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.

Layer-dependent stability of intracortical recordings and neuronal cell loss

“…Raw neural signals were band pass filtered at 250 Hz – 3 kHz using a 1st order Butterworth filter in MATLAB (MathWorks, Natick, MA). Even though, recent work has shown that there is valuable information in high frequency signals ( RaviPrakash et al, 2021 ), band-pass filtering, as the one used herein, has been a commonly used technique for spike sorting ( Quiroga, 2012 ). Spike sorting analysis was performed using Offline Sorter (V3, Plexon Inc. Dallas, TX).…”

Identifying the Neurocognitive Difference Between Two Groups Using Supervised Learning

“…The most accurate model KSVC selects the regions Frontal Sup Orb L and Cerebelum Crus2 R as the most important region, whereas none of the other methods identify Frontal Sup Orb L among the most important five regions. As there were very few results on this type of problem and even in a different setting so having a comparison is not possible [15,27].…”

“…[14] implemented several classification techniques to classify autistic and healthy patients and concluded that the neural networks outperformed other methods. [15] utilized the MRI scans of chess masters and novices to estimate morphometric connectivity. The authors introduced a novel functional morphometric similarity connectome by integrating functional and anatomical features, followed by classification using the SVM model after the feature selection technique is implemented.…”