Psychiatry Research: Neuroimaging最新文献

{"title":"Quantitative brain regional relaxometry metrics for early diagnosis of Parkinson's disease","authors":"Miao Chen ,&nbsp;PuYeh Wu ,&nbsp;Wenjia Wang ,&nbsp;YiFei Zhang ,&nbsp;Rui Zhang ,&nbsp;Jinpeng Qi ,&nbsp;Zhibo Liang ,&nbsp;Lihong Gao ,&nbsp;Gang Zhang","doi":"10.1016/j.pscychresns.2025.112120","DOIUrl":"10.1016/j.pscychresns.2025.112120","url":null,"abstract":"<div><h3>Background</h3><div>The early identification of Parkinson's disease (PD) prior to the emergence of motor symptoms is paramount for effective treatment and mitigation of disease progression. Moreover, early predictions and assessments of disease progression in certain patients are critical for timely clinical interventions.</div></div><div><h3>Purpose</h3><div>This study investigates the neuroanatomical alterations in various brain regions during the initial stages and progression of PD, to explore the potential of quantitative regional metrics as candidate imaging markers for early diagnosis and disease progression.</div></div><div><h3>Materials and Methods</h3><div>We enrolled 31 PD patients and 25 healthy controls (HCs), categorizing PD patients into early-stage Parkinson's (ESP) (<em>n</em> = 22) and advanced-stage Parkinson's (ASP)(<em>n</em> = 9) based on the Hoehn and Yahr grading. The study employed 3D T1BRAVO and synthetic MRI for data acquisition, followed by voxel-based morphometry (VBM) and extraction of T1, T2, and proton density (PrD) values. Comparative analysis of brain volume and regional relaxation metrics was performed among the groups. A classification model based on regions showing significant group differences was evaluated using internal cross-validation.</div></div><div><h3>Results</h3><div>Significant variations were identified in specific brain regions when comparing the ESP group with HCs, particularly in the right Calcarine_T1GM and left Cuneus_T1GM regions. Additionally, notable differences were discerned between the ESP group and the ASP group, specifically in the left Putamen_T1GM, left ParaHippocampal_T1WM, Precentral_T2WM, left ParaHippocampal_T2WM, Anterior Cingulate Cortex (ACC)_T2WM, and left Putamen_PDGM regions. Scatter plot analysis revealed a strong correlation between these brain regions (with the exception of left ParaHippocampal_T2WM and left Putamen_PDGM) and both the Hoehn and Yahr (H&amp;Y) and Movement Disorder Society (MDS) scores. Under internal cross-validation, T1-based gray-matter regional metrics demonstrated the most stable discriminative performance among the evaluated modalities. Cross-validated classification performance was moderate, particularly for distinguishing ESP from ASP, indicating limited but potentially informative progression-related signals.</div></div><div><h3>Conclusion</h3><div>Synthetic MRI–derived regional relaxometry reveals stage-related brain alterations in PD. T1-based gray-matter metrics show relatively robust performance under internal validation and may serve as candidate imaging markers associated with early disease-related changes and progression in Parkinson’s disease. However, all classification results should be regarded as exploratory and warrant further validation in larger, independent cohorts.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"357 ","pages":"Article 112120"},"PeriodicalIF":2.1,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinct neural correlates between suicide attempters with major depressive disorder and other psychiatric disorders: a multimodal imaging study","authors":"Woo-Sung Kim ,&nbsp;Soyolsaikhan Odkhuu ,&nbsp;Ariana Setiani ,&nbsp;Ling Li ,&nbsp;Ji-Young So ,&nbsp;Shahida Nazir ,&nbsp;Young-Chul Chung","doi":"10.1016/j.pscychresns.2025.112117","DOIUrl":"10.1016/j.pscychresns.2025.112117","url":null,"abstract":"<div><h3>Background</h3><div>Suicide attempters (SA) with mild diagnoses other than major depressive disorder (MDD) are underexplored. This study aimed to investigate neuroimaging abnormalities in SA with MDD (SA-MDD), SA with other psychiatric disorders (OPD) (SA-OPD), and healthy controls (HC) using MRI data. Associations between altered findings in the patient groups and anger, negative schema, or rumination were further examined.