A Review on Image- and Network-based Brain Data Analysis Techniques for Alzheimer's Disease Diagnosis Reveals a Gap in Developing Predictive Methods for Prognosis

Abstract

Unveiling pathological brain changes associated with Alzheimer's disease (AD) is a challenging task especially that people do not show symptoms of dementia until it is late. Over the past years, neuroimaging techniques paved the way for computer-based diagnosis and prognosis to facilitate the automation of medical decision support and help clinicians identify cognitively intact subjects that are at high-risk of developing AD. As a progressive neurodegenerative disorder, researchers investigated how AD affects the brain using different approaches: 1) image-based methods where mainly neuroimaging modalities are used to provide early AD biomarkers, and 2) network-based methods which focus on functional and structural brain connectivities to give insights into how AD alters brain wiring. In this study, we reviewed neuroimaging-based technical methods developed for AD and mild-cognitive impairment (MCI) classification and prediction tasks, selected by screening all MICCAI proceedings published between 2010 and 2016. We included papers that fit into image-based or network-based categories. The majority of papers focused on classifying MCI vs. AD brain states, which has enabled the discovery of discriminative or altered brain regions and connections. However, very few works aimed to predict MCI progression based on early neuroimaging-based observations. Despite the high importance of reliably identifying which early MCI patient will convert to AD, remain stable or reverse to normal over months/years, predictive models are still lagging behind.

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

“…In the context of classification related to the anatomical region of brain, Soussia et al [232] provided a review of 28 papers from 2010 to 2016 published in MICCAI. They reviewed neuroimaging-based technical methods developed for the Alzheimer Disease (AD) and Mild-Cognitive Impairment (MCI) classification tasks.…”

“…The majority of papers used MRI for dementia classification and few worked to predict MCI conversion to AD at later observations. We refer to [232] for the detailed discussions on the contributions reviewed by this article. Gutierrez et al [233] proposed a deep neural network, termed Multi-structure point network (MSPNet), for the shape analysis on multiple structures.…”