Background: Current evaluations of immersive virtual reality (VR) interventions for attention-deficit/hyperactivity disorder (ADHD) primarily rely on subjective clinical scales. This pilot study investigated the effects of an immersive VR intervention on brain functional networks in children with ADHD using resting-state functional magnetic resonance imaging (rs-fMRI) degree centrality (DC) and how they correlate with clinical symptoms. Methods: Fourteen children with attention-deficit/hyperactivity disorder (ADHD group) and 13 age- and sex-matched healthy controls (HC group) were recruited. All participants underwent rs-fMRI examinations within 2 days of baseline clinical assessment. DC values were calculated to evaluate spontaneous neural activity. Children in the ADHD group received a 16-week immersive VR intervention, administered once weekly for 30 minutes per session. The intervention comprised firefly cave navigation tasks and daily scenario-based social skills training. The study analyzed baseline differences in DC values between the two groups, as well as changes in clinical scores and DC values in the ADHD group before and after the intervention. Additionally, correlation analysis was performed to explore the association between changes in DC values and behavioral indicators. Results: At baseline, the ADHD group had significantly higher CPT and SNAP-IV inattention/hyperactivity scores (P<0.05), with abnormal degree centrality (DC) in cortico-striatal, cerebellar and prefrontal regions. After VR intervention, the ADHD group showed significant reductions in all clinical scores (P<0.05), accompanied by decreased DC in the right cuneus and left cerebellar Crus II area; only SNAP-IV inattention scores remained higher than HC group (P= 0.009). Changes in right cuneus DC were positively correlated with SNAP-IV inattention changes (r=0.64, P=0.013), and right middle orbitofrontal gyrus DC was positively correlated with CPT scores (r=0.57, P=0.033). Conclusion: In this pilot study, immersive VR intervention alleviated core ADHD symptoms. In addition, dynamic changes in degree centrality within specific brain regions may serve as potential neuroimaging biomarkers for monitoring therapeutic responses to VR intervention in children with ADHD.
The dimensional approach to autism spectrum disorder (ASD) considers ASD as the extreme of a dimension traversing through the general population, with autistic traits continuously distributed across the general population. Yet, their neurophysiological correlates in typically developing (TD) individuals remain underexplored. This study examines the associations between autistic traits, as measured by the autism spectrum quotient (AQ), and electroencephalogram (EEG) functional connectivity (FC) abnormalities in nonclinical populations. Resting-state EEG data were collected from 88 TD adults (43 males, 45 females; mean age 24.43 ± 5.61). To evaluate large-scale brain dynamics, 88 neurotypical subjects were measured across five frequency bands for FC metrics, including phase locking value (PLV), weighted phase lag index (WPLI), and phase lag index (PLI). We conducted multiple regression analyses between the AQ and FC across all EEG frequency bands, followed by Pearson correlations to examine the relationships between specific FC features and individual AQ subscales. Regression analyses revealed that the Delta/Theta band, particularly in PLV connectivity, significantly predicted autistic traits (FDR corrected p = 0.010 for both), with lower connectivity associated with greater autistic trait expression. Additionally, EEG Theta/Delta band PLV connectivity metrics revealed significant correlations with social skills and communication AQ subscales, core domains affected in ASD. Our findings demonstrate that EEG FC in low-frequency bands (Delta/Theta), specifically PLV connectivity, is associated with autistic traits in the general population, with correlation analyses revealing specific links to the social skills and communication AQ subscales. These results highlight Delta/Theta PLV connectivity as a sensitive physiological correlate of autistic trait expression in TD children.
Autism is a complex neurological condition characterized by repetitive behaviours, varied speech and difficulties in social interaction. Identifying autism spectrum using traditional methods alone is challenging due to its complexity and variability. In order to facilitate earlier and more accurate detection, Artificial intelligence (AI) methods have been developed to improve traditional diagnostic techniques. Standard diagnostic methods often struggle with reliability due to the variability and complexity of autistic individuals. To overcome these challenges, this study proposes a novel Deep Learning (DL) based framework for efficient and accurate autism spectrum detection. The input data are first pre-processed using an Adaptive Median Gaussian Filter (AMGF) to remove noise while preserving essential image features. Then, Generative Adversarial Networks (GANs) are employed for data augmentation, effectively overcoming dataset imbalance and security. Feature extraction is carried out using the Deep Multi-scale and multi-level enclosed attentional DarkNet (DMM-AND) model, which captures both fine-grained and high-level patterns. Finally, autism spectrum detection is performed using the Optimized Quadrangle Attention consolidated Convolutional Multi-scale Vision Transformer (OQA-CMVT) model. Here, the Random Spiral exploit chimp optimization algorithm (RS-COA) is used for the hyperparameter tuning process. The proposed technique achieves 97.96% accuracy for the autistic children's facial dataset and 98.04% for the Autism_Image_Data dataset.
