Nowadays, there is an active fi eld of research in neuroscience trying to fi nd
relations between neurofunctional abnormalities of brain structures and neurological
disorders. Previous statistical studies on brain functional Magnetic
Resonance Images (fMRI) have found Attention Defi cit Hyperactivity Disorder
(ADHD) patients are characterized by reduced activity in the inferior
frontal gyrus (IFG) during response inhibition tasks and in the Ventral Striatum
(VStr) during reward anticipation tasks.
Interpreting brain image experiments using fMRI requires analysis of complex
data and diff erent univariate or multivariate approaches can be chosen.
Recently, one analysis approach that has grown in popularity is the use of
machine learning algorithms to train classifiers to discriminate abnormal behavior
or other variables of interest from fMRI data.
The purpose of this work is to apply machine learning techniques to perform
fMRI group analysis in an adult population. We propose a multivariate
classifi er using diff erent discriminative features. Furthermore, we show how
temporal information of fMRI data can be taken into account to improve the
discrimination.
We demonstrate that our new approach is able to diagnose the ADHD
characteristics based on the activation in the executive functions. Previous
results on the response inhibition task did not find di fferences between activation
responses. Opposite to these results, we achieve accurate classifi cation
performance for this task. Moreover, in this case, we show that classi fication
rates can be signi cantly improved by incorporating temporal information
into the classi fier.