</div></div><div><h3>Methods</h3><div>We recruited SA who visited the emergency department and divided them into two groups: SA-MDD (<em>n</em> = 52) and SA-OPD (<em>n</em> = 24). Age-, sex-, and education-matched HC) (<em>n</em> = 100) participated in the study. Cortical thickness, surface area, and subcortical volume were measured using structural magnetic resonance imaging (MRI). Regional analysis and seed-based functional connectivity (FC) analyses were performed using resting-state functional MRI. Partial Pearson or Spearman correlation with psychological variables was conducted.</div></div><div><h3>Results</h3><div>Widespread decreases in cortical thickness and larger cortical surface areas in some regions were observed in both SA-MDD and SA-OPD. However, decreased hippocampal volume was observed only in SA-MDD. Altered FC was more prominent in SA-MDD than in SA-OPD. In addition, significant associations between altered seed-based FC and negative schema were observed in both groups. Importantly, the associations with anger and rumination were evident only in SA-OPD.</div></div><div><h3>Conclusions</h3><div>These findings suggest that SA-OPD have similar alterations in brain morphometry to SA-MDD but show less prominent FC alterations. The clinical implications of anger and rumination in SA-OPD warrant further investigation.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"357 ","pages":"Article 112117"},"PeriodicalIF":2.1,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145869102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinct subcortical connectivity patterns of opioid and stimulant use disorders: A resting-state fMRI study","authors":"Neli Atanasova ,&nbsp;Anna Todeva-Radneva ,&nbsp;Kristina Stoyanova ,&nbsp;Elena Psederska ,&nbsp;Elizabet Dzhambazova ,&nbsp;Drozdstoy Stoyanov ,&nbsp;Nikoleta Traykova ,&nbsp;Jasmin Vassileva","doi":"10.1016/j.pscychresns.2025.112116","DOIUrl":"10.1016/j.pscychresns.2025.112116","url":null,"abstract":"<div><div>This study investigated resting-state functional connectivity (rsFC) patterns in individuals with opioid use disorder (OUD), stimulant use disorder (StUD) and healthy controls (HC). Using seed-based analysis of key subcortical regions, we found distinct connectivity profiles associated with each substance type. OUD showed reduced connectivity between limbic/basal ganglia structures and sensorimotor regions, along with increased pallidum-angular gyrus connectivity compared to HC. StUD exhibited decreased striatal-default mode network and limbic-prefrontal connectivity relative to HC. Direct comparison between OUD and StUD revealed widespread corticostriatal, striato-cerebellar, and prefrontal-limbic hyperconnectivity in OUD compared to StUD. These substance-specific alterations in intrinsic brain organization may reflect differential neuroadaptations underlying the cognitive and behavioral manifestations of opioid versus stimulant use disorders. Our findings highlight the potential of rsFC patterns as a biomarker for distinguishing among different subtypes of addiction and informing targeted interventions.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"357 ","pages":"Article 112116"},"PeriodicalIF":2.1,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning based treatment remission prediction to transcranial direct current stimulation in bipolar depression using EEG power spectral density","authors":"Jijomon Chettuthara Moncy ,&nbsp;Wenyi Xiao ,&nbsp;Rachel D. Woodham ,&nbsp;Ali-Reza Ghazi-Noori ,&nbsp;Hakimeh Rezaei ,&nbsp;Elvira Bramon ,&nbsp;Philipp Ritter ,&nbsp;Michael Bauer ,&nbsp;Allan H. Young ,&nbsp;Yong Fan ,&nbsp;Cynthia H.Y. Fu","doi":"10.1016/j.pscychresns.2025.112115","DOIUrl":"10.1016/j.pscychresns.2025.112115","url":null,"abstract":"<div><div>Bipolar disorder is characterized by marked changes in mood and activity levels and is a leading cause of disability worldwide. We sought to investigate the application of deep learning methods to electroencephalogram (EEG) signals to predict clinical remission after 6 weeks of home-based transcranial direct current stimulation (tDCS) treatment. Pre-treatment resting-state EEG acquired from 21 bipolar participants was used for this work. A hybrid 1DCNN and GRU model, with input from power spectral density values of theta, beta and gamma frequency bands of the AF7 and TP10 electrodes, achieved a treatment remission prediction accuracy of 78.5% (sensitivity 81.4%, specificity 74.64%).