# Targeted Time‐Varying Functional Connectivity

## TL;DR

This paper introduces a new method to study brain connectivity that focuses on specific connections over time, revealing task-related information missed by traditional approaches.

## Contribution

The novel T-Targeted Time-Varying Functional Connectivity method models specific network connections' temporal dynamics.

## Key findings

- T-TVFC captures task-related information in thalamocortical connections not detected by traditional methods.
- Thalamocortical connectivity shows distinct temporal signatures compared to corticocortical networks during reasoning tasks.
- High-field fMRI data from 60 participants revealed unique cognitive processing insights using T-TVFC.

## Abstract

To elucidate the neurobiological basis of cognition, which is dynamic and evolving, various methods have emerged to characterise time‐varying functional connectivity (FC) and track the temporal evolution of functional networks. However, given a selection of regions, many of these methods are based on modelling all possible pairwise connections, diluting a potential focus of interest on individual connections. This is the case with the hidden Markov model (HMM), which relies on region‐by‐region covariance matrices across all pairs of selected regions, assuming that fluctuations in FC occur across all investigated connections; that is, that all connections are locked to the same temporal pattern. To address this limitation, we introduce Targeted Time‐Varying FC (T‐TVFC), a variant of the HMM that explicitly models the temporal fluctuations between two sets of regions in a targeted fashion, rather than across the entire connectivity matrix. In this study, we apply T‐TVFC to both simulated and real‐world data. Specifically, we investigate thalamocortical connectivity, hypothesising distinct temporal signatures compared to corticocortical networks. Given the thalamus's role as a critical hub, thalamocortical connections might contain unique information about cognitive processing that could be overlooked in a coarser representation. We tested these hypotheses on high‐field functional magnetic resonance data from 60 participants engaged in a reasoning task with varying complexity levels. Our findings demonstrate that the time‐varying interactions captured by T‐TVFC contain task‐related information not detected by more traditional decompositions.

This study introduces T
argeted Time‐Varying Functional Connectivity (T‐TVFC), a novel method that models the temporal dynamics of specific network connections. Applied to 7 T fMRI data, T‐TVFC allowed us to focus on task‐relevant corticothalamic dynamics that were overlooked by traditional whole‐network approaches.