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Herpes zoster (HZ) and postherpetic neuralgia (PHN), which are common chronic pain disorders, can cause long-term pain and negative emotions accompanied by structural brain changes; however, their temporal dynamics and causal relationships remain unknown. This study employed causal structural covariance network (CaSCN) analysis was used to explore gray matter volume (GMV) alterations across disease stages and their causal relationships. CaSCN refers to directed causal influences between brain regions based on structural covariance. This study employed a cross-sectional observational design. In this study, 157 treatment-naïve first-episode HZ and PHN patients (53 acute HZ, 53 subacute HZ and 52 chronic PHN) were enrolled, along with 85 sex- and age-matched healthy controls (HCs). Voxel-based morphometry (VBM) was applied to analyze high-resolution T1-weighted magnetic resonance images and measure GMV in each participant. On the basis of the results of the intergroup comparisons, the left pericalcarine cortex and bilateral thalamus (voxel-level p < 0.001, cluster-level p < 0.05) presented significant differences and were selected as seed regions for subsequent CaSCN analysis. Compared with healthy controls, patients with HZ and PHN presented stage-specific GMV changes, and areas such as the left pericalcarine cortex and bilateral thalamus presented GMV changes at the time of onset; causal structure analysis revealed that the left pericalcarine cortex and bilateral thalamus presented significant positive causal effects on the left middle occipital gyrus, left middle temporal gyrus, left angular gyrus, left cerebellum, right inferior temporal gyrus, left medial superior frontal gyrus, left cusai lobe and other brain regions. This study revealed dynamic patterns of GMV changes over time in HZ and PHN patients by CaSCN analysis, providing new perspectives for understanding the neuroimaging mechanisms of HZ and PHN and clarifying the causal relationships of brain structural alterations during disease progression.

BackgroundThe aberrance of gray matter morphology in migraineurs has been widely investigated. However, it remains largely unknown whether there are illness duration-related hierarchical changes in the gray matter structure.MethodsA total of 86 migraine without aura (MwoA) patients and 73 healthy controls were included. The Voxel-Based Morphometry approach was utilized to compare the gray matter volume (GMV) differences between MwoA patients and healthy controls. The Structural Covariance Network analysis was conducted to quantify the cross-regional synchronous alterations of gray matter structure in MwoA patients. The Causal Structural Covariance Network analysis was performed to describe the progressive and hierarchical changes in the gray matter network of patients in the pathological progression of migraine.ResultsMwoA patients had duration-stage related GMV hypertrophy in the left parahippocampus, as well as synergistic GMV aberrance in the parahippocampus and the medial inferior temporal gyrus and cerebellum. Moreover, the GMV alteration of the parahippocampus, and the surrounding hippocampus, amygdala, and bilateral anterior cerebellum, preceded and causally influenced the morphological changes of lateral parietal-temporal-occipital gyrus, as well as the motor cortex and prefrontal gyrus with the increasing illness duration in MwoA patients.ConclusionThe current study indicated that gray matter structural alterations in the medial inferior temporal gyrus, especially the parahippocampus, is a critical pathological characteristic in MwoA patients, which drives the gray matter structure alteration of other regions. These findings provide further evidence for understanding the progressive gray matter morphological changes in migraine and may facilitate the development of neuromodulation therapies targeting this procession.

Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices. Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations. VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network. Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.

Abstract Background and Hypothesis Schizophrenia is associated with widespread cortical thinning and abnormality in the structural covariance network, which may reflect connectome alterations due to treatment effect or disease progression. Notably, patients with treatment-resistant schizophrenia (TRS) have stronger and more widespread cortical thinning, but it remains unclear whether structural covariance is associated with treatment response in schizophrenia. Study Design We organized a multicenter magnetic resonance imaging study to assess structural covariance in a large population of TRS and non-TRS, who had been resistant and responsive to non-clozapine antipsychotics, respectively. Whole-brain structural covariance for cortical thickness was assessed in 102 patients with TRS, 77 patients with non-TRS, and 79 healthy controls (HC). Network-based statistics were used to examine the difference in structural covariance networks among the 3 groups. Moreover, the relationship between altered individual differentiated structural covariance and clinico-demographics was also explored. Study Results Patients with non-TRS exhibited greater structural covariance compared with HC, mainly in the fronto-temporal and fronto-occipital regions, while there were no significant differences in structural covariance between TRS and non-TRS or HC. Higher individual differentiated structural covariance was associated with lower general scores of the Positive and Negative Syndrome Scale in the non-TRS group, but not in the TRS group. Conclusions These findings suggest that reconfiguration of brain networks via coordinated cortical thinning is related to treatment response in schizophrenia. Further longitudinal studies are warranted to confirm if greater structural covariance could serve as a marker for treatment response in this disease.

