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Background: The high comorbidity and symptom overlap of generalized anxiety disorder (GAD), posttraumatic stress disorder (PTSD), and social anxiety disorder (SAD), has led to the study of their shared and disorder-specific neural substrates. However, the morphometric similarity network (MSN) differences among these disorders remain unknown., Methods: MSN derived from T1-weighted images in patients of GAD, PTSD, and SAD, and health controls (HC) using a Siemens 3T magnetic resonance imaging system. Covariance analysis and post hoc tests were used to investigate group differences. In addition, the relationship between MSN and clinical characteristics was analyzed., Results: Increased morphometric similarity (MS) between left bankssts (BA22, superior temporal cortex, STC) and right precentral gyrus, and decreased MS between left precentral gyrus and right cuneus_part1/part2, and between right rostral middle frontal cortex (rMFC) and right STC were common in GAD and PTSD relative to HC and SAD. Compared to the other three groups, SAD exhibited disorder-specific alterations of increased MS between right rMFC and right STC, and between left cuneus and right inferior parietal cortex. Additionally, increased regional MSN in left precentral gyrus was found in PTSD compared to HC and SAD. A mild positive correlation of the MS value between left bankssts and right precentral gyrus and the Hamilton Anxiety Rating Scale scores (uncorrected p = 0.041) was found in PTSD., Conclusions: Our study provides the first evidence for common and distinct brain MSN abnormalities underlying the pathophysiology of GAD, PTSD, and SAD, which may aid in differential diagnosis and determining potential disorder-specific intervention targets., Competing Interests: Declarations. Ethics approval and consent to participate: This study was performed in line with the principles of the Declaration of Helsinki. Therefore, the Medical Ethics Council of West China Hospital, Sichuan University, approved the current study which was registered on the Clinical Trials website (ChiCTR2100041598), and each participant signed a written informed consent form. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: Differentiating between patients with bipolar disorder (BD) and major depressive disorder (MDD) is clinically challenging. This study aimed to explore the potential of radiomic textural features for discriminating BD and MDD., Methods: A total 253 subjects (114 patients with BD, 139 patients with MDD) with T1-weighted MRI data were recruited. Radiomics features and gray matter volume (GMV) features were extracted from each brain region. A novel high-level MSN_II feature method based on radiomic features was proposed. And a total of 21 MSN features (5 MSN_I and 16 MSN_II) based on different combinations of the 5 types of radiomic textural feature were calculated. Classification models were constructed using various combinations of MSNs or GMV, and their performance and stability was evaluated through 2000 repeated experiments., Results: The model built with combined features (GMV and GMV + MSN_II_GLCM_GLSZM_NGTDM) showed the best classification performance (AUC = 0.896±0.058, ACC = 0.831±0.064) in the validation cohort. After MANOVA analysis and FDR correlation, the MSN_II_GLCM_GLSZM_NGTDM values in 4 regions (right rectus gyrus, right temporal pole: middle temporal gyrus, Vermis3 and Vermis10) showed significant difference between BD and MDD., Limitation: The main limitation of this study is that the data is derived from a single center without an external independent test set., Conclusions: Incorporating the high-level MSN_II based on radiomics features can improve the classification performance compared to models solely relying on GMV features alone. This result implied the potential application of the proposed high level MSN method and radiomics textural features on the MDD and BD clinical studies., Competing Interests: Declaration of competing interest The author(s) indicated no potential conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: The promoter variant rs17111237 in the CEP128 closely relates to radiotherapy (RT)‐related brain necrosis in nasopharyngeal carcinoma (NPC) patients. Purpose: To explore RT‐related dynamic alterations in brain morphology and their potential genetic mechanism, and to explore the modulatory effects of CEP128 genetic variants on RT‐related brain morphological alterations in NPC patients. Study Type: Prospective, longitudinal. Population: One hundred one patients with histopathologic ally‐proven NPC (age 41.64 ± 9.63, 46 male), analyzed at baseline (pre‐RT), 3‐months post‐RT and 6 months post‐RT, and 19 sex‐, age‐ and education‐matched healthy controls. Field Strength/Sequence: 3D gradient echo brain volume (3D‐BRAVO) and diffusion‐weighted single‐shot spin‐echo echo‐planar sequences at 3.0 T. Assessment: rs17111237 in CEP128 was detected by Sanger sequencing. Structural and diffusion images were processed with FreeSurfer and FSL. Morphometric similarity network (MSN) was constructed with nine cortical indices derived from structural and diffusion images. Statistical Tests: One‐way ANOVA, chi‐square test. Pearson's correlation analysis was conducted to measure the relationship