[Assessment of the knowledge of Lewy body disease by health care professionnels compared to Alzheimer's disease: result of a descriptive analytical study]. / Évaluation des connaissances par les professionnels de santé de la maladie à corps de Lewy comparativement à la maladie d'Alzheimer : résultat d'une étude analytique descriptive.

BACKGROUND: Dementia with Lewy body (DLB) is a common neurodegenerative disease that warrants specific care, which remains largely underdiagnosed. Our objective was to assess the knowledge of DLB by health professionals in comparison with that of Alzheimer's disease (AD), to better understand the reasons of its under-diagnosis. METHODS: We conducted a descriptive and analytical study processing the results of an online questionnaire submitted to French healthcare professionals between December 1, 2020 and March 1, 2021. RESULTS: A total of 490 healthcare professionals responded to the questionnaire. We observed a poorer knowledge of DLB compared to AD both subjective as highlighted on the self-assessment questionnaires and objective since the diagnostic criteria and therapeutic specificities were less known for DLB compared to AD. CONCLUSIONS: DLB appears as a disease that is still too poorly known by health professionals. To improve training is therefore a decisive objective in order to optimize the therapeutic care and support of patients with DLB and their relatives.

Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models.

Exploring time-varying connectivity networks in neurodegenerative disorders is a recent field of research in functional MRI. Dementia with Lewy bodies (DLB) represents 20% of the neurodegenerative forms of dementia. Fluctuations of cognition and vigilance are the key symptoms of DLB. To date, no dynamic functional connectivity (DFC) investigations of this disorder have been performed. In this paper, we refer to the concept of connectivity state as a piecewise stationary configuration of functional connectivity between brain networks. From this concept, we propose a new method for group-level as well as for subject-level studies to compare and characterize connectivity state changes between a set of resting-state networks (RSNs). Dynamic Bayesian networks, statistical and graph theory-based models, enable one to learn dependencies between interacting state-based processes. Product hidden Markov models (PHMM), an instance of dynamic Bayesian networks, are introduced here to capture both statistical and temporal aspects of DFC of a set of RSNs. This analysis was based on sliding-window cross-correlations between seven RSNs extracted from a group independent component analysis performed on 20 healthy elderly subjects and 16 patients with DLB. Statistical models of DFC differed in patients compared to healthy subjects for the occipito-parieto-frontal network, the medial occipital network and the right fronto-parietal network. In addition, pairwise comparisons of DFC of RSNs revealed a decrease of dependency between these two visual networks (occipito-parieto-frontal and medial occipital networks) and the right fronto-parietal control network. The analysis of DFC state changes thus pointed out networks related to the cognitive functions that are known to be impaired in DLB: visual processing as well as attentional and executive functions. Besides this context, product HMM applied to RSNs cross-correlations offers a promising new approach to investigate structural and temporal aspects of brain DFC.