Despite regional brain structural alterations having been reported in the patients with primary trigeminal neuralgia (PTN), the topological characteristics of structural covariance networks (SCNs) still remain unclear. This study applied graph theoretical analysis to investigate the abnormalities of the global and nodal network patterns of SCNs in PTN. Forty-five patients with PTN and 45 matched healthy controls (HCs) were recruited in this experiment. All of the participants underwent structural magnetic resonance imaging examinations and clinical assessment. The cortical thickness (CT) and cortical surface area (CSA) was extracted from 68 brain regions according to the Desikan atlas, and utilized to reconstruct the SCNs. Subsequently, graph theoretical analysis was performed to evaluate the aberrance of topological properties of the SCNs in patient group. Local cerebral atrophy was observed in scattered brain areas, especially in several frontal and medial parietal cortices in the patients with PTN. Specifically, notable changes of nodal degree, local efficiency and betweenness centrality based on CT and CSA were detected in orbitofrontal cortex, anterior cingulate cortex, posterior cingulate cortex, and precuneus. Network analysis revealed that the patient group showed decreased global efficiency of CT and CSA in varying degrees compared to those of HCs. These findings indicated a distinctive pattern of cortical reorganization of CT and CSA based SCNs in PTN, which is beneficial to understand the pathophysiological mechanism at the level of cortical structural network and provide potential targets for induced neuromodulation in this pain disorder. The registry name of this study in ClinicalTrials.gov: Magnetic Resonance Imaging Study on Patients with Trigeminal Neuralgia (MRI-TN). gov ID: NCT02713646. A link to the full application: https://clinicaltrials.gov/ct2/results?cond=%26;term=NCT02713646%26;cntry=%26;state=%26;city=%26;dist= .

Multilayer network analyses allow for the exploration of complex relationships across different modalities. Specifically, this study employed a novel method that integrates psychometric networks with structural covariance networks to explore the relationships between cognition, emotion and the brain. Psychological (NIH Toolbox Cognition Battery and NIH Toolbox Emotion Battery) and anatomical MRI (cortical volume) data were extracted from the Human Connectome Project Young Adult dataset (n = 1109). Partial correlation networks with graphical lasso regularisation and extended Bayesian information criterion tuning were used to model a psychometric bi-layer network consisting of seven cognitive nodes and four emotion nodes, as well as a neuro-psychometric tri-layer network consisting of these same nodes in addition to 24 brain nodes from the central executive and salience networks. Bridge strength centrality was used to identify nodes that bridged between layers. For the bi-layer network, it was found that stress was the only bridge node. For the tri-layer network, six bridge nodes were identified, with the left insula emerging as the most central. These findings demonstrate the utility of multilayer networks in integrating psychological and neurobiological data for the potential identification of targets to improve psychological wellbeing.

The diagnosis of autism spectrum disorder (ASD) is often hampered by its heterogeneity and reliance on time-consuming behavioral assessments. Automated neuroimaging-based diagnostic tools offer a promising alternative, but multi-site data integration often introduces variability, hindering the achievement of accurate and interpretable results. This study presents the Connectome Convolutional Transformer (CCTF), a multimodal deep learning framework that integrates functional and structural brain connectivity information from fMRI and sMRI modalities. The CCTF enriches feature representation by incorporating diverse functional connectivity metrics and structural covariance networks based on multiple morphological properties. It employs a connectome convolutional embedding module and transformer encoder to capture and refine brain connectivity patterns. In addition, a node-to-graph pooling layer facilitates the identification of potential ASD biomarkers. Evaluation on the multi-site ABIDE dataset demonstrated that CCTF outperformed state-of-the-art methods, achieving accuracies of [Formula: see text] for fMRI, [Formula: see text] for sMRI, and [Formula: see text] for the ensemble fMRI+sMRI model in intra-site cross-validation. In the inter-site leave-one-site-out cross-validation, the CCTF maintained its superiority, with the ensemble model reaching [Formula: see text] accuracy, underscoring its robustness and generalizability across different sites. The identified brain regions are consistent with established ASD neurobiology, underscoring CCTF's potential to advance the understanding of the neural mechanisms underlying this complex disorder.

