structural brain changes. However, the precise mechanisms through which WMH-associated grey matter volume (GMV) changes ultimately translate to cognitive decline remain unclear, particularly regarding propagation patterns and causal interactions within affected neural circuits. To investigate the progressive structural changes in WMH patients based on disease severity, we recruited 185 patients with cerebral small vessel disease and 40 healthy controls, who underwent magnetic resonance imaging scans. First, voxel-based morphometry analysis was performed to compare GMV differences between WMH patients and healthy controls, followed by subgroup analyses across different disease stages to identify key regions with significant morphological changes. Subsequently, causal structural covariance network analysis, modularity analysis, and functional decoding were employed to map the causal relationships of GMV changes, the hierarchical topography, and functional characteristics of the structural network throughout the WMH progression. Finally, mediation analysis was conducted to explore the relationships between WMH volume, GMV, and cognition, providing insights into the underlying causal pathways.The results revealed that GMV reductions originated in the right insula and progressively extended to cortical and subcortical regions with increasing disease severity. Causal structural covariance network analysis identified the right insula as a central hub, exerting causal effects on gray matter volume reductions in regions associated with executive function and attention. Modularity analysis and functional decoding further highlighted key pathways linking the right insula to cortico-subcortical networks involved in cognitive regulation and motor coordination. Additionally, compensatory GMV increases were observed in specific regions, suggesting neuroplastic responses to WMH-related damage. Mediation analysis demonstrated that GMV reductions significantly mediated the relationship between WMH volume and cognitive impairments, particularly in executive function and processing speed. Overall, the right insula acts as a critical hub driving hierarchical GMV atrophy and network disruption in WMH. Its early involvement and causal influence highlight its importance as a potential target for interventions to mitigate cognitive decline.

ABSTRACTThe tremor‐dominant (TD) subtype of Parkinson's disease (PD) is characterized by prominent tremor symptoms. However, the temporal and causal relationships between brain structural alterations in TD patients remain unexplored. A total of 61 TD patients and 61 matched healthy controls (HCs) were included in this study. The gray matter volume (GMV) of the bilateral precuneus (PCUN) was significantly reduced in TD patients. A structural covariance network analysis seeded with the left pallidum (PAL.L), which had the most significant differences, revealed a substantial reduction in covariance with precentral gyrus in TD patients. We performed a causal structural covariance network analysis using the TD duration as a pseudotime series. The PCUN, with the highest out‐degree in the cortex, regulates numerous regions, including the supplementary motor area and the extensive temporal lobe. Machine learning was utilized to construct a model that accurately assesses the surgical prognosis based on the above cortical volume and clinical scale, with the aim of assisting in clinical deep brain stimulation (DBS) treatment. These findings suggested a progressive pattern of GMV changes extending from the PAL.L to the PCUN region and continuing to other brain regions, providing insights into the progression of TD and enhancing DBS treatment strategies.

Brain network architecture is anticipated to influence future grey matter loss in individuals at Clinical High Risk (CHR) for psychosis. However, existing studies on grey matter structural network properties in CHR are scarce and constrained by small sample sizes. Here, we examined network topology differences comparing a) CHR versus healthy controls (HC); b) CHR who transitioned to psychosis (CHR-T) versus those who did not (CHR-NT); and c) different subsyndromes. We included structural scans from 1842 CHR individuals and 1417 HC individuals from 31 sites within the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. At the global level, CHR individuals exhibited lower structural covariance (q < 0.001; Cohen's d = 0.164) and less optimal structural network configuration than HC (lower global efficiency and clustering coefficient, d = 0.100,0.087, qs <= 0.027). Though no global difference between CHR-T and CHR-NT, network distinctiveness of the frontal and temporal surface area networks was higher in CHR-T than CHR-NT (d = 0.223,0.237) and HC (d = 0.208,0.219) (qs < 0.001). Network distinctiveness of the frontal cortical thickness network was lower in CHR-T (d = 0.218, q < 0.001) than CHR-NT and HC (d = 0.165, q < 0.001). Importantly, higher network distinctiveness was associated with worse positive symptoms in CHR-NT (frontal surface area, q = 0.008, R2 = 0.013) and at trend with worse negative symptoms in CHR-T (frontal thickness, q = 0.063, R2 = 0.049). Further, the brief intermittent psychotic syndrome subgroup showed more severe network alterations. Together, brain structural networks inform symptoms and the risk of transition to psychosis in CHR individuals.

