Alterations in individual structural covariance networks in patients with insomnia disorder

The frequent presentation of Alzheimer's disease (AD) with neuropsychiatric symptoms (NPS) in the context of normal or minimally-impaired cognitive function led to the concept of Mild Behavioral Impairment (MBI). While MBI's impact on subsequent cognitive decline is recognized, its association with brain network changes in biologically-defined AD remains unexplored. To investigate the correlation of structural covariance networks with MBI-C checklist sub-scores in biologically-defined AD patients. We analyzed 33 biologically-defined AD patients, ranging from mild cognitive impairment to early dementia, all characterized as amyloid-positive through cerebrospinal fluid analysis or amyloid positron emission tomography scans. Regional network properties were assessed through graph theory. Affective dysregulation correlated with decreased segregation and integration in the right inferior frontal gyrus (IFG). Impulse dyscontrol and social inappropriateness correlated positively with centrality and efficiency in the right posterior cingulate cortex (PCC). Global network properties showed a preserved small-world organization. This study reveals associations between MBI subdomains and structural brain network alterations in biologically-confirmed AD. The IFG's involvement is crucial for mood dysregulation, while the PCC could be involved in compensatory mechanisms for social cognition and impulse control. These findings underscore the significance of biomarker-based neuroimaging for the characterization of NPS across the AD spectrum.

Abstract Background Treatment-resistant depression (TRD) is associated with gray matter volume reduction compared to healthy controls (HC). Since depression has increasingly been recognized as a disorder of dysregulated neural networks, it is important to evaluate these structural changes in terms of brain networks. Structural covariance is a measurement that gauges the association strength of structural measures between two regions, which is used to examine network-level alteration of brain structure. Although several studies reported abnormal structural covariance in depression, few studies have thus far investigated structural covariance alteration in relation to TRD. Aims and Objectives In this study, we aimed to examine structural covariance alteration in TRD compared to HC. Methods T1 images were obtained from 116 patients with TRD and 54 HC who were recruited at Keio University. Brain volume was calculated for each of 85 regions of the DK atlas using FreeSurfer 6.0. Group differences in brain volumes were examined by analysis of covariance controlling for age and sex. Structural covariances of brain volumes across 3570 pairs of brain regions were obtained by partial correlation analysis using age, sex, and total intracranial volume as covariates. Network-based statistics were used to extract networks with group differences in structural covariances. This study was conducted after obtaining approval from the respective ethics review committees. Results There were no significant differences in brain volumes between the TRD and HC groups. The null hypothesis of equality in structural covariance between them was rejected using network-based statistics. We found a single network comprising connections with elevated structural covariance in patients with TRD compared with HC. The left nucleus accumbens, bilateral pericalcarine, and supramarginal gyrus had high degree centrality in the structural covariance network. Discussion and Conclusion Patients with TRD showed coordinated brain volume alterations in comparison with HC, even though there was no difference in brain volumes between them. Nucleus accumbens, an integral hub in the reward circuit, was one of the hubs of extracted network with elevated structural covariance in TRD, which is in line with a previous study that reported higher structural covariance of nucleus accumbens in depression. Further studies are warranted to examine structural covariance differences among TRD, non-TRD, and HC in order to confirm the link between structural alteration and pathophysiology of treatment resistance in depression.

