Frontiers in Neuroinformatics最新文献

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Identifying natural inhibitors against FUS protein in dementia through machine learning, molecular docking, and dynamics simulation.
IF 2.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2025-02-05 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1439090
Darwin Li
{"title":"Identifying natural inhibitors against FUS protein in dementia through machine learning, molecular docking, and dynamics simulation.","authors":"Darwin Li","doi":"10.3389/fninf.2024.1439090","DOIUrl":"10.3389/fninf.2024.1439090","url":null,"abstract":"<p><p>Dementia, a complex and debilitating spectrum of neurodegenerative diseases, presents a profound challenge in the quest for effective treatments. The FUS protein is well at the center of this problem, as it is frequently dysregulated in the various disorders. We chose a route of computational work that involves targeting natural inhibitors of the FUS protein, offering a novel treatment strategy. We first reviewed the FUS protein's framework; early forecasting models using the AlphaFold2 and SwissModel algorithms indicated a loop-rich protein-a structure component correlating with flexibility. However, these models showed limitations, as reflected by inadequate ERRAT and Verify3D scores. Seeking enhanced accuracy, we turned to the I-TASSER suite, which delivered a refined structural model affirmed by robust validation metrics. With a reliable model in hand, our study utilized machine learning techniques, particularly the Random Forest algorithm, to navigate through a vast dataset of phytochemicals. This led to the identification of nimbinin, dehydroxymethylflazine, and several other compounds as potential FUS inhibitors. Notably, dehydroxymethylflazine and cleroindicin C identified during molecular docking analyses-facilitated by AutoDock Vina-for their high binding affinities and stability in interaction with the FUS protein, as corroborated by extensive molecular dynamics simulations. Originating from medicinal plants, these compounds are not only structurally compatible with the target protein but also adhere to pharmacokinetic profiles suitable for drug development, including optimal molecular weight and LogP values conducive to blood-brain barrier penetration. This computational exploration paves the way for subsequent experimental validation and highlights the potential of these natural compounds as innovative agents in the treatment of dementia.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"18 ","pages":"1439090"},"PeriodicalIF":2.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11835945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143457473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM.
IF 2.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2025-01-29 eCollection Date: 2025-01-01 DOI: 10.3389/fninf.2025.1530047
Peipei Zeng, Shuimiao Kang, Fan Fan, Jiyuan Liu
{"title":"Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM.","authors":"Peipei Zeng, Shuimiao Kang, Fan Fan, Jiyuan Liu","doi":"10.3389/fninf.2025.1530047","DOIUrl":"10.3389/fninf.2025.1530047","url":null,"abstract":"<p><p>Anomaly detection is a typical binary classification problem under the condition of unbalanced samples, which has been widely used in various fields of data mining. For example, it can help detect heart murmurs when the heart is structurally abnormal, to tell if a newborn has congenital heart disease. Due to the low time and high efficiency, most work focuses on the semi- supervised anomaly detection method. However, the anomaly detection effect of this method is not high because of massive data with uneven samples and different noise. To improve the accuracy of anomaly detection under unbalanced sample conditions, we propose a new semi-supervised anomaly detection method (WCOS) based on semi-supervised clustering, which combines wavelet reconstruction, convolutional autoencoder, and one classification support vector machine. In this way, we can not only distinguish a small proportion of abnormal heart sounds in the huge data scale but also filter the noise through the noise reduction network, thus significantly improving the detection accuracy. In addition, we evaluated our method using real datasets. When the noise of sigma = 0.5, the AUC standard deviation of the WR-CAE-OCSVM is 19.2, 54.1, and 29.8% lower than that of WR-OCSVM, CAE-OCSVM and OCSVM, respectively. The results confirmed the higher accuracy of anomaly detection in WCOS compared to other state-of-the-art methods.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1530047"},"PeriodicalIF":2.5,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11813893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143406035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial: Recent applications of noninvasive physiological signals and artificial intelligence.