Opioid use disorder (OUD) has been linked to alterations in brain white matter microstructure, but evidence comparing pre-treatment and six-month buprenorphine-naloxone (BNX) treatment remains limited. This study examined changes in brain diffusion tensor imaging (DTI) metrics before and after six months of BNX treatment in individuals with OUD and assessed the influence of concurrent cannabis and tobacco use. This pre-post study included 25 individuals with OUD initiating BNX treatment and 25 healthy controls. All participants underwent 3-Tesla brain DTI scans at baseline and at six-month follow-up. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were quantified across 48 regions of interests defined by JHU White Matter Atlas. Linear mixed-effects models were applied to examine group and time effects. At follow-up, the OUD group demonstrated widespread increases in MD, AD, RD compared with baseline and healthy controls, involving commissural, projection, and association tracts. Compared with healthy controls, the OUD group at baseline showed lower FA in key commissural and projection pathways. White matter changes after 6-month BNX treatment are modest overall but might be influenced by continued cannabis and tobacco use. Addressing concurrent substance use may be important for optimizing neurobiological recovery during buprenorphine treatment.
INTRODUCTION:Lithium remains a cornerstone treatment for bipolar disorder, yet response rates are variable and predicting outcomes remains challenging. This study examined whether cortical thickness changes during lithium treatment serve and their association with clinical outcomes in bipolar depression, aiming to characterize the neurobiological correlates of treatment response. METHODS:Twenty-three adult outpatients (17 female, 6 male; 74% bipolar II; mean age 28.5 years), medication-free and treatment-naïve, received six-week low-therapeutic lithium monotherapy (mean level 0.47±0.19 mmol/L). Cortical thickness was assessed via 3T MRI across 68 regions using multivariate Bayesian GLMMs controlling for age, sex, and subtype. RESULTS:Forty-two of 68 regions showed moderate-to-strong evidence for cortical change. Increased thickness in right rostral anterior cingulate, right isthmus cingulate, right lateral orbitofrontal cortex, and right frontal pole were associated with remission. Remitters showed decreased prefrontal-cingulate thickness but increased visual-temporal thickness, suggesting localized neuroplastic effects specific to lithium response. DISCUSSION:Regional cortical thickness changes during lithium treatment represent promising neurobiological correlates of therapeutic response in bipolar depression, advancing understanding of the pathways underlying lithium's effects. Future studies should validate these findings in larger, prospective cohorts to assess their potential translational relevance.
Background Previous MRI studies have shown structural and functional brain changes in patients with post-traumatic stress disorder (PTSD). However, few studies have focused on brain changes between pre- and postcognitive processing therapy (CPT). Methods Twenty patients with PTSD participated in this study. They underwent MRI before and after 16-week of CPT, and the severity of illness was assessed using the Clinician-Administered PTSD Scale (CAPS-5) and the PTSD Checklist for DSM-5 (PCL-5). Changes in regional gray matter volume, cerebral blood flow, and regional microstructure of the white matter following CPT were evaluated using MRI. Results The clinical severity of PTSD improved after CPT. There was a significant increase in fractional anisotropy in the bilateral white matter regions adjacent to the insulae and in the right periaqueductal gray area after CPT. We also found an increase in the regional cerebral blood flow in the right frontal region at the trend level. Conclusions We detected some increases in MRI indices following treatment. These regions were frequently implicated in PTSD-related changes, suggesting that the recovery of these indices could be a biomarker for the prediction of the responsibility for the treatment of PTSD.
