Family history of alcohol use disorder is associated with brain structural and functional changes in healthy first-degree relatives.

BACKGROUND: Neuroimaging studies of vulnerability to Alcohol Use Disorder (AUD) have identified structural and functional variations which might reflect inheritable features in alcohol-naïve relatives of AUD individuals (FH+) compared to controls having no such family history (FH-). However, prior research did not simultaneously account for childhood maltreatment, any clinically significant disorder and maternal AUD. Therefore, we mainly aimed to investigate the brain structure and reward-related neural activations (fMRI), using whole-brain analysis in FH+ young adults with no prevalent confounders. METHODS: 46 FH+ and 45 FH- male and female participants had no severe childhood maltreatment exposure, neither any psychiatric disorder or AUD, nor a prenatal exposure to maternal AUD. We used a 3 T MRI coupled with a whole brain voxel-based method to compare between groups the grey matter volumes and activations in response to big versus small wins during a Monetary Incentive Delay task. The Childhood Trauma Questionnaire score was used as confounding variable in the analyses to account for the remaining variance between groups. RESULTS: Compared to FH- controls, FH+ participants had smaller grey matter volumes in the frontal and cingulate regions as well as in the bilateral nucleus accumbens and right insula. The FH+ participants' fMRI datasets denoted a blunted activation in the middle cingulum with respect to FH- controls' during the processing of reward magnitude, and a greater activation in the anterior cingulum in response to anticipation of a small win. CONCLUSIONS: Family history of alcohol use disorder is linked to structural and functional variations including brain regions involved in reward processes.

Benchmarking functional connectome-based predictive models for resting-state fMRI.

Functional connectomes reveal biomarkers of individual psychological or clinical traits. However, there is great variability in the analytic pipelines typically used to derive them from rest-fMRI cohorts. Here, we consider a specific type of studies, using predictive models on the edge weights of functional connectomes, for which we highlight the best modeling choices. We systematically study the prediction performances of models in 6 different cohorts and a total of 2000 individuals, encompassing neuro-degenerative (Alzheimer's, Post-traumatic stress disorder), neuro-psychiatric (Schizophrenia, Autism), drug impact (Cannabis use) clinical settings and psychological trait (fluid intelligence). The typical prediction procedure from rest-fMRI consists of three main steps: defining brain regions, representing the interactions, and supervised learning. For each step we benchmark typical choices: 8 different ways of defining regions -either pre-defined or generated from the rest-fMRI data- 3 measures to build functional connectomes from the extracted time-series, and 10 classification models to compare functional interactions across subjects. Our benchmarks summarize more than 240 different pipelines and outline modeling choices that show consistent prediction performances in spite of variations in the populations and sites. We find that regions defined from functional data work best; that it is beneficial to capture between-region interactions with tangent-based parametrization of covariances, a midway between correlations and partial correlation; and that simple linear predictors such as a logistic regression give the best predictions. Our work is a step forward to establishing reproducible imaging-based biomarkers for clinical settings.

Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation.

Estimating covariances from functional Magnetic Resonance Imaging at rest (r-fMRI) can quantify interactions between brain regions. Also known as brain functional connectivity, it reflects inter-subject variations in behavior and cognition, and characterizes neuropathologies. Yet, with noisy and short time-series, as in r-fMRI, covariance estimation is challenging and calls for penalization, as with shrinkage approaches. We introduce population shrinkage of covariance estimator (PoSCE) : a covariance estimator that integrates prior knowledge of covariance distribution over a large population, leading to a non-isotropic shrinkage. The shrinkage is tailored to the Riemannian geometry of symmetric positive definite matrices. It is coupled with a probabilistic modeling of the individual and population covariance distributions. Experiments on two large r-fMRI datasets (HCP n=815, CamCAN n=626) show that PoSCE has a better bias-variance trade-off than existing covariance estimates: this estimator relates better functional-connectivity measures to cognition while capturing well intra-subject functional connectivity.

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.

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 (Alzheimer's 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.

Predicting brain-age from multimodal imaging data captures cognitive impairment.

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.

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 biomarker of neurodegenerative diseases, such as Alzheimer's 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.

Female patient-specific finite element modeling of pelvic organ prolapse (POP).

Pelvic organ prolapse (POP) occurs only in women and becomes more common as women age. However, the surgical practices remain poorly evaluated. The realization of a simulator of the dynamic behavior of the pelvic organs is then identified as a need. It allows the surgeon to estimate the functional impact of his actions before his implementation. In this work, the simulation will be based on a patient-specific approach in which each geometrical model will be carried out starting from magnetic resonance image (MRI) acquisition of pelvic organs of one patient. To determine the strain and stress in the soft biological tissues, hyperelastic constitutive laws are used in the context of finite element analysis. The Yeoh model has been implemented into an in-house finite element code FER to model these organ tissues taking into account large deformations with multiple contacts. The 2D and 3D models are considered in this preliminary study and the results show that our method can help to improve the understanding of different forms of POP.

Automatic estimation of pelvic organ anatomical references.

Pelvic floor diseases cover pathologies of which physiopathology is not well understood. 2D sagittal MRI sequences used in the clinical assessment allow to visualize the dynamic behavior of the main organs involved (bladder, uterus-vagina and rectum). Clinicians use anatomical landmarks and measurements related to the pelvic organs in their pathology assessment. Usually, those tasks are performed manually which results in being both tedious and subject to operator dependency. A methodology is proposed to attempt a quantitative and objective characterization of the organ behaviors under abdominal strain condition. This approach automatically assesses the organ movements, through the estimation of characteristic angles (anorectal angle, uterovaginal angle, bladder inclination), and the tracking of anatomically significant points (anorectal angle vertex, uterovaginal angle vertex, bladder neck). From a multi-subject analysis, pathological organs have been distinguished from healthy ones, which shows the relevance of the computed features. In addition, a stability analysis has shown the soundness of the approach.