Abstract
Recently, machine learning methods have gained lots of attention from researchers seeking to analyze brain images such as Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to obtain a deeper understanding of the brain and such related diseases, for example, Alzheimer's disease. Finding the common patterns caused by a brain disorder through analysis of the functional connectivity (FC) network along with discriminating brain diseases from normal controls have long been the two principal goals in studying rs-fMRI data. The majority of FC extraction methods calculate the FC matrix for each subject and then use simple techniques to combine them and obtain a general FC matrix. In addition, the state-of-the-art classification techniques for finding subjects with brain disorders also rely on calculating an FC for each subject, vectorizing, and feeding them to the classifier. Considering these problems and based on multi-dimensional nature of the data, we have come up with a novel tensor framework in which a general FC matrix is obtained without the need to construct an FC matrix for each sample. This framework also allows us to reduce the dimensionality and create a novel discriminant function that rather than using FCs works directly with each sample, avoids vectorization in any step, and uses the test data in the training process without forcing any prior knowledge of its label into the classifier. Extensive experiments using the ADNI dataset demonstrate that our proposed framework effectively boosts the fMRI classification performance and reveals novel connectivity patterns in Alzheimer's disease at its early stages.
Highlights
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with a long pre-morbid asymptomatic period, which affects millions of elderly individuals worldwide (Caselli et al, 2004)
If X does not belong to the ith class, HighOrder Singular Value Decomposition (HOSVD) would naturally consider it as noise [based on ordering property (4)], since X is not similar to other samples and does not play a key role in reconstructing them, so its effect would be on the last slices of the core tensor and singular matrices, that is, slices with higher indices that are ignored in reconstruction (8)
We proposed a tensor framework for early Mild Cognitive Impairment (eMCI) diagnosis and functional connectivity (FC) construction
Summary
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with a long pre-morbid asymptomatic period, which affects millions of elderly individuals worldwide (Caselli et al, 2004). The predominant clinical symptoms of AD include a decline in some important brain cognitive and intellectual abilities such as memory, thinking, and reasoning. Detection is important for possible delay of the progression of mild MCI to moderate and severe stages (Folch et al, 2016). Diagnosis of MCI is difficult due to its mild symptoms of cognitive impairment, causing most computer-aided diagnosis to achieve lower-than-desired performance (Musha et al, 2013; Li R. et al, 2018). Precise diagnosis of AD, especially in its early warning stage, that is, early Mild Cognitive Impairment (eMCI), enables treatments to delay or even avoid such disorders
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