Richard M Leahy
University of Southern California, CA·Neuroscience
Brain cells communicate with electrical signals. Researchers record this activity in several ways: with electrodes on the scalp (electroencephalography, EEG), with sensors that pick up the brain's tiny magnetic fields (magnetoencephalography, MEG), and with electrodes placed inside the brain during clinical care (intracranial EEG).
- Fields
Neuroscience
Biomedical Engineering
Computer Science
- Topics
Brainstorm
electroencephalography
magnetoencephalography
- Grants
- 2 known grants, through Jul 2028
What they work on
- Develop Brainstorm tools for experiments that use naturalistic, non-repeating stimuli, including machine- and human-readable labels for uncontrolled events and behavior, so these can be linked to recorded brain activity.
- Build tools for intracranial EEG that locate and label complex electrode layouts by aligning CT scans to MRI, automatically tag interictal features (abnormal signals between seizures), and pinpoint neuronal sources using FEM-based head models.
- Create a hub framework so third-party analysis tools can be run, and their results viewed, from inside Brainstorm, and extend provenance tools that save analysis histories so any analysis can be fully reproduced.
- Package Brainstorm as a containerized BIDS App that analyzes EEG, MEG and intracranial data in the Brain Imaging Data Structure format without complex installation, adding parallel computing, GPU acceleration and Python interoperability.
More about the research
These recordings are used to study normal thinking and conditions such as epilepsy, schizophrenia, autism and Alzheimer's disease. Turning raw signals into answers takes complex software, and a result can only be trusted if other scientists can repeat the same analysis. Both projects center on Brainstorm, free, open-source software for analyzing and visualizing brain electrophysiology data. One project expands what Brainstorm can do. The other makes it easier to install, faster and more reproducible.
Why it matters
Many researchers studying epilepsy, schizophrenia, obsessive-compulsive disorder, autism and Alzheimer's disease rely on EEG and MEG to measure brain activity. Free, tested and well-documented analysis software lets more labs, including those without large computing teams, run these analyses. Repeatable workflows could make findings easier to trust and compare. Better tools for intracranial recordings could also help researchers working with data from epilepsy patients who have electrodes placed in the brain.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Arnaud Delorme
University of California-San Diego, CA·Neuroscience
Consolidate human EEG datasets from major international repositories into a single U.S.-hosted resource, linking each to its original source and pairing recordings with behavioral measures, stimulus events, response times and eye tracking.
Nishant Sinha
University of Pennsylvania, PA·Biomedical Engineering·Early-career
Build scalable pipelines that automatically aggregate and jointly analyze structural MRI, intracranial EEG (IEEG) and clinical data from multiple epilepsy centers, so analyses generalize across sites.
Matthew Banks
University of Wisconsin-Madison, WI·Neuroscience
Identify intracranial EEG biomarkers that distinguish conscious and unconscious states while epilepsy patients go under and emerge from general anesthesia.
The school
University of Southern California: 47 labs on PI Pages, $1.3B a year on research, 882 research doctorates a year.
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