The personalized Alzheimer's disease cortical thickness index predicts likely pathology and clinical progression in mild cognitive impairment

Abstract

Introduction An Alzheimer's disease (AD) biomarker adjusted for age-related brain changes should improve specificity for AD-related pathological burden. Methods We calculated a brain-age-adjusted “personalized AD cortical thickness index” (pADi) in mild cognitive impairment patients from the Alzheimer's Disease Neuroimaging Initiative. We performed receiver operating characteristic analysis for discrimination between patients with and without cerebrospinal fluid evidence of AD and logistic regression in an independent sample to determine if a dichotomized pADi predicted conversion to AD dementia. Results Receiver operating characteristic area under the curve was 0.69 and 0.72 in the two samples. Three empirical methods identified the same cut-point for pADi in the discovery sample. In the validation sample, 83% of pADi+ mild cognitive impairment patients were cerebrospinal fluid AD biomarker positive. pADi+ mild cognitive impairment patients (n = 63, 38%) were more likely to progress to AD dementia after 1 (odds ratio = 2.9) and 3 (odds ratio = 2.6) years. Discussion The pADi is a personalized, magnetic resonance imaging–derived AD biomarker that predicts progression to dementia.

Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations

“…In this study, we aimed to answer these questions using the cortical thickness data extracted from T 1 -weighted MR scans, deep neural network classifier, and transfer learning. Cortical thinning has been reported in AD/MCI patients (Du et al, 2007; Lerch et al, 2008; Lerch et al, 2005; Singh et al, 2006), and has been identified as imaging biomarkers for the identification of AD/MCI, as well as the progression from MCI to AD (Querbes et al, 2009; Racine et al, 2018; Schaerer et al, 2016). However, a substantial body of existing studies (Eskildsen et al, 2013; Wee et al, 2012) employed regional features, e.g., the mean cortical thickness within a region-of-interest (ROI), for classification, but did not incorporate the cortical geometry.…”

Mapping the patterns of cortical thickness in single- and multiple-domain amnestic mild cognitive impairment patients: a pilot study

“…The aim of our study was to identify the structural cortical thickness difference among AD, aMCI-m, aMCI-s and NC groups by both ROI-based and vertex-based methods. Based on previous studies on AD and aMCI subjects [21, 17], we hypothesized that both aMCI-m and aMCI-s groups might have significant cortical thickness decrease comparing to NC group, while AD group would be more likely to exhibit cortical thickness decrease than both aMCI-m and aMCI-s groups. In addition, the two subtypes of aMCI might demonstrate differential thickness decrease patterns in certain cortical regions ( e.g.…”

Personalised modelling with spiking neural networks integrating temporal and static information

“…The selected nearest neighbouring samples to an individual 𝑖 are used to build a PSNN model through the following steps using the NeuCube SNN architecture [5]:…”

“…The classification accuracy was also compared with traditional methods: Multiple Linear Regression (MLR), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and Evolving Clustering Method (ECM) [51] as shown in Table 2. The SVM optimal parameters that resulted in the best classification accuracy were found after performing the experiments several times with different parameter setting (polynomial degree within [2,5] and (RBF) kernel degree within [0.2, 1]). As shown in Table 2, when we used SVM for classification, the best accuracy was obtained using Kernel polynomial degree: 2.…”

“…In [4] a spiking neural network (SNN) architecture NeuCube for modelling brain data was proposed with the potential to be used for personalised modelling. Research [5] reported 83% accuracy of classifying patients with mild cognitive impairment (MCI) and those MCI patients with Alzheimer's disease (AD) using Magnetic Resonance Imaging (MRI) scans with respect to brain-age-related changes of an individual patient. In research [6], Quantum Associative Memories (QAM) was used for the diagnosis of tropical diseases (malaria symptoms versus different types of fevers) to improve the personal diagnostic process.…”