Automatic Classification Software for MRI brain scans: A Diagnostic tool

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A major challenge facing humankind is an aging global population and the associated increase in degenerating and debilitating diseases, such as Alzheimer's disease (AD). Early diagnosis is essential to improve patient quality of life and minimize social costs. Magnetic Resonance Imaging (MRI) is a well-established tool for studying brain abnormalities. Conventional MRI can only reveal unspecific brain atrophy in AD, but it has been demonstrated that "quantitative" MRI techniques, together with functional MRI (fMRI) can provide signatures of the onset of dementia (Bozzali et al., 2011). Resting-state fMRI is a relatively novel approach to detect spontaneous brain activity at rest (Greicius et al., 2004). Resting-state fMRI is potentially very powerful for the early diagnosis of dementia (Zhou et al., 2010). However, it is regarded as unsuitable for clinical use owing to the volume of data, the complex image analysis required, and the difficulty in the interpretation of results.A number of functional networks in the brain have been identified using Independent Component Analysis (ICA) deconstructions of resting-state fMRI data. However, the interpretation of the raw images and even the compressed ICA data requires an experienced human eye. In particular, once the networks have been identified, they are typically analysed separately using univariate statistical approaches. A multivariate approach providing a quick, automatic, objective classification and diagnosis of images would have a huge impact in clinical and research arenas. Our proposal is to illustrate proof of concept for the automatic classification of MRI imaging of the brain. We will use existing ICA decomposition of patient and control groups in resting conditions. Our plan is to use a machine learning technique, specifically a Bayesian classifier using Gaussian mixtures, which has been used in Astronomical research. We will code a prototype diagnostic tool, based on resting state fMRI, which can be tested by the CISC group. Once validated, this approach can be easily extended to the joint analysis of other MRI modalities.This proposal brings together the Astronomy Centre and the Clinical Imaging Science Centre (CISC) at the University of Sussex. The Astronomy Centre brings extensive expertise in statistical analysis and software development while the CISC brings extensive human-classified data sets and deep understanding of the clinical problems and expertise in resting state fMRI and other MRI techniques.