IF 2.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2025-01-16 eCollection Date: 2025-01-01 DOI: 10.3389/fninf.2025.1543103
Irma N Angulo, Eduardo Iáñez, Andres Ubeda
{"title":"Editorial: Recent applications of noninvasive physiological signals and artificial intelligence.","authors":"Irma N Angulo, Eduardo Iáñez, Andres Ubeda","doi":"10.3389/fninf.2025.1543103","DOIUrl":"https://doi.org/10.3389/fninf.2025.1543103","url":null,"abstract":"","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1543103"},"PeriodicalIF":2.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11779702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143079233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Power spectral analysis of voltage-gated channels in neurons.
IF 2.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2025-01-15 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1472499
Christophe Magnani, Lee E Moore
{"title":"Power spectral analysis of voltage-gated channels in neurons.","authors":"Christophe Magnani, Lee E Moore","doi":"10.3389/fninf.2024.1472499","DOIUrl":"https://doi.org/10.3389/fninf.2024.1472499","url":null,"abstract":"<p><p>This article develops a fundamental insight into the behavior of neuronal membranes, focusing on their responses to stimuli measured with power spectra in the frequency domain. It explores the use of linear and nonlinear (quadratic sinusoidal analysis) approaches to characterize neuronal function. It further delves into the random theory of internal noise of biological neurons and the use of stochastic Markov models to investigate these fluctuations. The text also discusses the origin of conductance noise and compares different power spectra for interpreting this noise. Importantly, it introduces a novel sequential chemical state model, named <i>p</i> <sub>2</sub>, which is more general than the Hodgkin-Huxley formulation, so that the probability for an ion channel to be open does not imply exponentiation. In particular, it is demonstrated that the <i>p</i> <sub>2</sub> (without exponentiation) and <i>n</i> <sup>4</sup> (with exponentiation) models can produce similar neuronal responses. A striking relationship is also shown between fluctuation and quadratic power spectra, suggesting that voltage-dependent random mechanisms can have a significant impact on deterministic nonlinear responses, themselves known to have a crucial role in the generation of action potentials in biological neural networks.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"18 ","pages":"1472499"},"PeriodicalIF":2.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143064901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository: rationale and blueprint. 多中心急性缺血性卒中成像和临床数据(MAGIC)存储库:原理和蓝图。
IF 2.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2025-01-07 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1508161
Hakim Baazaoui, Stefan T Engelter, Henrik Gensicke, Lukas S Enz, Marios Psychogios, Matthias Mutke, Patrik Michel, Davide Strambo, Alexander Salerno, Henk A Marquering, Paul J Nederkoorn, Nabila Wali, Stephanie Tanadini-Lang, Björn Menze, Ezequiel de la Rosa, Kaiyuan Yang, Gian Marco De Marchis, Tolga D Dittrich, Francesco Valletta, Manon Germann, Carlo W Cereda, João Pedro Marto, Lisa Herzog, Patrick Hirschi, Zsolt Kulcsar, Susanne Wegener
{"title":"The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository: rationale and blueprint.","authors":"Hakim Baazaoui, Stefan T Engelter, Henrik Gensicke, Lukas S Enz, Marios Psychogios, Matthias Mutke, Patrik Michel, Davide Strambo, Alexander Salerno, Henk A Marquering, Paul J Nederkoorn, Nabila Wali, Stephanie Tanadini-Lang, Björn Menze, Ezequiel de la Rosa, Kaiyuan Yang, Gian Marco De Marchis, Tolga D Dittrich, Francesco Valletta, Manon Germann, Carlo W Cereda, João Pedro Marto, Lisa Herzog, Patrick Hirschi, Zsolt Kulcsar, Susanne Wegener","doi":"10.3389/fninf.2024.1508161","DOIUrl":"10.3389/fninf.2024.1508161","url":null,"abstract":"<p><strong>Purpose: </strong>The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository is a collaboration established in 2024 by seven stroke centres in Europe. MAGIC consolidates clinical and radiological data from acute ischemic stroke (AIS) patients who underwent endovascular therapy, intravenous thrombolysis, a combination of both, or conservative management.</p><p><strong>Participants: </strong>All centres ensure accuracy and completeness of the data. Only patients who did not refuse use of their routine data collected during or after their hospital stay are included in the repository. Approvals or waivers are obtained from the responsible ethics committees before data exchange. A formal data transfer agreement (DTA) is signed by all contributing centres. The centres then share their data, and files are stored centrally on a safe server at the University Hospital Zurich. There, patient identifiers are removed and images are algorithmically de-faced. De-identified structured clinical data are connected to the imaging data by a new identifier. Data are made available to participating centres which have entered into a DTA for stroke research projects.