Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI

Abstract

Multivariate pattern analysis and statistical machine learning techniques are attracting increasing interest from the neuroimaging community. Researchers and clinicians are also increasingly interested in the study of functional-connectivity patterns of brains at rest and how these relations might change in conditions like Alzheimer's disease or clinical depression. In this study we investigate the efficacy of a specific multivariate statistical machine learning technique to perform patient stratification from functional-connectivity patterns of brains at rest. Whilst the majority of previous approaches to this problem have employed support vector machines (SVMs) we investigate the performance of Bayesian Gaussian process logistic regression (GP-LR) models with linear and non-linear covariance functions. GP-LR models can be interpreted as a Bayesian probabilistic analogue to kernel SVM classifiers. However, GP-LR methods confer a number of benefits over kernel SVMs. Whilst SVMs only return a binary class label prediction, GP-LR, being a probabilistic model, provides a principled estimate of the probability of class membership. Class probability estimates are a measure of the confidence the model has in its predictions, such a confidence score may be extremely useful in the clinical setting. Additionally, if miss-classification costs are not symmetric, thresholds can be set to achieve either strong specificity or sensitivity scores. Since GP-LR models are Bayesian, computationally expensive cross-validation hyper-parameter grid-search methods can be avoided. We apply these methods to a sample of 77 subjects; 27 with a diagnosis of probable AD, 50 with a diagnosis of a-MCI and a control sample of 39. All subjects underwent a MRI examination at 3T to obtain a 7minute and 20second resting state scan. Our results support the hypothesis that GP-LR models can be effective at performing patient stratification: the implemented model achieves 75% accuracy disambiguating healthy subjects from subjects with amnesic mild cognitive impairment and 97% accuracy disambiguating amnesic mild cognitive impairment subjects from those with Alzheimer's disease, accuracies are estimated using a held-out test set. Both results are significant at the 1% level.

Advancing Alzheimer’s research: A review of big data promises

“…Liu et al improved the predication of MCI to AD conversion using local linear embedding (LLE) (37). Hinrichs et al designed a multi-kernel learning (MKL) framework (38) and later applied Bayesian Gaussian process logistic regression (GP-LR) models to differentiate MCI patients from HC and AD patients (39). Deep learning techniques were applied to classify various stages of AD progression using MRI scans from ADNI database (40).…”

Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments

“…Despite the protection toward bias offered by a datadriven approach and a sample of comparable or larger size than that of other studies, [16][17][18][19][20] the outcome is still the result of feature and algorithm definition. Although we selected "standard" cognitive tests and segmentation/parcellation atlases, and 2 basic machine-learning algorithms, we cannot rule out the possibility that other methodological choices might have yielded slightly different patterns of findings.…”

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI

“…To date, several studies have employed machine learning approaches for automatic identification of patients with MCI and AD (Challis et al, 2015;Davatzikos et al, 2011), and some also performed classification of these patients using undirected graph measures (Jie et al, 2013;Jie et al, 2014;Khazaee et al, 2015a, b, c;Li et al, 2013b;Wang et al, 2013;Wee et al, 2012). Undirected graph measures may not be able to adequately characterize the information flow among the regions within the brain network.…”