BackgroundThe prevalence of type 2 diabetes mellitus (T2DM) is steadily increasing, with central nervous system complications commonly manifesting as mild cognitive impairment and dementia. However, the neuropathophysiological mechanisms underlying T2DM-related cognitive dysfunction remain poorly understood.MethodThis study used voxel-based morphometry (VBM) and seed-to-voxel structural covariance network (SCN) analyses to investigate alterations in cerebellar gray matter volume (GMV) and SCNs in T2DM, as well as their associations with cognitive performance. Intergroup differences were assessed using two-sample t-tests with Gaussian random field correction.ResultsVBM analysis revealed significant GMV reductions in the bilateral cerebellar crus I, left lobules I–IV, left crus II, left lobule IX, and right lobule VIIb in T2DM participants. Seed-to-voxel SCN analysis further demonstrated decreased covariance between the left crus I and the left middle temporal gyrus, middle occipital gyrus, and angular gyrus, along with increased covariance between the left lobules I–IV and the right caudate nucleus. Correlation analysis revealed that GMV of the left crus I was positively associated with Clock Drawing Test scores, while GMV of the right crus I was positively correlated with Auditory Verbal Learning Test (AVLT) scores. In addition, GMV of the right lobule VIIb was positively associated with both AVLT and Grooved Pegboard Test (GPT) scores, and GMV of the left lobule IX was positively correlated with GPT scores. With respect to network integrity, reduced SCN connectivity between the left crus I and the default mode network (DMN) was negatively correlated with AVLT and the color word test performance, whereas enhanced SCN connectivity between the left lobules I–IV and the right caudate nucleus was negatively correlated with AVLT scores and was positively correlated with Trail Making Test-A performance.ConclusionBy integrating VBM and SCN approaches, this study demonstrated that cerebellar GMV atrophy and abnormal structural covariance in T2DM were closely associated with cognitive dysfunction. These findings highlight the role of disrupted cerebro-cerebellar connectivity in the pathophysiology of T2DM-related cognitive impairment.

Adolescent depression presented higher risk of suicide than adult depression. However, the neurophysiological mechanisms underlying this phenomenon have not been elucidated. We aimed to identify structural covariance network alterations in depressed adolescents with suicidal behaviors to provide novel neuroimaging evidence for this condition. 64 first-episode, treatment-naïve depressed adolescent patients with suicidal behaviors and 48 healthy controls were enrolled. Nonnegative matrix factorization was used to identify the structural covariance networks. The Kullback-Leibler divergence method was applied to estimate the interregional relationships between the altered brain networks. Correlation analyses were conducted between altered brain networks and clinical characteristics. Patients had lower gray matter volumes in the anterior default mode network (DMN), visual network, sensorimotor network, and right executive control network than healthy controls. Morphological connections were altered in the anterior DMN, visual network, and right executive control network in patients. Correlation analyses revealed negative associations between morphological connections in anterior DMN-visual networks and illness duration in the patient group. This study revealed abnormal gray matter attributes in the anterior DMN, visual network, sensorimotor network, and executive control network in first-episode and treatment-naïve adolescent depression with suicide, which might reflect disease traits and provide essential neurobiological evidence for behavioral disturbances in depression.

