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

TL;DR

This paper reviews methods for identifying stimulus-driven neural activity patterns in multi-patient intracranial recordings, addressing challenges posed by variable electrode locations and presenting various modeling approaches.

Contribution

It provides a comprehensive overview of approaches for analyzing stimulus-driven neural activity in multi-patient intracranial data, highlighting recent methodological advances.

Findings

Comparison of multiple modeling approaches for intracranial data

Illustrative examples from recent literature

Discussion of intracranial recording challenges

Abstract

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.