In current medical practice, patients undergoing treatment for depression typically must wait four to six weeks before clinicians can assess their response to medication, due to the delayed onset of noticeable effects from antidepressants. Identifying treatment response at an earlier stage is of great importance, as it can reduce both the emotional and economic burden associated with prolonged treatment. We present a novel Motif Discovery Framework (MDF) that extracts dynamic features from EEG time series data to distinguish between treatment responders and non-responders in depression. Our findings show that MDF can predict treatment response with high precision as early as the 7th day of treatment, significantly reducing the waiting time for patients. Furthermore, we demonstrate that MDF generalizes well to classification tasks in other psychiatric conditions, including schizophrenia, Alzheimer’s disease, and dementia. Overall, our experiments show that MDF outperforms relevant benchmarks. The high precision of our classification framework underscores the potential of EEG dynamic properties-represented as motifs-to support clinical decision-making and ultimately enhance patient quality of life.
{"title":"EEG-based classification in psychiatry using motif discovery","authors":"Melanija Kraljevska , Kateřina Hlaváčková-Schindler , Lukas Miklautz , Claudia Plant","doi":"10.1016/j.neuri.2025.100242","DOIUrl":"10.1016/j.neuri.2025.100242","url":null,"abstract":"<div><div>In current medical practice, patients undergoing treatment for depression typically must wait four to six weeks before clinicians can assess their response to medication, due to the delayed onset of noticeable effects from antidepressants. Identifying treatment response at an earlier stage is of great importance, as it can reduce both the emotional and economic burden associated with prolonged treatment. We present a novel Motif Discovery Framework (MDF) that extracts dynamic features from EEG time series data to distinguish between treatment responders and non-responders in depression. Our findings show that MDF can predict treatment response with high precision as early as the 7th day of treatment, significantly reducing the waiting time for patients. Furthermore, we demonstrate that MDF generalizes well to classification tasks in other psychiatric conditions, including schizophrenia, Alzheimer’s disease, and dementia. Overall, our experiments show that MDF outperforms relevant benchmarks. The high precision of our classification framework underscores the potential of EEG dynamic properties-represented as motifs-to support clinical decision-making and ultimately enhance patient quality of life.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100242"},"PeriodicalIF":0.0,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618463","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}
Brain hemorrhage, or Intracranial Hemorrhage (ICH), is a critical medical condition requiring rapid diagnosis. Existing Convolutional Neural Network (CNN) models often struggle to differentiate similar hemorrhage subtypes like Epidural (EDH) and Subdural (SDH) due to a lack of specific spatial feature identification. This study aims to develop a robust classification framework to address this challenge. We propose an ensemble framework combining two complementary models. The first is an attention-gated 2D CNN designed to highlight subtle hemorrhagic regions. The second is a multi-level Discrete Wavelet Transform (DWT) model that analyzes images in the frequency domain to capture deeper contextual and textural information from the 3D brain volume. The proposed ensemble model was evaluated on the RSNA, CQ500, and a new GMC clinical dataset. The empirical study demonstrates that our model consistently outperforms state-of-the-art methods across standard evaluation metrics, including accuracy, macro-averaged AUC-ROC, specificity, sensitivity, and F1-score. The novel ensembling of an attention-gated CNN and a DWT-based model provides a more comprehensive feature representation, leading to significantly improved accuracy and robustness in ICH classification, particularly in distinguishing challenging subtypes like EDH and SDH.
