Classification of individuals based on Sparse Representation of brain cognitive patterns: A functional MRI study

Abstract

Many neurological disorders can change patterns of brain activity observed in functional imaging studies. These functional differences may be useful for classification of individuals into diagnostic categories. However, due to the high dimensionality of the input feature space and small set of subjects that are usually available, classification based on fMRI data is not trivial. Here, we evaluate the use of a Sparse Representation Analysis method within a Fisher Linear Discriminant (FLD) classification method, taking functional patterns characteristic of different cognitive tasks as the data input. As a test dataset, with a clear 'gold-standard' classification, we attempt to classify individuals as young, or older, based only on functional activation patterns in a speech listening task. Thirty two young (age: 19-26) and older (age: 57-73) adults (16 each) were scanned while listening to noise and to sentences degraded with noise, half of which contained meaningful context that could be used to enhance intelligibility. Different functional contrast images were used within K-SVD to generate basis activation sources and their corresponding sparse modulation profiles. Sparse modulation profiles were used in a FLD framework to classify individuals into the young and older categories. The results demonstrate the feasibility of the general approach, and confirm the potential applicability of the proposed method for real-world diagnostic problems.

Multimodal Imaging Genetic Analysis of Schizophrenia Based on Sparse Representation and Dictionary Learning

“…In Reference [19], the authors were the first to use the SDL model to denoise fMRI time course. Ramezani et al [20] used SDL to classify brain cognitive patterns in fMRI data, where L 1 norm constraints were applied to the sparse coding matrix. Zhao et al [21] adopted the SDL to identify brain functional networks.…”