</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"357 ","pages":"Article 112115"},"PeriodicalIF":2.1,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145869103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG/MEG-based biomarkers of cognitive training effects in schizophrenia: A systematic review","authors":"Alp Eren Yüce ,&nbsp;Işın Yaman ,&nbsp;Serdar Gündoğdu","doi":"10.1016/j.pscychresns.2025.112112","DOIUrl":"10.1016/j.pscychresns.2025.112112","url":null,"abstract":"<div><div>This systematic review aims to evaluate the effects of cognitive training methods on brain electrical activity in patients with schizophrenia, focusing on studies published between January 2010 and October 2025 in the PubMed and Web of Science databases. Twenty experimental and quasi-experimental studies were included in the study, examining various cognitive training methods, including auditory, visual, emotional, and social skills training. These studies measured Electroencephalography (EEG) and Magnetoencephalography (MEG) metrics, including oscillations, mismatch negativity (MMN), and event-related potentials (ERPs), to assess changes in neural activity associated with schizophrenia. Findings demonstrate that different cognitive training methods may improve neural oscillations, particularly in the gamma and alpha bands, and may enhance cognitive functions such as working memory, attention, and emotional regulation. While some studies have shown limited effects on specific EEG and MEG indices, the overall evidence suggests that personalized cognitive interventions can promote neuroplasticity and cognitive recovery in individuals with schizophrenia. These findings show the potential of integrating EEG/MEG-based biomarkers into clinical practices to enhance the effect of treatment and improve neural outcomes for schizophrenia patients.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"357 ","pages":"Article 112112"},"PeriodicalIF":2.1,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146012244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disease and state dependent neural markers in adolescents with BD. Understanding the neural bases of mania, depression and remission in a data fusion approach","authors":"Xiaoping Yi ,&nbsp;Xueying Wang ,&nbsp;Mingzhao Ma ,&nbsp;Jinfan Zhang ,&nbsp;Feifei Wu ,&nbsp;Haimiao Huang ,&nbsp;Qian Xiao ,&nbsp;Xiaoqun Liu ,&nbsp;Alessandro Grecucci","doi":"10.1016/j.pscychresns.2025.112113","DOIUrl":"10.1016/j.pscychresns.2025.112113","url":null,"abstract":"<div><h3>Background</h3><div>Pediatric bipolar disorder (PBD) is a severe and disabling condition marked by alternating episodes of mania and depression, intermitted with periods of remission. A critical challenge in the field is to elucidate the neural mechanisms underlying these distinct mood states, as manic and depressive presentations reflect profoundly different clinical phenotypes. Despite their relevance, the functional and structural brain alterations associated with manic, depressed, and remitted states in PBD remain poorly understood.</div></div><div><h3>Method</h3><div>In this study, data on Resting-State Regional Homogeneity (ReHo) and grey matter concentration (GMC) were considered from 58 PBD patients and 21 healthy controls (HC), matched for age, gender, education, and IQ. The analysis was conducted using Parallel ICA, a multivariate data-driven approach that identifies joint patterns of covariation across resting-state functional and gray matter features.</div></div><div><h3>Results</h3><div>Among the nine RS-GM components identified, distinct disease- and state-dependent networks were detected. GM2, a cortico-limbic network, differentiated PBD from HC, suggesting a neural substrate of bipolar disorder independent of mood state. GM9, including the cingulate and the precuneus, distinguished manic from depressed states, while RS7, including the fusiform and occipital regions, was specific to depression compared to HC. RS1, including regions of the DMN, differentiated manic from remitted states.</div></div><div><h3>Conclusions</h3><div>These findings provide new fresh understanding on the common and separate functional and structural abnormalities displayed during mania, depression and remission phases in bipolar patients.