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The progression of neurodegenerative diseases, such as Alzheimer’s Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with 𝑁 = 897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores. This work is supported by the European Union’s Horizon 2020 research and innovation programme (grant n◦ 848158). M. Lorenzi is supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR) (ANR-19-P3IA-0002). G. Piella is supported by ICREA under the ICREA Academia programme. This publication is part of the project PCI2021-122044-2A, funded by the project ERA-NET NEURON Cofund2, by MCIN/AEI/10.13039/501100011033/ and by the European Union “NextGenerationEU”/PRTR. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Disease progression modeling (DPM) of Alzheimer's disease (AD) aims at revealing long term pathological trajectories from short term clinical data. Along with the ability of providing a data-driven description of the natural evolution of the pathology, DPM has the potential of representing a valuable clinical instrument for automatic diagnosis, by explicitly describing the biomarker transition from normal to pathological stages along the disease time axis. In this work we reformulated DPM within a probabilistic setting to quantify the diagnostic uncertainty of individual disease severity in an hypothetical clinical scenario, with respect to missing measurements, biomarkers, and follow-up information. We show that the staging provided by the model on 582 amyloid positive testing individuals has high face validity with respect to the clinical diagnosis. Using follow-up measurements largely reduces the prediction uncertainties, while the transition from normal to pathological stages is mostly associated with the increase of brain hypo-metabolism, temporal atrophy, and worsening of clinical scores. The proposed formulation of DPM provides a statistical reference for the accurate probabilistic assessment of the pathological stage of de-novo individuals, and represents a valuable instrument for quantifying the variability and the diagnostic value of biomarkers across disease stages.

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.

Aujourd’hui, il y a un besoin croissant d’harmonisation et d’innovation dans les evaluations cognitives et comportementales. Les outils actuels sont parfois trop invasifs, couteux ou demande des temps de realisations trop important dans le cadre d’une simple consultation. De ce faite de nouvelles methodes ecologiquement valides et sensibles pourraient etre utiles pour ameliorer l’accessibilite en tant que depistage de premiere ligne dans la population souffrant de troubles neuropsychiatriques. Les technologies de l’information et de la communication (TIC) sont des solutions non invasives et qui ont montrees une utilite pour identifier les sujets aux premiers stades cliniques des maladies neurodegeneratives [1] , [2] . Les recherches actuelles s’orientent sur l’interet des ICT a un stade pre clinique, comme marqueur d’evolution, au cours des essais therapeutiques et dans les troubles psychiatriques [3] , [4] . Cette session a pour objectif d’illustrer les scenario d’utilisation d’outils numeriques novateurs, qui pourraient etre utilises pour le depistage a grande echelle et pour le suivi des patients dans les essais cliniques. Lea Domain, interne de psychiatrie au centre hospitalier Guillaume-Regnier de Rennes presentera les resultats preliminaires de l’etude DEFLUENCE. Cette etude a pour objectif de determiner si les alterations qualitatives aux tests de fluences verbales mesurees de facon automatisee peuvent constituer un bio marqueur pronostic de l’evolution de la depression. Alexandra Konig, neuropsychologue et chercheuse au laboratoire CoBTeK presentera l’application Δelta sur tablette qui permet aux cliniciens de faire passer et d’analyser automatiquement des tests neuropsychologiques classiques meme a distance a l’aide de l’intelligence artificielle (IA), de l’analyse automatisee de l’expression faciale et de la voix. Un exemple d’une analyse psycholinguistique informatisee d’une entrevue clinique sera egalement presentee. Clement Abi Nader, doctorant dans l’equipe Epione INRIA presentera les travaux portant sur la modelisation de l’evolution de la maladie d’Alzheimer a partir de donnees cliniques longitudinales acquises. Cette approche consiste a developper des algorithmes integrant des donnees heterogenes (imagerie, donnees biologiques, capteurs, donnees cliniques).