{"title":"Attention-Gated CNN and discrete wavelet transform based ensemble framework for brain hemorrhage classification","authors":"Srutanik Bhaduri , Rasel Mondal , Prateek Sarangi , Vinod Kumar Kurmi , Swati Goyal , Lovely Kaushal , Mahek Sodani , Tanmay Basu","doi":"10.1016/j.neuri.2025.100243","DOIUrl":"10.1016/j.neuri.2025.100243","url":null,"abstract":"<div><div>Brain hemorrhage, or Intracranial Hemorrhage (ICH), is a critical medical condition requiring rapid diagnosis. Existing Convolutional Neural Network (CNN) models often struggle to differentiate similar hemorrhage subtypes like Epidural (EDH) and Subdural (SDH) due to a lack of specific spatial feature identification. This study aims to develop a robust classification framework to address this challenge. We propose an ensemble framework combining two complementary models. The first is an attention-gated 2D CNN designed to highlight subtle hemorrhagic regions. The second is a multi-level Discrete Wavelet Transform (DWT) model that analyzes images in the frequency domain to capture deeper contextual and textural information from the 3D brain volume. The proposed ensemble model was evaluated on the RSNA, CQ500, and a new GMC clinical dataset. The empirical study demonstrates that our model consistently outperforms state-of-the-art methods across standard evaluation metrics, including accuracy, macro-averaged AUC-ROC, specificity, sensitivity, and F1-score. The novel ensembling of an attention-gated CNN and a DWT-based model provides a more comprehensive feature representation, leading to significantly improved accuracy and robustness in ICH classification, particularly in distinguishing challenging subtypes like EDH and SDH.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100243"},"PeriodicalIF":0.0,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145580157","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}
Pub Date : 2025-10-28DOI: 10.1016/j.neuri.2025.100241
Sayma Alam Suha, Rifat Shahriyar
Fetal neurodevelopment is a complex process of neural growth during pregnancy, where early detection of abnormalities is vital, and deep learning offers promising techniques for this purpose. The objective of this systematic review is to investigate deep learning applications in fetal neurodevelopment, aiming to synthesize cutting-edge research, examine methodologies, identify research gaps, and propose a federated learning framework. Following PRISMA 2020 guidelines, 55 peer-reviewed articles were selected from an initial 900 records across major databases and additional sources where each article was examined through six specific data extraction criteria. Peer-reviewed articles from 2005 to 2025, specifically those exploring automated deep learning for fetal neurodevelopment using clinical images were included, while non-deep learning analyses were excluded. Risk of bias was qualitatively assessed based on design, data diversity, validation, and reporting. Key scopes of the studies included brain segmentation and regionalization (50.91%), structural measurement (12.73%), image reconstruction, enhancement and synthesis (21.82%) and predictive modeling and clinical classification (14.55%) which also distinguishes between tasks involving pixel-level analysis and image-level predictions. The 55 included studies used diverse datasets (753 to 433,000 images) as well as synthetic image data in some recent works covering wide-ranging gestational ages, mainly using MRI and ultrasound images. The systematic analysis explicitly categorizes each study by task type, applied methodology (U-Net variants, transformer-based models, CNNs, implicit neural representations), and corresponding evaluation metrics—segmentation (DSC, IoU, HD95), classification (Accuracy, Precision, AUC), regression (MAE, RMSE, R2), and reconstruction (PSNR, SSIM), facilitating standardized performance comparisons and establishing clear benchmarks for future research in automated fetal brain imaging. Significant gaps that were identified include inadequate data diversity, privacy measures, limited clinical interpretability and validity of AI models, and insufficient integration of multimodal data. To address these challenges, a unified framework is proposed that integrates multimodal data fusion, explainable artificial intelligence (XAI) paradigms, and federated learning architectures complemented by synthetic data generation techniques to ensure robust privacy preservation in real-world application. This work was not specifically funded, and the review was not registered.
