Novel EEG Analytics for Alzheimer’s Continuum: Biomarker Discovery, Machine Learning Classification, and Hypotheses on Cholinergic and Neurovascular Dysfunctions

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Title:	Novel EEG Analytics for Alzheimer’s Continuum: Biomarker Discovery, Machine Learning Classification, and Hypotheses on Cholinergic and Neurovascular Dysfunctions
Authors:	朱國大;Chu, Kwo-Ta
Contributors:	認知與神經科學研究所
Keywords:	靜息態腦電圖;振幅調變;阿茲海默症;全息希爾伯譜分析;腦心軸;resting-state EEG (rsEEG);amplitude modulation (AM);Alzheimer’s disease (AD);Holo-Hilbert Spectral Analysis (HHSA);Brain-heart axis
Date:	2025-07-11
Issue Date:	2025-10-17 12:47:36 (UTC+8)
Publisher:	國立中央大學
Abstract:	本論文運用先進的靜息態腦電圖 (EEG) 生物標記物，探討神經認知老化和阿茲海默症 (AD) 背後的神經生物學機制。基於全息希爾伯譜分析 (HHSA) 和 FOOOF 參數化等新型分析方法，本研究闡明了反映阿茲海默症病程中膽鹼能功能障礙和神經血管損傷的獨特腦電圖特徵。第一項主要發現是，腦電圖減慢（以個體 α 波峰值頻率 (IAPF) 降低和 α 波功率衰減為特徵）與阿茲海默症患者基底前腦膽鹼能功能障礙密切相關。其次，β 波和 γ 波段高頻振蕩的振幅調變 (AM) 減弱表明神經血管耦合中斷，這與阿茲海默症血管病變的新證據一致。第三，隨著疾病進展，腦電圖非週期指數的增加提示突觸興奮性降低，這為突觸病理生理學提供了新的見解。基於這些發現，並考慮到透過腦電圖測量的體力活動誘導神經可塑性的證據，本論文提出腦電圖作為一種靈敏、非侵入性的工具，可用於監測運動神經科學中的大腦健康和神經可塑性反應。此外，在阿茲海默症的背景下，本論文強調，許多潛在的可改變的失智症風險因素可以透過體力活動有效地緩解，體力活動是一種已被充分證實的非藥物失智症預防干預措施。這些電生理生物標記在早期疾病檢測、進展分期和療效監測方面展現出強大的潛力。此外，將腦電圖指標與臨床和分子標記物結合，為旨在減緩認知能力下降的精準診斷和個人化介入策略鋪平了道路。本論文強調了基於腦電圖的神經生理標記的轉化前景，它是一種可方便取得且經濟有效的工具，可以推進阿茲海默症研究和臨床照護，同時也支持身體活動在促進神經認知恢復力方面的作用。;This dissertation investigates the neurobiological mechanisms underlying neurocognitive aging and Alzheimer′s disease (AD) using advanced resting-state electroencephalography (EEG) biomarkers. Building upon novel analytic approaches, such as Holo-Hilbert Spectral Analysis (HHSA) and FOOOF parameterization, this research elucidates distinct EEG signatures that reflect cholinergic dysfunction and neurovascular impairment across the AD continuum. The first major finding is that EEG slowing, characterized by reduced individual alpha peak frequency (IAPF) and attenuated alpha power, robustly correlates with basal forebrain cholinergic deficits in AD patients. Second, diminished amplitude modulation (AM) of high-frequency oscillations in the beta and gamma bands indicates disrupted neurovascular coupling, consistent with emerging evidence of vascular pathology in AD. Third, an increased EEG aperiodic exponent with disease progression suggests a shift toward synaptic hypo-excitability, providing novel insights into synaptic pathophysiology. Building on these findings and considering evidence of physical activity-induced neuroplasticity measured via EEG, this thesis proposes EEG as a sensitive, noninvasive tool for monitoring brain health and neuroplastic responses in sports neuroscience. Furthermore, within the context of AD, it highlights that many potential modifiable risk factors for dementia can be effectively mitigated through physical activity, which stands as a well-documented non-pharmacological intervention for dementia prevention. These electrophysiological biomarkers demonstrate strong potential for early disease detection, staging disease progression, and monitoring therapeutic efficacy. Moreover, integrating EEG metrics with clinical and molecular markers paves the way for precision diagnostics and personalized intervention strategies aimed at mitigating cognitive decline. This thesis highlights the translational potential of EEG-based neurophysiological markers as accessible and cost-effective tools to advance both Alzheimer′s disease (AD) research and clinical care, while also underscoring the role of physical activity in promoting neurocognitive resilience.
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