Negative symptoms of schizophrenia (SCZ), particularly amotivation, are prominent across both SCZ and bipolar disorder (BD). While orbitofrontal cortex (OFC) alterations have been implicated in the development of negative symptoms, their contributions across disorders remain to be established. Here, we examined how OFC thickness and network associations relate to amotivation compared to diminished expression across the BD-SCZ spectrum. We included 50 individuals with SCZ, 49 with BD, and 122 controls. We assessed amotivation and diminished expression and estimated thickness in the medial and lateral OFC as regions of interest as well as 64 other cortical regions. Across BD and SCZ, reduced right lateral and bilateral medial OFC thickness were specifically associated with amotivation, but not diminished expression or other clinical factors. We then generated intra-individual OFC structural covariance networks to evaluate how the system-level embedding of the OFC would link to brain-wide cortical maps of negative symptoms. We found that medial OFC covariance networks spatially correlated with the brain-wide cortical alterations of both negative symptom dimensions. Further analyses in independent SCZ data from the ENIGMA consortium (n = 4474) revealed associations with lateral OFC covariance networks. Finally, the brain-wide cortical alterations of amotivation were significantly correlated with normative functional and structural white-matter connectivity profiles of the right medial and left lateral OFC as well as adjacent prefrontal and limbic regions. Our work identifies OFC alterations as a possible transdiagnostic signature of amotivation and provides insights into network associations underlying the system-wide cortical alterations of negative symptoms across SCZ and BD.

Schizophrenia is a psychiatric disorder hypothesized to result from disturbed brain connectivity. Structural covariance networks (SCN) describe the shared variation in morphological properties emerging from coordinated neurodevelopmental processes, This study evaluates the potential of SCNs as diagnostic biomarker for schizophrenia. We compared the diagnostic value of two SCN computation methods derived from regional gray matter volume (GMV) in 154 patients with a diagnosis of first episode psychosis or recurrent schizophrenia (PAT) and 366 healthy control individuals (HC). The first method (REF-SCN) quantifies the contribution of an individual to a normative reference group's SCN, and the second approach (KLS-SCN) uses a symmetric version of Kulback-Leibler divergence. Their diagnostic value compared to regional GMV was assessed in a stepwise analysis using a series of linear support vector machines within a nested cross-validation framework and stacked generalization, all models were externally validated in an independent sample (NPAT=71, NHC=74), SCN feature importance was assessed, and the derived risk scores were analyzed for differential relationships with clinical variables. We found that models trained on SCNs were able to classify patients with schizophrenia and combining SCNs and regional GMV in a stacked model improved training (balanced accuracy (BAC)=69.96%) and external validation performance (BAC=67.10%). Among all unimodal models, the highest discovery sample performance was achieved by a model trained on REF-SCN (balanced accuracy (BAC=67.03%). All model decisions were driven by widespread structural covariance alterations involving the somato-motor, default mode, control, visual, and the ventral attention networks. Risk estimates derived from KLS-SCNs and regional GMV, but not REF-SCNs, could be predicted from clinical variables, especially driven by body mass index (BMI) and affect-related negative symptoms. These patterns of results show that different SCN computation approaches capture different aspects of the disease. While REF-SCNs contain valuable information for discriminating schizophrenia from healthy control individuals, KLS-SCNs may capture more nuanced symptom-level characteristics similar to those captured by PCA of regional GMV.

Cortical thickness and structural covariance network alterations in cerebral amyloid angiopathy: A graph theoretical analysis.