{"title":"Deep learning for fetal brain imaging: A systematic review and framework towards privacy-preserving neurodevelopmental informatics","authors":"Sayma Alam Suha, Rifat Shahriyar","doi":"10.1016/j.neuri.2025.100241","DOIUrl":"10.1016/j.neuri.2025.100241","url":null,"abstract":"<div><div>Fetal neurodevelopment is a complex process of neural growth during pregnancy, where early detection of abnormalities is vital, and deep learning offers promising techniques for this purpose. The objective of this systematic review is to investigate deep learning applications in fetal neurodevelopment, aiming to synthesize cutting-edge research, examine methodologies, identify research gaps, and propose a federated learning framework. Following PRISMA 2020 guidelines, 55 peer-reviewed articles were selected from an initial 900 records across major databases and additional sources where each article was examined through six specific data extraction criteria. Peer-reviewed articles from 2005 to 2025, specifically those exploring automated deep learning for fetal neurodevelopment using clinical images were included, while non-deep learning analyses were excluded. Risk of bias was qualitatively assessed based on design, data diversity, validation, and reporting. Key scopes of the studies included brain segmentation and regionalization (50.91%), structural measurement (12.73%), image reconstruction, enhancement and synthesis (21.82%) and predictive modeling and clinical classification (14.55%) which also distinguishes between tasks involving pixel-level analysis and image-level predictions. The 55 included studies used diverse datasets (753 to 433,000 images) as well as synthetic image data in some recent works covering wide-ranging gestational ages, mainly using MRI and ultrasound images. The systematic analysis explicitly categorizes each study by task type, applied methodology (U-Net variants, transformer-based models, CNNs, implicit neural representations), and corresponding evaluation metrics—segmentation (DSC, IoU, HD95), classification (Accuracy, Precision, AUC), regression (MAE, RMSE, R<sup>2</sup>), and reconstruction (PSNR, SSIM), facilitating standardized performance comparisons and establishing clear benchmarks for future research in automated fetal brain imaging. Significant gaps that were identified include inadequate data diversity, privacy measures, limited clinical interpretability and validity of AI models, and insufficient integration of multimodal data. To address these challenges, a unified framework is proposed that integrates multimodal data fusion, explainable artificial intelligence (XAI) paradigms, and federated learning architectures complemented by synthetic data generation techniques to ensure robust privacy preservation in real-world application. This work was not specifically funded, and the review was not registered.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100241"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465619","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}
Pub Date : 2025-10-28DOI: 10.1016/j.neuri.2025.100238
Esteban A. Alarcón-Braga , Samuel Gruffaz , Cécile Delagarde , Axel Roques , Jean-Clément Riff , Laurent Oudre , Clément Dubost
Existing methods to detect depth of sedation do not fully adjust to the characteristics of the ICU population. The aim of this study is to evaluate the performance of a two-channel EEG in predicting the depth of sedation in ICU patients. The electroencephalographic signal of 21 patients admitted to the ICU were analyzed, and EEG features were calculated. These served as inputs in 2 machine learning models: Random Forest Classifier (RFC) and Support Vector Machine (SVM). The depth of sedation was assessed using the Richmond Agitation-Sedation Scale (RASS). Patients with RASS scores of −4/−5 were classified as “Deeply Sedated”, otherwise they were classified as “Not Deeply Sedated”. In the general models, all EEG features were used, after which sequential feature selection was conducted to improve performance and reduce the number of variables (reduced models). The general models showed a moderate ability to discriminate between sedation categories (RFC: average , SVM: average ). This ability was improved in the reduced models (RFC: average , SVM: average ). It was observed that decreasing the number of features in the reduced SVM model from 6 to 3 features could achieve a similar performance while simplifying the model (SVM: average ). An exploratory analysis showed that the individual feature with the best performance was Beta Power–EEG2. Overall, the 2-channel EEG has a moderate power to discriminate between different states of sedation and may not be useful in this purpose if used as a single predictor.
