Abstract
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.
Highlights
The human brain is a distributed processing machine, with even the most basic tasks requiring the cooperation of neurons in multiple brain regions
Rather than asking to what degree each voxel responds to one experimental condition versus another, multivariate pattern analysis (MVPA) turns the question around, asking instead whether – and which – patterns of brain activity across many voxels are characteristic of the brain during one experimental condition versus another, or of one clinical population versus another
While MVPA techniques have yet to be widely adopted in pediatric neuroimaging, we expect to see applications in several domains in coming years
Summary
The human brain is a distributed processing machine, with even the most basic tasks requiring the cooperation of neurons in multiple brain regions. Multivariate pattern analysis (MVPA) approaches to studying the brain have been gaining momentum (e.g., Kay et al, 2008; Mitchell et al, 2008) These methods are designed to identify spatial and/or temporal patterns in the data that differentiate between cognitive tasks or subject groups. Brain activity, i.e., neuronal firing, is in itself just a means of communicating with other neurons, and it is clear that even the simplest cognitive tasks are not accomplished solely by the neurons contained in individual voxels This suggests that analysis techniques designed to learn distributed spatial patterns that best distinguish one condition from another (Kriegeskorte et al, 2006; De Martino et al, 2008; Pereira et al, 2009) may be more sensitive than univariate techniques. An important challenge in pediatric clinical neuroimaging is identifying patterns of brain activity or structure that reliably precede disease onset (Koutsouleris et al, 2009), or can distinguish treatment responders from non-responders
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