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Shi Liu

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JBHI Journal 2025 Journal Article

A Habenula Neural Biomarker Simultaneously Tracks Weekly and Daily Symptom Variations During Deep Brain Stimulation Therapy for Depression

  • Shi Liu
  • Yu Qi
  • Shaohua Hu
  • Ning Wei
  • Jianmin Zhang
  • Junming Zhu
  • Hemmings Wu
  • Hailan Hu

Objective: Deep brain stimulation (DBS) targeting the lateral habenula (LHb) is a promising therapy for treatment-resistant depression (TRD) but its clinical effect has been variable, which can be improved by adaptive DBS (aDBS) guided by a neural biomarker of depression symptoms. Existing neural biomarkers, however, cannot simultaneously track slow and fast symptom dynamics, do not sufficiently respond to stimulation parameters, and lack neurobiological interpretability, which hinder their use in developing aDBS. Methods: We conducted a study on one TRD patient who achieved remission following a 41-week LHb DBS treatment, during which we assessed slow symptom variations using weekly clinical ratings and fast variations using daily self-reports. We recorded daily LHb local field potentials (LFP) concurrently with the reports during the entire treatment process. We then used machine learning methods to identify a personalized depression neural biomarker from spectral and temporal LFP features. Results: The neural biomarker was identified from classification of high and low depression symptom states with a cross-validated accuracy of 0. 97. It further simultaneously tracked both weekly (slow) and daily (fast) depression symptom variation dynamics, achieving test data explained variance of 0. 74 and 0. 63 respectively and responded to DBS frequency alterations. Finally, it can be neurobiologically interpreted as indicating LHb excitatory and inhibitory balance changes during DBS treatment. Conclusion: By collecting and analyzing a unique personalized dataset of weekly and daily LFP recordings and symptom evaluations, we identified a high-performance neural biomarker for depression during LHb DBS. Significance: Our results hold promise to facilitate future aDBS for treating TRD.

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ICML Conference 2025 Conference Paper

CoMemo: LVLMs Need Image Context with Image Memory

  • Shi Liu
  • Weijie Su 0002
  • Xizhou Zhu
  • Wenhai Wang
  • Jifeng Dai

Recent advancements in Large Vision-Language Models built upon Large Language Models have established aligning visual features with LLM representations as the dominant paradigm. However, inherited LLM architectural designs introduce suboptimal characteristics for multimodal processing. First, LVLMs exhibit a bimodal distribution in attention allocation, leading to the progressive neglect of middle visual content as context expands. Second, conventional positional encoding schemes fail to preserve vital 2D structural relationships when processing dynamic high-resolution images. To address these limitations, we propose CoMemo - a dual-path architecture that combines a Co ntext image path with an image Memo ry path for visual processing, effectively alleviating visual information neglect. Additionally, we introduce RoPE-DHR, a novel positional encoding mechanism that employs thumbnail-based positional aggregation to maintain 2D spatial awareness while mitigating remote decay in extended sequences. Evaluations across seven benchmarks, including long-context comprehension, multi-image reasoning, and visual question answering, demonstrate CoMemo’s superior performance compared to conventional LVLM architectures. Project page is available at https: //lalbj. github. io/projects/CoMemo/.

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v2026.09.13