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 3 T to obtain a 7 minute and 20 second 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.  A broad goal of neuroimaging research is to develop effective, reliable clinical tools for the early detection and diagnosis of a range of neurological conditions such as dementia, depression and attention deficit hyperactivity disorder. Machine learning (ML) seems a promising route to help achieve such objectives. ML is the study of algorithms and computational techniques that use previous examples in the form of multivariate datasets to help make future predictions. One application of a ML prediction algorithm in the context of neuroimaging would be to make clinical diagnoses from subject's functional MRI (fMRI) scans. Alongside providing a computational and statistical framework within which to make predictions from multivariate observations, ML can also provide insights into what multivariate features of the data are most relevant for making accurate predictions. In the context of neuroimaging for patient stratification those features correspond to biomarkers of disease states. In this paper we present a ML technique to perform patient stratification between healthy control subjects and either amnesic mild cognitive impairment (a-MCI) or Alzheimer's disease subjects. Subject classifications are made from the functional-connectivity scores of their brains inferred from resting state fMRI (rsfMRI) scans. Previous rsfMRI patient stratification studies have applied support vector machines (SVMs) to make inter-group classifications. Our approach here NeuroImage 112 (2015) 232-243
Challis et al. REF applied Bayesian Gaussian Process Logistic Regression for disease classification.
Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI
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Psychology Medicine Computer Science