Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players

Abstract

A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Toward this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm.

Keywords: MRI; functional connectivity; functional morphometric similarity; machine learning; morphometric similarity.

Figure 1

Generating the Morphometric Similarity Network (MSN) from anatomical images where morphometric measures are extracted from the cortical surface of the T1-weighted MRI and Pearson's correlation is used to build the MSN.

Figure 2

Generating the Functional Morphometric Similarity Connectome (FMSC) from the anatomical and function images. Left: Morphometric measures are extracted from the cortical surface of the T1-weighted MRI. Right: Functional connectivity is generated from the surface of the rs-fMRI and node degrees (ND) are extracted via sparsification (thresholding the edge strength). The morphometric and functional measures are combined to generate the FMSC. NS, node strength; @, thresholded at.

Figure 3

Top-10 connections identified in classification of chess masters vs. amateur players. (A) Functional connections. (B) Morphometric connections. Table 5 lists the names of the ROIs in these top connections.

Figure 4

Functional anatomical connections differentiating chess masters from amateur players. (A) Common functional and morphometric connections. (B) Functional-morphometric connections. Table 5 lists the names of the ROIs in these top connections.