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Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping

Overview of attention for article published in PLoS Computational Biology, October 2012
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Title
Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping
Published in
PLoS Computational Biology, October 2012
DOI 10.1371/journal.pcbi.1002707
Pubmed ID
Authors

Russell A. Poldrack, Jeanette A. Mumford, Tom Schonberg, Donald Kalar, Bishal Barman, Tal Yarkoni

Abstract

Neuroimaging research has largely focused on the identification of associations between brain activation and specific mental functions. Here we show that data mining techniques applied to a large database of neuroimaging results can be used to identify the conceptual structure of mental functions and their mapping to brain systems. This analysis confirms many current ideas regarding the neural organization of cognition, but also provides some new insights into the roles of particular brain systems in mental function. We further show that the same methods can be used to identify the relations between mental disorders. Finally, we show that these two approaches can be combined to empirically identify novel relations between mental disorders and mental functions via their common involvement of particular brain networks. This approach has the potential to discover novel endophenotypes for neuropsychiatric disorders and to better characterize the structure of these disorders and the relations between them.

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X Demographics

The data shown below were collected from the profiles of 29 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 276 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 3%
United Kingdom 3 1%
Switzerland 2 <1%
Germany 2 <1%
Canada 2 <1%
Nigeria 1 <1%
Japan 1 <1%
China 1 <1%
Unknown 256 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 74 27%
Student > Ph. D. Student 53 19%
Student > Bachelor 28 10%
Student > Master 25 9%
Professor 16 6%
Other 53 19%
Unknown 27 10%
Readers by discipline Count As %
Psychology 68 25%
Neuroscience 39 14%
Agricultural and Biological Sciences 30 11%
Medicine and Dentistry 26 9%
Computer Science 24 9%
Other 46 17%
Unknown 43 16%