Psychiatry Research: Neuroimaging最新文献

Acute alcohol use reduces brain glucose metabolism while increasing uptake of acetate, a byproduct of alcohol. This metabolic shift persists in individuals with alcohol use disorder (AUD) and may offer a treatment target. Recent studies show that ketone therapies can lessen alcohol withdrawal and cravings. In this study, we tested whether a single dose of a ketone ester (KE) supplement affects brain energy use and alcohol craving. Ten participants (five with AUD, five healthy controls) received two FDG-PET brain scans-one after taking 395 mg/kg KE and one at baseline-in a randomized order. Additionally, five AUD participants underwent magnetic resonance spectroscopy to measure cingulate β-hydroxybutyrate (BHB). KE lowered blood glucose and increased BHB in both groups. Brain scans revealed a 17% reduction in glucose metabolism, especially in the frontal, occipital, and cingulate cortices, as well as the hippocampus, amygdala, and insula. No major differences were observed between AUD and control groups. KE significantly reduced alcohol craving in AUD participants and tripled cingulate BHB levels. These findings suggest that a single KE dose can rapidly shift brain energy use from glucose to ketones, and may help reduce cravings in AUD, supporting its potential as a therapeutic approach.

Background: Major psychiatric disorders (MPDs), including schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and obsessive-compulsive disorder (OCD), have been classified as distinct diagnostic categories based on clinical symptoms. However, neuroimaging studies suggest that these MPDs share similar brain structure and functional characteristics. The present study aim to explore potential subtypes of MPDs based on gray matter volume (GMV) in a trans-diagnostic sample.

Methods: We involved 84 patients with MPDs (22 SZ, 22 BD, 18 MDD, 22 OCD) and 39 healthy controls (HCs). Voxel-based morphometry (VBM) and dynamic functional connectivity (dFC) analyses were used to investigate the GMV and the dynamic characteristics of the whole brain at rest. The heterogeneity through discriminant analysis (HYDRA) approach was applied to identify the subtypes of MPDs.

Results: Two distinct subtypes were identified, subtype-I (S-I) showed increased GMVs in subcortical brain regions, while subtype-II (S-II) displayed decreased GMVs in cerebral cortex regions. Both S-I and S-II patients showed reduced dFC values within the connected edges at rest. The negative correlation between total GMVs and disease duration were observed in S-II patients.

Conclusions: The present results may contribute to understand the pathogenesis and biological classification of MPDs.

Deficits in visual working memory (vWM) are a fundamental cognitive characteristic of schizophrenia; however, the dynamic spatiotemporal characterization of their neural mechanisms remains unclear. The present study employs multivariate pattern classification (MVPC) and searchlight analysis to investigate neural signaling differences between patients with schizophrenia (PSZ) and healthy controls (HCS) during a vWM task. A total of 46 participants (22 PSZ, 24 HCS) completed the change detection task (1T/2T/4T). Contralateral delay activity (CDA) was extracted through ERP analysis. MVPC was employed in the temporal dimension, while a searchlight approach was employed in the spatial dimension to decode memory load (1T/2T/4T) and memory side (left/right) information. CDA amplitude was significantly lower in the PSZ group (p = .04). MVPC analysis indicated that decoding accuracy in the PSZ group was significantly lower than that in the HCS group during the 176-656 ms window (pcorrected < 0.05), suggesting reduced discriminability of multivariate ERP patterns during the delay period. Searchlight analysis revealed broadly reduced decoding across the scalp in PSZ, with the strongest group differences over posterior parieto-occipital scalp electrodes (peaking around PO3/PO4), which is consistent with prior evidence implicating posterior parietal systems in vWM maintenance. This study reveals the spatiotemporal dynamics of vWM deficits in schizophrenia using ERP decoding approaches and may inform future development of neuromarker-guided cognitive interventions.

Early and accurate diagnosis of Alzheimer's Disease (AD) is critical for effective disease management and progression delay. Researches have been done in past towards better study of Alzheimer's, but advancements in feature engineering-cum-learning methodologies have still created scope to overcome the limits of previous methods and achieve more accurate modelling and classification. Here, we propose a novel model, LEFF-ViT (Locally Enhanced Feedforward Vision Transformer), for AD classification along with a framework culminating an idea of using separate segmented brain subregions as a marked feature engineering element. For this Segmentation of MRI images are done to extract White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) regions using a Deep Residual Squeeze-Inception U-Net (De-RIS U-Net). Subsequently, a novel DWFE-Net is employed to extract discriminative spatial features. Finally, LEFF-ViT integrates a Vision Transformer with Multi-Head Self-Attention and a Locally Enhanced Feedforward Network (LFFN) to effectively capture both local and global contextual information for accurate classification. The experimental results demonstrate that the proposed model achieves an accuracy of 98.68 %, a sensitivity of 96.45 %, a specificity of 98.17 %, a Dice score of 96.36 %, and a Jaccard index of 92.31 %, which nearly outperforms the existing state-of-the-art methods across multiple evaluation metrics.

Increasing evidence explores the potential of combining psychotherapy with transcranial direct current stimulation (tDCS) to facilitate meaningful clinical changes. A recent study outlines a protocol for a randomized controlled trial of tDCS-enhanced exposure and response prevention (ERP) for obsessive-compulsive disorder. In light of the variable efficacy reported for tDCS monotherapy, we discuss the theoretical support for this combined approach, emphasizing the activity-selectivity hypothesis and state-dependent fear extinction. Furthermore, we suggest that therapeutic benefits might emerge from broader network synergy and connectivity reconfiguration rather than local excitability changes alone, indicating the potential value of multimodal neuroimaging in elucidating these dynamic mechanisms.

Mind wandering (MW) refers to a shift from task-related, stimulus-driven thoughts to internally generated thoughts. While commonly experienced, particularly among individuals with attention deficit/hyperactivity disorder (ADHD), it remains understudied in children, especially through objective measures such as neural markers. This study aimed, first, to examine differences in neural activation and functional connectivity related to MW between typically developing children and those diagnosed with ADHD. Second, it explored how these neural markers relate to sustained attention in children with ADHD, with the goal of identifying objective indicators for diagnosis. Electroencephalography (EEG) was used to assess current source density and lagged functional connectivity across 13 brain regions associated with MW. Participants completed the Integrated Visual and Auditory Continuous Performance Test (IVA), which measures auditory and visual sustained attention. Children with ADHD showed distinct patterns of neural activation and connectivity, including increased delta and decreased beta connectivity. These changes were accompanied by increased activity in the default mode network (DMN) and impaired regulation by the executive control network (ECN). In addition to the DMN (commonly linked to MW), several non-DMN regions and their connectivity were also associated with various aspects of sustained attention, including focus, vigilance, comprehension, and persistence. These findings highlight the contribution of MW to attentional deficits in ADHD and underscore its potential as a measurable and clinically meaningful feature of ADHD psychopathology, with important implications for both diagnosis and intervention.