Neural fingerprinting is the identification of individuals in a cohort based on neuroimaging recordings of brain activity. In magneto- and electroencephalography (M/EEG), it is common practice to use second-order statistical measures, such as correlation or connectivity matrices, when neural fingerprinting is performed. These measures or features typically require coupling between signal channels and often ignore the individual temporal dynamics. In this study, we show that, following recent advances in multivariate time series classification, such as the development of the RandOm Convolutional KErnel Transformation (ROCKET) classifier, it is possible to perform classification directly on short time segments from MEG resting-state recordings with remarkably high classification accuracies. In a cohort of 124 subjects, it was possible to assign windows of time series of 1 s in duration to the correct subject with above 99% accuracy. The achieved accuracies are vastly superior to those of previous methods while simultaneously requiring considerably shorter time segments.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10547883 | PMC |
| http://dx.doi.org/10.3389/fnins.2023.1229371 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Metallomic Classification of Pulmonary Nodules Using Blood by Deep-Learning-Boosted Synchrotron Radiation X-ray Fluorescence.
Environ Health (Wash)
January 2025
CAS-HKU Joint Laboratory of Metallomics on Health and Environment, & CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, & Beijing Metallomics Facility, & National Consortium for Excellence in Metallomics, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China.
Ambient air pollution is an important contributor to increasing cases of lung cancer, which is a malignant cancer with the highest mortality among all cancers. It primarily manifests in the form of pulmonary nodules, but not all will develop into lung cancer. Therefore, it is highly desired to distinguish between benign and malignant pulmonary nodules for the early prevention and treatment of lung cancer.
View Article and Find Full Text PDFComparative Analysis of Recurrent Neural Networks with Conjoint Fingerprints for Skin Corrosion Prediction.
J Chem Inf Model
January 2025
Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
Skin corrosion assessment is an essential toxicity end point that addresses safety concerns for topical dosage forms and cosmetic products. Previously, skin corrosion assessments required animal testing; however, differences in skin architecture and ethical concerns regarding animal models have fostered the advancement of alternative methods such as and models. This study aimed to develop deep learning (DL) models based on recurrent neural networks (RNNs) for classifying skin corrosion of chemical compounds based on chemical language notation, molecular substructure, physicochemical properties, and a combination of these three properties called conjoint fingerprints.
View Article and Find Full Text PDFMGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction.
BMC Bioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China.
Background: Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional prediction methods relying on demographic and genetic data often fall short in accuracy and robustness. Recent graph-based models, while promising, frequently neglect the critical role of atomic interactions and fail to integrate drug fingerprints with SMILES for comprehensive molecular graph construction.
View Article and Find Full Text PDFDecoding ruminative reflection in healthy individuals: The role of triple network connectivity.
Int J Clin Health Psychol
October 2024
Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China.
Ruminative reflection has been linked to enhanced executive control in processing internally represented emotional information, suggesting it may serve as an adaptive strategy for emotion regulation. Investigating the neural substrates of reflection can deepen our understanding of its adaptive properties. This study used network-based statistic (NBS)-Predict methodology to identify resting state functional connectivity (FC)-based predictors of ruminative reflection in a healthy sample.
View Article and Find Full Text PDFNeurobiological fingerprints of negative symptoms in schizophrenia identified by connectome-based modeling.
Psychiatry Clin Neurosci
January 2025
Department of Radiology, and Functional and Molecular Imaging key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
Aim: As a central component of schizophrenia psychopathology, negative symptoms result in detrimental effects on long-term functional prognosis. However, the neurobiological mechanism underlying negative symptoms remains poorly understood, which limits the development of novel treatment interventions. This study aimed to identify the specific neural fingerprints of negative symptoms in schizophrenia.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!