A feature-based approach to combine functional MRI, structural MRI and EEG brain imaging data.
The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via a joint histogram visualization technique. Evaluation of which combination of fused data is most useful is determined by using the Kullback-Leibler divergence. We demonstrate our method on a data set composed of functional MRI data from two tasks, structural MRI data, and EEG data collected on patients with schizophrenia and healthy controls. We show that combining data types can improve our ability to distinguish differences between groups.
Calhoun
V
V
Olin Neuropsychiatry Res. Center, Yale Univ., New Haven, CT 06520, USA. vince.calhoun@yale.edu
Adali
T
T
Liu
J
J
D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D002555:Cerebrospinal Fluid; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials; D006439:Hemodynamics; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010487:Periaqueductal Gray / Q000502:physiology