Panic disorder (PD) is a psychiatric condition marked by recurring, unexpected panic attacks characterized by intense fear and rapid-onset physiological symptoms. Functional magnetic resonance imaging (fMRI) in individuals with PD has revealed abnormalities in several brain regions associated with fear processing, emotional regulation, and interoception. A systematic search was conducted in PubMed and Embase to review the fMRI changes associated with PD published until April 2024. Eighteen studies were included, primarily employing resting-state fMRI and examining functional connectivity. Consistent findings included amygdala and insula hyperactivity and reduced connectivity within the default mode network, particularly in the posterior cingulate cortex and precuneus. Altered connectivity patterns were also observed in the prefrontal cortex, anterior cingulate cortex, and hippocampus. Task-based fMRI studies revealed increased activity in fear-related regions that correlated with clinical measures of PD severity. This review consolidates the evidence of neural network disruptions in PD, emphasizing dysregulated fear processing, emotional regulation, and self-referential thinking. These findings highlight the potential of fMRI to elucidate the pathophysiology of PD and inform targeted interventions. However, the limited number of available studies, small sample sizes, and methodological heterogeneity, particularly among task-based fMRI studies, warrant cautious interpretation.
Background: Persistent midline brain cavities-cavum septi pellucidi (CSP), cavum vergae (CV), and cavum veli interpositi (CVI)-arise from incomplete embryonic obliteration and are proposed markers of disrupted perinatal neurodevelopment of limbic and commissural structures.This study investigated their prevalence, twodimensional morphometry, and clinical correlates in schizophrenia spectrum disorders. Methods: MRI scans from 252 patients and 260 matched healthy controls were analyzed. CSP was classified using three established systems; CVI was evaluated against an empirically derived height threshold. Multivariate logistic regression, adjusted for age, sex, MR field strength, and septum pellucidum length, assessed the independent association of anomalies with group membership. Results: Large CSP and large CVI were significantly more prevalent in patients. CSP transverse width was significantly greater in patients, remaining independently associated with group membership after adjustment for anteroposterior length and covariates. ROC analyses showed significant, modest discriminative performance. A significant sex interaction was observed, with large CSP and large CVI being associated with group membership in female participants. Conclusions: Midline anomalies exhibit size-dependent associations with schizophrenia spectrum disorders, with CSP transverse width and CVI height providing dimensional information beyond conventional length-based classification. These findings are consistent with the neurodevelopmental hypothesis of schizophrenia, while individual-level diagnostic utility remains limited.
Background Body dysmorphic disorder (BDD) is prevalent in cosmetic surgery populations, but its neurobiological mechanisms remain unclear. Objective To identify brain functional and structural alterations in BDD using multimodal MRI. Methods Twenty BDD patients and 20 healthy controls were recruited. After quality control, 16 per group were included in final analyses. ALFF, ReHo, functional connectivity (FC), VBM, and SBM analyses were performed, with correlations to clinical severity. Results BDD patients showed increased ALFF in right cerebellar lobule VIII and increased ReHo in left cerebellar lobule VIII. FC revealed enhanced mPFC-rectus gyrus connectivity and reduced PCC connectivity with left inferior parietal lobule and right midcingulate cortex. Exploratory structural analyses suggested increased gray matter in left middle occipital gyrus, decreased cerebellar/parietal volumes, altered white matter, cortical thickening in left pars triangularis, thinning in left inferior parietal lobule, and gyrification changes, but these did not survive multiple-comparison correction and require independent replication. Left inferior parietal cortical thickness positively correlated with BDD severity. Conclusions BDD involves abnormalities in default mode, cerebellar-cortical, and visual processing networks, providing preliminary neurobiological insights into BDD. These group-level findings are exploratory and do not yet support individual-level applications. Given the modest sample size, replication in larger cohorts is required.
Substance addiction is a major health problem that may cause structural and functional changes in the central nervous system. Since the retina is an extension of the brain, it may provide an indirect biological window for observing these changes. Optical Coherence Tomography (OCT) enables non-invasive, micron-level imaging of retinal layers. Although clinical studies have investigated retinal alterations related to substance addiction using OCT, artificial intelligence-based automatic detection studies remain limited. In this study, a hybrid deep learning and machine learning framework is proposed for detecting methamphetamine addiction from OCT images. A new deep feature extraction architecture, ResNet50-OCT, was designed. It was pre-trained on the Stable ImageNet-1K dataset, and multi-layer deep features were extracted from OCT images. These high-dimensional features were reduced using the Multi-Resolution Feature Selection strategy, which combines different feature ranking algorithms and selection ratios. The selected features were classified using Support Vector Machines with different kernels. High-performing model outputs were fused through Threshold Filtered Iterative Majority Voting. The framework was evaluated on 3,859 OCT images from 193 individuals using 10-fold cross-validation and LOSO validation. Accuracy values of 89.43% and 74.07% were obtained, respectively, showing the potential of OCT-based AI for methamphetamine addiction detection.
Schizophrenia (SCH) is a severe neuropsychiatric disorder characterized by abnormalities in perception, cognition, and behavioral functioning, making early and objective diagnosis a major challenge in clinical practice. Electroencephalography (EEG) has gained significant attention as a non-invasive and cost-effective modality for identifying neural biomarkers associated with SCH. In this study, a novel hybrid deep learning framework is proposed for automated SCH classification using raw EEG recordings. The proposed methodology employs a CBiL-Net framework that integrates one-dimensional Convolutional Neural Networks (1D-CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM), networks to simultaneously learn discriminative spatial features and complex temporal dependencies inherent in EEG signals. Initially, EEG data undergo preprocessing involving numerical standardization and elimination of invalid samples, followed by segmentation into fixed-length temporal sequences to preserve sequential neural dynamics. The stacked convolutional layers perform multi-level feature extraction, while bidirectional recurrent layers capture long-range contextual dependencies from both forward and backward temporal directions. Fully connected layers with dropout regularization are incorporated in the final classification stage. The proposed framework was evaluated on two benchmark EEG datasets for SCH, namely the IBIB PAN dataset and the Moscow dataset. Experimental results demonstrated strong generalization performance, achieving 89.15% classification accuracy, 83.78% precision, 89.86% F1-score, 96.88% sensitivity, and 81.54% specificity on the IBIB PAN dataset, while yielding significantly superior performance on the Moscow dataset with 99.60% accuracy, 97.66% precision, 98.81% F1-score, 100.00% sensitivity, and 98.92% specificity. Additionally, to assess robustness in a stricter evaluation setting, 5-fold stratified cross-validation was performed, during which the proposed model achieved an average accuracy of 61.80% and an AUC of 68.03%.
