Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment

Abstract

Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset.

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

“…In addition, the early diagnosis is also helpful for selecting suitable patients for clinical trials. Numerous studies [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] use neuroimaging techniques to detect pathology associated with AD and to predict the MCI-to-AD conversion. Among them, structural magnetic resonance imaging (MRI) has been the most extensively used imaging modality in the detection and prediction of AD as it is widely available and offers good diagnostic accuracy with moderate costs.…”

“…In order to reduce the large inter-subject variability, MR images are usually spatially registered to a common space for comparison [14], [20], [23]. Different studies have used different registration techniques to align anatomies at different levels of detail.…”

“…Different studies have used different registration techniques to align anatomies at different levels of detail. For example, affine registration was used in [23] while more accurate nonrigid registrations were utilized in other studies [14]. In [22], the registration in SPM5 was compared to the more accurate registration method DARTEL.…”

An image‐based model of brain volume biomarker changes in Huntington's disease

“…The EBM provides a simple and robust tool for investigating disease patterns and estimating patient stages in a fully data‐driven manner. In familial and sporadic Alzheimer's disease,8, 10 as well as frontotemporal dementia,11 EBMs have provided staging systems with predictive power at least as good as pattern matching techniques, for example 12, 13. In contrast to pattern matching, however, the EBMs provide uniquely fine‐grained temporal patterns of atrophy, enhancing disease understanding, and a well‐defined staging system for stratification.…”

Apolipoprotein E ε-4 as a genetic determinant of Alzheimer’s disease heterogeneity

“…51 Young et al developed a Gaussian predictive model integrating multimodal data gathered from volumetric magnetic resonance imaging, fludeoxyglucose positron emission tomography, determination of cerebrospinal fluid t-tau, p-tau, and aβ42 levels, and APOE genotyping, which correlated with conversion rates from MCI to AD. 52 A higher frequency of the ε-4 allele observed in patients with primarily amnestic MCI further suggests an increased risk of conversion to AD in this subgroup. 53 These findings are in accordance with results reported by Fagan et al, who showed that baseline fludeoxyglucose positron emission tomography measurements and episodic memory loss predicted the rate of conversion from MCI to AD, whereas the p-tau181p/Aβ1-42 ratio predicted further cognitive decline.…”