Gray Matter

BackgroundSuicidal ideation (SI) is very common in patients with major depressive disorder (MDD). However, its neural mechanisms remain unclear. The anterior cingulate cortex (ACC) region may be associated with SI in MDD patients. This study aimed to elucidate the neural mechanisms of SI in MDD patients by analyzing changes in gray matter volume (GMV) in brain structures in the ACC region, which has not been adequately studied to date.MethodsAccording to the REST-meta-MDD project, this study subjects consisted of 235 healthy controls and 246 MDD patients, including 123 MDD patients with and 123 without SI, and their structural magnetic resonance imaging data were analyzed. The 17-item Hamilton Depression Rating Scale (HAMD) was used to assess depressive symptoms. Correlation analysis and logistic regression analysis were used to determine whether there was a correlation between GMV of ACC and SI in MDD patients.ResultsMDD patients with SI had higher HAMD scores and greater GMV in bilateral ACC compared to MDD patients without SI (all p ConclusionsOur findings suggest that GMV of ACC may be associated with SI in patients with MDD and is a sensitive biomarker of SI.

Periventricular heterotopia (PH), a common form of gray matter heterotopia associated with developmental delay and drug-resistant seizures, poses a challenge in understanding its neurophysiological basis. Human cerebral organoids (hCOs) derived from patients with causative mutations in FAT4 or DCHS1 mimic PH features. However, neuronal activity in these 3D models has not yet been investigated. Here we show that silicon probe recordings reveal exaggerated spontaneous spike activity in FAT4 and DCHS1 hCOs, suggesting functional changes in neuronal networks. Transcriptome and proteome analyses identify changes in neuronal morphology and synaptic function. Furthermore, patch-clamp recordings reveal a decreased spike threshold specifically in DCHS1 neurons, likely due to increased somatic voltage-gated sodium channels. Additional analyses reveal increased morphological complexity of PH neurons and synaptic alterations contributing to hyperactivity, with rescue observed in DCHS1 neurons by wild-type DCHS1 expression. Overall, we provide new comprehensive insights into the cellular changes underlying symptoms of gray matter heterotopia.

IntroductionPremature ovarian insufficiency (POI), marked by ovarian function loss before age 40, is linked to a higher risk of dementia, including Alzheimer's disease (AD). However, the associated brain structural changes remain poorly understood.MethodsWe analyzed T1-weighted and diffusion tensor imaging in 33 idiopathic POI women and 51 healthy controls, using voxel-based, surface-based morphometry, and network analyses to assess gray matter volume (GMV), cortical thickness, and brain connectivity.ResultsWomen with POI showed significant GMV and cortical thickness reductions in the frontal, parietal, and temporal regions (p DiscussionThis preliminary study suggests early neurodegenerative patterns in POI, potentially contributing to dementia risk. Further research is needed to explore the underlying mechanisms and potential interventions.HighlightsWe evaluated brain structural changes in participants with idiopathic premature ovarian insufficiency (POI). The observed brain alterations in POI participants closely resemble those seen in early dementia, including regions specifically associated with Alzheimer's disease (AD). These findings highlight the critical need for early interventions to reduce the long-term risks of cognitive impairment and dementia in women with POI.

ImportanceInflammation is increasingly implicated in the pathophysiology of mood and psychotic disorders. Integrating blood biomarkers and brain imaging may help uncover mechanistic pathways and guide targeted interventions.ObjectiveTo identify shared and distinct multivariate patterns of peripheral inflammation and gray matter volume (GMV) in early-stage depressive and psychotic disorders using a transdiagnostic machine learning approach.Design, setting, and participantsThe naturalistic multicenter PRONIA study was conducted between February 2014 and May 2019 with a follow-up period of up to 36 months; baseline data were analyzed between August 2021 and April 2024. Eight sites, including inpatient and outpatient facilities, in 5 European countries (Germany, Italy, Switzerland, Finland, and the United Kingdom) were included. The study included individuals with recent-onset depression (ROD, n = 163) or psychosis (ROP, n = 177) or clinical high-risk states for psychosis (CHR-P, n = 172), all with minimal medication exposure, and healthy control (HC) individuals (n = 166).ExposuresStructural magnetic resonance imaging (MRI), peripheral assays of cytokines (eg, interleukin [IL] 6, IL-1β, tumor necrosis factor [TNF] α, C-reactive protein [CRP], brain-derived neurotrophic factor [BDNF], S100 calcium-binding protein B [S100B]); clinical assessments; neurocognitive testing.Main outcomes and measuresAfter data collection, sparse partial least squares was used to identify latent brain-blood signatures. Support vector machine classification evaluated psychosocial and neurocognitive predictors of signature expression using repeated nested cross-validation.ResultsA total of 678 participants (346 [51.0%] female; median [IQR] age, 24.0 [20.9-28.9] years) were included. Four signatures were identified. A psychosis signature (ρ = 0.27; P = .002) differentiated ROP from CHR-P with elevated IL-6, TNF-α, and reduced CRP, alongside GMV shifts in corticothalamic circuits. A depression signature (ρ = 0.19; P = .02) differentiated ROD from HC individuals with elevated IL-1β, IL-2, IL-4, S100B, and BDNF and GMV reductions in limbic regions. Additional signatures reflected age (ρ = 0.67) and sex or MRI quality (ρ = 0.53). Psychosocial features, including a differential childhood trauma pattern, predicted both the psychosis (balanced accuracy [BAC] = 67.2%) and depression (BAC = 78.0%) signatures. Cognitive performance predicted only the psychosis signature (BAC = 65.1%).Conclusions and relevanceIn this study, early-stage depression and psychosis exhibited distinct neurobiological signatures involving immune and neuroanatomical markers, challenging fully dimensional disease models. These signatures are shaped by childhood trauma and cognition and may support biologically informed early interventions.