AIM:This study aimed to examine volumetric differences in thalamic nuclei among patients with schizophrenia, schizoaffective disorder, and healthy controls to identify disorder-specific patterns of thalamic involvement METHODS: The study included 45 patients aged 18-65 years (25 with schizophrenia and 20 with schizoaffective disorder) without physical or neurological disease, and 26 healthy controls. Participants were assessed using the Positive and Negative Syndrome Scale, Clinical Global Impression scale, and Pittsburgh Sleep Quality Index; the Young Mania Rating Scale and Hamilton Depression Rating Scale were additionally applied in the schizoaffective group. Volumes of thalamic nuclei groups (anterior, medial, intralaminar, posterior, lateral, and ventral) and their subnuclei were measured using a probabilistic atlas-based segmentation method. Group differences were evaluated using multivariate analysis of covariance, controlling for age and intracranial volume. RESULTS:Patients with schizoaffective disorder showed significantly reduced total right thalamic volume (p=0.001) and reduced volumes of the ventral posterolateral (p=0.006) and ventromedial nuclei (p=0.006) compared with schizophrenia patients and healthy controls. Reductions were also observed in medial and intralaminar nuclei associated with limbic function, attention,pain. CONCLUSION:Schizoaffective disorder shows greater right-sided thalamic involvement than schizophrenia. These nucleus-level differences may help clarify the pathophysiology of schizoaffective disorder and support diagnostic relevance.
Recent research into the neural mechanisms underlying autism spectrum increasingly relies on brain network analysis; however, conventional models remain limited in their ability to capture directed causal interactions between brain regions. To address this limitation, we propose a directional graph attention network (DGAT) as a proof-of-concept framework for autism classification using directed effective connectivity. DGAT takes Granger causality matrices as input and employs a dual-branch attention architecture to model incoming and outgoing information flows separately, then adaptively fuses the resulting bidirectional embeddings through learnable weights to more explicitly characterize driver-response relationships between regions. In addition, the model incorporates multiscale node descriptors, including temporal statistics, graph-theoretic centrality measures, and global graph metrics, to enhance representational capacity. Under nested cross-validation, DGAT achieved competitive performance on key metrics (accuracy: 71.99%, AUC: 75.15%, specificity: 73.07%) and produced more favorable results than support vector machines, random forests, graph convolutional networks, and GAT based on undirected functional connectivity. These findings suggest that DGAT may serve as a promising exploratory framework and offer a novel perspective for disease classification based on directed brain networks.
BACKGROUND:Current neuroimaging research on paranoid traits remains limited. Existing studies have largely relied on small samples, focused on categorical diagnoses or transient paranoid states, and examined either structural or functional measures in isolation. Crucially, the joint contribution of brain structure and intrinsic functional activity to paranoid personality traits (PPT) in the general population and their relationship with other psychological traits, remains unknown OBJECTIVES: The present study aimed to identify network-level neural markers of paranoid personality traits in a large sample by integrating gray matter morphology and resting-state brain activity, to test the hypothesis that networks associated with social and affective dysfunctions, predict PTT METHODS: We applied an unsupervised multimodal data-fusion approach (parallel ICA) to gray matter concentration and fractional ALFF in 197 healthy individuals. Paranoid personality traits were assessed dimensionally. In complementary analyses, we examined whether multimodal component loadings captured trait-related variance beyond demographic covariates RESULTS: Analyses identified a resting-state component encompassing the precuneus and angular gyrus, partially overlapping with the default mode network, significantly associated with paranoid personality traits. This functional component was positively correlated with a gray matter component including orbitofrontal and insular regions indicating a linked structural-functional pattern. CONCLUSIONS:By jointly modeling gray matter and resting-state activity, this study provides the first multimodal evidence of network-level markers underlying paranoid personality traits in the general population.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that causes discrepancies in social interaction and behavioral changes. The developments of neuroimaging techniques, like Magnetic Resonance Imaging (MRI) is employed to detect brain abnormalities. Due to the heterogeneity of disease severity and symptoms, the detection of ASD is difficult. To solve such complexity, a novel model named Fractional Painting Training Based Optimization trained Quantum Convolution Searched Binary Neural Network (FPTO_QCSBNN) is proposed for ASD detection in cloud. A cloud-based detection system offers the analysis and storage of large-scale neuroimages. Moreover, it provides faster diagnosis with scalable storage. Initially, the cloud system is simulated, and pre-processing is done using Mid-Point filter and Region of Interest (ROI) extraction. Image enhancement is done by gamma correction method, and pivotal region is extracted using functional connectivity. The optimal grid selection in pivotal region extraction is done using FPTO, and features are extracted from enhanced image. Using features and pivotal region extracted image, QCSBNN detects ASD, and it is trained by FPTO. Furthermore, developed FPTO_QCSBNN attains the accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) of 91.37%, 91.32%, and 91.89%.
