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    This summary is machine-generated.

    A new deep learning framework, NeuroCLIP, integrates EEG and fNIRS data for a reliable biomarker of methamphetamine dependence. This approach enhances objective assessment of addiction and treatment effectiveness, correlating with craving scores.

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    Area of Science:

    • Neuroscience
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Methamphetamine dependence is a global health issue.
    • Current assessment relies on subjective self-reports, limiting reliability.
    • Existing neuroimaging biomarkers from EEG or fNIRS have individual limitations.

    Purpose of the Study:

    • To develop a robust, data-driven biomarker for methamphetamine addiction using multimodal neuroimaging.
    • To overcome limitations of single-modality neuroimaging and subjective assessments.
    • To objectively evaluate the efficacy of treatments like repetitive transcranial magnetic stimulation (rTMS).

    Main Methods:

    • Proposed NeuroCLIP, a deep learning framework integrating simultaneous EEG and fNIRS data.
    • Employed a progressive learning strategy for multimodal data fusion.
    • Validated the framework by comparing its discriminative capabilities against single-modality models.

    Main Results:

    • NeuroCLIP significantly improved discrimination between individuals with methamphetamine dependence and healthy controls.
    • The framework demonstrated objective, brain-based evaluation of rTMS treatment efficacy.
    • The multimodal biomarker showed a strong correlation with psychometrically validated craving scores, establishing trustworthiness.

    Conclusions:

    • NeuroCLIP provides a robust and reliable multimodal biomarker for methamphetamine addiction.
    • This approach enhances objective clinical assessment and treatment evaluation.
    • The findings support the use of integrated EEG-fNIRS data for addiction neuroscience research.