Model connectivity: leveraging the power of encoding models to overcome the limitations of functional connectivity

Abstract

Functional connectivity (FC) is the most popular method for recovering functional networks of brain areas with fMRI. However, because FC is defined as temporal correlations in brain activity, FC networks are confounded by noise and lack a precise functional role. To overcome these limitations, we developed model connectivity (MC). MC is defined as similarities in encoding model weights, which quantify reliable functional activity in terms of interpretable stimulus- or task-related features. To compare FC and MC, both methods were applied to a naturalistic story listening dataset. FC recovered spatially broad networks that are confounded by noise, and that lack a clear role during natural language comprehension. By contrast, MC recovered spatially localized networks that are robust to noise, and that represent distinct categories of semantic concepts. Thus, MC is a powerful data-driven approach for recovering and interpreting the functional networks that support complex cognitive processes.

Bilingual language processing relies on shared semantic representations that are modulated by each language

“…First, we used a cross-validated clustering approach ( model connectivity 40 ) to identify semantic clusters from the estimated model weights. Five clusters best summarized the distribution of model weights across participants and languages (Figure S9).…”

“…Weight Clustering A clustering approach was used to separate voxels into groups that represent similar concepts 40 . Model weights for each participant and language were projected to a standard template space (fsAverage 32 ).…”