Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images

TL;DR

This paper introduces a multimodal, multiscale deep neural network framework that combines MRI and FDG-PET images to improve early diagnosis of Alzheimer's Disease, achieving high accuracy in predicting conversion within three years.

Contribution

The paper presents a novel deep learning approach integrating multiple imaging modalities and scales for more accurate early AD diagnosis, outperforming existing methods.

Findings

Achieved 85.68% accuracy in predicting AD conversion within 3 years.

Outperformed existing literature in discrimination ability.

Validated through cross-validation experiments.

Abstract

Alzheimer's Disease (AD) is a progressive neurodegenerative disease. Amnestic mild cognitive impairment (MCI) is a common first symptom before the conversion to clinical impairment where the individual becomes unable to perform activities of daily living independently. Although there is currently no treatment available, the earlier a conclusive diagnosis is made, the earlier the potential for interventions to delay or perhaps even prevent progression to full-blown AD. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo view into the structure and function (glucose metabolism) of the living brain. It is hypothesized that combining different image modalities could better characterize the change of human brain and result in a more accuracy early diagnosis of AD. In this paper, we proposed a novel framework to discriminate normal control(NC) subjects from subjects with AD pathology (AD and NC, MCI subjects convert to AD in future). Our novel approach utilizing a multimodal and multiscale deep neural network was found to deliver a 85.68\% accuracy in the prediction of subjects within 3 years to conversion. Cross validation experiments proved that it has better discrimination ability compared with results in existing published literature.