Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications

Journal of Clinical Medicine, Год журнала: 2025, Номер 14(2), С. 550 - 550

Опубликована: Янв. 16, 2025

The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding the brain, unlocking new possibilities in research, diagnosis, therapy. This review explores how AI’s cutting-edge algorithms—ranging from deep learning to neuromorphic computing—are revolutionizing by enabling analysis complex neural datasets, neuroimaging electrophysiology genomic profiling. These advancements are transforming early detection neurological disorders, enhancing brain–computer interfaces, driving personalized medicine, paving way for more precise adaptive treatments. Beyond applications, itself has inspired AI innovations, with architectures brain-like processes shaping advances algorithms explainable models. bidirectional exchange fueled breakthroughs such as dynamic connectivity mapping, real-time decoding, closed-loop systems that adaptively respond states. However, challenges persist, including issues data integration, ethical considerations, “black-box” nature many systems, underscoring need transparent, equitable, interdisciplinary approaches. By synthesizing latest identifying future opportunities, this charts a path forward integration neuroscience. From harnessing multimodal cognitive augmentation, fusion these fields not just brain science, it reimagining human potential. partnership promises where mysteries unlocked, offering unprecedented healthcare, technology, beyond.

Язык: Английский

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The history, current state and future possibilities of the non-invasive brain computer interfaces

Medicine in Novel Technology and Devices, Год журнала: 2025, Номер 25, С. 100353 - 100353

Опубликована: Фев. 1, 2025

Язык: Английский

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Neuroimage analysis using artificial intelligence approaches: a systematic review

Medical & Biological Engineering & Computing, Год журнала: 2024, Номер 62(9), С. 2599 - 2627

Опубликована: Апрель 26, 2024

Язык: Английский

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Determination of the Time-frequency Features for Impulse Components in EEG Signals

Neuroinformatics, Год журнала: 2025, Номер 23(2)

Опубликована: Янв. 23, 2025

Язык: Английский

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Artificial Intelligence in Cardiac Emergencies: A Review

Indian Journal of Clinical Cardiology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 3, 2025

Sudden cardiac arrest is a major public health problem as it accounts for nearly 1,000 deaths per day worldwide. An estimated 80% of these occur outside hospitals, with less than 20% survival out-of-hospital victims and around 30% in-hospital victims. Delays in recognizing sudden initiating high-quality cardiopulmonary resuscitation result significant neurological problems like post-anoxic coma vegetative states. Human expertise integrated artificial intelligence will contribute to dramatic improvement outcomes by aiding emergency physicians making critical decisions the management prognostication patient outcomes.

Язык: Английский

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GEM-CRAP: a fusion architecture for focal seizure detection

Journal of Translational Medicine, Год журнала: 2025, Номер 23(1)

Опубликована: Апрель 5, 2025

Identification of seizures is essential for the treatment epilepsy. Current machine-learning and deep-learning models often perform well on public datasets when classifying generalized with prominent features. However, their performance was less effective in detecting brief, localized seizures. These seizure-like patterns can be masked by fixed brain rhythms. Our study proposes a supervised multilayer hybrid model called GEM-CRAP (gradient-enhanced modulation CNN-RES, attention-like, pre-policy networks), three parallel feature extraction channels: CNN-RES module, an amplitude-aware channel attention-like mechanisms, LSTM-based layer integrated into recurrent neural network. The trained Xuanwu Hospital HUP iEEG dataset, including intracranial, cortical, stereotactic EEG data from 83 patients, covering over 8500 labeled electrode channels classification (wakefulness sleep). A post-SVM network used secondary training accuracy below 80%. We introduced average deviation rate metric to assess seizure detection accuracy. For datasets, achieved 97% intracranial cortical sequences 95% mixed sequences, deviations 5%. In it maintained 94% wakefulness around 90% during sleep. SVM improved 10%. Additionally, strong positive correlation found between distribution temporal states. enhances focal epilepsy through adaptive adjustments attention achieving higher precision robustness complex signal environments. Beyond improving interval detection, excels identifying analyzing specific epileptic waveforms, such as high-frequency oscillations. This advancement may pave way more precise diagnostics provide suitable artificial intelligence algorithm closed-loop neurostimulation.

