Prediction of Clinical Scores from Neuroimaging Data with Censored Likelihood Gaussian Processes

In this paper, we explore the use of Censored Likelihoods in Gaussian Process Regression when predicting bounded clinical scores from neuroimaging data. The standard approach, which uses a Gaussian Likelihood, does not respect the fact that the clinical scores are bounded, and so may produce suboptimal models. Conversely, Censored Likelihoods explicitly model the restricted range of such clinical scores and carry this property through inference. We apply both the standard approach and the Censored Likelihood approach to the prediction of the MMSE score from structural MRI. Overall, we find small improvements in mean squared error when using the Censored Likelihood and in addition, the censored models are more favoured from a Bayesian perspective. We also discuss the qualitative nature of the predictions of the two approaches

Stability-based multivariate mapping using SCoRS

Recently we proposed a feature selection method based on stability theory (SCoRS - Survival Count on Random Subspaces) and showed that the proposed approach was able to improve classification accuracy using different datasets. In the present work we propose: (i) an extension of SCoRS using reproducibility instead of model accuracy as the parameter optimization criterion and (ii) a procedure to estimate the rate of false positive selection associated with the set of features obtained. Our results using the proposed framework showed that, as expected, the optimal parameter was more stable across the cross-validation folds, the spatial map displaying the features selected was less noisy and there was no decrease in classification accuracy. In addition, our results suggest that the estimated false positive rate for the features selected by SCoRS is under 0.05 for both optimization approaches, nevertheless lower when optimizing reproducibility in comparison with the standard optimization approach

Evidence for bias of genetic ancestry in resting state functional MRI

Resting state functional magnetic resonance imaging (rsfMRI) is a popular imaging modality for mapping the functional connectivity of the brain. Rs-fMRI is, just like other neuroimaging modalities, subject to a series of technical and subject level biases that change the inferred connectivity pattern. In this work we predicted genetic ancestry from rs-fMRI connectivity data at very high performance (area under the ROC curve of 0.93). Thereby, we demonstrated that genetic ancestry is encoded in the functional connectivity pattern of the brain at rest. We hypothesize that these observed differences are a result of known ethnicity-related variations in head and brain morphology, which may be carried forward through the rs-fMRI processing pipeline, rather than true neuronal differences. In any case, genetic ancestry constitutes a bias that should be accounted for in the analysis of rs-fMRI data

Predictive Modelling using Neuroimaging Data in the Presence of Confounds

When training predictive models from neuroimaging data, we typically have available non-imaging variables such as age and gender that affect the imaging data but which we may be uninterested in from a clinical perspective. Such variables are commonly referred to as 'confounds'. In this work, we firstly give a working definition for confound in the context of training predictive models from samples of neuroimaging data. We define a confound as a variable which affects the imaging data and has an association with the target variable in the sample that differs from that in the population-of-interest, i.e., the population over which we intend to apply the estimated predictive model. The focus of this paper is the scenario in which the confound and target variable are independent in the population-of-interest, but the training sample is biased due to a sample association between the target and confound. We then discuss standard approaches for dealing with confounds in predictive modelling such as image adjustment and including the confound as a predictor, before deriving and motivating an Instance Weighting scheme that attempts to account for confounds by focusing model training so that it is optimal for the population-of-interest. We evaluate the standard approaches and Instance Weighting in two regression problems with neuroimaging data in which we train models in the presence of confounding, and predict samples that are representative of the population-of-interest. For comparison, these models are also evaluated when there is no confounding present. In the first experiment we predict the MMSE score using structural MRI from the ADNI database with gender as the confound, while in the second we predict age using structural MRI from the IXI database with acquisition site as the confound. Considered over both datasets we find that none of the methods for dealing with confounding gives more accurate predictions than a baseline model which ignores confounding, although including the confound as a predictor gives models that are less accurate than the baseline model. We do find, however, that different methods appear to focus their predictions on specific subsets of the population-of-interest, and that predictive accuracy is greater when there is no confounding present. We conclude with a discussion comparing the advantages and disadvantages of each approach, and the implications of our evaluation for building predictive models that can be used in clinical practice

A multimodal multiple kernel learning approach to Alzheimer's disease detection

In neuroimaging-based diagnostic problems, the combination of different sources of information as MR images and clinical data is a challenging task. Their simple combination usually does not provides an improvement if compared with using the best source alone. In this paper, we deal with the well known Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset tackling the AD versus Control task. We use a recently proposed multiple kernel learning approach, called EasyMKL, to combine a huge amount of basic kernels in synergy with a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our new approach, called EasyMKLFS, outperforms baselines (e.g. SVM) and state-of-the-art methods as recursive feature elimination and SimpleMKL

Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines

Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer’s disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer the following questions: is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images) enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: (i) we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease). (ii) When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. (iii) We found important gender differences, and the possible causes of that finding are discussed

Predicting numerical processing in naturalistic settings from controlled experimental conditions

Machine learning research is interested in building models based on a training set that can then be applied to new data, whether this unseen data comes from new examples (e.g. New subjects, other tasks) or new features (e.g. Different modalities). In this work, we present a simple approach to transfer learning using intracranial EEG (also known as electrocorticographic, ECoG) data from three patients. More specifically, we aimed at detecting numerical processing during naturalistic settings based on a model trained with controlled experimental conditions. Our results showed significant prediction accuracy of numerical events in naturalistic settings when considering a priori knowledge of the target task

Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach

Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson’s disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process

SCoRS - a method based on stability for feature selection and mapping in neuroimaging.

Feature selection (FS) methods play two important roles in the context of neuroimaging based classification: potentially increase classification accuracy by eliminating irrelevant features from the model and facilitate interpretation by identifying sets of meaningful features that best discriminate the classes. Although the development of FS techniques specifically tuned for neuroimaging data is an active area of research, up to date most of the studies have focused on finding a subset of features that maximizes accuracy. However, maximizing accuracy does not guarantee reliable interpretation as similar accuracies can be obtained from distinct sets of features. In the current paper we propose a new approach for selecting features: SCoRS (Survival Count on Random Subsamples) based on a recently proposed Stability Selection theory. SCoRS relies on the idea of choosing relevant features that are stable under data perturbation. Data are perturbed by iteratively subsampling both features (subspaces) and examples. We demonstrate the potential of the proposed method in a clinical application to classify depressed patients versus healthy individuals based on fMRI data acquired during visualization of happy faces