{"title":"Detecting the depth of sedation in the intensive care unit using a 2-channel electroencephalogram: An analysis with 2 machine learning models","authors":"Esteban A. Alarcón-Braga , Samuel Gruffaz , Cécile Delagarde , Axel Roques , Jean-Clément Riff , Laurent Oudre , Clément Dubost","doi":"10.1016/j.neuri.2025.100238","DOIUrl":"10.1016/j.neuri.2025.100238","url":null,"abstract":"<div><div>Existing methods to detect depth of sedation do not fully adjust to the characteristics of the ICU population. The aim of this study is to evaluate the performance of a two-channel EEG in predicting the depth of sedation in ICU patients. The electroencephalographic signal of 21 patients admitted to the ICU were analyzed, and EEG features were calculated. These served as inputs in 2 machine learning models: Random Forest Classifier (RFC) and Support Vector Machine (SVM). The depth of sedation was assessed using the Richmond Agitation-Sedation Scale (RASS). Patients with RASS scores of −4/−5 were classified as “Deeply Sedated”, otherwise they were classified as “Not Deeply Sedated”. In the general models, all EEG features were used, after which sequential feature selection was conducted to improve performance and reduce the number of variables (reduced models). The general models showed a moderate ability to discriminate between sedation categories (RFC: average <span><math><mtext>F1-score</mtext><mo>=</mo><mn>0.60</mn></math></span>, SVM: average <span><math><mtext>F1-score</mtext><mo>=</mo><mn>0.59</mn></math></span>). This ability was improved in the reduced models (RFC: average <span><math><mtext>F1-score</mtext><mo>=</mo><mn>0.65</mn></math></span>, SVM: average <span><math><mtext>F1-score</mtext><mo>=</mo><mn>0.72</mn></math></span>). It was observed that decreasing the number of features in the reduced SVM model from 6 to 3 features could achieve a similar performance while simplifying the model (SVM: average <span><math><mtext>F1-score</mtext><mo>=</mo><mn>0.72</mn></math></span>). An exploratory analysis showed that the individual feature with the best performance was Beta Power–EEG2. Overall, the 2-channel EEG has a moderate power to discriminate between different states of sedation and may not be useful in this purpose if used as a single predictor.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"6 1","pages":"Article 100238"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977036","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}
Pub Date : 2025-10-28DOI: 10.1016/j.neuri.2025.100240
Bar Lehmann, Andrei V. Medvedev
Objective
Power-to-power cross-frequency coupling (CFC) is a novel method to index the dynamic spatio-temporal interactions between brain rhythms, including high frequency oscillations (HFOs). This research evaluates this promising method's capacity for seizure detection with intracranial EEG. Seizures can be conceptualized as composites of different electrographic patterns including (1) spike, (2) ripple-on-spike, and (3) ripple-on-oscillation. This study also performs a basic CFC analysis of each of these components which has potential to further the understanding of epileptogenic processes.
Methods
In this study, deep learning networks including Stacked Sparse Autoencoder (SSAE) and Long Short Term Memory (LSTM) are trained to detect seizures and help characterize CFC patterns for these three common seizure components. The analysis uses intracranial EEG (iEEG) records from the ieeg.org (Mayo Clinic files) database. Temporal Lobe Epilepsy (TLE) seizures () from 26 patients were analyzed along with segments of background activity. Power-to-power coupling was calculated between all frequencies 1–250 Hz pairwise using the EEGLAB toolbox. CFC matrices of seizure and background activity were used as training or testing inputs to the autoencoder.
Results
The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 90.2%, specificity of 96.8% and overall accuracy of 93.4%. The three seizure components (spike, ripple-on-spike, ripple-on-oscillation) were also observed to have unique CFC signatures.
Conclusions
The results provide evidence both for (1) the relevance of power-to-power coupling (PPC) for TLE seizure detection in iEEG, as well as (2) there existing unique PPC signatures of three common seizure components.
