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
Center for Interdisciplinary Brain Sciences Research, Stanford University School of Medicine, Palo Alto, CA, USA
Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA
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.