A new approach to digitized cognitive monitoring: validity of the SelfCog in Huntington's disease

Cognitive deficits represent a hallmark of neurodegenerative diseases, but evaluating their progression is complex. Most current evaluations involve lengthy paper-and-pencil tasks which are subject to learning effects dependent on the mode of response (motor or verbal), the countries’ language or the examiners. To address these limitations, we hypothesized that applying neuroscience principles may offer a fruitful alternative. We thus developed the SelfCog, a digitized battery that tests motor, executive, visuospatial, language and memory functions in 15 min. All cognitive functions are tested according to the same paradigm, and a randomization algorithm provides a new test at each assessment with a constant level of difficulty. Here, we assessed its validity, reliability and sensitivity to detect decline in early-stage Huntington’s disease in a prospective and international multilingual study (France, the UK and Germany). Fifty-one out of 85 participants with Huntington’s disease and 40 of 52 healthy controls included at baseline were followed up for 1 year. Assessments included a comprehensive clinical assessment battery including currently standard cognitive assessments alongside the SelfCog. We estimated associations between each of the clinical assessments and SelfCog using Spearman’s correlation and proneness to retest effects and sensitivity to decline through linear mixed models. Longitudinal effect sizes were estimated for each cognitive score. Voxel-based morphometry and tract-based spatial statistics analyses were conducted to assess the consistency between performance on the SelfCog and MRI 3D-T1 and diffusion-weighted imaging in a subgroup that underwent MRI at baseline and after 12 months. The SelfCog detected the decline of patients with Huntington’s disease in a 1-year follow-up period with satisfactory psychometric properties. Huntington’s disease patients are correctly differentiated from controls. The SelfCog showed larger effect sizes than the classical cognitive assessments. Its scores were associated with grey and white matter damage at baseline and over 1 year. Given its good performance in longitudinal analyses of the Huntington’s disease cohort, it should likely become a very useful tool for measuring cognition in Huntington’s disease in the future. It highlights the value of moving the field along the neuroscience principles and eventually applying them to the evaluation of all neurodegenerative diseases

Les jeux de ficelle en Nouvelle-Calédonie : une recherche de terrain inédite de Françoise Ozanne-Rivierre (1941-2007)

Cet article présente pour la première fois un corpus de jeux de ficelle inédit, collecté en Nouvelle-Calédonie par Françoise Ozanne-Rivierre dans les années 1960 et retrouvé dans les archives de Jean-Claude Rivierre après son décès en janvier 2018. Ce corpus de plus de 50 jeux de ficelle recueillis dans l’aire paicî-cèmuhî vient enrichir très utilement les rares publications existantes portant sur les jeux de ficelle kanak. La première partie de cet article propose une mise en contexte du travail de Françoise Ozanne-Rivierre sur les jeux de ficelle, tel qu’il nous est parvenu au travers de quelques carnets de notes, accompagnés d’un texte dactylographié et manuscrit. Outre la présentation du corpus collecté, l’étude de ces documents permet de préciser la méthodologie mise en œuvre par la chercheure. La seconde partie donne un aperçu des procédures impliquées dans ce corpus, en présentant étape par étape deux (séries de) jeux de ficelle, illustrées par des photographies, dont certaines réalisées par les époux Rivierre.This article presents the unpublished corpus of string figures collected in New Caledonia by the linguist Françoise Ozanne-Rivierre in the 1960s. Found in Jean-Claude Rivierre’s archives, after his death in 2018, this corpus of more than 50 string figures from the paicî-cèmuhî area is a highly valuable contribution, expanding the few publications there are on Kanak string figures. The first part of this article contextualizes Françoise Ozanne-Rivierre’s work on string figures, consisting of a few notebooks, accompanied by a typed manuscript. In addition to containing the collated corpus, these documents allow us to highlight the methodology implemented by the researcher. The second part affords an insight into the procedures involved in this corpus, while presenting the making of two (series of) string figures, illustrated by photographs, some of which were taken by the Rivierres themselves

Information about action outcomes differentially affects learning from self-determined versus imposed choices