Schizophrenia is associated with widespread gray matter reduction. This is influenced by the underlying connectivity, resulting in covarying patterns of structural changes that are more pronounced in treatment-resistant individuals. However, it remains uncertain whether a distinct networkof brain regions, with specific neurotransmitter basis, forms the substrate for treatment resistance in schizophrenia. We investigated the structural covariance networks (SCN) in 198 individuals; 55 with treatment-resistant schizophrenia (TRS) and 79 without TRS (non-TRS) in active symptomatic phase, and 64 healthy controls (HC) using Calhoun's Source-Based Morphometry. We mapped the putative neurotransmitter basis of the SCNs using a PET-based chemoarchitectural atlas. Twelve independent components (i.e., SCNs) were identified. A prefrontal-limbic SCN had lower gray matter volume (GMV) in TRS compared to HC and non-TRS (F = 7.757, p < 0.001, FDR-corrected). Spatial correlation with chemoarchitectural atlas revealed predominant contributions from serotonergic [5HT1b and 5HT2a], glutamatergic [mGluR5], histaminergic [H3], and opioid [MOR] receptors for thisTRS-related SCN (all pspin-permutation < 0.05, FDR-corrected). A differentSCN comprised of dorsal fronto-temporal and parieto-occipital regions, notassociated with any specific neurotransmitter distribution, exhibited reduced GMV in both TRS and non-TRS groupsvs. HC (F = 7.239, p < 0.001, FDR-corrected). Amidst the generic GMV reduction that is shared with non-TRS patients, patients with TRS have specific prefrontal-limbic structural deficits with a unique non-dopaminergic chemoarchitecture. These findings indicate a putative molecular and structural basis for poor treatment response, guiding the development of second- and third-line pharmacotherapies for TRS.

BackgroundMild cognitive impairment (MCI) is a heterogeneous disorder with significant individual variabilities in clinical and biological features. Abnormal inter-regional structural covariance suggests disruption of the brain structural network in MCI. Most studies have examined group-level structural covariance alterations while ignoring individual-level differences. Hence, we aimed to investigate the heterogeneity of MCI using individual differential structural covariance network (IDSCN) analysis.MethodsT1-weighted images of 596 MCI patients and 309 cognitively normal (CN) were collected from the ADNI database as discovery dataset, and 122 MCI and 117 CN from the OASIS-3 dataset as validation cohort. We constructed each patient’s IDSCN using regional gray matter volume and applied K-means clustering analysis to identify MCI subtypes based on significantly altered covariance edges. Then, clinical features, brain structure, and gene expression profiles were evaluated for each subtype.ResultsIn the ADNI dataset, MCI patients exhibited significant alterations in structural covariance edges, mainly involving the hippocampus, parahippocampal gyrus, and amygdala. Two robust MCI subtypes were identified. Subtype 1 showed faster disease progression relative to subtype 2, which was validated in the independent OASIS-3 dataset. Significant differences between two subtypes were found in clinical cognition and biomarkers, cerebral atrophy patterns, and enriched genes for metal ion transport and neuron projection development. Finally, correlation analysis and functional annotation further revealed that the affected edges were related to cognitive performance and implicated in memory and emotion terms.ConclusionsIn summary, these findings offer new perspectives into understanding the heterogeneity of MCI and facilitate strategies for future precision medicine.

PurposeTo explore the alterations of gray matter volume (GMV) and structural covariant network (SCN) in unilateral frontal lobe low-grade gliomas (FLGGs).Materials and methodsThe three dimensional (3D) T1 structural images of 117 patients with unilateral FLGGs and 68 age- and sex-matched healthy controls (HCs) were enrolled. The voxel-based morphometry (VBM) analysis and graph theoretical analysis of SCN were conducted to investigate the impact of unilateral FLGGs on the brain structure. This represents the first structural MRI study integrating both voxel-level morphometric changes and network-level reorganization patterns in unilateral FLGGs.ResultsThrough VBM analysis, we found that unilateral FLGGs can cause increased GMV in contralesional amygdala, calcarine, and angular gyrus, ipsilesional amygdala as well as vermis_6. The SCN of contralesional cerebrum, ipsilesional unaffected regions and cerebellum in both patients and HCs have typical small-world properties (Sigma > 1, Lambda ≈ 1 and Gamma > 1). Compared to HCs, global and nodal network metrics changed significantly in patients.ConclusionThe combination of VBM and SCN analysis revealed both focal GMV enlargement and topological alterations in patients with unilateral FLGGs, and provide a novel perspective of cross regional morphological collaborative changes for understanding the glioma-related neuroadaptation. These findings may suggest potential neuroimaging correlates of adaptive changes, which could inform future investigations into personalized treatment approaches.Clinical trial numberNot applicable.

