# Identifying stimulus-driven neural activity patterns in multi-patient intracranial recordings

arXiv: https://arxiv.org/abs/2202.01933 · HTML: https://mlsanity.com/paper/2202.01933/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 166 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 14 of 14)

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- [A hierarchical independent component analysis model for longitudinal Neuroimaging studies](https://mlsanity.com/paper/1808.01557/) (2018)
- [Inter-regional ECoG correlations predicted by communication dynamics, geometry, and correlated gene expression](https://mlsanity.com/paper/1706.06088/) (2017)

## Abstract (arXiv)

Identifying stimulus-driven neural activity patterns is critical for studying the neural basis of cognition. This can be particularly challenging in intracranial datasets, where electrode locations typically vary across patients. This chapter first presents an overview of the major challenges to identifying stimulus-driven neural activity patterns in the general case. Next, we will review several modality-specific considerations and approaches, along with a discussion of several issues that are particular to intracranial recordings. Against this backdrop, we will consider a variety of within-subject and across-subject approaches to identifying and modeling stimulus-driven neural activity patterns in multi-patient intracranial recordings. These approaches include generalized linear models, multivariate pattern analysis, representational similarity analysis, joint stimulus-activity models, hierarchical matrix factorization models, Gaussian process models, geometric alignment models, inter-subject correlations, and inter-subject functional correlations. Examples from the recent literature serve to illustrate the major concepts and provide the conceptual intuitions for each approach.