Mehdi Rahim

Predicting brain-age from multimodal imaging data captures cognitive impairment

Abstract The disparity between the chronological age of an individual and their brain‐age measured based on biological information has the potential to offer clinically relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain‐age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain‐imaging data improves age prediction. Using cortical anatomy and whole‐brain functional connectivity on a large adult lifespan sample (N=2354, age 19–82), we found that multimodal data improves brain‐based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain‐age measure was robust to confounding effects: head motion did not drive brain‐based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N=475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain‐age prediction to confounds, generalizability across sites, and sensitivity to clinically‐relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders. HighlightsBrain‐based age prediction is improved with multimodal neuroimaging data.Participants with cognitive impairment show increased brain aging.Age prediction models are robust to motion and generalize to independent datasets from other sites.

Integrating Multimodal Priors in Predictive Models for the Functional Characterization of Alzheimer's Disease

Functional brain imaging provides key information to characterize neurodegenerative diseases, such as Alzheimers disease AD. Specifically, the metabolic activity measured through fluorodeoxyglucose positron emission tomography FDG-PET and the connectivity extracted from resting-state functional magnetic resonance imaging fMRI, are promising biomarkers that can be used for early assessment and prognosis of the disease and to understand its mechanisms. FDG-PET is the best suited functional marker so far, as it gives a reliable quantitative measure, but is invasive. On the other hand, non-invasive fMRI acquisitions do not provide a straightforward quantification of brain functional activity. To analyze populations solely based on resting-state fMRI, we propose an approach that leverages a metabolic prior learned from FDG-PET. More formally, our classification framework embeds population priors learned from another modality at the voxel-level, which can be seen as a regularization term in the analysis. Experimental results show that our PET-informed approach increases classification accuracy compared to pure fMRI approaches and highlights regions known to be impacted by the disease.

Joint prediction of multiple scores captures better individual traits from brain images

To probe individual variations in brain organization, population imaging relates features of brain images to rich descriptions of the subjects such as genetic information or behavioral and clinical assessments. Capturing common trends across these measurements is important: they jointly characterize the disease status of patient groups. In particular, mapping imaging features to behavioral scores with predictive models opens the way toward more precise diagnosis. Here we propose to jointly predict all the dimensions (behavioral scores) that make up the individual profiles, using so-called multi-output models. This approach often boosts prediction accuracy by capturing latent shared information across scores. We demonstrate the efficiency of multi-output models on two independent resting-state fMRI datasets targeting different brain disorders (Alzheimers Disease and schizophrenia). Furthermore, the model with joint prediction generalizes much better to a new cohort: a model learned on one study is more accurately transferred to an independent one. Finally, we show how multi-output models can easily be extended to multi-modal settings, combining heterogeneous data sources for a better overall accuracy.

Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction

Functional connectivity describes neural activity from resting-state functional magnetic resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomark-er of neurodegenerative diseases, such as Alzheimers disease (AD), where the connectome can be an indicator to assess and to understand the pathology. However, it only provides noisy measurements of brain activity. As a consequence, it has shown fairly limited discrimination power on clinical groups. So far, the reference functional marker of AD is the fluorodeoxyglucose positron emission tomography (FDG-PET). It gives a reliable quantification of metabolic activity, but it is costly and invasive. Here, our goal is to analyze AD populations solely based on rs-fMRI, as functional connectivity is correlated to metabolism. We introduce transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is learned from an independent FDG-PET dataset to improve functional connectivity-based prediction of AD. The prior acts as a regularization of connectivity learning and improves the estimation of discriminative patterns from distinct rs-fMRI datasets. Our approach is a two-stage classification strategy that combines several seed-based connectivity maps to cover a large number of functional networks that identify AD physiopathology. Experimental results show that our transmodal approach increases classification accuracy compared to pure rs-fMRI approaches, without resorting to additional invasive acquisitions. The method successfully recovers brain regions known to be impacted by the disease.