BACKGROUND:To examine spatiotemporal modifications in electroencephalogram (EEG) microstates among individuals diagnosed with schizophrenia utilizing a five-class model, and to evaluate the potential of microstate parameters as diagnostic biomarkers. METHODS:Resting-state EEG data were collected from 122 patients with schizophrenia and 72 age- and sex-matched healthy controls. EEG recordings were preprocessed and analyzed using a standardized pipeline, applying a five-class microstate framework (A-E). Temporal parameters, transition probabilities, and spatial topographies were quantified. Group comparisons were conducted, and the diagnostic utility of microstate features was evaluated using receiver operating characteristic analysis. The study also investigated the correlations between various microstate metrics and the severity of symptoms, as measured by Positive and Negative Syndrome Scale (PANSS) scores. RESULTS:Significant differences were observed across all five microstate classes between the groups. Patients with schizophrenia showed shorter durations and increased occurrences of all microstates, altered transition patterns, and altered topographic distributions. Notably, reduced duration of microstate A, associated with auditory and linguistic processing, demonstrated the strongest discriminatory power, with an AUC of 0.93, sensitivity of 93.1 %, and specificity of 83.6 %. Despite allowing for group discrimination, microstate metrics were not significantly correlated with symptom severity as measured by the PANSS within the group of patients with schizophrenia. CONCLUSIONS:This study presents robust evidence of disrupted EEG microstate dynamics in patients with schizophrenia. The reduced duration of microstate A may serve as a potential neurophysiological biomarker, independent of symptom severity, for diagnostic differentiation purposes.
Alzheimer’s disease (AD) is an irreversible neurodegenerative syndrome that affects memory, cognitive abilities and behaviour. Detecting AD in the early stage is crucial to improve the quality of life. However, traditional diagnostic approaches and manual analysis of neuroimaging data are slow, subjective and lead to human mistakes. Existing machine learning techniques often have difficulty in identifying complex patterns in high-dimensional biomedical data. These drawbacks emphasize the necessity for a more efficient and automated diagnostic system. This study introduced a new deep learning based hybrid framework for classifying and predict progression of AD. The method comprises three main steps: data acquisition, feature extraction and classification. Initially, EEG signals are collected from the CAU-EEG dataset. Then, features such as time domain features, frequency domain features and time frequency domain features are extracted. Finally, classification is performed by dilated convolutions attention based long short term memory (DC-ALSTM). Investigational results show that the proposed model outperforms existing baseline methods. DC-ALSTM achieved a classification 99.26% accuracy, 99.21% precision, recall at 99.23% and 99.22% F1-score, which indicates outstanding diagnostic capability.
One of the neuro-developmental conditions is called Autism Spectrum Disorder (ASD), which causes changes in the behavior of the patients, and it delays language and social interactions. Details about the functional activity of the brain are provided by Magnetic Resonance Imaging (MRI). Studying each MRI scan of the patients is laborious and time-consuming for doctors and specialists. To tackle these limitations, this paper develops an advanced deep learning diagnosis method. In the beginning, the necessary MRI images are gathered from the available data resource. The input brain images are subjected to an Ensemble Deep Convolutional Neural Network (EDCNN) for feature extraction, which makes the diagnosis easier by reducing the complexities. The ensemble model is created by the integration of the Visual Geometry Group (VGG16), Residual Network (ResNet), and Inception approaches. Further, the resultant features are fused with weights that are optimized using the Improved Random Uniform Number-aided Humboldt Squid Optimization Algorithm (IRUN-HSOA); thus, the weighted fused feature is obtained. The resultant weighted fused feature is fed into Attention-based Residual Long Short-Term Memory (ARLSTM) for the ASD diagnosis. Further, the developed model is compared with different state-of-the-art techniques, and the suitability of the model is discussed for prospects.
Brain-derived neurotrophic factor (BDNF) has been proposed as a potential biological correlate of repetitive transcranial magnetic stimulation (rTMS). However, its relationship with clinical and cognitive outcomes in mood disorders remains unclear. In this prospective exploratory pre-post study, 28 adults with major depressive disorder or bipolar disorder underwent 12 sessions of high-frequency left dorsolateral prefrontal cortex (DLPFC) rTMS while continuing pharmacological treatment. Assessments included Hamilton Rating Scale for Depression (HDRS-24), Wisconsin Card Sorting Test (WCST), plasma BDNF, and baseline glymphatic efficiency (diffusion tensor image analysis along the perivascular space (DTI-ALPS)). Over the study period, HDRS-24 scores and WCST perseverative errors decreased, whereas the number of WCST categories completed did not significantly change at the group level; peripheral plasma BDNF also increased. Greater BDNF change ratios were associated with higher baseline ALPS indices and with individual variability in WCST categories completed, but not with changes in depressive symptoms or perseverative errors. Given the uncontrolled design, these longitudinal changes should be interpreted as associations observed over time rather than treatment effects attributable to rTMS. Baseline glymphatic efficiency may therefore reflect an individual biological characteristic associated with neurotrophic responsiveness to rTMS. These exploratory findings require replication in larger controlled studies using standard clinical protocols.
