bioRxivNeurosciencePreprint
Multi-Timescale Neural Adaptation Failure as a Mechanistic Signature of Major Depression
Yun Hui YangiD, Yanli Zhao, Kun Xie, Chenfan Song, Yilun Huang, Anhui Kong, Shiqi HuangiD, Zixuan Xing, Wenyi Wang, Nils MuhlertiD, Rebecca ElliottiD, Shuping TaniD, Zhenhong HeiD, Dandan ZhangiD
Abstract
Major depression (MD) is usually characterized in terms of momentary abnormalities, such as negative bias or emotional blunting, derived from paradigms that measure only the first response to a stimulus. Yet depressive experience in daily life is repetitive and cumulative. Here we test the hypothesis that a failure of neural adaptation across repetitions is a core feature of MD. Using a repeated negative-stimulus paradigm, we quantified adaptation at micro (within-trial) and macro (across-session) scales via electroencephalography in 28 MD patients and 34 healthy controls, measuring both conventional event-related potentials and large-scale traveling-wave propagation. MD patients exhibited pervasive maladaptation: neural rigidity at the micro scale (absent repetition suppression) and progressive regulatory depletion at the macro scale (escalating emotional reactivity with declining cognitive control). Across analyses, adaptation-based metrics revealed larger group differences than single-shot indices, and classifiers built on adaptation features--especially traveling-wave dynamics--outperformed single-shot models (task AUC = 0.83 vs 0.78) and generalized to two independent datasets (up to AUC = 0.91). These findings challenge conventional symptom-based characterizations of MD, establishing adaptation failure--not initial reactivity--as the mechanistically primary pathological signature. This reframing provides a unified mechanistic explanation for rumination and cognitive inflexibility while offering a standardized, low-cost biomarker requiring minimal electrodes ([≤]8 channels), enabling large-scale screening in resource-limited settings.
- organism: human
- assay: electronic health records
- research focus: benchmarking
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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