Neuroscience informatics最新文献

{"title":"Letter to editor regarding “A systematic review and meta-analysis on the diagnostic accuracy of artificial intelligence and computer-aided diagnosis of lumbar prolapsed intervertebral disc”","authors":"Estanislao Arana","doi":"10.1016/j.neuri.2025.100235","DOIUrl":"10.1016/j.neuri.2025.100235","url":null,"abstract":"","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100235"},"PeriodicalIF":0.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart web interface for student mental health prediction using machine learning with blockchain technology","authors":"Mishu Deb Nath ,&nbsp;Md. Khabir Uddin Ahamed ,&nbsp;Omayer Ahmed ,&nbsp;Tanvir Ahmed ,&nbsp;Sujit Roy ,&nbsp;Mohammed Nasir Uddin","doi":"10.1016/j.neuri.2025.100236","DOIUrl":"10.1016/j.neuri.2025.100236","url":null,"abstract":"<div><div>Student mental health is becoming a growing global concern, with more students facing psychological stress, anxiety, and related disorders. These mental health challenges often develop gradually and, if ignored, can negatively affect a student's academic performance and personal life. Early detection is essential, but high costs, limited resources, and time constraints often hinder it. The study proposes a machine learning-based approach to predict and assess student mental health, addressing this problem. Using rich psychological and behavioral data, the system can identify early signs of mental distress. An extensive evaluation of 12 machine learning models identified the top six performers. Logistic regression, Decision Tree, Extra Tree, Adaboost, Gradient Boosting, and XGBoost. Among these, the fine-tuned Random Forest algorithm achieved the highest performance, with an impressive accuracy of 95.6%. To ensure practical implementation, a Streamlit-based application was developed. This application enables educators and mental health professionals to perform real-time analysis and receive predictions in a clear and user-friendly format. The study incorporates blockchain technology to ensure the secure handling of sensitive data. Data collected through the Web interface, such as responses to mental health questionnaires, is securely stored using blockchain technology. This integrated system offers a reliable and scalable solution for monitoring and supporting student mental health.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100236"},"PeriodicalIF":0.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional MRI in hypertension – A systematic review of brain connectivity, regional activity, and cognitive impairment","authors":"Sathya Sabina Muthu ,&nbsp;Suresh Sukumar ,&nbsp;Rajagopal Kadavigere ,&nbsp;Shivashankar K.N. ,&nbsp;K. Vaishali ,&nbsp;Ramesh Babu M.G. ,&nbsp;Hari Prakash Palaniswamy ,&nbsp;Abhimanyu Pradhan ,&nbsp;Winniecia Dkhar ,&nbsp;Nitika C. Panakkal ,&nbsp;Sneha Ravichandran ,&nbsp;Dilip Shettigar ,&nbsp;Poovitha Shruthi Paramashiva","doi":"10.1016/j.neuri.2025.100233","DOIUrl":"10.1016/j.neuri.2025.100233","url":null,"abstract":"<div><div>Hypertension is increasingly recognized as a key contributor to cognitive decline and brain structure and function alterations. Functional Magnetic Resonance Imaging (fMRI) provides a non-invasive means to detect early disruptions in neural networks before clinical symptoms of cognitive impairment emerge. This systematic review explored the application of fMRI in assessing brain functional changes and cognitive performance in individuals with hypertension. A comprehensive search of electronic databases identified eight relevant studies, most of which employed resting-state fMRI techniques. Findings majorly demonstrated that hypertension is associated with altered connectivity within key neural networks, including the default mode network, frontoparietal network, and salience network. Additional observations included reduced regional homogeneity and changes in low-frequency fluctuations. These neural alterations were linked to decreased memory, executive function, and attention. While the findings support the potential of fMRI as an early biomarker for hypertension-related cognitive impairment, the evidence remains limited by the small number of studies and geographic concentration. Nonetheless, fMRI holds promise for clinical application in identifying individuals at risk and guiding timely interventions. Additional longitudinal studies with broader geographic representation are necessary to confirm these insights and facilitate the integration of fMRI into the routine evaluation and management of hypertension-related brain alterations.