{"title":"Power-to-power cross-frequency coupling as a novel approach for temporal lobe seizure detection and analysis","authors":"Bar Lehmann, Andrei V. Medvedev","doi":"10.1016/j.neuri.2025.100240","DOIUrl":"10.1016/j.neuri.2025.100240","url":null,"abstract":"<div><h3>Objective</h3><div>Power-to-power cross-frequency coupling (CFC) is a novel method to index the dynamic spatio-temporal interactions between brain rhythms, including high frequency oscillations (HFOs). This research evaluates this promising method's capacity for seizure detection with intracranial EEG. Seizures can be conceptualized as composites of different electrographic patterns including (1) spike, (2) ripple-on-spike, and (3) ripple-on-oscillation. This study also performs a basic CFC analysis of each of these components which has potential to further the understanding of epileptogenic processes.</div></div><div><h3>Methods</h3><div>In this study, deep learning networks including Stacked Sparse Autoencoder (SSAE) and Long Short Term Memory (LSTM) are trained to detect seizures and help characterize CFC patterns for these three common seizure components. The analysis uses intracranial EEG (iEEG) records from the ieeg.org (Mayo Clinic files) database. Temporal Lobe Epilepsy (TLE) seizures (<span><math><mi>n</mi><mo>=</mo><mn>120</mn></math></span>) from 26 patients were analyzed along with segments of background activity. Power-to-power coupling was calculated between all frequencies 1–250 Hz pairwise using the EEGLAB toolbox. CFC matrices of seizure and background activity were used as training or testing inputs to the autoencoder.</div></div><div><h3>Results</h3><div>The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 90.2%, specificity of 96.8% and overall accuracy of 93.4%. The three seizure components (spike, ripple-on-spike, ripple-on-oscillation) were also observed to have unique CFC signatures.</div></div><div><h3>Conclusions</h3><div>The results provide evidence both for (1) the relevance of power-to-power coupling (PPC) for TLE seizure detection in iEEG, as well as (2) there existing unique PPC signatures of three common seizure components.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100240"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465624","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}
Neuroscience research publications encompass a vast wealth of knowledge. Accurately retrieving existing information and discovering new insights from this extensive literature is essential for advancing the field. However, when knowledge is dispersed across multiple sources, current state-of-the-art retrieval methods often struggle to extract the necessary information. A knowledge graph (KG) can integrate and link knowledge from multiple sources. However, existing methods for constructing KGs in neuroscience often rely on labeled data and require domain expertise. Acquiring large-scale, labeled data for a specialized area like neuroscience presents significant challenges. This work proposes novel methods for constructing KG from unlabeled large-scale neuroscience research corpus utilizing large language models (LLM), neuroscience ontology, and text embeddings. We analyze the semantic relevance of neuroscience text segments identified by LLM for building the knowledge graph. We also introduce an entity-augmented information retrieval algorithm to extract knowledge from the KG. Several experiments were conducted to evaluate the proposed approaches. The results demonstrate that our methods significantly enhance knowledge discovery from the unlabeled neuroscience research corpus. The performance of the proposed entity and relation extraction method is comparable to the existing supervised method. It achieves an F1 score of 0.84 for entity extraction from the unlabeled data. The knowledge obtained from the KG improves answers to over 52% of neuroscience questions from the PubMedQA dataset and questions generated using selected neuroscience entities.
{"title":"Entity-augmented neuroscience knowledge retrieval using ontology and semantic understanding capability of LLM","authors":"Pralaypati Ta , Sriram Venkatesaperumal , Keerthi Ram , Mohanasankar Sivaprakasam","doi":"10.1016/j.neuri.2025.100237","DOIUrl":"10.1016/j.neuri.2025.100237","url":null,"abstract":"<div><div>Neuroscience research publications encompass a vast wealth of knowledge. Accurately retrieving existing information and discovering new insights from this extensive literature is essential for advancing the field. However, when knowledge is dispersed across multiple sources, current state-of-the-art retrieval methods often struggle to extract the necessary information. A knowledge graph (KG) can integrate and link knowledge from multiple sources. However, existing methods for constructing KGs in neuroscience often rely on labeled data and require domain expertise. Acquiring large-scale, labeled data for a specialized area like neuroscience presents significant challenges. This work proposes novel methods for constructing KG from unlabeled large-scale neuroscience research corpus utilizing large language models (LLM), neuroscience ontology, and text embeddings. We analyze the semantic relevance of neuroscience text segments identified by LLM for building the knowledge graph. We also introduce an entity-augmented information retrieval algorithm to extract knowledge from the KG. Several experiments were conducted to evaluate the proposed approaches. The results demonstrate that our methods significantly enhance knowledge discovery from the unlabeled neuroscience research corpus. The performance of the proposed entity and relation extraction method is comparable to the existing supervised method. It achieves an F1 score of 0.84 for entity extraction from the unlabeled data. The knowledge obtained from the KG improves answers to over 52% of neuroscience questions from the PubMedQA dataset and questions generated using selected neuroscience entities.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100237"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465738","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}
Pub Date : 2025-10-28DOI: 10.1016/j.neuri.2025.100239
Liu Zhenyi , Ye Cun Chun
Objective
This study explores the use of Convolutional Neural Networks (CNNs) to analyze House-Tree-Person (HTP) drawings for the classification of depression severity, addressing the subjectivity and limitations of traditional psychological assessment methods.
