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

Understand

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.