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study of hybrid decision tree–deep learning models in the detection of intracranial arachnoid cysts","authors":"Aziz Ilyas Ozturk ,&nbsp;Osman Yıldırım ,&nbsp;Ebru İdman ,&nbsp;Emrah İdman","doi":"10.1016/j.neuri.2025.100234","DOIUrl":"10.1016/j.neuri.2025.100234","url":null,"abstract":"<div><div>Intracranial arachnoid cysts are fluid-filled lesions within the arachnoid membrane, which pose significant diagnostic challenges due to their varying sizes, subtle radiographic characteristics, and often unclear clinical correlations. Traditional diagnostic methods, such as MRI or CT imaging, rely on expert interpretation but suffer from issues like inter-observer variability and diagnostic delays, especially for small or atypically located cysts. To address these challenges, this study integrates machine learning (ML) and deep learning (DL) techniques into neuroimaging diagnostics, introducing three novel hybrid models: DecisionTree-ViT, DecisionTree-Random Forest, and DecisionTree-ResNet50. The DecisionTree-Random Forest hybrid model showed remarkable performance, achieving 96.3% accuracy and 0.98 AUC in differentiating arachnoid cysts from normal cerebrospinal fluid spaces and other intracranial cystic lesions. This model combines deep learning's pattern recognition strengths with decision tree transparency, meeting the clinical need for both accuracy and explainability. The DecisionTree-ResNet50 variant excelled in detecting small (&lt;1 cm) cysts, with a sensitivity of 89.7%, outperforming standalone ResNet50 (82.4%). Specialized contrast-enhancement protocols and anatomically constrained augmentation techniques were applied to address class imbalance and improve model calibration. The DecisionTree-ViT model also demonstrated strong performance, with 94% accuracy and well-calibrated confidence estimates, making it reliable for clinical decision-making. The study compares these hybrid models against pure deep learning and traditional machine learning approaches, highlighting their superior performance in challenging diagnostic scenarios. The integrated interpretability features allow radiologists to validate algorithmic findings, fostering trust in AI-assisted diagnostics. This research showcases the potential of hybrid AI models to transform neuroimaging diagnostics and improve patient outcomes.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100234"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing spatiotemporal neural activation patterns in electrocorticography recordings of human speech production by mutual information","authors":"Julio Kovacs ,&nbsp;Dean Krusienski ,&nbsp;Minu Maninder ,&nbsp;Willy Wriggers","doi":"10.1016/j.neuri.2025.100232","DOIUrl":"10.1016/j.neuri.2025.100232","url":null,"abstract":"<div><h3>Background</h3><div>Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.</div></div><div><h3>New Method</h3><div>We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, we also implemented a novel “masked analysis”, which excludes periods of silence, and compared it with the standard (unmasked) analysis.</div></div><div><h3>Results</h3><div>Our findings show that previous results, obtained through more complex statistical methods, can be reproduced using CC with an appropriate threshold cutoff. Moreover, both standard MI and CC are influenced by broad transitions between silence and speech, but masking allows the detection of intrinsic correspondences between the two signals, revealing more localized activity.</div></div><div><h3>Comparison with existing methods</h3><div>Compared to the standard CC, masked MI highlights early prefrontal and premotor activations emerging ∼440 ms before speech onset. It also identifies sharper, anatomically coherent activations in key speech-related areas, demonstrating improved sensitivity to the fine-grained spatiotemporal dynamics of continuous speech production.</div></div><div><h3>Conclusion</h3><div>These findings deepen our understanding of the neural pathways underlying speech and underscore the potential of masked MI for advancing neural decoding in future speech-based brain-computer interface applications.