Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach

Abstract

Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

High‐order resting‐state functional connectivity network for MCI classification

“…We also provide some citations showing the importance of corresponding brain regions. We can see that many of these selected brain regions are consistent with the observations reported in the previous literatures (Echávarri, et al, 2011; Jacobs, et al, 2012; Kosicek and Hecimovic, 2013; Salvatore, et al, 2015). For example, Echávarri et al (Echávarri, et al, 2011) have observed that parahippocampal gyrus and hippocampus are important biomarkers, while the former can discriminate better than the latter in terms of AD diagnosis.…”

Graph analysis of structural brain networks in Alzheimer’s disease: beyond small world properties

“…Multiple studies have found structural MRI (sMRI), a useful diagnostic tool that can contribute to detecting AD-related modifications before the development of clinical symptoms (Raskin et al 2015;Xie et al 2015;Teipel et al 2015;Besson et al 2015;Goveas et al 2015). A different way in which sMRI can assist in early AD diagnosis is by providing data sets for a distinct group of recently developed analytical procedures involving advanced mathematical and statistical methods such as machine learning (Gorji and Haddadnia 2015;Klöppel et al 2008;Salvatore et al 2015;Zhan et al 2015) and graph theory, the formal study of networks (Supekar et al 2008;Stam et al 2009;He et al 2008).…”

Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's Disease: A Methodological Review

“…Short runs emphasis, Long runs emphasis, Graylevel non-uniformity, Run-length non-uniformity, Run percentage [78], [83]- [85] D. Classification methods Classification is used for the identification of patterns features of interest into the classes they belong. Moreover, machine-learning techniques have the potential to classify MR features without requiring a priori hypotheses from where this information may be coded in the images [86]. When classifiers are used, image samples are divided into two sets, training and testing [87].…”