We present a feature selection method for neuroimaging techniques to find objective criteria for diagnosis of schizophrenia. The method is based on kernel alignment with the ideal kernel using Support Vector Machines (SVM) in order to detect relevant features for the diagnostic task.

The method has been applied to a dataset obtained using multichannel MagnetoEncephalograpy (MEG), from a set individuals composed by patients with chronic schizophrenia stable compensate, patients with the same diagnosis but in an acute exacerbation state, and a control group. The diagnosis of the schizophrenia is characterized in this paper as differences of synchronism between different parts of the brain, so correlations among sensors readings for different brain areas are used as features. All signal frequency bands are also analyzed, from δ to high frequency γ, to find the best band for diagnosis.

One of the main advantages of the proposed method is that it is less prone to overfitting than other approaches. This requirement is essential in neuroimaging where the number of features representing recordings is usually very large compared with the number of recordings. Another advantage is the ablility to visualize brain areas showing different correlations in control individuals compared with correlations in patients. The proposed methodology can be easily applied to other pathologies.