Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI.
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.
Department of Physics and Astronomy, University of Sussex, Falmer, East Sussex BN1 9QH, UK.
Edward
E
Challis
Department of Physics and Astronomy, University of Sussex, Falmer, East Sussex BN1 9QH, UK.
Peter
P
Hurley
Neuroimaging Laboratory, Santa Lucia Foundation, Via Ardeatina 306, Roma, Italy.
Laura
L
Serra
Neuroimaging Laboratory, Santa Lucia Foundation, Via Ardeatina 306, Roma, Italy.
Marco
M
Bozzali
Department of Physics and Astronomy, University of Sussex, Falmer, East Sussex BN1 9QH, UK.
Seb
S
Oliver
Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, University of Sussex, Falmer, East Sussex BN1 9PR, UK. Electronic address: m.cercignani@bsms.ac.uk.
Mara
M
Cercignani
D000368:Aged; D000375:Aging; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology* / Q000523:psychology; D060825:Cognitive Dysfunction / Q000473:pathology* / Q000523:psychology; D004522:Educational Status; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016015:Logistic Models; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015999:Multivariate Analysis; D009434:Neural Pathways; D009483:Neuropsychological Tests; D016011:Normal Distribution; D011336:Probability; D012146:Rest / Q000502:physiology*; D012727:Sex Characteristics
Dementia; Functional connectivity; Machine learning