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7 Years of Developing Seed Techniques for Alzheimer’s Disease Diagnosis Using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis

  • Mayssa Soussia
  • Islem RekikEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11843)

Abstract

Unveiling pathological brain changes associated with Alzheimer’s disease (AD) and its earlier stages including mild cognitive impairment (MCI) is a challenging task especially that patients 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 exceptional review paper, we screened MICCAI proceedings published between 2010 and 2016 and IPMI proceedings published between 2011 and 2017, where ‘seed’ technical ideas generally get published, to identify neuroimaging-based technical methods developed for AD and MCI classification and prediction tasks. We included papers that fit into image-based or network-based categories. We found out that 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 from early 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 that foresee MCI evolution are still lagging behind.

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Authors and Affiliations

  1. 1.BASIRA Lab, Faculty of Computer and InformaticsIstanbul Technical UniversityIstanbulTurkey
  2. 2.Department of Electrical EngineeringThe National Engineering School of TunisTunisTunisia

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