Dopamine Transporter and Reward Anticipation in a Dimensional Perspective: A Multimodal Brain Imaging Study

Dopamine function and reward processing are highly interrelated and involve common brain regions afferent to the nucleus accumbens, within the mesolimbic pathway. Although dopamine function and reward system neural activity are impaired in most psychiatric disorders, it is unknown whether alterations in the dopamine system underlie variations in reward processing across a continuum encompassing health and these disorders. We explored the relationship between dopamine function and neural activity during reward anticipation in 27 participants including healthy volunteers and psychiatric patients with schizophrenia, depression, or cocaine addiction, using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) multimodal imaging with a voxel-based statistical approach. Dopamine transporter (DAT) availability was assessed with PET and [11C]PE2I as a marker of presynaptic dopamine function, and reward-related neural response was assessed using fMRI with a modified Monetary Incentive Delay task. Across all the participants, DAT availability in the midbrain correlated positively with the neural response to anticipation of reward in the nucleus accumbens. Moreover, this relationship was conserved in each clinical subgroup, despite the heterogeneity of mental illnesses examined. For the first time, a direct link between DAT availability and reward anticipation was detected within the mesolimbic pathway in healthy and psychiatric participants, and suggests that dopaminergic dysfunction is a common mechanism underlying the alterations of reward processing observed in patients across diagnostic categories. The findings support the use of a dimensional approach in psychiatry, as promoted by the Research Domain Criteria project to identify neurobiological signatures of core dysfunctions underling mental illnesses.

Population-Shrinkage of Covariance to Estimate Better Brain Functional Connectivity

Brain functional connectivity, obtained from functional Magnetic Resonance Imaging at rest (r-fMRI), reflects inter-subject variations in behavior and characterizes neuropathologies. It is captured by the covariance matrix between time series of remote brain regions. With noisy and short time series as in r-fMRI, covariance estimation calls for penalization, and shrinkage approaches are popular. Here we introduce a new covariance estimator based on a non-isotropic shrinkage that integrates prior knowledge of the covariance distribution over a large population. The estimator performs shrinkage tailored to the Riemannian geometry of symmetric positive definite matrices, coupled with a probabilistic modeling of the subject and population covariance distributions. Experiments on a large-scale dataset show that such estimators resolve better intra- and inter-subject functional connectivities compared existing covariance estimates. We also demonstrate that the estimator improves the relationship across subjects between their functional-connectivity measures and their behavioral assessments.

Comparing functional connectivity based predictive models across datasets

Resting-state functional Magnetic Resonance Imaging (rs-fMRI) holds the promise of easy-to-acquire and widespectrum biomarkers. However, there are few predictivemodeling studies on resting state, and processing pipelines all vary. Here, we systematically study resting state functionalconnectivity (FC)-based prediction across three different cohorts. Analysis pipelines consist of four steps: Delineation of brain regions of interest (ROIs), ROI-level rs-fMRI time series signal extraction, FC estimation and linear model classification analysis of FC features. For each step, we explore various methodological choices: ROI set selection, FC metrics, and linear classifiers to compare and evaluate the dominant strategies for the sake of prediction accuracy. We achieve good prediction results on the three different targets. With regard to pipeline selection, we obtain consistent results in two pipeline steps -FC metrics and linear classifiers- that are vital in the diagnosis of rs-fMRI based disease biomarkers. Regarding brain ROIs selection, we observe that the effects of different diseases are best characterized by different strategies: Schizophrenia discrimination is best performed in dataset-specific ROIs, which is not clearly the case for other pathologies. Overall, we outline some dominant strategies, in spite of the specificity of each brain disease in term of FC pattern disruption.

Multi-output predictions from neuroimaging: assessing reduced-rank linear models

Typical neuroimaging studies analyze associations between physiological or behavioral traits and brain structure or function. Some rely on predicting these scores from neuroimaging data. To explain association between brain features and multiple traits, reduced-rank regression (RRR) models are often used, such as canonical correlation analysis (CCA) and partial least squares (PLS). These methods estimate latent variables, or canonical modes, that maximize the covariations between neuroimaging features and behavioral scores. Here, we investigate theoretically and empirically the extent to which reduced-rank models predict out-of-sample clinical scores from functional connectivity. Experiments on a schizophrenia dataset show that i) significant correlations between canonical modes do not necessarily mean accurate generalization on unseen data, and ii) better accuracy is achieved when taking into account regularized covariance between scores.