Purpose Diagnosing diabetic retinopathy (DR) in the pre-clinical stage is crucial to reversing DR. This study aimed to compare the retinal thickness changes between healthy controls (HCs) and diabetics without retinopathy (DWORs). For the first time, we would like to introduce the concept of network modularity analysis in studying retinal networks to demonstrate disrupted retinal layer organization as evidence of subclinical retinopathy. Methods This was a cross-sectional study on 156 eyes of HCs and 78 eyes of DWORs. Retinal layer thickness was measured on Spectralis OCT (Heidelberg Engineering, Heidelberg, Germany). Average thickness values from the outer ring of the ETDRS grid (Avg_O) and the inner ring (Avg_I) were calculated for each layer. Mean retinal thicknesses for each layer between the two groups were compared using the t-test. Age-related thickness changes were compared between the groups using Fisher's r-to-z transform. Group-based structural covariance networks were estimated for both DWORs and HCs. Optimal community architecture was estimated using Louvain's modularity. Results Inner retinal layers, namely RNFL_C (HC: 10.16 ± 2.48 µm versus DWOR: 10.85 ± 2.23 µm; p=0.023) and INL_Avg_I (HC: 39.9 ± 3.7 µm versus DWOR: 40.9 ± 3.16 µm; p=0.035), were thicker in the DWOR group compared to the HC group. Outer retinal layers, namely OR_C (HC: 89.9 ± 3.8 µm versus DWOR: 88.7 ± 3.6 µm; p=0.017) and OR_Avg_I (HC: 81.4 ± 3.16 µm versus DWOR: 80.5 ± 2.28 µm; p=0.02), were thinner in the DWOR group compared to the HC group. The central sub-field showed an age-related thickening in retinal nerve fiber layer (RNFL) (r=0.117, p=0.04), GCL (r=0.078, p=0.17), inner plexiform layer (r=0.137, p=0.01), inner nuclear layer (INL) (r=0.29, p≤0.001), outer plexiform layer (r=0.256, p<0.001), and outer nuclear layer (r=0.197, p=0.001) layers in the HC group, which was not seen in the DWOR group. There was an abnormal increase in modularity among DWORs compared to HCs (Qhc=0.47, Qdowr=0.51, p=1.6x10-8). In the DWOR group, we noted a disruption in the community architecture and minimal inter-community interactions compared to HCs. Conclusion RNFL and INL are thicker in DWORs compared to HCs. Outer retinal layers are thinner in DWORs compared to HCs. On modularity analysis, we noted a disruption in the community architecture in the DWOR group compared to the HC group.

Tinnitus is considered a neurological disorder affecting both auditory and nonauditory networks. This study aimed to investigate the structural brain covariance network in tinnitus patients and analyze its altered topological properties. Fifty three primary tinnitus patients and 67 age- and sex-matched healthy controls (HCs) were included. Gray matter volume (GMV) of each participant was extracted using voxel-based morphometry, a group-level structural covariance network (SCN) was constructed based on the GMV of each participant, and graph theoretic analyses were performed using graph analysis toolbox (GAT). The differences in the topological properties of SCN between both groups were compared and analyzed. Both groups exhibited small-world attributes. Compared with HCs, tinnitus patients had significantly higher characteristic path length, lambda, transitivity, and assortativity (p < 0.05), and significantly lower global efficiency (p < 0.05). Tinnitus patients had higher clustering coefficient and reduced gamma and modularity, but neither was remarkable. The hubs in tinnitus network focused on the temporal lobe. In addition, the tinnitus network was found to be reduced in robustness to targeted attacks compared with HCs. Besides, a significant negative correlation between Tinnitus Handicap Inventory (THI) score and GMV in the left angular gyrus (r = -0.283, p = 0.040) as well as left superior temporal pole (r = -0.282, p = 0.041) were identified. Tinnitus patients showed reduced small-world properties, altered hub nodes, and reduced ability to respond to targeted attacks in brain network. The GMV in the left angular gyrus and left superior temporal pole showed significant negative correlation with tinnitus distress (THI score), indicating potential therapeutic target.

Abnormal structural covariance network in major depressive disorder: Evidence from the REST-meta-MDD project

Methamphetamine (METH) dependence is a globally significant public health concern with no efficacious treatment. Trait impulsivity is associated with the initiation, maintenance, and recurrence of substance abuse. However, the presence of these associations in METH addiction, as well as the underlying neurobiological mechanisms, remains incompletely understood. We scanned 110 individuals with METH use disorder (MUDs) and 55 matched healthy controls (HCs) using T1-weighted imaging and assessed their drug use characteristics and trait impulsivity. Surface-based morphometry and graph theoretical analysis were used to investigate group differences in brain morphometry and network attributes. Partial correlations were conducted to investigate the relationships between brain morphometric changes, drug use parameters, and trait impulsivity. Mediation analyses examined how trait impulsivity and drug craving influenced the link between brain morphometric change and MUD severity in patients. MUDs exhibited thinner thickness in the left fusiform gyrus and right pars opercularis, as well as diminished small-world properties in their structural covariance networks (SCNs) compared to HCs. Furthermore, reduced cortical thickness in the right pars opercularis was linked to motor impulsivity (MI) and MUD severity, and the association between the right pars opercularis thickness and MUD severity was significantly mediated by both MI and cue-induced craving. These findings suggest that MUDs exhibit distinct brain structural abnormalities in both the cortical thickness and SCNs and highlight the critical role of impulse control in METH addiction. This insight may offer a potential neurobiological target for developing therapeutic interventions to treat addiction and prevent relapse.