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"357 ","pages":"Article 112113"},"PeriodicalIF":2.1,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145912795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural correlates of personal agency in borderline personality disorder - An fMRI investigation using attachment-informed autobiographical memories","authors":"Emily L Matthews ,&nbsp;Ely M Marceau ,&nbsp;Charlotte C van Schie ,&nbsp;Brin FS Grenyer","doi":"10.1016/j.pscychresns.2025.112111","DOIUrl":"10.1016/j.pscychresns.2025.112111","url":null,"abstract":"<div><div>Individuals with borderline personality disorder (BPD) have identity disturbance and difficulty with self/other distinction which may be linked to difficulties in personal agency. Neuroimaging research has helped inform underlying deficits linked to sociocognitive functioning, however, no research has specifically investigated neural correlates of personal agency in BPD. We used an attachment-informed autobiographical memory recall task to investigate behavioural and neural mechanisms of personal agency in BPD. Individuals with BPD (<em>n</em> = 34) and nonclinical controls (NCC, <em>n</em> = 35) recalled attachment and neutral memories during functional magnetic resonance imaging (fMRI). Behavioural analyses of affect, agency, and vividness of memories, and event-related fMRI analysis compared neutral and attachment-informed memories. Parametric modulation identified changes in neural activation associated with increasing agency. People with BPD felt less agentic and more negative during recall of attachment-informed memories, compared to NCCs. Agency was similar between groups for neutral memories. We found hyperactivation in the temporal parietal junction (TPJ) and superior temporal gyrus particularly during memories involving more distant attachment figures. Differential activation between BPD and NCC groups based on level of agency was found, including in the right-TPJ. Personal agency is influenced by relational contextual factors, with individuals with BPD being affected in agency when recalling memories with attachment figures. The neural hyperactivation findings provide evidence for how attachment distress or negative relational experiences may be associated with poor personal agency. This has implications for interpersonal functioning in BPD and highlights the importance and challenges of fostering personal agency in treatment.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"356 ","pages":"Article 112111"},"PeriodicalIF":2.1,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145834632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meta-analysis of aberrant white matter microstructure and gray matter volume in autism spectrum disorder","authors":"Yu Jiang ,&nbsp;Qiuying Tao ,&nbsp;Ying Wei ,&nbsp;Shengli Shi","doi":"10.1016/j.pscychresns.2025.112110","DOIUrl":"10.1016/j.pscychresns.2025.112110","url":null,"abstract":"<div><h3>Objective</h3><div>Our aim was to conduct a comprehensive meta-analysis of structural neuroimaging studies investigating white matter (WM) microarchitecture and gray matter volume (GMV) abnormalities in patients with autism spectrum disorder (ASD).</div></div><div><h3>Methods</h3><div>Totally 24 diffusion tensor imaging studies using tract-based spatial statistics (TBSS) or voxel-based analysis (VBA) approach were included in this meta-analysis. The discrepancies in WM fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity between ASD patients and typically developing (TD) individuals and the results of two common methods were compared. Besides, we detect GMV alternations by analyzing 44 voxel-based morphometry studies in ASD and comparing the difference in different developing stages.</div></div><div><h3>Results</h3><div>Compared with TD, ASD exhibited significantly WM microstructure alterations in corpus callosum, cortico-spinal projections, anterior thalamic projections, superior longitudinal fasciculus and middle cerebellar peduncles. There is some heterogeneity in aberrant WM microstructure between the TBSS and VBA subgroup. ASD had increased GMV in gyrus rectus, superior and inferior temporal gyrus, and precentral gyrus, and decreased GMV in cerebellum, anterior cingulate/paracingulate gyri, cingulum, and inferior parietal gyri. ASD displayed an age-related trajectory of brain morphometry.