•Optimism is associated with a pattern of gyrification-based structural covariance network (SCN).•Gyrification-based SCN of optimistic individuals exhibited higher global efficiency and local efficiency.•Gyrification-based SCN of optimistic individuals were characterized by a pronounced betweenness centrality pattern. Dispositional optimism (hereinafter, optimism), as a vital character strength, reflects the tendency to hold generalized positive expectancies for future outcomes. A great number of studies have consistently shown the importance of optimism to a spectrum of physical and mental health outcomes. However, less attention has been given to the intrinsic neurodevelopmental patterns associated with interindividual differences in optimism. Here, we investigated this important question in a large sample comprising 231 healthy adolescents (16–20 years old) via structural magnetic resonance imaging and behavioral tests. We constructed individual structural covariance networks based on cortical gyrification using a recent novel approach combining probability density estimation and Kullback-Leibler divergence and estimated global (global efficiency, local efficiency and small-worldness) and regional (betweenness centrality) properties of these constructed networks using graph theoretical analysis. Partial correlations adjusted for age, sex and estimated total intracranial volume showed that optimism was positively related to global and local efficiency but not small-worldness. Partial least squares correlations indicated that optimism was positively linked to a pronounced betweenness centrality pattern, in which twelve cognition-, emotion-, and motivation-related regions made robust and reliable contributions. These findings remained basically consistent after additionally controlling for family socioeconomic status and showed significant correlations with optimism scores from 2.5 years before, which replicated the main findings. The current work, for the first time, delineated characteristics of the cortical gyrification covariance network associated with optimism, extending previous neurobiological understandings of optimism, which may navigate the development of interventions on a neural network level aimed at raising optimism.

Insomnia disorder (ID) is neurobiologically heterogeneous and often eludes characterization by traditional group-level neuroimaging. Subtyping based on neuroimaging and clinical data offers a promising strategy for identifying biologically and clinically meaningful ID subgroups. To address this need, we developed a Gray Matter Population Graph Attention Autoencoder (GM-PGAAE) to subtype insomnia disorder in a cohort comprising 140 patients diagnosed with ID and 57 matched healthy controls. Each subject was represented as a node defined by atlas-based gray matter (GM) volumes. Population edges combined imaging-derived intersubject correlations with clinical similarity via a Hadamard product, generating an adjacency matrix that jointly encodes structural and phenotypic relationships. A Graph Attention Autoencoder learned low-dimensional embeddings that adaptively weighted informative intersubject connections, and clustering these embeddings identified distinct subtypes. Regional and network-level differences were further assessed using Voxel-Based Morphometry (VBM) and individualized differential structural covariance networks (IDSCNs). Through this framework, two ID subtypes were identified. Compared with Subtype 2, Subtype 1 showed higher symptom severity and greater GM reductions–particularly in the cerebellar vermis, thalamus, middle occipital cortex, fusiform gyrus, and paracentral lobule–alongside negative associations between GM volume and clinical scores. IDSCNs further revealed reduced thalamocortical and subcortical Z-scores in Subtype 1, indicating subtype-specific network alterations. Overall, GM-PGAAE integrates structural MRI and clinical measures to derive individualized embeddings and delineate biologically distinct ID subtypes.

The structural covariance network (SCN) has provided a perspective on the large‐scale brain organization impairment in the Alzheimer's Disease (AD) continuum. However, the successive structural impairment across brain regions, which may underlie the disrupted SCN in the AD continuum, is not well understood. In the current study, we enrolled 446 subjects with AD, mild cognitive impairment (MCI) or normal aging (NA) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The SCN as well as a casual SCN (CaSCN) based on Granger causality analysis were applied to the T1‐weighted structural magnetic resonance images of the subjects. Compared with that of the NAs, the SCN was disrupted in the MCI and AD subjects, with the hippocampus and left middle temporal lobe being the most impaired nodes, which is in line with previous studies. In contrast, according to the 194 subjects with records on CSF amyloid and Tau, the CaSCN revealed that during AD progression, the CaSCN was enhanced. Specifically, the hippocampus, thalamus, and precuneus/posterior cingulate cortex (PCC) were identified as the core regions in which atrophy originated and could predict atrophy in other brain regions. Taken together, these findings provide a comprehensive view of brain atrophy in the AD continuum and the relationships among the brain atrophy in different regions, which may provide novel insight into the progression of AD. A casual model combined with structural covariance network (SCN) was provided and applied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We found the asynchronism neurodegeneration progress may underlying the disrupted SCN previously reported, and hippocampus, thalamus, and precuneus/posterior cingulate cortex (PCC) were identified as the core regions in which atrophy originated and could predict atrophy in other brain regions.

The 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. We 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  < 0.001, cluster-wise FWE corrected  < 0.05). Our findings revealed that all 25 ipsilesional thalamic subfields in CS were atrophied (  < 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. The 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.

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 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.