ABSTRACTThe behavioural variant of frontotemporal dementia (bvFTD) is a younger‐onset dementia syndrome characterised by early atrophy of frontoinsular cortices, manifesting in profound socioemotional disturbances. Converging evidence from correlational, data‐driven, and computational approaches indicates large‐scale network degeneration in bvFTD. While the insula is consistently implicated, it remains unclear whether insular atrophy causally impacts progressive large‐scale structural network alterations in bvFTD. Eighty‐two patients with clinically probable bvFTD were classified as very mild/mild (n = 35), moderate (n = 30), and severe (n = 17) using the CDR plus NACC FTLD. Grey matter volume comparison between the entire bvFTD group and a healthy control group matched for age and education identified the left anterior insula as the initial maximal site of atrophy in bvFTD. To determine potential causal effects of insular atrophy on network‐based dysfunction in bvFTD, a voxel‐wise causal structural covariance network (CaSCN) was constructed based on pseudo‐time‐series morphometric data using the left anterior insula as the seed region. Sex, age, years of education, total intracranial volume (TIV), and scanning site were included as covariates, along with the difference between the sum of boxes score for the CDR plus NACC FTLD across the two pseudo–time points. Finally, an event‐based model (EBM) was applied to confirm the sequence of regional atrophy precipitated by left anterior insula atrophy, which emerged in the CaSCN analysis. BvFTD patients in the very mild/mild disease subgroup showed predominant atrophy of frontotemporal (e.g., insula, middle frontal gyrus), limbic (e.g., hippocampus, amygdala), and subcortical (e.g., putamen, nucleus accumbens) structures. Widespread grey matter atrophy was evident in the moderate bvFTD subgroup, extending to the middle cingulate, paracingulate gyri, and the thalamus, which progressed to posterior brain regions, including the fusiform gyrus and the cerebellum in the severe subgroup. Importantly, the CaSCN and event‐based model analysis reinforced the disease‐staging results by revealing progression of atrophy from the initial seed region of the left anterior insula to the orbitofrontal cortex, putamen/nucleus accumbens, anterior cingulate cortex, dorsolateral prefrontal cortex, inferior temporal gyrus, and supramarginal gyrus, before progressing posteriorly to the lingual gyrus. Using causal structural covariance network analysis and event‐based modelling, our findings indicate a causal role for the left anterior insula in driving the spread of pathology in bvFTD through well‐delineated functional brain networks known to support higher‐order cognitive and socioemotional processing. By capturing the direction of atrophy progression, our findings hold utility for potentially monitoring and tracking the efficacy of novel therapeutics on brain function in bvFTD.

After a subcortical stroke, structural and functional alterations have often been observed in brain regions far from the lesion, a phenomenon suspected but not yet confirmed as diaschisis. This study investigated the existence and patterns of diaschisis following subcortical stroke. This observational, single-center, cross-sectional study was conducted in China from August 2014 to September 2023. We recruited patients with subcortical stroke with hemiplegia and unilateral lesions in the basal ganglia and corona radiata. Healthy controls were recruited from the community using an age- and sex-matching strategy with the patients. Patients were categorized by the National Institutes of Health Stroke Scale scores: mild (<5) and moderate (5-15) deficits. Using magnetic resonance imaging techniques, we analyzed gray matter volume alterations with voxel-based morphometry and causal structural networks with the causal structural covariance network method. Of the 566 patients initially screened, 102 were enrolled. Due to recruitment constraints, only 46 healthy controls were recruited, limiting successful matching. The final analysis included 90 patients and 44 controls. The patient group (62 males and 28 females) had a mean age of 60.64±9.93 years, while the healthy group (23 males and 21 females) had a mean age of 59.43±5.01 years. We found significant gray matter volume loss in the medial superior frontal gyrus and identified both positive and negative directional connectivity patterns between this region and other areas in the prefrontal cortex (inferior frontal gyrus), temporal regions (superior and middle temporal gyri), limbic structures (insula, cingulate gyrus, and parahippocampus), and precentral. Notably, the causal network in patients with mild deficits was more complex. These findings support the existence of structural diaschisis following subcortical stroke, centered in the prefrontal cortex. This study underscores the importance of brain-wide imaging markers and may contribute to developing brain stimulation targets.

