Deep Learning CNN and Recurrence Analysis for Alpha Gamma EEG Biomarkers in Fragile X Syndrome

2026-08-01

AI Summary

Individuals with Fragile X Syndrome (FXS) exhibit aberrant alpha/gamma-band electroencephalographic (EEG) oscillations closely linked to impairments in inhibitory control, sensory processing, and cognition; however, efficient automated methods for identifying reliable biomarkers remain lacking. This study proposes a multimodal deep learning framework that, for the first time, integrates convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and joint modeling of time–frequency and nonlinear dynamical features—including recurrence plot analysis—specifically tailored to alpha and gamma EEG signals. In subject-independent evaluations, the proposed approach significantly outperforms unimodal baselines, with the combined alpha–gamma representation achieving the highest discriminative performance. These results underscore the framework’s potential for facilitating automated FXS identification, diagnosis, and treatment monitoring.

Abstract

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.

Problem

• Fragile X Syndrome
• EEG biomarkers
• alpha oscillations
• gamma oscillations
• neurodevelopmental disorder

Innovation

• deep learning
• recurrence plot
• CNN-LSTM hybrid
• EEG biomarkers
• alpha-gamma oscillations