Identifying stimulus-driven neural activity patterns in multi-patient intracranial recordings
This is an incremental review chapter for researchers in neuroscience and neuroimaging, focusing on methodological considerations without presenting new results.
The paper tackles the challenge of identifying stimulus-driven neural activity patterns in multi-patient intracranial recordings, where electrode locations vary across patients, by reviewing and discussing various within-subject and across-subject modeling approaches such as generalized linear models and hierarchical matrix factorization.
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.