Researchers at Boston University have developed PhysMAP, a machine learning tool that distinguishes between different types of neurons based solely on their unique electrical signatures. The tool addresses a major hurdle in neuroscience: understanding which specific cells are active during brain recordings.
PhysMAP can identify cells implicated in psychiatric disorders, such as those linked to schizophrenia and major depressive disorder, directly from in vivo recordings. This enables the study of how specific neural circuits fail in real-time, providing a roadmap for targeted therapies.
Key Details
- PhysMAP identifies circuitopathies: disorders like schizophrenia and depression that arise from dysfunctional interactions between specific cell types.
- The algorithm combines multiple electrical signatures to isolate individual neuron types (e.g., parvalbumin or somatostatin cells) within a recording.
- No genetic engineering is needed; PhysMAP works using only electrical recordings, unlike previous methods requiring optotagging.
- The tool was trained and validated using seven public datasets, demonstrating the value of open-source data.
Background
When probes are inserted into the brain, the electrical activity of neurons is recorded. However, brains consist of cell types with different roles, and without identifying them, pathological states cannot be fully understood. PhysMAP separates the "voices" of individual cell types by combining complementary features of their electrical signatures.
Statements
Corresponding author Chandramouli Chandrasekaran stated that PhysMAP allows for the study of interacting cell types in intact and altered neural circuits implicated in psychiatric disorders. The tool also illustrates the power of open data sharing, as scientists enabled the development of new tools without additional experiments.
Potential Applications
- Studying psychiatric disorders arising from circuit dysfunction without genetic manipulation.
- Informing development of future therapeutic strategies.
- The previous version, WaveMAP, was deployed in the first human recordings with Neuropixels. PhysMAP is more powerful and can identify specific cell types implicated in disorders: parvalbumin-positive cells in schizophrenia or Dravet syndrome, and somatostatin-positive cells in major depressive disorder.
Funding and Publication
The research was supported by NIH grants, the Moorman-Simon Interdisciplinary Career Development Professorship, the Whitehall Foundation, and the Brain and Behavior Research Foundation. The study appears in Nature Communications.