BackgroundThe thalamus, along with its component nuclei, possesses extensive connections with various brain regions and is engaged in diverse functions. However, it is unknown whether the gray matter volume (GMV) covariance networks of thalamic subfields are selectively affected in chronic capsular stroke.MethodsWe recruited 45 patients with chronic right capsular strokes (CS) and 93 normal controls (NC) from three centers. The thalamus was segmented into 25 subfields using FreeSurfer (v7.1.1). A general linear model was applied to investigate intergroup differences in the GMV covariance network of each thalamic subfield with each voxel of the entire brain between CS and NC, correcting for confounders such as age, gender, total intracranial volume (TIV), and scanners (voxel-wise p < 0.001, cluster-wise FWE corrected p < 0.05).ResultsOur findings revealed that all 25 ipsilesional thalamic subfields in CS were atrophied (p < 0.05, FDR correction). Among these, 16 ipsilesional thalamic subfields (including AV, LD, LP, VLa, VLp, VPL, VM, CeM, CL, MDm, LGN, PuM, PuI, CM, Pf, and Pt) exhibited significantly subfield-specific increased GMV covariance connectivity with the anterior orbital gyrus, superior occipital gyrus, calcarine, anterior cingulate cortex, precentral gyrus, and other regions. Additionally, although none of the contralesional thalamic subfields demonstrated regional GMV changes, 3/25 showed subfield-specific increased GMV covariance connectivity with the ipsilesional anterior orbital gyrus and subcortex.ConclusionThe GMV covariance networks of thalamic subfields are selectively involved in patients with chronic capsular stroke, which affect not only the ipsilesional thalamic subfields but also the contralesional ones.

Predicting cognitive aging through brain structural covariance networks: A decade of longitudinal insights using source-based morphometry.

Distinct neural mechanisms underlying cognitive difficulties in preterm children born at different stages of prematurity

Mapping grey matter and network abnormalities in seizure-onset patterns via stereotactic EEG.

ABSTRACTBackgroundThe high heterogeneity in vestibular migraine (VM) complicates understanding its precise pathophysiological mechanisms and identifying potential biomarkers. This study investigated the heterogeneity in VM using a newly proposed method called Individualized Differential Structural Covariance Network (IDSCN) analysis.MethodsStructural T1‐weighted MRI scans were performed on 55 patients with VM and 65 healthy controls, and an IDSCN was constructed for each patient. We studied the extent of heterogeneity in the IDSCNs, summarized the distribution of differential edges, and clustered the patients into subtypes with the shared differential edges. Imaging–clinical association analyses were conducted on both the subtype classification and the differential edges exhibiting significant inter‐subtype differences.ResultsPatients with VM demonstrated notable heterogeneity in the number of significantly altered IDSCN edges, while sharing several common differential connections that were mainly distributed among the parietal, subcortical, and cerebellar regions. Two robust and distinct neuroanatomical subtypes of VM were identified, which were associated with headache frequency. The differential edge between the left paracentral lobule and right pallidum was associated with both headache frequency and occurrence.ConclusionsThese findings indicate the importance of considering individual differences in VM research and may offer insights for precise diagnosis and individualized treatment of the disease.

Children represent a particularly vulnerable group to the long-term consequences of COVID-19 due to their ongoing neurodevelopment. This study aimed to identify transient and persistent structural alterations in children recovering from the infection by comparing pretreatment and posttreatment MRI scans and to evaluate differences in brain morphology and network organization relative to age- and sex-matched healthy controls. A retrospective cohort of 26 children aged 8-12years with confirmed COVID-19 was compared to 26 healthy controls. All participants underwent high-resolution T1-weighted MRI on a 3T scanner using identical acquisition protocols. Standard VBM and SBM pipelines were applied to quantify cortical volume, thickness, and sulcal depth, followed by SCN analysis to construct correlation matrices based on gray matter metrics. Graph theoretic metrics, including clustering coefficients, eigenpath lengths, small-worldness, and global/local efficiencies, were computed under different network sparsity thresholds. Cortical volume analyses revealed reductions in regions including the cingulate cortex, hippocampus, and superior temporal gyrus among children post-COVID-19, with within-group comparisons showing decreases in the left middle cingulate cortex (7.4-6.9cm3), left postcentral gyrus (12.2-10.8cm3), and right anterior cingulate cortex (2.1-1.8cm3). Partial recovery of sulcal depth and cortical thickness was observed in the superior temporal gyrus (sulcal depth from 210.3 to 198.5mm2, thickness from 2.34 to 2.15mm). Structural covariance network analysis demonstrated lower global efficiency and higher small-worldness in the post-COVID-19 group compared to controls, along with increased characteristic path length, whereas local connectivity measures (clustering coefficient and local efficiency) remained relatively stable. Children recovering from COVID-19 may exhibit structural brain changes and network connectivity disruptions, some of which show partial resolution over time, whereas others persist. Long-term follow-up through comprehensive neuroimaging and clinical evaluation is necessary to clarify the potential impact on development.