The human brain is responsible for a wide range of a person’s behavioral and cognitive capabilities. The functionality of the brain is affected by various disorders like schizophrenia, epilepsy, and Alzheimer. This study presents a novel framework for the automated detection of schizophrenia using EEG-based Weighted Effective Brain Connectivity Networks (WEBCNs). The proposed method introduces a new network descriptor called Weighted Directed Ordinal Connection (WDOC) that integrates causal directionality, connection strength, and ordinal relation between edges to capture complex brain dynamics. EEG signals from schizophrenia patients and healthy controls are preprocessed and transformed into WEBCNs using four causal connectivity estimators: Directed Transfer Function (DTF), Granger Causality (GC), Partial Directed Coherence (PDC), and Transfer Entropy (TE). WDOC-based features are extracted and classified using multiple machine learning algorithms, including KNN, SVM (linear, polynomial, RBF), and Random Forest. Among all models, the SVM with RBF kernel achieved the best performance, yielding 94.44% accuracy, 95% precision, 94% recall, and 89% kappa score for PDC-based networks. Structural and statistical analyses confirm distinct topological alterations in the causal flow between frontal and parietal regions in schizophrenia. The results demonstrate that WDOC-based characterization enhances discriminative power and interpretability in effective brain network analysis.
Neuroimaging plays a critical role in the diagnosis of Alzheimer's disease (AD), with Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) providing detailed structural and functional information for deep learning (DL) based classification. However, their high cost and limited availability restrict widespread clinical use. Computed Tomography (CT), while affordable and widely accessible, is diagnostically insufficient for detecting subtle neurodegenerative changes associated with early AD. To address this limitation, this study proposes a multimodal DL framework that enhances CT-based AD diagnosis by leveraging complementary feature representations learned from MRI. A custom convolutional neural network (CNN) was trained and evaluated using paired CT and MRI data from the Open Access Series of Imaging Studies (OASIS-3). A total of 772 participants with available MRI and CT scans were selected based on Clinical Dementia Rating (CDR) scores and corresponding clinical diagnoses. Participants were categorized as Normal Control (NC) (CDR = 0, n = 300), mild cognitive impairment (MCI) (CDR = 0.5, n = 250), or AD (CDR ≥ 1, n = 222). The overall sex distribution comprised 352 males and 420 females. The CT-only model achieved an accuracy of 84%, with 92% sensitivity and 83% specificity for AD classification. The proposed multimodal model demonstrated superior performance, achieving 92% accuracy, 95% sensitivity, and 91% specificity. Importantly, during CT-only inference, the multimodal framework retained high diagnostic accuracy in identifying disease status, indicating effective transfer of MRI-derived features to CT. These results demonstrate a scalable solution for improving AD detection using CT imaging in resource-limited healthcare.
Neuroimaging studies in familial high-risk (FHR) individuals are vital for identifying vulnerability markers independent of overt illness. However, research on purely non-prodromal FHR cohorts using comparative multimodal approaches remains limited. This study addresses this gap through multimodal MRI analysis-including cortical morphometry, white matter microstructure, tractography, and functional connectivity-in non-prodromal FHR for psychosis (FHR-P, n = 18), bipolar disorder (FHR-BD, n = 19), and healthy controls (HC, n = 25). FHR-BD showed increased right inferior parietal surface area and right middle temporal volume compared to HC. Conversely, FHR-P exhibited reduced right superior frontal cortical thickness compared to FHR-BD and decreased left pallidum volume compared to HC. White matter analysis revealed significantly lower fractional anisotropy in FHR-P compared to both FHR-BD and HC. FHR-BD showed higher axial diffusivity than HC in the forceps minor, uncinate fasciculus, and right-fronto-occipital fasciculus. No significant differences were found in network-based statistics or graph theoretical measures. These findings reveal shared and distinct neurobiological alterations in non-prodromal FHR-P and FHR-BD, suggesting that grey and white matter disruptions constitute endophenotypes even without clinical symptoms. The lack of network-level findings may reflect the modest sample size, requiring further investigation in larger cohorts.