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morphometric characterization of early- and late-onset Parkinson's disease: An ROI-based study of classification and correlation","authors":"Sadhana Kumari ,&nbsp;Bharti Rana ,&nbsp;Shefali Chaudhary ,&nbsp;Roopa Rajan ,&nbsp;S. Senthil Kumaran ,&nbsp;Achal Kumar Srivastava ,&nbsp;Leve Joseph Devarajan","doi":"10.1016/j.neuri.2025.100228","DOIUrl":"10.1016/j.neuri.2025.100228","url":null,"abstract":"<div><h3>Introduction</h3><div>Parkinson's disease (PD) is associated with progressive neurodegeneration, particularly involving cortico-basal ganglia-thalamo-cortical circuits that underlie motor and cognitive functions. We investigated the morphological brain features derived from structural MRI to differentiate early (EOPD) and late-onset PD (LOPD) from age-related healthy controls.</div></div><div><h3>Methods</h3><div>3D T1-weighted MRI was acquired in 114 subjects (27 EOPD, 32 YHC, 28 LOPD, and 27 OHC). Gray matter volume (GMV), white matter volumes (WMV), fractal dimension (FD), gyrification index (GI), and cortical thickness (CT) were extracted using CAT12 software. Three tasks, (i) identification of statistically significant regions, (ii) automatic diagnosis using machine learning using individual and combined features, and (iii) correlation study were performed to quantify the relationship between morphological features and clinical variables.</div></div><div><h3>Results</h3><div>EOPD exhibited a reduction in GMV and cortical complexity in frontal, parietal and temporal lobes compared to YHC. We achieved the highest classification accuracy of 89.06% using FD and CT for EOPD vs YHC, 90.91% using GMV, WMV and FD for LOPD vs OHC and 89.29% using WMV and FD for EOPD vs LOPD after data augmentation for class balancing. EOPD revealed a negative correlation of GMV with UPDRS II (in medial frontal cortex, precuneus and supplementary motor cortex), FD with UPDRS III in pericalcarine; GI and UPDRS II in transverse temporal and pars opercularis; CT with UPDRS III in superior frontal regions.</div></div><div><h3>Conclusion</h3><div>Distinct morphometric changes were observed in patients with EOPD and LOPD in comparison with HC, suggesting the utility of morphological measures in early diagnosis of PD.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100228"},"PeriodicalIF":0.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing neuromolecular imaging classification in low-data regimes with generative machine learning: A case study in HDAC PET/MR imaging of alcohol use disorder","authors":"Tyler N. Meyer ,&nbsp;Olga Andreeva ,&nbsp;Roger D. Weiss ,&nbsp;Wei Ding ,&nbsp;Iris Shen ,&nbsp;Changning Wang ,&nbsp;Ping Chen ,&nbsp;Tewodros Mulugeta Dagnew","doi":"10.1016/j.neuri.2025.100225","DOIUrl":"10.1016/j.neuri.2025.100225","url":null,"abstract":"<div><h3>Introduction</h3><div>Positron Emission Tomography (PET) is a vital modality for investigating brain related disorders. However, data scarcity especially for novel molecular targets like neuroepigenetic enzymes combined with difficult-to-recruit patient populations limits the development of machine learning (ML) models. Our primary objective is to enhance single-subject classification of neuromolecular imaging data and facilitate biomarker discovery. We demonstrate our approach using histone deacetylase (HDAC) PET/MR imaging in Alcohol Use Disorder (AUD).</div></div><div><h3>Methods</h3><div>We propose <em>Catalysis Training pipeline</em>, a framework that augments real imaging data with high-quality synthetic data generated by a Wasserstein Conditional Generative Adversarial Network (WCGAN). Using [¹¹C]Martinostat PET/MR imaging, we extracted 1-D standardized uptake value ratio (SUVR) tabular features representing HDAC enzyme expression density across eight cingulate subregions. These were used to train and test ML classifiers, including Support Vector Machine (SVM), XGBoost, and Random Forest, under leave-one-out cross-validation.</div></div><div><h3>Results</h3><div>Integrating synthetic data in the training process improved classification accuracy significantly: +26% for XGBoost and Random Forest (from 59% to 85%), and +18% for SVM (from 70% to 88%). Synthetic samples improved model generalizability. Key hemispheric and subregional cingulate HDAC patterns were also identified as potential biomarkers.</div></div><div><h3>Conclusion</h3><div>Our results demonstrate that generative AI can help overcome data scarcity in low-data regime neuroimaging applications. Catalysis Training provides a scalable strategy to enhance ML-driven biomarker discovery and disease classification, especially for rare or difficult-to-study disorders like AUD. Clinically, cingulate HDAC expression measured by [¹¹C]Martinostat PET/MR shows promise as an objective biomarker for AUD, complementing DSM-based diagnosis and informing novel treatment strategies.