Demixed shared component analysis of neural population data from multiple brain areas

TL;DR

This paper introduces demixed shared component analysis (dSCA), a novel method for disentangling task-related information shared across multiple brain regions from neural population data, enhancing interpretability of inter-area neural dynamics.

Contribution

dSCA extends a single-area demixing method to multiple brain regions, enabling the extraction of shared task-relevant components from complex neural data.

Findings

dSCA reveals shared information dynamics across brain areas during decision-making.

Application to rodent and macaque data uncovers new insights into inter-area computation.

Method improves interpretability of multi-region neural population analyses.

Abstract

Recent advances in neuroscience data acquisition allow for the simultaneous recording of large populations of neurons across multiple brain areas while subjects perform complex cognitive tasks. Interpreting these data requires us to index how task-relevant information is shared across brain regions, but this is often confounded by the mixing of different task parameters at the single neuron level. Here, inspired by a method developed for a single brain area, we introduce a new technique for demixing variables across multiple brain areas, called demixed shared component analysis (dSCA). dSCA decomposes population activity into a few components, such that the shared components capture the maximum amount of shared information across brain regions while also depending on relevant task parameters. This yields interpretable components that express which variables are shared between different brain regions and when this information is shared across time. To illustrate our method, we reanalyze two datasets recorded during decision-making tasks in rodents and macaques. We find that dSCA provides new insights into the shared computation between different brain areas in these datasets, relating to several different aspects of decision formation.