Язык: Английский

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AI-Enhanced Neurophysiological Assessment

Advances in psychology, mental health, and behavioral studies (APMHBS) book series, Год журнала: 2025, Номер unknown, С. 33 - 64

Опубликована: Янв. 3, 2025

Advancements in artificial intelligence (AI) are revolutionizing neurophysiology, enhancing precision and efficiency assessing brain nervous system function. AI-driven neurophysiological assessment integrates machine learning, deep neural networks, advanced data analytics to process complex from electroencephalography, electromyography techniques. This technology enables earlier diagnosis of neurological disorders like epilepsy Alzheimer's by detecting subtle patterns that may be missed human analysis. AI also facilitates real-time monitoring predictive analytics, improving outcomes critical care neurorehabilitation. Challenges include ensuring quality, addressing ethical concerns, overcoming computational limits. The integration into neurophysiology offers a precise, scalable, accessible approach treating disorders. chapter discusses the methodologies, applications, future directions assessment, emphasizing its transformative impact clinical research fields.

Язык: Английский

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Dynamic reconstruction of electroencephalogram data using RBF neural networks

Frontiers in Neuroscience, Год журнала: 2025, Номер 19

Опубликована: Март 28, 2025

Electroencephalography (EEG) is widely used for analyzing brain activity; however, the nonlinear and nature of EEG signals presents significant challenges traditional analysis methods. Machine has shown great promise in addressing these limitations. This study proposes a novel approach using Radial Function (RBF) neural networks optimized by Particle Swarm Optimization (PSO) to reconstruct dynamics extract age-related characteristics. recordings were collected from 142 participants spanning multiple age groups. Signals preprocessed through bandpass filtering (1-35 Hz) Independent Component Analysis (ICA) artifact removal. network was trained on time-series data with PSO employed optimize model parameters identify fixed points reconstructed system. Statistical analyses including ANOVA Kruskal-Wallis tests performed assess differences fixed-point coordinates. The RBF demonstrated high accuracy signal reconstruction across different frequency normalized root mean square error (NRMSE) 0.0671 ± 0.0074 Pearson correlation coefficient 0.0678. Spectral time-frequency confirmed s capability accurately capture oscillations. Importantly coordinates revealed distinct age-related. These findings suggest that can serve as quantitative markers aging providing new insights into age-dependent changes dynamics. proposed method offers computationally efficient interpretable potential applications neurological diagnosis cognitive research.

Язык: Английский

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A Lightweight Multi-Mental Disorders Detection Method Using Entropy-Based Matrix from Single-Channel EEG Signals

Brain Sciences, Год журнала: 2024, Номер 14(10), С. 987 - 987

Опубликована: Сен. 28, 2024

Background: Mental health issues are increasingly prominent worldwide, posing significant threats to patients and deeply affecting their families social relationships. Traditional diagnostic methods subjective delayed, indicating the need for an objective effective early diagnosis method. Methods: To this end, paper proposes a lightweight detection method multi-mental disorders with fewer data sources, aiming improve procedures enable patient detection. First, proposed takes Electroencephalography (EEG) signals as acquires brain rhythms through Discrete Wavelet Decomposition (DWT), extracts approximate entropy, fuzzy permutation sample entropy establish entropy-based matrix. Then, six kinds of conventional machine learning classifiers, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), Generalized Additive Model (GAM), Linear Discriminant Analysis (LDA), Decision Tree (DT), adopted matrix achieve task. Their performances assessed by accuracy, sensitivity, specificity, F1-score. Concerning these experiments, three public datasets schizophrenia, epilepsy, depression utilized validation. Results: The analysis results from identifies representative single-channel (schizophrenia: O1, epilepsy: F3, depression: O2), satisfying classification accuracies (88.10%, 75.47%, 89.92%, respectively) minimal input. Conclusions: Such impressive when considering sources concern, which also improves interpretability features in EEG, providing reliable approach advancing insights into underlying mechanisms pathological states.

Язык: Английский

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Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Март 21, 2024

Deep learning methods are increasingly being applied to raw electroencephalogram (EEG) data. However, if these models be used in clinical or research contexts, explain them must developed, and for combining explanations across large numbers of developed counteract the inherent randomness existing training approaches. Model visualization-based explainability EEG involve structuring a model architecture such that its extracted features can characterized have potential offer highly useful insights into patterns they uncover. Nevertheless, been underexplored within context multichannel EEG, combine their folds not yet developed. In this study, we present two novel convolutional neural network-based architectures apply automated major depressive disorder diagnosis. Our obtain slightly lower classification performance than baseline architecture. 50 folds, find individuals with MDD exhibit higher β power, potentially δ brain-wide correlation is most strongly represented right hemisphere. This study provides multiple key represents significant step forward domain explainable deep EEG. We hope it will inspire future efforts eventually enable development contribute both care medical discoveries.

Язык: Английский

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