</p><p><strong>Repository setup: </strong>Initially, MAGIC is set to comprise initial and first follow-up imaging of 2,500 AIS patients. Clinical data consist of a comprehensive set of patient characteristics and routine prehospital metrics, treatment and laboratory variables.</p><p><strong>Outlook: </strong>Our repository will support research by leveraging the entire range of routinely collected imaging and clinical data. This dataset reflects the current state of practice in stroke patient evaluation and management and will enable researchers to retrospectively study clinically relevant questions outside the scope of randomized controlled clinical trials. New centres are invited to join MAGIC if they meet the requirements outlined here. We aim to reach approximately 10,000 cases by 2026.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"18 ","pages":"1508161"},"PeriodicalIF":2.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11747442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143002715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial: Protecting privacy in neuroimaging analysis: balancing data sharing and privacy preservation. 编辑:保护神经影像分析中的隐私:平衡数据共享和隐私保护。
IF 2.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2025-01-07 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1543121
Rashid Mehmood, Mariana Lazar, Xiaohui Liang, Juan M Corchado, Simon See
{"title":"Editorial: Protecting privacy in neuroimaging analysis: balancing data sharing and privacy preservation.","authors":"Rashid Mehmood, Mariana Lazar, Xiaohui Liang, Juan M Corchado, Simon See","doi":"10.3389/fninf.2024.1543121","DOIUrl":"10.3389/fninf.2024.1543121","url":null,"abstract":"","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"18 ","pages":"1543121"},"PeriodicalIF":2.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11746894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143002683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection.
IF 2.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2025-01-07 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1507217
Sofia Biju Francis, Jai Prakash Verma
{"title":"Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection.","authors":"Sofia Biju Francis, Jai Prakash Verma","doi":"10.3389/fninf.2024.1507217","DOIUrl":"10.3389/fninf.2024.1507217","url":null,"abstract":"<p><strong>Introduction: </strong>The prevalence of age-related brain issues has risen in developed countries because of changes in lifestyle. Alzheimer's disease leads to a rapid and irreversible decline in cognitive abilities by damaging memory cells.</p><p><strong>Methods: </strong>A ResNet-18-based system is proposed, integrating Depth Convolution with a Squeeze and Excitation (SE) block to minimize tuning parameters. This design is based on analyses of existing deep learning architectures and feature extraction techniques. Additionally, pre-trained ResNet-18 models were created with and without the SE block to compare ROC and accuracy values across different hyperparameters.</p><p><strong>Results: </strong>The proposed model achieved ROC values of 95% for Alzheimer's Disease (AD), 95% for Cognitively Normal (CN), and 93% for Mild Cognitive Impairment (MCI), with a maximum test accuracy of 88.51%. However, the pre-trained model with SE had 93.26% accuracy and ROC values of 98%, 99%, and 98%, while the model without SE had 94%, 97%, and 94% ROC values and 92.41% accuracy.</p><p><strong>Discussion: </strong>Collecting medical data can be expensive and raises ethical concerns. Small data sets are also prone to local minima issues in the cost function. A scratch model that experiences extensive hyperparameter tuning may end up being either overfitted or underfitted. Class imbalance also reduces performance. Transfer learning is most effective with small, imbalanced datasets, and pre-trained models with SE blocks perform better than others. The proposed model introduced a method to reduce training parameters and prevent overfitting from imbalanced medical data. Overall performance findings show that the suggested approach performs better than the state-of-the-art techniques.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"18 ","pages":"1507217"},"PeriodicalIF":2.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11752122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143022894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Leveraging deep learning for robust EEG analysis in mental health monitoring. 在精神健康监测中利用深度学习进行鲁棒脑电图分析。
IF 2.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2025-01-03 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1494970
Zixiang Liu, Juan Zhao
