Neural connectivity during various activities [behavioral mode]

Here's the first two tweets in a stream of 8 tweets:

We used factor analysis to derive a measure of intrinsic connectivity that persists across rest and 8 different task states. Results suggest that (when treated equally) resting state is an especially poor measure of intrinsic connectivity compared with other task states 2/n pic.twitter.com/FOrKCJc3By

— Michael W. Cole (@TheColeLab) April 25, 2022

Here's the abstract and author's summary from the article, Latent functional connectivity underlying multiple brain states:

Abstract

Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to reflect the brain’s intrinsic network architecture, which is thought to be broadly relevant because it persists across brain states (i.e., is state-general). However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting shared connectivity patterns across many brain states, better captures state-general intrinsic FC relative to measures derived from resting state alone. We estimated latent FC independently for each connection using leave-one-task-out factor analysis in seven highly distinct task states (24 conditions) and resting state using fMRI data from the Human Connectome Project. Compared with resting-state connectivity, latent FC improves generalization to held-out brain states, better explaining patterns of connectivity and task-evoked activation. We also found that latent connectivity improved prediction of behavior outside the scanner, indexed by the general intelligence factor (g). Our results suggest that FC patterns shared across many brain states, rather than just resting state, better reflect state-general connectivity. This affirms the notion of “intrinsic” brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.

Author Summary

The initial promise of resting-state fMRI was that it would reflect “intrinsic” functional relationships in the brain free from any specific task context, yet this assumption has remained untested until recently. Here we propose a latent variable method for estimating intrinsic functional connectivity (FC) as an alternative to rest FC. We show that latent FC outperforms rest FC in predicting held-out FC and regional activation states in the brain. Additionally, latent FC better predicts a marker of general intelligence measured outside of the scanner. We demonstrate that the latent variable approach subsumes other approaches to combining data from multiple states (e.g., averaging) and that it outperforms rest FC alone in terms of generalizability and predictive validity.

This article speaks to an idea that was, I believe, first articulated by Warren McCulloch. He argued that for each specific behavioral mode (and here)– hunting, eating, sex, sleep, etc. – there is a particular pattern of brain activity, some regions are more active than others. That is, the brain doesn't have specific modules for each activity, but rather specific patterns of activation over the whole brain.