Le but de cette presentation est d’illustrer notre contribution a l’elaboration de methodes d’apprentissage statistique robustes et interpretables, afin d’analyser des donnees issues d’essais cliniques pour l’etude de pathologies telles que l’apathie ou la maladie d’Alzheimer. En particulier, nous nous interesserons a des approches detectant automatiquement la relation jointe entre biomarqueurs de grande dimension tels que les images medicales, l’information genetique ou des tests psychologiques. Nous presenterons egalement nos travaux portant sur la modelisation de l’evolution de la maladie d’Alzheimer a partir de donnees cliniques longitudinales acquises sur une courte duree. Nous verrons comment nos methodes permettent d’identifier differents types de progressions de biomarqueurs impliques dans le processus pathologique. Ces methodes peuvent etre appliquees sur des donnees de grande dimension, notamment des images medicales acquises selon differentes modalites. Enfin, nous montrerons comment ces methodes peuvent etre utilisees comme reference statistique afin d’evaluer et de predire le stade de la maladie a partir de donnees cliniques non etudiees par nos algorithmes.

International audience; A potential overlap between bipolar disorder (BD) and borderline personality disorder (BPD) has been recently proposed. We aimed to assess similarities and differences of brain structural features in BD and BPD. Twenty-six in-patients with BPD, 14 with BD and 40 age and sex-matched healthy controls (HC) underwent structural magnetic resonance (MR). Voxel-based morphometry analysis with Statistical Parametric Mapping (SPM) was used to localize and quantify gray (GM) and white matter (WM) abnormalities in BD and BPD compared to HC and to identify those specifically affected in each patient group (p

Introduction It has been extensively shown that the insurgence of dementia and cognitive decline is strongly tied to the presence of stroke and brain vascular damage. However, while hypertension is a major risk factor for stroke, the relationship between cardiac abnormalities such as high blood pressure or arrhythmias, with dementia still needs to be elucidated. Currently, large biobank studies offer the chance of jointly analyzing the common variation between cardiovascular pathologies and neurodegeneration. Methods From the UK Biobank database, we have image information from 10,000 individuals. From these, we have selected 4424 participants, which had all the complete image information, cardiac and brain images. For each individual, we extracted cardiovascular and brain related phenotypes. The cardiovascular information includes : systolic blood pressure, diastolic blood pressure, cardiac output and stroke volume. As brain volumetric indicators, we took into account grey matter (GM) and white matter (WM) volumes, and the ventricles volume. All the volumes were normalized by the total head volume. The relationship among the different variables was studied through canonical correlation analysis (CCA). Results and discussion Fig. 1 shows the comparison between the canonical loadings obtained from CCA, as well as the loadings obtained when regressing out age from the model. When age is not taken into account, the main predictor of the correlation is the systolic blood pressure, followed by diastolic blood pressure. These variables are correlated with the shrinkage of the brain volume (WM and GM) and with the increase of ventricles volume. When age is explicitly accounted for, we observe that cardiac output and stroke volume become the dominant factors, being mostly associated with GM loss and ventricles volumes increase. We hypothesize that in the first scenario age was strongly implicitly associated with blood pressures and brain changes. After removing the influence of age, we can observe how GM decreases as cardiac output and stroke volume increase, while WM shrinkage has disappeared. This indicates an underlying relationship among cardiovascular indicators and brain volumes, which should be further explored. We plan to explore this relationship in the future, developing unsupervised multivariate methods and biophysical models that allow us to obtain additional features that can help us to characterize this behavior and hopefully identify pathological subjects.

Introduction The aim of this study is to develop a generative and probabilistic statistical learning model for the joint analysis of heterogeneous biomedical data. The model will be applied to the investigation of neurological disorders from collections of brain imaging, body sensors, biological and clinical data available in current large-scale health databases. The resulting methodological framework will be tested on the UK Biobank, as well as on pathology-specific clinical data, as provided by the ADNI, or INSIGHT initiatives. Methods We propose a variational approximation of Bayesian Canonical Correlation Analysis (CCA). The proposed formulation is inspired by current advanced in variational learning, and offers the potential to scale to high-dimensional observations, such as medical images and arrays of biological data. We proved that the variational lower bound can be optimized through modern learning libraries such as Torch and TensorFlow. Results We currently benchmarked the method with respect to classical CCA on both synthetic data and on the classical benchmarking datasets in machine learning (IRIS dataset). With respect to the synthetic dataset ( Fig. 1 A), we observed a strong agreement between the score components computed with classical CCA and our method. Moreover, the classification results on IRIS showed that the two methods essentially provide the same latent representation ( Fig. 1 B). Conclusion Our method shows promising results for the future application to medical data. The method is computationally efficient and scalable, hence able to process complex multivariate multidimensional datasets. We expect to highlight meaningful relationship among biomarkers that could be used to develop optimal strategies for disease classification, quantification, and prediction. In the future, the proposed approach will be tested in several experimental settings : – classification/stratification ; – prediction and imputation from a set of observed data (e.g., predict biological and clinical output from medical imaging information).