International audienceThe valence of new information influences learning rates in humans: good news tends to receive more weight than bad news. We investigated this learning bias in four experiments, by systematically manipulating the source of required action (free versus forced choices), outcome contingencies (low versus high reward) and motor requirements (go versus no-go choices). Analysis of model-estimated learning rates showed that the confirmation bias in learning rates was specific to free choices, but was independent of outcome contingencies. The bias was also unaffected by the motor requirements, thus suggesting that it operates in the representational space of decisions, rather than motoric actions. Finally, model simulations revealed that learning rates estimated from the choice-confirmation model had the effect of maximizing performance across low- and high-reward environments. We therefore suggest that choice-confirmation bias may be adaptive for efficient learning of action–outcome contingencies, above and beyond fostering person-level dispositions such as self-esteem

Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis

BackgroundDementia is defined as a cognitive decline that affects functional status. Longitudinal ageing surveys often lack a clinical diagnosis of dementia though measure cognition and daily function over time. We used unsupervised machine learning and longitudinal data to identify transition to probable dementia.MethodsMultiple Factor Analysis was applied to longitudinal function and cognitive data of 15,278 baseline participants (aged 50 years and more) from the Survey of Health, Ageing, and Retirement in Europe (SHARE) (waves 1, 2 and 4-7, between 2004 and 2017). Hierarchical Clustering on Principal Components discriminated three clusters at each wave. We estimated probable or "Likely Dementia" prevalence by sex and age, and assessed whether dementia risk factors increased the risk of being assigned probable dementia status using multistate models. Next, we compared the "Likely Dementia" cluster with self-reported dementia status and replicated our findings in the English Longitudinal Study of Ageing (ELSA) cohort (waves 1-9, between 2002 and 2019, 7840 participants at baseline).ResultsOur algorithm identified a higher number of probable dementia cases compared with self-reported cases and showed good discriminative power across all waves (AUC ranged from 0.754 [0.722-0.787] to 0.830 [0.800-0.861]). "Likely Dementia" status was more prevalent in older people, displayed a 2:1 female/male ratio, and was associated with nine factors that increased risk of transition to dementia: low education, hearing loss, hypertension, drinking, smoking, depression, social isolation, physical inactivity, diabetes, and obesity. Results were replicated in ELSA cohort with good accuracy.ConclusionsMachine learning clustering can be used to study dementia determinants and outcomes in longitudinal population ageing surveys in which dementia clinical diagnosis is lacking

Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis

Abstract Background Dementia is defined as a cognitive decline that affects functional status. Longitudinal ageing surveys often lack a clinical diagnosis of dementia though measure cognition and daily function over time. We used unsupervised machine learning and longitudinal data to identify transition to probable dementia. Methods Multiple Factor Analysis was applied to longitudinal function and cognitive data of 15,278 baseline participants (aged 50 years and more) from the Survey of Health, Ageing, and Retirement in Europe (SHARE) (waves 1, 2 and 4–7, between 2004 and 2017). Hierarchical Clustering on Principal Components discriminated three clusters at each wave. We estimated probable or “Likely Dementia” prevalence by sex and age, and assessed whether dementia risk factors increased the risk of being assigned probable dementia status using multistate models. Next, we compared the “Likely Dementia” cluster with self-reported dementia status and replicated our findings in the English Longitudinal Study of Ageing (ELSA) cohort (waves 1–9, between 2002 and 2019, 7840 participants at baseline). Results Our algorithm identified a higher number of probable dementia cases compared with self-reported cases and showed good discriminative power across all waves (AUC ranged from 0.754 [0.722–0.787] to 0.830 [0.800–0.861]). “Likely Dementia” status was more prevalent in older people, displayed a 2:1 female/male ratio, and was associated with nine factors that increased risk of transition to dementia: low education, hearing loss, hypertension, drinking, smoking, depression, social isolation, physical inactivity, diabetes, and obesity. Results were replicated in ELSA cohort with good accuracy. Conclusions Machine learning clustering can be used to study dementia determinants and outcomes in longitudinal population ageing surveys in which dementia clinical diagnosis is lacking

Linguistique et ethno-linguistique océaniennes

Numéro publié avec le soutien du LACITO - UMR 7107

L’industrie alimentaire du futur passe par le numérique.: Alimentation et numérique Feuille de route ANIA

il s'agit d'un type de produit dont les métadonnées ne correspondent pas aux métadonnées attendues dans les autres types de produit : REPORTL’industrie alimentaire du futur passe par le numérique.. Alimentation et numérique Feuille de route ANI