Abstract
Multivariate pattern recognition approaches have become a prominent tool in neuroimaging data analysis. These methods enable the classification of groups of participants (e.g. controls and patients) on the basis of subtly different patterns across the whole brain. This study demonstrates that these methods can be used, in combination with automated morphometric analysis of structural MRI, to determine with great accuracy whether a single subject has been engaged in regular mental training or not. The proposed approach allowed us to identify with 94.87% accuracy (p<0.001) if a given participant is a regular meditator (from a sample of 19 regular meditators and 20 non-meditators). Neuroimaging has been a relevant tool for diagnosing neurological and psychiatric impairments. This study may suggest a novel step forward: the emergence of a new field in brain imaging applications, in which participants could be identified based on their mental experience.
Highlights
Pioneers in neuroscience studied patients with lesions and associated behavioural abnormalities, such as the classic case of Phineas Gage [1], in order to determine aspects of brain function
It was possible to identify whether a participant belonged to the regular meditator or non-meditator group with 94.87% accuracy (37 participants from 39, p,0.001, accuracy estimated from firstlevel leave-one-subject-out) using Support Vector Machine (SVM) analysis of the volumetric data from several brain regions
Support vector machines seem to be a promising tool for use in disease studies, but we investigated whether this technique could classify healthy participants on the basis of their mental training experience in meditation
Summary
Pioneers in neuroscience studied patients with lesions and associated behavioural abnormalities, such as the classic case of Phineas Gage [1], in order to determine aspects of brain function. Subtle differences in images were still difficult to identify accurately, until the application of Machine Learning methods for classification of brain images, such as Support Vector Machine (SVM [3]). These computational methods of pattern recognition have been used to aid discrimination of clinical brain pathologies associated with identifiable behavioural disorders [4,5]. Non-meditators required greater neural activation compared to regular meditators to achieve equivalent behavioural performance This supports the hypothesis that meditation training results in greater efficiency via improved sustained attention and impulse control. A pattern recognition approach based on SVM and feature selection was applied as a tool for automated classification
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