Convergent evidence has been collected to support that Alzheimer’s disease (AD) is associated with reduction in hippocampal volume based on anatomical magnetic resonance imaging (MRI) and impaired functional connectivity based on functional MRI. Radiomics texture analysis has been previously successfully used to identify MRI biomarkers of several diseases, including AD, mild cognitive impairment and multiple sclerosis. In this study, our goal was to determine if MRI hippocampal textures, including the intensity, shape, texture and wavelet features, could be served as an MRI biomarker of AD. For this purpose, the texture marker was trained and evaluated from MRI data of 48 AD and 39 normal samples. The result highlights the presence of hippocampal texture abnormalities in AD, and the possibility that texture may serve as a neuroimaging biomarker for AD.
The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated
informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a
novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The
functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity
pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity
patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace
distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed
to select independent components for constructing the most discriminative functional connectivity pattern. The
discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and
31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising
classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies
discriminative functional networks that are informative for schizophrenia diagnosis.