Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets

TLDR

Functional connectivity, defined as the temporal correlation of neurophysiological indices across brain regions, is investigated using positron emission tomography during verbal fluency tasks to identify distributed brain systems. The study proposes that the identified distributed brain system exhibits an attentional bias. The authors applied a recursive principal‑component analysis tailored for large PET datasets to extract functional connectivity patterns. Two independent principal components were identified: a dominant component corresponding to an intentional brain system previously observed in verbal fluency studies, and a secondary component comprising a distributed network including the anterior cingulate and Wernicke's area that tracks monotonic time effects.

Abstract

The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.