Scholarlink

<p>Neurons of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological recordings can measure the activity of many neurons simultaneously, identifying cell types during these experiments remains difficult. Here we present PhysMAP, a framework adapted from multiomics data analysis that weights multiple electrophysiological modalities simultaneously to obtain interpretable multimodal representations. We apply PhysMAP to seven datasets and demonstrate that these multimodal representations are better aligned with known transcriptomically-defined cell types than any single modality alone. We then show that this alignment allows PhysMAP to better identify putative cell types in the absence of ground truth. We also demonstrate how annotated datasets can transfer labels to unannotated recordings and confirm that inferred cell types exhibit properties consistent with ground truth. Crucially, we show that PhysMAP can also be used to iteratively detect batch effects which confound classification. Together, these results establish PhysMAP as a tool for studying multiple cell types simultaneously and gaining insight into neural circuit dynamics.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A multimodal approach for visualizing and identifying electrophysiological cell types in vivo

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月15日

查看全文
  • Eric Kenji Lee,
  • Asım E. Gül,
  • Greggory Heller,
  • Anna Lakunina,
  • Han Yu,
  • Andrew Shelton,
  • Shawn Olsen,
  • Nicholas A. Steinmetz,
  • Cole Hurwitz,
  • Santiago Jaramillo,
  • Pawel F. Przytycki,
  • Chandramouli Chandrasekaran

摘要

Neurons of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological recordings can measure the activity of many neurons simultaneously, identifying cell types during these experiments remains difficult. Here we present PhysMAP, a framework adapted from multiomics data analysis that weights multiple electrophysiological modalities simultaneously to obtain interpretable multimodal representations. We apply PhysMAP to seven datasets and demonstrate that these multimodal representations are better aligned with known transcriptomically-defined cell types than any single modality alone. We then show that this alignment allows PhysMAP to better identify putative cell types in the absence of ground truth. We also demonstrate how annotated datasets can transfer labels to unannotated recordings and confirm that inferred cell types exhibit properties consistent with ground truth. Crucially, we show that PhysMAP can also be used to iteratively detect batch effects which confound classification. Together, these results establish PhysMAP as a tool for studying multiple cell types simultaneously and gaining insight into neural circuit dynamics.