This randomised clinical trial, involving patients with advanced colorectal cancer, was carried out to compare the effectiveness of accelerated folinic acid (FA) plus 5-fluorouracil (5-FU) with that of the conventional regimen of 5-FU alone. Both regimens were administered with simulataneous supportive care. 185 patients were eligible: 94 were randomly allocated to receive FA 200 mg/m2 i.v. plus 5-FU 400 mg/m2 i.v. on days 1-5 every 3 weeks; and 91 to receive 5-FU 400 mg/m2 i.v. on days 1-5 every 4 weeks. The response rate was 33.3% in the accelerated FA/5-FU and 18.6% in the 5-FU arm (P = 0.045). Median survival was 13.5 months in the FA/5-FU arm and 7.5 months in the 5-FU arm (P = 0.039). Toxicity was mild and slightly more pronounced in the FA/5-FU arm (P = 0.078). This study indicates that, in patients with advanced colorectal cancer, accelerated chemotherapy with FA and 5-FU and simultaneous supportive care is capable of achieving a higher response rate and longer survival than conventional 5-FU alone, without severe toxicity.

Background/Aims: Colon cancer is one of the major health problems in industrialized countries, and its incidence appears to be increasing. Surgical resectability is the most important prognostic determinant, although despite apparently curative surgery, recurrent tumors are common. Metastatic disease cannot be cured, and thus, there is a need for better adjuvant therapies. Methods: Two hundred and thirty-nine patients with surgically resected colon cancer in Dukes' stage B 2 or C were randomly assigned to chemotherapy or observation alone to determine whether adjuvant chemotherapy could effectively reduce the rate of cancer recurrence. One hundred and twenty-one patients in stage B 2 and 118 patients in stage C were enrolled in the study. Adjuvant treatment consisted of folinic acid 200 mg/m 2 , intravenously, plus 5-fluorouracil 400 mg/m 2 , intravenously, on days 1–5 every 4 weeks for 12 cycles. Results: In stage B 2 , no significant difference between the adjuvant arm and the observation arm was noted. In stage C, adjuvant chemotherapy produced an advantage over observation in terms of a reduction in cancer recurrence rate with prolongation of a disease-free interval ( P = 0.0016) and an improvement in overall survival ( P = 0.0025). Conclusions: This study shows that folinic acid plus 5-fluorouracil adjuvant chemotherapy is effective in patients with surgically resected Dukes' stage C colon carcinoma.

We examined the effects of a two-thirds hepatectomy in the adult rat on the activities of the three L-threonine-degrading enzymes, L-threonine dehydratase, L-threonine aldolase and L-threonine dehydrogenase. Noticeable variations were observed which did not occur in either sham-operated or turpentine-treated rats and were not linked to food intake. They were considered specific to the regenerating liver. When the reactions were followed in vitro, L-threonine deaminase and L-threonine aldolase were significantly lower for the first 12-24 h: L-threonine dehydrogenase decreased only after 48 h. These results are linked to a decrease in the enzyme concentration in the tissue. L-Serine and L-threonine liver concentrations increased 2-3-fold during the same periods. When the activities were evaluated in vivo, the levels of the first two enzymes remained constant for 24 h, but increased after 48 h; L-threonine dehydrogenase increased between 12 and 48 h. The in vivo activity of the enzymes was reflected by total L-threonine degradation, which had a single sharp peak at 48 h. The asynchronous variations in enzyme activity are related to the differences in protein metabolism which occur in the regenerating liver, and are the consequence of a new transient differential control. The changes observed are significant in liver regeneration; they regulate the consumption and the serum and liver levels of L-serine and L-threonine, setting them aside for protein synthesis. They minutely control the flux of amino acids toward gluconeogenesis, since, during the first 48 h after partial hepatectomy, the production of glucose is ensured principally by lactate; the contribution of L-threonine seems to be more significant only at 48 h. These findings are useful in the study of the regulation of the enzymes involved in amino acid metabolism during liver regeneration.