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.
Highlights
Functional connectivity networks (FCNs) are representative of relationships between spatially separated brain regions
We adapt sparse learning methods for the brain analysis of different groups of healthy subjects, where one group consists of professional chess players and a second group of amateur subjects
We propose a novel functional morphometric similarity connectome (FMSC) by combining the anatomical and functional information and enabling sparse learning
Summary
Functional connectivity networks (FCNs) are representative of relationships between spatially separated brain regions. The study of FCN, as a technique for diagnosis of clinical conditions, has gained an increase in popularity due to their high test-retest reliability and reproducibility (Zuo and Xing, 2014). While methods that use machine learning approaches for analyzing FCNs have shown great promise (Wee et al, 2012; Chen et al, 2016; He et al, 2018), FCN-based classification methods could suffer from high dimensionality issues where there are more features than the available data. While there is evidence that FCNbased methods are useful for studying various clinical conditions, these have not yet been widely adapted to analyze two groups of healthy subjects. We propose to extend the applicability of sparse learning methods from diseased subjects to healthy subjects consisting of professional chess players (grand masters) and amateurs
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