A multimodal approach for visualizing and identifying electrophysiological cell types in vivo
Authors:
- Eric Kenji LeeELEric Kenji Lee
- Greggory HellerGHGreggory Heller
- Anna LakuninaALAnna Lakunina
- Han YuHYHan Yu
- Andrew SheltonASAndrew Shelton
- Shawn OlsenSOShawn Olsen
- Nicholas A. SteinmetzNSNicholas A. Steinmetz
- Cole HurwitzCHCole Hurwitz
- Santiago JaramilloSJSantiago Jaramillo
- Pawel F. PrzytyckiPPPawel F. Przytycki
- Chandramouli ChandrasekaranCCChandramouli Chandrasekaran
Open Access
- 15 April 2026
- journal article
- research article
- Published by Springer Nature in Nature Communications
- Vol. 17 (1)
Abstract
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.Keywords
Funding Information
- National Institute of Neurological Disorders and Stroke (R00NS092972)
- National Institute of Neurological Disorders and Stroke (R01NS121409)
- National Institute of Neurological Disorders and Stroke (R21NS135361)
- National Institute of Neurological Disorders and Stroke (R01NS122969)
- National Institute of Neurological Disorders and Stroke (F31NS131018)
- National Institute of Neurological Disorders and Stroke (U01NS113252)
- National Institute of Neurological Disorders and Stroke (U01NS113252)
- National Institute of Neurological Disorders and Stroke (U01NS113252)
- National Institute of Neurological Disorders and Stroke (RF1NS131993)
- Whitehall Foundation (2019-12-77)
- Brain and Behavior Research Foundation (27923)
- National Institute on Deafness and Other Communication Disorders (R01DC01553)
- National Institute on Deafness and Other Communication Disorders (R01DC01553)
This publication has 98 references indexed in Scilit:
- Distinct roles of PV and Sst interneurons in visually induced gamma oscillationsCell Reports, 2025
- A deep learning strategy to identify cell types across species from high-density extracellular recordingsCell, 2025
- Circuit dynamics of superficial and deep CA1 pyramidal cells and inhibitory cells in freely moving macaquesCell Reports, 2024
- Transcriptomic cell type structures in vivo neuronal activity across multiple timescalesCell Reports, 2023
- Considerations for the Use of Viral Vectors in Nonhuman Primates2023
- Cognition and the single neuron: How cell types construct the dynamic computations of frontal cortexCurrent Opinion in Neurobiology, 2022
- Adjusting the adjusted Rand IndexComputational Statistics, 2022
- Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic CellsCell, 2020
- Comprehensive Integration of Single-Cell DataCell, 2019
- Evaluation of neuronal connectivity: Sensitivity of cross-correlationBrain Research, 1985