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This article is part of the Research Topic The developing human brain

Review ARTICLE

Front. Hum. Neurosci., 23 October 2009 | http://dx.doi.org/10.3389/neuro.09.032.2009

Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations

1
Center for Interdisciplinary Brain Sciences Research, Stanford University School of Medicine, Palo Alto, CA, USA
2
Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA
3
Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA
Analyses of functional and structural imaging data typically involve testing hypotheses at each voxel in the brain. However, it is often the case that distributed spatial patterns may be a more appropriate metric for discriminating between conditions or groups. Multivariate pattern analysis has been gaining traction in neuroimaging of adult healthy and clinical populations; studies have shown that information present in neuroimaging data can be used to decode intentions and perceptual states, as well as discriminate between healthy and diseased brains. While few studies to date have applied these methods in pediatric populations, in this review we discuss exciting potential applications for studying both healthy, and aberrant, brain development. We include an overview of methods and discussion of challenges and limitations.
Keywords:
multivariate pattern classification, development, MRI, fMRI, clinical
Citation:
Bray S, Chang C and Hoeft F (2009). Applications of Multivariate Pattern Classification Analyses in Developmental Neuroimaging of Healthy and Clinical Populations. Front. Hum. Neurosci. 3:32. doi: 10.3389/neuro.09.032.2009
Received:
31 July 2009;
 Paper pending published:
03 September 2009;
Accepted:
29 September 2009;
 Published online:
23 October 2009.

Edited by:

Elizabeth D. O´Hare, Helen Wills Neuroscience Institute, University of California at Berkeley, USA

Reviewed by:

Rajeev D.S. Raizada, Dartmouth College, USA
John-Dylan Haynes, Bernstein Center for Computational Neuroscience, Germany
Copyright:
© 2009 Bray, Chang and Hoeft. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
*Correspondence:
Signe Bray, Center for Interdisciplinary Brain Sciences Research, Stanford University, 401 Quarry Lane, Palo Alto, CA 94301, USA. e-mail: signeb@stanford.edu