发表机构
School of Information Technology (SoIT), University of Cincinnati; Division of Child and Adolescent Psychiatry Cincinnati Children’s Hospital Medical Center（辛辛那提大学信息技术学院; 辛辛那提儿童医院医学中心儿童与青少年精神病学科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出整合CNN、LSTM与RP分析的多表征深度学习框架，通过分析FXS患者α、γ频段EEG信号，实现优于单模态基线的自动表型表征，为FXS生物标志物开发提供了可扩展方法。
AI 中文摘要
脆性X综合征（FXS）是一种神经发育障碍，由脆性X智力低下蛋白（FMRP）表达降低引发，导致突触可塑性受损、皮层过度兴奋及网络同步功能障碍。脑电图（EEG）为研究这些机制提供了无创窗口，且始终显示与抑制控制、感觉加工及认知相关的α（8至12Hz）和γ（30至100Hz）振荡异常。本文提出一种多表征深度学习框架，用于自动表征FXS的EEG表型，该框架整合了卷积神经网络（CNN）、长短期记忆（LSTM）网络及递归图（RP）分析。将限带EEG信号分解为α和γ分量，并转换为互补表征，包括编码非线性递归结构的时间特征序列、时频图及RP图像。CNN模块从基于图像的表征中学习具有区分性的空间-光谱及动态纹理，LSTM模块对振荡活动的时间调制进行建模；混合CNN-LSTM架构可联合捕捉空间、时间及非线性依赖关系。受试者独立评估显示，该混合模型优于单模态基线，其中γ特征提供了强区分能力，而α-γ整合产生了最佳整体性能。这些发现表明，结合非线性表征的深度学习是用于FXS的EEG生物标志物开发的可扩展方法，在转化环境中具有诊断、分层及治疗监测的潜在应用价值。
英文摘要
Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by reduced expression of fragile X mental retardation protein (FMRP), leading to disrupted synaptic plasticity, cortical hyperexcitability, and impaired network synchronization. Electroencephalography (EEG) provides a noninvasive window into these mechanisms and consistently reveals abnormalities in alpha (8 to 12 Hz) and gamma (30 to 100 Hz) oscillations that relate to inhibitory control, sensory processing, and cognition. This paper proposes a multi representation deep learning framework for automated characterization of FXS EEG phenotypes by integrating convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and recurrence plot (RP) analysis. Band limited EEG signals are decomposed into alpha and gamma components and transformed into complementary representations, including temporal feature sequences, time frequency maps, and RP images encoding the nonlinear recurrence structure. CNN modules learn discriminative spatial-spectral and dynamical textures from image based representations, while LSTM modules model temporal modulation of oscillatory activity; a hybrid CNN LSTM architecture jointly captures spatial, temporal, and nonlinear dependencies. Subject-independent evaluation demonstrates that the hybrid model outperforms single modality baselines, with gamma features providing strong discriminative power and alpha gamma integration yielding the best overall performance. These findings support deep learning with nonlinear representations as a scalable approach for EEG biomarker development in FXS, with potential utility for diagnosis, stratification, and treatment monitoring in translational settings.
Comments14 pages, 4 figures, conference
Journal refSpringer ISBCom 2026
