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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Brain Imaging Analysis Can Identify Participants under Regular Mental Training

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Sato, Joao R. [1] ; Kozasa, Elisa H. [2, 3] ; Russell, Tamara A. [4] ; Radvany, Joao [2] ; Mello, Luiz E. A. M. [5] ; Lacerda, Shirley S. [2] ; Amaro, Jr., Edson [2]
Total Authors: 7
[1] Univ Fed Abc, Santo Andre - Brazil
[2] Hosp Israelita Albert Einstein, Inst Cerebro, Sao Paulo - Brazil
[3] UNIFESP Univ Fed Sao Paulo, Dept Psychobiol, Sao Paulo - Brazil
[4] Kings Coll London, Inst Psychiat, London WC2R 2LS - England
[5] Univ Fed Sao Paulo, Dept Physiol, Sao Paulo - Brazil
Total Affiliations: 5
Document type: Journal article
Source: PLoS One; v. 7, n. 7 JUL 3 2012.
Web of Science Citations: 7

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. (AU)

FAPESP's process: 10/01394-4 - Statistical and computational methods for discriminating of anatomic changes, mental states and identification of brain connectivity: an integrative approach based on MRI, fMRI and EEG
Grantee:João Ricardo Sato
Support type: Regular Research Grants