# NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis

arXiv: https://arxiv.org/abs/2507.20189 · HTML: https://mlsanity.com/paper/2507.20189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 27 Jul 2025.

## Builds on (most cited first, 10 of 10)

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- [Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity](https://mlsanity.com/paper/2305.11675/) (2023)
- [Deep Learning in fNIRS: A review](https://mlsanity.com/paper/2201.13371/) (2022)
- [Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond](https://mlsanity.com/paper/2210.06295/) (2022)
- [C2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction](https://mlsanity.com/paper/2110.13674/) (2021)
- [Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings](https://mlsanity.com/paper/2410.12866/) (2024)
- [Reliability and validity of TMS-EEG biomarkers](https://mlsanity.com/paper/2207.08456/) (2022)
- [MMFusion: Multi-modality Diffusion Model for Lymph Node Metastasis Diagnosis in Esophageal Cancer](https://mlsanity.com/paper/2405.09539/) (2024)
- [Towards Unified Neural Decoding with Brain Functional Network Modeling](https://mlsanity.com/paper/2506.12055/) (2025)

## Abstract (arXiv)

Methamphetamine dependence poses a significant global health challenge, yet its assessment and the evaluation of treatments like repetitive transcranial magnetic stimulation (rTMS) frequently depend on subjective self-reports, which may introduce uncertainties. While objective neuroimaging modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer alternatives, their individual limitations and the reliance on conventional, often hand-crafted, feature extraction can compromise the reliability of derived biomarkers. To overcome these limitations, we propose NeuroCLIP, a novel deep learning framework integrating simultaneously recorded EEG and fNIRS data through a progressive learning strategy. This approach offers a robust and trustworthy biomarker for methamphetamine addiction. Validation experiments show that NeuroCLIP significantly improves discriminative capabilities among the methamphetamine-dependent individuals and healthy controls compared to models using either EEG or only fNIRS alone. Furthermore, the proposed framework facilitates objective, brain-based evaluation of rTMS treatment efficacy, demonstrating measurable shifts in neural patterns towards healthy control profiles after treatment. Critically, we establish the trustworthiness of the multimodal data-driven biomarker by showing its strong correlation with psychometrically validated craving scores. These findings suggest that biomarker derived from EEG-fNIRS data via NeuroCLIP offers enhanced robustness and reliability over single-modality approaches, providing a valuable tool for addiction neuroscience research and potentially improving clinical assessments.