</div></div><div><h3>Conclusion</h3><div>Our main findings reveal the variable WM microarchitecture and GMV in ASD and offer a neuroanatomical framework that may guide multimodal and longitudinal research.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"356 ","pages":"Article 112110"},"PeriodicalIF":2.1,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive network based fuzzy inference system efficientnet for autism spectrum disorder detection with optimization based pivotal region extraction","authors":"Satish Muppidi ,&nbsp;Kishore Bhamidipati ,&nbsp;Grandhi Siva Sankar ,&nbsp;Anupkumar M. Bongale ,&nbsp;Ramesh Babu P","doi":"10.1016/j.pscychresns.2025.112109","DOIUrl":"10.1016/j.pscychresns.2025.112109","url":null,"abstract":"<div><div>Autism Spectrum Disorder (ASD) refers to abnormal neural activity that may increase the risk of premature brain development issues. Many traditional treatment methods are available for ASD, but they require continuous assessment and analysis of patient behavior. However, traditional models are time-consuming and often lead to unclear outcomes, significantly impacting patient’s social lives and communication ability. Hence, a novel hybrid approach is developed, namely, Adaptive Network Based Fuzzy Inference System EfficientNet (ANFIS-EffNet), which combines the ability of Adaptive Network Based Fuzzy Inference (ANFIS) and EfficientNet by modifying their layers. At first, the input autism brain image is given to the pre-processing stage, performed using Anisotropic diffusion and Region of Interest (ROI) extraction. Next, functional connectivity-based pivotal region extraction is done by Adam War Strategy Optimization (AWSO), a combination of the Adam optimization algorithm and War Strategy Optimization (WSO) technique. Next, feature extraction is carried out by including Learned Invariant Feature Transform (LIFT) and statistical methods. At last, ANFIS-EffNet is employed to detect autism spectrum disorder. The ANFIS-EffNet model has achieved outstanding performance with an accuracy of 93.219 %, sensitivity of 93.670 % and specificity of 93.840 % respectively.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"356 ","pages":"Article 112109"},"PeriodicalIF":2.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145828333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent electroencephalogram feature engineering for rapid mental health diagnosis","authors":"Ammu Kripalal ,&nbsp;C. Sekar","doi":"10.1016/j.pscychresns.2025.112103","DOIUrl":"10.1016/j.pscychresns.2025.112103","url":null,"abstract":"<div><div>Schizophrenia is one of the serious disorders and, if left untreated, can result in a range of problems with cognition, behavior, and emotions that affect every area of life. Diagnosis based on behavioral and clinical investigations remains difficult with schizophrenia symptoms which are complex and heterogenic. Early detection of schizophrenia is essential for the timely treatment leading to betterment of the life of patients. In this study based on machine learning algorithms, we have identified the relevant set of features from the electroencephalogram (EEG) signal to improve the classification accuracy of patients with schizophrenia and healthy controls. Combinations of these identified relevant features have been used to diagnose schizophrenia.Furthermore, we validated this same feature set as the high performing feature subset on an independent dataset, confirming its robustness and generalizability. The results show that the selected features from the EEG signal achieve the highest accuracy of 94.7% and 96.4% for Logistic Regression (LR) and Support Vector Machines (SVM) respectively with reduced data. Reduction in training data with this feature selection enhances the performance of edge devices that are optimized for applications such as brain computer interfaces, neurological disorder detection, cognitive state monitoring, and neurofeedback training.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"356 ","pages":"Article 112103"},"PeriodicalIF":2.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}