Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia

Frontiers in Neurology, Journal Year: 2023, Volume and Issue: 14

Published: Oct. 12, 2023

Introduction Dementia is a neurological disorder associated with aging that can cause loss of cognitive functions, impacting daily life. Alzheimer's disease (AD) the most common dementia, accounting for 50–70% cases, while frontotemporal dementia (FTD) affects social skills and personality. Electroencephalography (EEG) provides an effective tool to study effects AD on brain. Methods In this study, we propose use shallow neural networks applied two sets features: spectral-temporal functional connectivity using four methods. We compare three supervised machine learning techniques CNN models classify EEG signals / FTD control cases. also evaluate different measures from frequency bands considering multiple thresholds. Results discussion showed CNN-based achieved highest accuracy 94.54% AEC in test dataset when all connections, outperforming conventional methods providing potentially additional early diagnosis tool.

Language: Английский

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Extracting interpretable signatures of whole-brain dynamics through systematic comparison

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(12), P. e1012692 - e1012692

Published: Dec. 23, 2024

The brain’s complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for given application. Here, we address this limitation by systematically comparing diverse, interpretable features both intra-regional activity and inter-regional functional coupling from resting-state magnetic resonance imaging (rs-fMRI) data, demonstrating our method case–control comparisons four neuropsychiatric disorders. Our findings generally support use linear time-series analysis techniques rs-fMRI analyses, while also identifying new ways to quantify informative fMRI structures. While simple representations performed surprisingly well (e.g., within single brain region), combining with improved performance, underscoring distributed, multifaceted changes in comprehensive, data-driven introduced here enables systematic identification interpretation quantitative signatures multivariate applicability beyond neuroimaging diverse scientific problems involving time-varying systems.

Language: Английский

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Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome coupled oscillators model

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 28, 2024

Multi-state metastability in neuroimaging signals reflects the brain's flexibility to transition between network configurations response changing environments or tasks. We modeled these dynamics with a Kuramoto of 90 nodes oscillating at an intrinsic frequency 40 Hz, interconnected using human brain structural connectivity strengths and delays. simulated this model for 30 min generate multi-state metastability. identified global coupling delay parameters that maximize spectral entropy, proxy At operational point, multiple frequency-specific coherent sub-networks spontaneously emerge across oscillatory modes, persisting periods 140 4300 ms, reflecting flexible sustained dynamic states. The topography aligns empirical resting-state data. Additionally, periodic components EEG spectra from young healthy participants correlate maximal metastability, while away point sleep anesthesia spectra. Our findings suggest metastable functional observed data specific interactions connection delays, providing platform study mechanisms underlying cognition.

Language: Английский

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