Methods
A dataset of 1,020 HTP drawings from adults aged 25–30 was collected, consisting of 432 healthy controls, 336 patients with moderate depression, and 252 patients with severe depression. The drawings were labeled based on the Hamilton Depression Scale (HAMD). A CNN model was trained and optimized using cross-validation to extract and classify depression-related visual features. The model's performance was evaluated using accuracy, recall, F1-score, and area under the ROC curve (AUC).
Results
The CNN model demonstrated a classification accuracy of 89% for distinguishing normal and depressed individuals, with an AUC of 0.96. In differentiating moderate from severe depression, the model achieved an AUC of 1.00, indicating near-perfect classification. The extracted features, such as line clarity and detail richness, correlated with depression severity, confirming their diagnostic relevance.
Conclusion
The study validates CNN-based image analysis as an effective and objective method for depression assessment using HTP drawings. The model not only improves accuracy but also offers potential applications in automated mental health screening. Future research should integrate multimodal data, such as speech and physiological signals, to enhance diagnostic precision.
{"title":"Feature analysis of depression patients' house-tree-person drawings using convolutional neural networks","authors":"Liu Zhenyi , Ye Cun Chun","doi":"10.1016/j.neuri.2025.100239","DOIUrl":"10.1016/j.neuri.2025.100239","url":null,"abstract":"<div><h3>Objective</h3><div>This study explores the use of Convolutional Neural Networks (CNNs) to analyze House-Tree-Person (HTP) drawings for the classification of depression severity, addressing the subjectivity and limitations of traditional psychological assessment methods.</div></div><div><h3>Methods</h3><div>A dataset of 1,020 HTP drawings from adults aged 25–30 was collected, consisting of 432 healthy controls, 336 patients with moderate depression, and 252 patients with severe depression. The drawings were labeled based on the Hamilton Depression Scale (HAMD). A CNN model was trained and optimized using cross-validation to extract and classify depression-related visual features. The model's performance was evaluated using accuracy, recall, F1-score, and area under the ROC curve (AUC).</div></div><div><h3>Results</h3><div>The CNN model demonstrated a classification accuracy of 89% for distinguishing normal and depressed individuals, with an AUC of 0.96. In differentiating moderate from severe depression, the model achieved an AUC of 1.00, indicating near-perfect classification. The extracted features, such as line clarity and detail richness, correlated with depression severity, confirming their diagnostic relevance.</div></div><div><h3>Conclusion</h3><div>The study validates CNN-based image analysis as an effective and objective method for depression assessment using HTP drawings. The model not only improves accuracy but also offers potential applications in automated mental health screening. Future research should integrate multimodal data, such as speech and physiological signals, to enhance diagnostic precision.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 4","pages":"Article 100239"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416057","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}
Pub Date : 2025-09-30DOI: 10.1016/j.neuri.2025.100235
Estanislao Arana
{"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}
Pub Date : 2025-09-30DOI: 10.1016/j.neuri.2025.100236
Mishu Deb Nath , Md. Khabir Uddin Ahamed , Omayer Ahmed , Tanvir Ahmed , Sujit Roy , Mohammed Nasir Uddin
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.
{"title":"Smart web interface for student mental health prediction using machine learning with blockchain technology","authors":"Mishu Deb Nath , Md. Khabir Uddin Ahamed , Omayer Ahmed , Tanvir Ahmed , Sujit Roy , 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}
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.
{"title":"Functional MRI in hypertension – A systematic review of brain connectivity, regional activity, and cognitive impairment","authors":"Sathya Sabina Muthu , Suresh Sukumar , Rajagopal Kadavigere , Shivashankar K.N. , K. Vaishali , Ramesh Babu M.G. , Hari Prakash Palaniswamy , Abhimanyu Pradhan , Winniecia Dkhar , Nitika C. Panakkal , Sneha Ravichandran , Dilip Shettigar , 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}
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