Importance:Early alcohol initiation (before age 15) is associated with adverse outcomes. Understanding mechanisms behind early alcohol initiation is essential for informing prevention efforts.Objective:To examine whether structural covariance network properties at ages 9–10 years predict early alcohol initiation.Design:Case-control, population-based study design.Setting:Data from the Adolescent Brain Cognitive Development study were used. Baseline structural brain imaging data (ages 9–10) were used for generation and comparison of structural covariance networks. Data from baseline to 4-year follow-up (≤age 15) assessments were used to determine alcohol initiation.Participants:Participants were excluded if they reported consuming a full drink of alcohol at baseline, or did not meet imaging inclusion criteria. Controls were excluded if they had not yet been assessed or were missing substance use data at 4-year follow-up. In total, 3,878 participants met study criteria, of which 182 participants initiated alcohol. Structural covariance network properties were compared between the full sample and a 1:1 propensity-matched sample based on age, sex, race, ethnicity, religion, parental education, prenatal alcohol exposure, and baseline alcohol sipping.Main Outcomes and Measures:Structural covariance networks were estimated using regional cortical thickness and volume measurements. Measures of network segregation (modularity, clustering coefficient), integration (characteristic path length, global efficiency), and resilience (degree assortativity) were compared between groups. Early alcohol initiation was defined as consuming a full drink between baseline and 4-year follow-upResults:Alcohol initiators (n=182, median[IQR] age, 10.3[9.9–10.8]; 101 female[55.5%]) demonstrated lower network segregation (modularity: area-under-the-curve[AUC] difference[95%CI]=-0.017[-0.017,-0.007], p=0.030; clustering coefficient: AUC[95%CI]=-0.026[-0.027,-0.012], p=0.0495) and higher network integration (characteristic path length: AUC[95%CI]=-0.106[-0.099,-0.046], p=0.020; global efficiency: AUC[95%CI]=0.011[0.005,0.011], p=0.010), compared to non-initiators (n=3,696, median[IQR] age, 9.9[9.4–10.4]; 1750 female[47.4%]) when controlling for age, sex, and mean cortical thickness. Within the matched sample, only differences in network integration were preserved (characteristic path length: AUC[95%CI]=-0.044[-0.032,0.035], p=0.010; global efficiency: AUC[95%CI]=0.003[-0.003,0.003], p=0.040). There were no differences between full or matched samples when comparing cortical volume structural covariance networks.Conclusions and Relevance:Differences in cortical thickness structural covariance network properties at ages 9–10 predicted alcohol initiation before age 15. These findings suggest cortical thickness network topology may reflect a neuroanatomical risk marker for early alcohol initiation.

This study investigated brain structural covariance network (SCN) topological changes and alertness in temporal lobe epilepsy (TLE) with and without focal to bilateral tonic-clonic seizures (FBTCS). Seventy-eight subjects, including 32 TLE patients with FBTCS (TLE-FBTCS), 46 TLE patients without FBTCS (TLE-FS), and 42 healthy controls (HCs), underwent the Attention Network Test to assess alertness and volumetric MRI scans. SCNs were constructed and analyzed using graph theory. Results showed that TLE-FS patients had lower total cerebral volume than HCs, and the lowest volume was observed in the TLE-FBTCS group. Compared to HCs and TLE-FBTCS patients, TLE-FS patients exhibited increased small-worldness, normalized clustering coefficient, global efficiency, and modularity, but decreased normalized characteristic shortest path length and assortativity. Specific brain regions, such as the hippocampus, thalamus, and superior temporal sulcus, showed changes in nodal clustering coefficients and efficiency in TLE-FS patients. Further analysis revealed decreased intrinsic/phasic alertness in TLE-FBTCS patients. Correlation analysis indicated that SCN topological properties were associated with alertness in TLE-FS patients but not in TLE-FBTCS patients. These findings suggest that TLE-FS and TLE-FBTCS patients show different changes in SCN integration and segregation, with TLE-FS alertness linked to SCN topological properties, providing insights into TLE's neuropathological mechanisms.