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100225"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring KAN as a next-generation replacement for MLPs in EEG-based seizure detection","authors":"Eman Allogmani","doi":"10.1016/j.neuri.2025.100226","DOIUrl":"10.1016/j.neuri.2025.100226","url":null,"abstract":"<div><div>Epilepsy is a chronic neurological disorder characterized by recurrent seizures due to abnormal brain activity. Accurate detection of seizures from electroencephalogram (EEG) signals is critical, but it is often challenged by signal noise and class imbalance in real-world data. In this study, we systematically evaluate Kolmogorov–Arnold Networks (KANs)—a recent neural architecture based on the Kolmogorov–Arnold representation theorem—as an alternative to Multi-Layer Perceptrons (MLPs) for EEG-based seizure classification, with a focus on model robustness under noisy conditions. This is the first comprehensive evaluation of KAN's robustness under multiplicative noise in the context of EEG seizure detection. Experiments were conducted using two widely used EEG datasets: the Bonn dataset and the CHB-MIT Scalp EEG dataset. Across multiple network configurations and varying levels of multiplicative noise, we assess performance using F1 Score, AUROC, AUPRC, Sensitivity, and Specificity. Our findings show that KAN achieves more stable performance than MLPs under noisy conditions, particularly in smaller architectures. These results suggest that KAN may offer a robust and generalizable approach for seizure detection in noise-prone clinical settings.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding memory with explainable AI: A large-scale EEG-based machine learning study of encoding vs. retrieval","authors":"Mohammed Tawshif Hossain ,&nbsp;Adnan Sami Sarker ,&nbsp;Arnab Chowdhury ,&nbsp;Rajesh Mitra ,&nbsp;Raiyan Rahman ,&nbsp;M.R.C. Mahdy","doi":"10.1016/j.neuri.2025.100227","DOIUrl":"10.1016/j.neuri.2025.100227","url":null,"abstract":"<div><div>Understanding the distinct neural signatures that differentiate memory encoding from retrieval remains a key challenge in cognitive neuroscience. This study applies machine learning to EEG data from the Penn Electrophysiology of Encoding and Retrieval Study (PEERS), involving 100 participants across over 400 sessions, to classify these cognitive states. We used Discrete Wavelet Transform (DWT) on EEG signals from six critical brain regions and evaluated seven machine learning models. Gradient Boosting emerged as the most effective classifier, achieving 81.97% accuracy and a 91.62% AUC. To interpret this performance, we applied Explainable AI (XAI) methods, specifically SHapley Additive exPlanations (SHAP). This analysis revealed that theta-band relative energy, especially in the Left and Right Anterior Superior (LAS/RAS) regions, was the most influential predictor. Low theta-band energy and RMS values were particularly indicative of encoding states. Topographic maps provided further validation, showing significant neural differences in anterior regions, notably within the theta range. However, the study is limited by the use of a fixed 2.5 s analysis window and demographic skew in the dataset, which may affect generalizability. Future work should address these issues through varied windowing strategies and more diverse populations. This study advances understanding of cognitive memory processes and supports the development of adaptive, memory-aware AI systems, contributing to both neuroscience and neurotechnology.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of physical activity and cardiorespiratory fitness on brain morphology among overweight and obese populations: A systematic review and meta-analysis of neuroimaging studies","authors":"Dilip Shettigar ,&nbsp;Suresh Sukumar ,&nbsp;Rajagopal Kadavigere ,&nbsp;K. Vaishali ,&nbsp;Nitika C. Panakkal ,&nbsp;Winniecia Dkhar ,&nbsp;Abhimanyu Pradhan ,&nbsp;Baskaran Chandrasekaran ,&nbsp;Hari Prakash Palaniswamy ,&nbsp;Poovitha Shruthi Paramashiva ,&nbsp;Sneha Ravichandran ,&nbsp;Sathya Sabina Muthu ,&nbsp;Koustubh Kamath","doi":"10.1016/j.neuri.2025.100224","DOIUrl":"10.1016/j.neuri.2025.100224","url":null,"abstract":"","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 3","pages":"Article 100224"},"PeriodicalIF":0.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}