BACKGROUND:Ketamine may alleviate treatment-resistant depression (TRD) primarily through glutamatergic modulation, with downstream dopaminergic activation. Iron plays an important role in monoaminergic metabolism, that is also implicated in the pathophysiology of TRD. Both Quantitative Susceptibility Mapping (QSM) and Effective Transverse Relaxation Rate (R2*) mapping can determine the extent of iron deposition in the brain. Given that abnormal iron accumulation may reflect dopamine dysfunction, we hypothesized that baseline magnetic substances could predict ketamine's antidepressant effects in patients with TRD. METHODS:We used data from a double-blind, randomized placebo-controlled trial followed by an extended single-arm open-label study to assess the efficacy of repeated intravenous ketamine in Japanese patients with TRD (jRCTs031210124). This study analyzed the data from the participants who underwent QSM and R2* mapping before receiving ketamine in either phase. Multivariable regression analyses were conducted to explore the association between baseline magnetic susceptibility and R2* with change in MADRS total and subdomain scores. RESULTS:This study included 17 patients with TRD (7 women; mean ± standard deviation age, 42.9 ± 10.6 years). Baseline magnetic susceptibility in the right nucleus accumbens negatively correlated with the change in MADRS retardation symptom scores (β = -0.73, p = 0.003). Moreover, baseline R2* in the left amygdala was negatively associated with the change in MADRS vegetative symptom scores (β = -0.71, p = 0.004). CONCLUSIONS:Baseline magnetic substances in the right nucleus accumbens and the left amygdala may be biomarkers to predict the effect of repeated ketamine infusions in patients with TRD.
Suicide is the leading global cause of death, particularly challenging in adolescent health. The current findings on brain regions associated with suicide are often confounded by environmental factors. Moreover, the emergence and persistence of suicidality remain largely unknown. Using the Adolescent Brain Cognitive Development study, we analyzed neuroimaging, suicidality, and environmental measures from 11,220 participants at baseline and 1-year-follow-up. Participants with low family-neighborhood-school environmental risk (risk score below mean) were grouped by suicidality changes across two timepoints: risk to risk (R-R), risk to no risk (R-NR), no risk to risk (NR-R), and no risk to no risk (NR-NR). Propensity score matching was performed on demographic variables, comparisons of brain volumes, cortical thickness, and surface area were conducted between R-R and R-NR groups, as well as NR-NR and NR-R groups, with matched sample size. Our results showed reduced gray matter volume in temporal cortex, parahippocampal, pallidum and hippocampus in the comparisons between NR-R and NR-NR (emergence of suicidality). Conversely, comparisons between R-R and R-NR (persistent suicidality) showed reduced gray matter volume in superior temporal, visual cortex and default mode network. These findings suggest that baseline differences in brain regions are distinctly associated with the emergence and persistence of suicidality among adolescents.
BACKGROUND:Acupuncture has demonstrated antidepressant efficacy; however, the brain's immediate response to stimulation in patients with major depressive disorder (MDD) remains unclear. We investigated cerebral blood flow (CBF) dynamics during and after acupuncture in patients with MDD via arterial spin labeling (ASL)-MRI. METHODS:Eleven patients with recurrent MDD and 14 healthy controls (HCs) underwent ASL-MRI at baseline, during, and after acupuncture (LI4, PC6, ST36, LV3). As an exploratory study, absolute CBF changes were analyzed using a flexible factorial design (voxelwise uncorrected P 〈 0.01; cluster-size 〉 350 voxels). RESULTS:In patients with MDD, acupuncture elicited increased CBF in the postcentral and prefrontal regions, with sustained activation in the middle frontal gyrus (DLPFC) poststimulation. Conversely, CBF decreased in the amygdala and posterior cingulate cortex following stimulation. Compared with HCs, patients with MDD exhibited a distinct neural perfusion pattern characterized by significantly greater recruitment of somatosensory-cognitive networks and attenuated activation in emotion-memory circuits. CONCLUSION:Acupuncture elicited distinct perfusion responses in MDD, shifting from maladaptive limbic dominance toward enhanced prefrontal engagement. These findings suggest that acupuncture stimulation is associated with perfusion patterns consistent with potential modulation of limbic-cortical networks in MDD.