Progressive gray matter alterations in the Meige's syndrome and across sub-types.

18. Disrupted Intra-Thalamic and Thalamo-Cortical Structural Covariance Networks in Posttraumatic Stress Disorder

Parkinson's disease (PD) typically presents with unilateral symptoms in early stages, starting on one side and progressing, with the onset side showing more severe motor symptoms even after bilateralization. This asymmetry may reflect complex interactions among multiple brain regions and their network connections. In this study, we aimed to use surface-based morphometry (SBM) and structural covariance networks (SCNs) to investigate the differences in brain structure and network characteristics between patients with left-onset PD (LPD) and right-onset PD (RPD). A total of 51 LPD and 49 RPD patients were recruited. Clinical assessments included the Unified Parkinson's Disease Rating Scale motor section, Hoehn and Yahr stage, Mini-Mental State Examination, Parkinson's Disease Questionnaire, and Beck Depression Inventory. All participants underwent 3 T structural MRI. FreeSurfer was used to perform vertex-wise comparisons of cortical surface area (CSA) and cortical thickness (CT), whereas the Brain Connectivity Toolbox was implemented to construct and analyze the structural covariance networks. In patients with LPD, we found reduced CSA in the right supramarginal gyrus (SMG), right precuneus (PCUN), left inferior parietal lobule (IPL), and left lingual gyrus (LING) compared to RPD, while no significant differences in CT were found between the two groups. The CSA of the right PCUN showed a significant positive correlation with MMSE score in LPD patients. In our SCNs analysis, LPD patients exhibited increased normalized characteristic path length and decreased small-world index in CSA-based networks, while in CT-based networks, they showed increased small-world index and global efficiency compared to RPD. No significant differences in nodal characteristics were observed in either CSA-based or CT-based networks between the two groups. In patients with LPD, reductions in CSA observed in the right PCUN, right SMG, left IPL, and left LING may be associated with cognitive impairments and hallucinations among non-motor symptoms of PD. Additionally, the SCNs of LPD and RPD patients show significant differences in global topology, but regional node characteristics do not reflect lateralization differences. These findings offer new insights into the mechanisms of symptom lateralization in PD from the perspective of brain regional structure and network topology.

Cerebral small vessel disease (CSVD) is a heterogeneous cerebrovascular syndrome with a variety of pathological mechanisms and clinical manifestations. A majority of research have shown that CSVD is associated with reduced expression of structural covariance networks (SCNs), but most of these SCN studies based on the group-level, which limits their ability to reflect individual variations in heterogeneous diseases. The purpose of this study is to analyze the structural covariance aberrations in patients with cerebral small vessels by utilizing individualized differential structural covariance network (IDSCN) analysis to explore the differences in SCNs and depressive states at the individual-level. A total of 22 CSVD patients with depression (CSVD+D) and 34 healthy controls (HCs) were included in this study. IDSCNs were constructed for each subject based on regional gray matter volumes derived from their T1-weighted MRI images. The unpaired-sample t-test was used to compare the IDSCNs between the two groups to obtain the differential structural covariance edge and its distribution. Finally, correlation analyses were performed between the differential edge, the total CSVD imaging burden and Hamilton Rating Scale for Depression (HAMD) score. (1) Compared with HCs, the CSVD+D patients exhibited heterogeneous distributions of differential structural covariance edge, with the differential edge located between the caudate nucleus and the cerebellum. (2) There was a significant positive correlation between the total CSVD imaging burden and HAMD scores in CSVD patients with depression (r = 0.692, p < 0.001). This study analyzed the IDSCNs between CSVD+D patients and HCs, which may indicate that the individual structural covariance aberrations between the caudate nucleus and cerebellum may contribute to depression in CSVD patients. Additionally, the higher total CSVD imaging burden of CSVD patients may indicate more severe depression. This finding suggests that early magnetic resonance imaging (MRI) assessment in CSVD patients may facilitate the early identification of depressive states and their severity in the near future.