BackgroundMajor Depressive Disorder (MDD) is a highly prevalent neurological disorder, characterized by multidimensional symptoms that are associated with structural abnormalities across multiple brain networks. There remains a lack of systematic research into the core regions and brain maturational disruption underlying the symptoms of MDD. In this study, we aimed to assess aberrant gray matter volume (GMV) and structural covariance network (SCN) in patients with MDD compared to healthy controls.MethodsT1-weighted anatomical images of 159 patients with MDD and 121 matched healthy controls were acquired. 17-item Hamilton Depression Rating Scale (HAMD-17) was utilized to assess the clinical symptoms of MDD. Voxel-based morphometry was utilized to assess the core aberrancies of GMV in patients with MDD; a novel Gaussian kernel-based density estimation was employed to construct the individual-based SCN, network-based statistic was applied to investigate the interregional structural coordinated changes; Pearson’s correlation was applied to assess the association between these abnormalities and clinical severity in MDD.ResultsPatients with MDD showed increased GMV mainly in the basal ganglia (putamen), limbic system (parahippocampal gyrus, amygdala), inferior temporal gyrus and olfactory, while decreased GMV in the diencephalic nuclei (thalamus) and precentral gyrus. SCN analyses reveal an abnormal network centered on the pallidum and hippocampus as core nodes, which encompasses three functional subnetworks: the emotional regulation network, sensorimotor network, and cognitive control network. Moreover, the decreased GMV in the thalamus and increased structural coordination between the pallidum and the parahippocampal gyrus is significantly correlated with patients’ HAMD-17 scores.ConclusionOur findings suggest the pathophysiology of MDD may primarily lie in the abnormal morphology and interregional coordinated development of the basal ganglia-limbic system. The current results provided novel supplementary evidence for the hypothesis of structural aberrations in MDD.

Sudden sensorineural hearing loss (SSNHL) is associated with abnormal changes in the brain's central nervous system. Previous studies on the brain networks of SSNHL have primarily focused on functional connectivity within the brain. However, in addition to functional connectivity, structural connectivity also plays a crucial role in brain networks. Moreover, traditional functional connectivity analyses often overlook the spatial and temporal characteristics of connectivity changes and fail to provide directional information and causal relationships. This study utilized Structural Covariance Network (SCN), multilayer network analysis, and Dynamic Causal Modeling (DCM) to investigate the cross-scale changes in neural network structure and function in SSNHL patients with accompanying cognitive and emotional disorders. We collected 3D-T1 structural magnetic resonance image data and functional magnetic resonance image data from 70 SSNHL patients and 81 healthy controls (HCs). SCN analysis was performed based on gray matter volume, and multilayer network analysis was used to calculate node switching rates. Based on the results of multilayer network analysis, six nodes exhibiting significant inter-group differences in node switching rates were selected as regions of interest (ROIs). DCM was then conducted to explore the causal relationships of functional connectivity between these nodes. Based on SCN, there were no significant inter-group differences in global network properties between SSNHL and HCs. At the node level, the left precentral gyrus in SSNHL showed a significant decrease in node efficiency. In the multilayer network analysis, SSNHL showed a significantly increased node switching rate at the level of the Left Superior Frontal Gyrus (L.SFG), Left Supplementary Motor Area (L.SMA), Left Superior Parietal Gyrus (L.SPG), Right Superior Parietal Gyrus (R.SPG), Right Inferior Parietal Lobe(R.IPL), and Left Thalamus (L.THA). Furthermore, the node switching rate of L.SFG showed a significant negative correlation with the Self-Rating Anxiety Scale (SAS) scores. DCM analysis of these six nodes revealed differences in the functional effective connectivity between the left superior parietal gyrus (L.SPG) and the left supplementary motor area (L.SMA), which were positively correlated with the AVLT-delay scores. These findings suggest that SSNHL patients experience structural and functional remodeling of the cerebral cortex, with hearing loss leading to the reallocation of cognitive resources. This provides new insights into understanding the potential mechanisms between cross-scale networks and cognitive-emotional disorders in SSNHL.

