利用基础模型实现基于脑电图的阿尔茨海默病诊断
Leveraging a Foundation Model for the EEG-Based Diagnosis of Alzheimer's Disease
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中文总结 AI 辅助
本研究针对阿尔茨海默病诊断挑战，采用预训练于2500小时脑电图的大型脑模型LaBraM，结合随机森林分类器，仅用8秒脑电片段即达89.36%的ROC-AUC，优于传统方法，可提取临床相关生物标志物。
中文摘要 AI 辅助
阿尔茨海默病（AD）的生物学异质性构成了关键的诊断挑战，传统线性方法无法捕捉非线性神经动力学。为解决该问题，我们提出了利用大型脑模型（LaBraM）的诊断框架，该模型在超过2500小时的脑电图（EEG）数据上进行预训练。通过将这些高维潜在嵌入与非线性随机森林分类器相结合，我们的方法能有效分离出稳健的疾病标志物。在严格的受试者独立5折交叉验证协议下，该方法在区分痴呆患者与健康对照时，获得了89.36%±3.49%的ROC-AUC、81.45%±4.43%的PR-AUC以及82.44%±4.34%的平衡准确率，且仅使用8秒的EEG片段，优于传统的频谱基线方法，包括带功率和参数化振荡特征（FOOOF）。事后遮挡分析证实，该模型捕捉到了临床验证的生物标志物，即枕-额区α和θ节律的退化。额外的神经生理学对齐分析显示，LaBraM预测的痴呆概率越高，与更差的认知表现、更严重的临床症状、更高的θ和α相对功率以及更高的非周期指数显著相关。这些发现表明，深度潜在表征能从噪声信号中提取临床相关特征，实现精准、快速且数据高效的诊断。
英文摘要
Biological heterogeneity in Alzheimer's Disease (AD) poses a critical diagnostic challenge, particularly for traditional linear methods that fail to capture non-linear neural dynamics. To address this, we propose a diagnostic framework utilizing the Large Brain Model (LaBraM), pretrained on over 2,500 hours of EEG data. By integrating these high-dimensional latent embeddings with a non-linear Random Forest classifier, our approach effectively isolates robust disease markers. Under a rigorous subject-independent 5-fold cross-validation protocol, the method achieves an ROC-AUC of 89.36% +/- 3.49%, PR AUC of 81.45% +/- 4.43%, and Balanced Accuracy of 82.44% +/- 4.34% in distinguishing dementia patients from healthy controls. Notably, this performance uses only 8-second EEG segments, surpassing traditional spectral baselines, including band-power and parameterized oscillatory features (FOOOF). Post-hoc occlusion analysis confirms the model captures clinically validated biomarkers, specifically occipital-frontal Alpha and Theta rhythm degradation. Additional neurophysiological alignment analysis demonstrated that higher LaBraM-predicted dementia probability significantly correlated with worse cognitive performance, greater clinical severity, increased theta and alpha relative power, and higher aperiodic exponent. These findings demonstrate that deep latent representations extract clinically relevant signatures from noisy signals, enabling precise, rapid, and data-efficient diagnosis.
发表机构
- University of California, San Diego（加利福尼亚大学圣地亚哥分校）
- HippoScreen Neurotech Corp.（HippoScreen神经科技公司）
- Swartz Center for Computational Neuroscience（Swartz计算神经科学中心）
机构由 AI 辅助整理，请以论文原文为准。