{"title":"Leveraging deep learning for robust EEG analysis in mental health monitoring.","authors":"Zixiang Liu, Juan Zhao","doi":"10.3389/fninf.2024.1494970","DOIUrl":"10.3389/fninf.2024.1494970","url":null,"abstract":"<p><strong>Introduction: </strong>Mental health monitoring utilizing EEG analysis has garnered notable interest due to the non-invasive characteristics and rich temporal information encoded in EEG signals, which are indicative of cognitive and emotional conditions. Conventional methods for EEG-based mental health evaluation often depend on manually crafted features or basic machine learning approaches, like support vector classifiers or superficial neural networks. Despite the potential of these approaches, they often fall short in capturing the intricate spatiotemporal relationships within EEG data, leading to lower classification accuracy and poor adaptability across various populations and mental health scenarios.</p><p><strong>Methods: </strong>To overcome these limitations, we introduce the EEG Mind-Transformer, an innovative deep learning architecture composed of a Dynamic Temporal Graph Attention Mechanism (DT-GAM), a Hierarchical Graph Representation and Analysis (HGRA) module, and a Spatial-Temporal Fusion Module (STFM). The DT-GAM is designed to dynamically extract temporal dependencies within EEG data, while the HGRA models the brain's hierarchical structure to capture both localized and global interactions among different brain regions. The STFM synthesizes spatial and temporal elements, generating a comprehensive representation of EEG signals.</p><p><strong>Results and discussion: </strong>Our empirical results confirm that the EEG Mind-Transformer significantly surpasses conventional approaches, achieving an accuracy of 92.5%, a recall of 91.3%, an F1-score of 90.8%, and an AUC of 94.2% across several datasets. These findings underline the model's robustness and its generalizability to diverse mental health conditions. Moreover, the EEG Mind-Transformer not only pushes the boundaries of state-of-the-art EEG-based mental health monitoring but also offers meaningful insights into the underlying brain functions associated with mental disorders, solidifying its value for both research and clinical settings.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"18 ","pages":"1494970"},"PeriodicalIF":2.5,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11739345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143002686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial: Emerging trends in large-scale data analysis for neuroscience research. 社论:神经科学研究中大规模数据分析的新趋势。
IF 2.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2024-12-20 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1538787
Farouk S Nathoo, Olave E Krigolson, Fang Wang
{"title":"Editorial: Emerging trends in large-scale data analysis for neuroscience research.","authors":"Farouk S Nathoo, Olave E Krigolson, Fang Wang","doi":"10.3389/fninf.2024.1538787","DOIUrl":"https://doi.org/10.3389/fninf.2024.1538787","url":null,"abstract":"","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"18 ","pages":"1538787"},"PeriodicalIF":2.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11695274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks. 用判别分析和神经网络对短期记忆任务中基于roi的fMRI数据分类。
IF 2.5 4区 医学
Frontiers in Neuroinformatics Pub Date : 2024-12-20 eCollection Date: 2024-01-01 DOI: 10.3389/fninf.2024.1480366
Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T Podolak, Paweł Oświęcimka
{"title":"Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks.","authors":"Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T Podolak, Paweł Oświęcimka","doi":"10.3389/fninf.2024.1480366","DOIUrl":"https://doi.org/10.3389/fninf.2024.1480366","url":null,"abstract":"<p><p>Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging. In this contribution, we used machine learning techniques to classify tasks in a working memory experiment and identify the brain areas involved in processing information. We employed classical discriminators and neural networks (convolutional and residual) to differentiate between brain responses to distinct types of visual stimuli (visuospatial and verbal) and different phases of the experiment (information encoding and retrieval). The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. Additionally, we developed an algorithm that took into account feature correlations to estimate the most important brain regions for the model's accuracy. Our findings suggest that from the perspective of considered models, brain signals related to the resting state have a similar degree of complexity to those related to the encoding phase, which does not improve the model's accuracy. However, during the retrieval phase, the signals were easily distinguished from the resting state, indicating their different structure. The study identified brain regions that are crucial for processing information in working memory, as well as the differences in the dynamics of encoding and retrieval processes. Furthermore, our findings indicate spatiotemporal distinctions related to these processes. The analysis confirmed the importance of the basal ganglia in processing information during the retrieval phase. The presented results reveal the benefits of applying machine learning algorithms to investigate working memory dynamics.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"18 ","pages":"1480366"},"PeriodicalIF":2.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11695337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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