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.

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

Mobility impairment is common in multiple sclerosis, especially in older adults with multiple sclerosis (OAMS). Grey matter (GM) changes are well documented in MS, and GM atrophy is common in older adults. The relationship between GM changes and mobility disability in OAMS is unknown. We sought to identify GM patterns associated with gait speed in OAMS and healthy older controls, using structural covariance network analysis. OAMS (n = 102; 64.8 ± 4.4 years) and healthy controls (n = 106; 68.2 ± 7.3 years) underwent brain MRI and gait assessments; structural covariance networks were constructed to elucidate brain regions with significant associations between GM volume and 25-foot walk gait speed. We used voxel-wise linear regression analyses to elucidate per-network subregions with significant correlations with gait speed. Voxel-wise moderation analysis tested for group differences in these associations. Across the entire cohort, the following networks demonstrated significant gait speed associations: bilateral hippocampus, bilateral caudate/pallidum/putamen, bilateral thalamus/putamen, right middle temporal gyrus and multiple cerebellar regions. There were no significant group-by-network interaction effects. In summary, structural network analysis reveals unique brain patterns of gait speed in older adults, but these patterns are common amongst healthy older adults and OAMS and highlight the importance of cerebellar and subcortical networks in supporting gait speed.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00415-025-13373-w.

Systemic heterogeneity in autism spectrum disorder revealed by individualized structural covariance network analysis.

Reorganization of cortical individualized differential structural covariance network is associated with regional morphometric changes in chronic subcortical stroke.

Background: Epilepsy is a common symptom in patients with diffuse lower‐grade glioma (DLGG). However, the specific role of cortical alterations in glioma‐related epilepsy (GRE) remains unclear. This study is aimed at investigating the reorganization of cortical architecture and network changes associated with GRE.Materials and Methods: High‐resolution T1‐weighted and T2‐weighted images were acquired from patients with DLGG (GRE = 68, non‐GRE = 79) and 94 healthy controls (HCs). Cortical thickness and myelin content were calculated using the Human Connectome Project pipeline. Characteristics of structural covariance networks were computed using graph theory and network‐based statistic. Cortical thickness, myelin content, and network characteristics were compared among three groups. A GRE individual prediction model was constructed using an automated machine learning approach.Results: Compared with HCs, both GRE and non‐GRE groups exhibited cortical thinning in the tumor ipsilateral hemisphere, whereas there was cortical thickening in the contralateral hemisphere. Regarding the connectome characteristics, both GRE and non‐GRE groups showed decreased nodal efficiency and connections in multiple regions. When comparing GRE with non‐GRE, the GRE group exhibited more pronounced cortical thickening and demyelination in the contralateral orbitofrontal gyrus and superior frontal gyrus, with further decreased connections in the sensorimotor network, default mode network, and salience network. Finally, an XGBoost model based on cortical features enabled classification of GRE individuals with an accuracy of 0.80 and an AUC of 0.87.Conclusion: These findings deepen our understanding of the comprehensive cortical alterations in patients with DLGG and simultaneously provide novel insights into the potential pathophysiological mechanisms underlying GRE.

Individual structural covariance connectome reveals aberrant brain developmental trajectories associated with childhood maltreatment.

BackgroundMajor depressive disorder (MDD) is a complex and heterogeneous disorder, and this heterogeneity poses a significant challenge for advancing precision medicine in patients with MDD. MRI-based subtyping analysis has been widely employed to address the heterogeneity of MDD patients. In this study, we investigated the subtypes of first-episode and drug-naive (FEDN) MDD patients based on the individualized structural covariance network (IDSCN).MethodsIn this study, we used T1-weighted anatomical images of 164 FEDN MDD patients and 164 healthy controls from the REST-meta-MDD consortium. The IDSCN of participants was obtained using the network template perturbation method. Subtypes of FEDN MDD were identified using k-means clustering analysis, and differences in neuroimaging findings and clinical symptoms between the identified subtypes were compared using two-sample t-tests.ResultsThis study identified two subtypes of FEDN MDD: subtype 1 (n = 117) and subtype 2 (n = 47) by characterizing 10 edges that were significantly altered in at least 5% of patients (i.e., 8 patients) in the IDSCN. Compared with subtype 2, subtype 1 had significantly higher anxiety symptom scores, stronger structural covariance edges in 9 edges within the thalamus, and a significantly reduced gray matter volume (GMV) in the frontal and parietal regions, and in the thalamus.ConclusionsOur results suggest that patients with FEDN MDD can be classified into two different subtypes based on their IDSCN, providing an important reference for personalized treatment and precision medicine for patients with FEDN MDD.

