Monotonicity in graph theoretic summaries of fMRI data acquired during human learning.

ABSTRACT:

Introduction

Behavioral performance during associative learning typically improves monotonically; performance on each successive iteration of the task is no worse (and typically better) than on the previous one. It is unclear whether connectomic measures of brain function (from fMRI data acquired during learning) also increase monotonically. We used a well-established associative learning paradigm to test for the possible co-observance of monotonicity in behavior and connectomics.

Methods

fMRI data were summarized using two distinct connectomic (i.e., graph theoretic) measures: (a) Betweenness Centrality (of nodes) and (b) Average Shortest Path Length (i.e., a measure of network efficiency) across the graph. To broaden our study's breath, in addition to healthy controls (

SUBMITTER: Bhatt D 

PROVIDER: S-EPMC12497747 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature