Objective To assess the contribution of regional grey matter (GM) atrophy and functional disconnection in determining the level of cognitive decline in patients with Alzheimer's disease (AD) at different clinical stages. Methods Ten patients with amnesic mild cognitive impairment (a-MCI), 11 patients with probable AD and 10 healthy controls were recruited. T1 volumes were obtained from each subject and postprocessed according to an optimised voxel based morphometry protocol. Resting state functional MRI data were also collected from the same individuals and analysed to produce connectivity maps after identification of the default mode network (DMN) by independent component analysis. Results Compared with healthy controls, both AD and a-MCI patients showed a similar regional pattern of brain disconnection between the posterior cingulate cortex (PCC) and the medial prefrontal cortex and the rest of the brain. Conversely, the distribution of GM atrophy was significantly more restricted in a-MCI than in AD patients. Interestingly, the PCC showed reduced connectivity in a-MCI patients in the absence of GM atrophy, which was, in contrast, detectable at the stage of fully developed AD. Conclusions This study indicates that disconnection precedes GM atrophy in the PCC, which is a critical area of the DMN, and supports the hypothesis that GM atrophy in specific regions of AD brains likely reflects a long term effect of brain disconnection. In this context, our study indicates that GM atrophy in PCC accompanies the conversion from MCI to AD.

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.

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