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Unveiling pathological brain changes associated with Alzheimer's disease (AD)is a challenging task especially that people do not show symptoms of dementiauntil it is late. Over the past years, neuroimaging techniques paved the wayfor computer-based diagnosis and prognosis to facilitate the automation ofmedical decision support and help clinicians identify cognitively intactsubjects that are at high-risk of developing AD. As a progressiveneurodegenerative disorder, researchers investigated how AD affects the brainusing different approaches: 1) image-based methods where mainly neuroimagingmodalities are used to provide early AD biomarkers, and 2) network-basedmethods which focus on functional and structural brain connectivities to giveinsights into how AD alters brain wiring. In this study, we reviewedneuroimaging-based technical methods developed for AD and mild-cognitiveimpairment (MCI) classification and prediction tasks, selected by screening allMICCAI proceedings published between 2010 and 2016. We included papers that fitinto image-based or network-based categories. The majority of papers focused onclassifying MCI vs. AD brain states, which has enabled the discovery ofdiscriminative or altered brain regions and connections. However, very fewworks aimed to predict MCI progression based on early neuroimaging-basedobservations. Despite the high importance of reliably identifying which earlyMCI patient will convert to AD, remain stable or reverse to normal overmonths/years, predictive models are still lagging behind.

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