The COVID-19 pandemic has raised significant concerns regarding its impact on the central nervous system, including the brain. While the effects on adult populations are well documented, less is known about its implications for pediatric populations. This study investigates alterations in cortical metrics and structural covariance networks (SCNs) based on the Local Gyrification Index (LGI) in children with mild COVID-19, alongside changes in non-invasive MRI proxies related to glymphatic function. We enrolled 19 children with COVID-19 and 22 age-comparable healthy controls. High-resolution T1-weighted and diffusion-weighted MRI images were acquired. Cortical metrics, including thickness, surface area, volume, and LGI, were compared using vertex-wise general linear models. SCNs were analyzed for differences in global and nodal metrics, and MRI proxies, including diffusion tensor imaging along the perivascular space and choroid plexus (CP) volume, were also assessed. Our results showed increased cortical area, volume, and LGI in the left superior parietal cortex, as well as increased cortical thickness in the left lateral occipital cortex among children with COVID-19. SCN analysis revealed altered network topology and larger CP volumes in the COVID group, suggesting virus-induced neuroinflammation. These findings provide evidence of potential brain alterations in children following mild COVID-19, emphasizing the need for further investigation into long-term neurodevelopmental outcomes.

Resolving heterogeneity of early-onset major depressive disorder through individual differential structural covariance network analysis.

The aim of this study was to investigate the relationship between obesity (OB) progression and brain structural changes. T1-weighted magnetic resonance images were acquired from 258 participants with overweight (OW) or OB and 74 participants with normal weight. Participants with OW or OB were divided into four groups according to BMI grades. Two-sample t tests compared disparities between the four subgroups and the participants with normal weight. We used causal structural covariance networks to examine the progressive impact of OB on brain structure. With increasing BMI values, reductions in gray matter volume originated in the left caudate nucleus, medial orbitofrontal cortex, and left insula and expanded to the right hippocampus and left lateral orbitofrontal cortex and then to the right parahippocampal gyrus, left precuneus, and left dorsolateral prefrontal cortex (p < 0.05, false discovery rate corrected). The left caudate nucleus and medial orbitofrontal cortex are the primary hubs of the directional network, exhibiting positive causality to the right hippocampus and left dorsolateral prefrontal cortex. Moreover, the right hippocampus is identified as an important transition hub. These findings suggest that changes in gray matter volume in individuals with OB may originate from reward/motivation processing regions, subsequently progressing to inhibitory control/learning memory regions, providing a new reference direction for clinical intervention and treatment of OB.

In this study, we aim to explore the changes in network graph theory indices of structural covariance networks (SCNs) in PHN patients with different disease durations. High-resolution T1 magnetic resonance images were collected from 109 subjects. We constructed SCNs based on cortical thickness data and analyzed the changes in global and regional network measures of PHN patients and herpes zoster (HZ) patients, and get hubs of each group. (1) PHN patients with a disease duration >6 months had reduced global efficiency (P=0.035) and increased characteristic shortest path length (P=0.028). (2) Nodal efficiency of the right pars opercularis was greater in both HZ and PHN patients with a disease duration of 1 to 3 months (P<0.001); in PHN patients with a disease duration > 6 months, the nodal degree of the left pars triangularis and nodal efficiency of the right middle temporal gyrus were greater (P<0.001). (3) The right supramarginal gyrus was the common hub of healthy controls (HCs) and HZ patients, the right pars opercularis was the common hub of HZ patients and PHN patients with a disease duration of 1 to 3 months, and the bilateral superior frontal gyrus was the common hub of HZ patients and PHN patients with a disease duration >6 months. There have changes in SCN indices in PHN patients with different disease durations. PHN patients with a disease duration >6 months had increased SCN integration and diminished information transfer capability between nodes, which complemented the topological properties of previous PHN networks. Eglobal and Lp can be considered as potential imaging markers for future clinical restaging of PHN.

Non-alcoholic fatty liver disease is associated with structural covariance network reconfiguration in cognitively unimpaired adults with type 2 diabetes.