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.

Radiation-induced brain injury (RBI) is a common complication in patients with nasopharyngeal carcinoma (NPC) who have undergone radiotherapy (RT), which is characterized by significant cognitive and psychological impairments. Although radiation-induced regional structural abnormalities have been well-reported, the effects of RT on the whole brain structural covariance networks are mostly unknown. Here, we performed a source-based morphometry (SBM) study to solve this issue. In this cross-sectional study, 131 NPC patients with pre- and post-RT were stratified into pre-RT (n=47) and post-RT (n=84) groups. The SBM method was adopted to investigate the radiation-induced alterations in structural covariance networks in patients with NPC. Compared to the pre-RT group, our SBM analyses revealed increased z-scores in the independent component 05 (IC05; mainly located in the posterior cingulate, precuneus areas, and superior parietal lobe) (P=0.040) and decreased z-scores in the temporal-occipital network (P=0.015) and cerebellar network (P=0.023) in post-RT NPC patients. Compared to the pre-RT group, voxel-based morphometry (VBM) revealed reduced gray matter volume in the left temporal lobe, cerebellum, bilateral thalamus, left insular, and occipital lobe in the post-RT group. Notably, a significant negative correlation was observed between the mean radiation doses of the right temporal lobe and the z-score of the cerebellar network (r=-0.349, P=0.027). This present study revealed radiation-induced changes in structural covariance networks and cortical volume in patients with NPC. These findings shed some light on the neural basis of symptom patterns in RBI and may support the development of new intervention strategies to prevent progression to radiation-induced brain necrosis.

Abstract BackgroundThe intricate and heterogeneous phenotypes associated with neuropsychiatric symptoms (NPSs) encumber exploration of their role in the neuropathology and underlying biological mechanisms of Alzheimer’s disease (AD) continuum.MethodAn individual‐level Regional Radiomics Similarity Network (R2SN) for 487 patients with AD continuum (376 with NPSs vs. 111 without NPSs) were developed to find the R2SN connections associated with NPSs and refine the subtypes of NPS in the AD continuum. Distinct brain network dysfunction, multimodal neuroimaging burden, and clinical measures/progression of each NPS subtype were analyzed. Gene set enrichment analysis (GSEA) was used to explore the biological mechanisms underpinning the various NPS subtypes and their connections to the biological pathways leading to the AD continuum.ResultThree NPS subtypes were identified based on 300 distinct key R2SN connections. Compared to AD patients without NPS, the first NPS subtype (sNPS) and third NPS subtype (moNPS) exhibited significant opposite pattern of brain connectivity damage, while the second NPS subtype (miNPS) showed minimal differences (Figure 1). Moreover, both sNPS and moNPS subtypes exhibited diminished performance at baseline and rapid decline in the MMSE and MoCA scores, while no statistically significant difference was discerned between the miNPS subtype and those without NPS. Furthermore, both sNPS and moNPS subtypes exhibited reduced regional grey matter volume and cortical thickness in the frontal, temporal, and parietal lobules, while miNPS subtype lacks significant structural brain changes but exhibited higher corrected cerebral blood flow in the inferior frontal gyrus and anterior cingulate cortex, suggesting early compensatory cerebral hyperperfusion (Figure 2). GSEA unveiled the shared and subtype‐specific gene pathways, elucidating each subtype’s unique biological mechanisms associated with the contribution of NPS‐related specific brain connectome dysfunctions to AD progression (Figure 3).ConclusionThis is the first study to identify three distinct NPS subtypes in the AD continuum through a data‐driven approach, bridging the gap in the knowledge on the contributions of NPSs to the onset and development of AD. Our study constitutes a step toward resolving stagnant research progress on NPS management strategies based on clinical symptoms, and offers new insights into precise interventions for these patients.