On the Brain Networks of Complex Problem Solving

TL;DR

This paper investigates the dynamic brain network interactions during complex problem solving, specifically in planning and execution phases, using a novel fMRI-based network construction model to identify key regions and their connectivity patterns.

Contribution

It introduces a new method for constructing dynamic functional brain networks from fMRI data, highlighting differences in network properties between planning and execution phases.

Findings

More hubs are active during planning than execution.

Clusters of interconnected regions are stronger during planning.

Identified key brain regions involved in problem-solving.

Abstract

Complex problem solving is a high level cognitive process which has been thoroughly studied over the last decade. The Tower of London (TOL) is a task that has been widely used to study problem-solving. In this study, we aim to explore the underlying cognitive network dynamics among anatomical regions of complex problem solving and its sub-phases, namely planning and execution. A new brain network construction model establishing dynamic functional brain networks using fMRI is proposed. The first step of the model is a preprocessing pipeline that manages to decrease the spatial redundancy while increasing the temporal resolution of the fMRI recordings. Then, dynamic brain networks are estimated using artificial neural networks. The network properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions. The major similarities and dissimilarities of the network structure of planning and execution phases are highlighted. Our findings show the hubs and clusters of densely interconnected regions during both subtasks. It is observed that there are more hubs during the planning phase compared to the execution phase, and the clusters are more strongly connected during planning compared to execution.