between CEP128 gene‐expression level in human brain and MSN alterations. Repeated analysis of variance performed to assess group differences in MSN and the modulatory effects of the CEP128 gene within patients. Significance level: P < 0.05, false‐discovery rate correction. Results: RT‐related significant widespread MSN alterations were observed in the cortices of NPC patients. Notably, regional MSN alterations had a weak but significant negative correlation with the cortical pattern of CEP128 gene expression (r = −0.152). Furthermore, rs17111237 in the CEP128 had significant modulatory effects on the observed MSN alterations in NPC patients, with the modulatory effects being most obvious at 3 months post‐RT. Conclusions: MSN has potential to serve as a sensitive biomarker to detect RT‐related brain injury. Inter‐brain regional and inter‐patient variability of RT‐related brain injuries may be attributed to the cortical expression of the CEP128 gene and the modulatory effects of the promoter variant rs17111237 in CEP128. Evidence Level: 2 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Aims: Generalised epilepsy is thought to involve distributed brain networks. However, the molecular and cellular factors that render different brain regions more vulnerable to epileptogenesis remain largely unknown. We aimed to investigate epilepsy-related morphometric similarity network (MSN) abnormalities at the macroscale level and their relationships with microscale gene expressions at the microscale level. Methods: We compared the MSN of genetic generalised epilepsy with generalised tonic-clonic seizure patients (GGE-GTCS, n = 101) to demographically matched healthy controls (HC, n = 150). Cortical MSNs were estimated by combining seven morphometric features derived from structural magnetic resonance imaging for each individual. Regional gene expression profiles were derived from brain-wide microarray measurements provided by the Allen Human Brain Atlas. Results: GGE-GTCS patients exhibited decreased regional MSNs in primary motor, prefrontal and temporal regions and increases in occipital, insular and posterior cingulate cortices, when compared with the HC. These case-control neuroimaging differences were validated using split-half analyses and were not affected by medication or drug response effects. When assessing associations with gene expression, genes associated with GGE-GTCS-related MSN differences were enriched in several biological processes, including 'synapse organisation', 'neurotransmitter transport' pathways and excitatory/inhibitory neuronal cell types. Collectively, the GGE-GTCS-related cortical vulnerabilities were associated with chromosomes 4, 5, 11 and 16 and were dispersed bottom-up at the cellular, pathway and disease levels, which contributed to epileptogenesis, suggesting diverse neurobiologically relevant enrichments in GGE-GTCS. Conclusions: By bridging the gaps between transcriptional signatures and in vivo neuroimaging, we highlighted the importance of using MSN abnormalities of the human brain in GGE-GTCS patients to investigate disease-relevant genes and biological processes. [ABSTRACT FROM AUTHOR]

Antipsychotic treatment has been conceived to alter brain connectivity, but it is unclear how the changes of network phenotypes relate to the underlying transcriptomics. Given DNA methylation (DNAm) may alter transcriptional levels, we further integrated an imaging-transcriptomic-epigenetic analysis to explore multi-omics treatment response biomarkers. Forty-two treatment-naive first-episode schizophrenia patients were scanned by TI weighted (T1W) imaging and DTI before and after 8-week risperidone monotherapy, and their peripheral blood genomic DNAm values were examined in parallel with MRI scanning. Morphometric similarity network (MSN) quantified with DTI and T1W data were used as a marker of treatment-related alterations in interareal cortical connectivity. We utilized partial least squares (PLS) to examine spatial associations between treatment-related MSN variations and cortical transcriptomic data obtained from the Allen Human Brain Atlas. Longitudinal MSN alterations were related to treatment response on cognitive function and general psychopathology symptoms, while DNAm values of 59 PLS1 genes were on negative and positive symptoms. Virtual-histology transcriptomic analysis linked the MSN alterations with the neurobiological, cellular and metabolic pathways or processes, and assigned MSN-related genes to multiple cell types, specifying neurons and glial cells as contributing most to the transcriptomic associations of longitudinal changes in MSN. We firstly reveal how brain-wide transcriptional levels and cell classes capture molecularly validated cortical connectivity alterations after antipsychotic treatment. Our findings represent a vital step towards the exploration of treatment response biomarkers on the basis of multiple omics rather than a single omics type as a strategy for advancing precise care. • The neural network MSN and DNAm modalities may be complementary in predicting treatment response. • Virtual histology study identified specific cell classes enriched for the treatment-related MSN variations. • This study represents a vital step to advance precise and personalized care. [ABSTRACT FROM AUTHOR]