Schizophrenia patients with auditory hallucinations have distinct morphological abnormalities, but whether this population have a progressive gray matter atrophy pattern and specific transmission chain of causal effects remains unclear. This study was designed to construct a causal structural covariance network in schizophrenia patients with persistent auditory hallucinations. T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group) and 83 healthy controls (HC group). Stage-specific independent t tests of gray matter volume (GMV) comparisons between the two groups were used to depict the GMV atrophic pattern and locate the atrophic origin. In the pAH group, the causal structural covariance network (CaSCN) was constructed to map causal effects between the atrophic origin and other regions as the auditory hallucination severity increased. With the ascending of hallucinatory severity, GMV reductions began from the thalamus, bilateral medial frontal gyri, left Rolandic operculum, and left calcarine, and expanded to other frontal and temporal regions, hippocampal complex, insula, anterior cingulate gyri, fusiform, and cerebellum. Using the peak region (thalamus) as the causal origin in the network, transitional nodes including the right opercular part of the inferior frontal gyrus, bilateral postcentral gyri, left thalamus, and right middle frontal gyrus received the casual information and projected to target nodes from the frontal, temporal, parietal, and occipital cortices, limbic system, and cerebellum. Our study revealed causal effects from the thalamus and a specific transmission pattern of causal information within the network, indicating a thalamic-cortical-cerebellar circuitry dysfunction related to auditory hallucinations.

BackgroundAdolescence is a period marked by high vulnerability to onset of depression. Neuroimaging studies have revealed considerableatrophy of brain structure in patients with major depressive disorder (MDD). However, the causal structural networks underpinning gray matter atrophies in depressed adolescents remain unclear. This study aimed to examine the initial gray matter alterations in MDD adolescents and investigate their causal relationships of abnormalities within brain structural networks.MethodsFirst-episode adolescent patients with MDD (n = 80, age = 15.57 ± 1.78) and age- and sex-matched healthy controls (n = 82, age = 16.11 ± 2.76) were included. We analyzed T1-weighted structural images using voxel-based morphometry to identify gray matter alterations in patients and the disease stage-specific abnormalities. Granger causality analysis was then conducted to construct causal structural covariance networks. We also identified potential pathways between the causal source and target.ResultsCompared to controls, MDD patients with shorter illness duration showed gray matter atrophy in localized brain regions such as ventral medial prefrontal cortex (vmPFC), anterior cingulate cortex, and insula. With a prolonged course of MDD, gray matter atrophy extended to widespread brain areas. Causal network results demonstrated that early abnormalities had positive effects on the default mode, frontoparietal networks, and reward circuits. Moreover, vmPFC demonstrated the highest out-degree value, possibly representing the initial source of brain abnormality in adolescent depression.ConclusionsThese findings revealed the progression of gray matter atrophy in adolescent depression and demonstrated the directional influences between initial localized alterations and subsequent deterioration in widespread brain networks.

Characteristics of the cortico-striato-thalamo-cerebellar structural covariance network in Meige syndrome.

Altered structural covariance networks in nonsuicidal self-injury: Implications for socio-affective dysfunctions.