Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD

Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD

This article explores various preprocessing tools that select/create features to help a learner produce a classifier that can use fMRI data to effectively discriminate Attention-Deficit Hyperactivity Disorder (ADHD) patients from healthy controls. We consider four different learning tasks: predictin...

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Bibliographic Details
Main Authors: Gagan S Sidhu, Nasimeh eAsgarian, Russell eGreiner, Matthew R G Brown
Format: Article
Language:English
Published: Frontiers Media S.A. 2012-11-01
Series:Frontiers in Systems Neuroscience
Subjects:
fMRI
machine learning
diagnosis
ADHD
classifier
FFT
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnsys.2012.00074/full

Internet

http://journal.frontiersin.org/Journal/10.3389/fnsys.2012.00074/full

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