المؤلفون: Alexandra, König, Radia, Zeghari, Rachid, Guerchouche, Minh, Duc Tran, François, Bremond, Nicklas, Linz, Hali, Lindsay, Kai, Langel, Inez, Ramakers, Pascale, Lemoine, Vincent, Bultingaire, Philippe, Robert

المساهمون: Psychiatrie & Neuropsychologie, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Cognition Behaviour Technology (CobTek), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice)-Université Côte d'Azur (UCA), Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI), Maastricht University [Maastricht], Centre Hospitalier de Digne Les Bains

المصدر: BMJ Open, 11(9):e047083. BMJ Publishing Group
BMJ Open, Vol 11, Iss 9 (2021)
BMJ Open
BMJ Open, 2021, 11 (9), pp.e047083. ⟨10.1136/bmjopen-2020-047083⟩

مصطلحات موضوعية: MINI-MENTAL-STATE, DIAGNOSIS, VALIDATION, [SCCO]Cognitive science, Cognition, old age psychiatry, Humans, Speech, health informatics, Aged, DECLINE, clinical trials, DEMENTIA, Reproducibility of Results, DEPRESSION, Telemedicine, ALZHEIMERS-DISEASE, Observational Studies as Topic, Mental Health, APATHY, Medicine, Feasibility Studies

الوصف: International audience; Introduction Early detection of cognitive impairments is crucial for the successful implementation of preventive strategies. However, in rural isolated areas or so-called ‘medical deserts’, access to diagnosis and care is very limited. With the current pandemic crisis, now even more than ever, remote solutions such as telemedicine platforms represent great potential and can help to overcome this barrier. Moreover, current advances made in voice and image analysis can help overcome the barrier of physical distance by providing additional information on a patients’ emotional and cognitive state. Therefore, the aim of this study is to evaluate the feasibility and reliability of a videoconference system for remote cognitive testing empowered by automatic speech and video analysis. Methods and analysis 60 participants (aged 55 and older) with and without cognitive impairment will be recruited. A complete neuropsychological assessment including a short clinical interview will be administered in two conditions, once by telemedicine and once by face-to-face. The order of administration procedure will be counterbalanced so half of the sample starts with the videoconference condition and the other half with the face-to-face condition. Acceptability and user experience will be assessed among participants and clinicians in a qualitative and quantitative manner. Speech and video features will be extracted and analysed to obtain additional information on mood and engagement levels. In a subgroup, measurements of stress indicators such as heart rate and skin conductance will be compared. Ethics and dissemination The procedures are not invasive and there are no expected risks or burdens to participants. All participants will be informed that this is an observational study and their consent taken prior to the experiment. Demonstration of the effectiveness of such technology makes it possible to diffuse its use across all rural areas (‘medical deserts’) and thus, to improve the early diagnosis of neurodegenerative pathologies, while providing data crucial for basic research. Results from this study will be published in peer-reviewed journals.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::9b25f5e9d0203121e212f085005224afTest
https://cris.maastrichtuniversity.nl/en/publications/a2e9a2b1-3a41-4424-be22-ff385ce33601Test

المؤلفون: Sarah Gregory, Nicklas Linz, Alexandra König, Kai Langel, Hannah Pullen, Saturnino Luz, John Harrison, Craig W Ritchie

المساهمون: Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

المصدر: BMJ Open
BMJ Open, 2022, 12, ⟨10.1136/bmjopen-2021-052250⟩
Gregory, S, Linz, N, König, A, Langel, K, Pullen, H, Luz, S, Harrison, J & Ritchie, C W 2022, ' Remote data collection speech analysis and prediction of the identification of Alzheimer’s disease biomarkers in people at risk for Alzheimer’s disease dementia: the Speech on the Phone Assessment (SPeAk) prospective observational study protocol ', BMJ Open, vol. 12, no. 3, pp. e052250 . https://doi.org/10.1136/bmjopen-2021-052250Test

مصطلحات موضوعية: [SCCO]Cognitive science, Observational Studies as Topic, Alzheimer Disease, Artificial Intelligence, Surveys and Questionnaires, Humans, Speech, Cognitive Dysfunction, General Medicine, Middle Aged, Biomarkers, Aged, Randomized Controlled Trials as Topic

الوصف: IntroductionIdentifying cost-effective, non-invasive biomarkers of Alzheimer’s disease (AD) is a clinical and research priority. Speech data are easy to collect, and studies suggest it can identify those with AD. We do not know if speech features can predict AD biomarkers in a preclinical population.Methods and analysisThe Speech on the Phone Assessment (SPeAk) study is a prospective observational study. SPeAk recruits participants aged 50 years and over who have previously completed studies with AD biomarker collection. Participants complete a baseline telephone assessment, including spontaneous speech and cognitive tests. A 3-month visit will repeat the cognitive tests with a conversational artificial intelligence bot. Participants complete acceptability questionnaires after each visit. Participants are randomised to receive their cognitive test results either after each visit or only after they have completed the study. We will combine SPeAK data with AD biomarker data collected in a previous study and analyse for correlations between extracted speech features and AD biomarkers. The outcome of this analysis will inform the development of an algorithm for prediction of AD risk based on speech features.Ethics and disseminationThis study has been approved by the Edinburgh Medical School Research Ethics Committee (REC reference 20-EMREC-007). All participants will provide informed consent before completing any study-related procedures, participants must have capacity to consent to participate in this study. Participants may find the tests, or receiving their scores, causes anxiety or stress. Previous exposure to similar tests may make this more familiar and reduce this anxiety. The study information will include signposting in case of distress. Study results will be disseminated to study participants, presented at conferences and published in a peer reviewed journal. No study participants will be identifiable in the study results.

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce6d50b44ac615958f2bc509c898ccacTest
https://doi.org/10.1136/bmjopen-2021-052250Test

المؤلفون: Jan Alexandersson, Johannes Tröger, Jutta Kray, Philippe Robert, Alexandra König, Nicklas Linz, Jessica Peter

المساهمون: Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI), Cognition Behaviour Technology (CobTek), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice), Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Psychiatric University Clinic [Bern] (UPD), Faculty of Medicine [Bern], University of Bern-University of Bern, Saarland University [Saarbrücken], This research was partially funded by the EIT Digital WellbeingActivity 17074, ELEMENT. The data was partially collected during theEU FP7 Dem@Care project, grant agreement 288199. The authors liketo thank Hali Lindsay and Katja Häuser for helpful feedback on anearlier version of the manuscript., Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice)-Université Côte d'Azur (UCA)

المصدر: Neuropsychologia
Neuropsychologia, Elsevier, 2019, 131, pp.53-61. ⟨10.1016/j.neuropsychologia.2019.05.007⟩
Neuropsychologia, 2019, 131, pp.53-61. ⟨10.1016/j.neuropsychologia.2019.05.007⟩

مصطلحات موضوعية: Male, Cognitive Neuroscience, Semantic analysis (machine learning), Experimental and Cognitive Psychology, Disease, Semantic speech analysis, Neuropsychological Tests, MCI (mild cognitive impairment), 050105 experimental psychology, Speech Disorders, 03 medical and health sciences, Behavioral Neuroscience, [SCCO]Cognitive science, 0302 clinical medicine, Qualitative analysis, Alzheimer Disease, medicine, Semantic memory, Dementia, Verbal fluency test, Humans, Speech, 0501 psychology and cognitive sciences, Control (linguistics), Aged, Aged, 80 and over, 05 social sciences, Alzheimer's disease, Temporal analysis, medicine.disease, Clinical Practice, Female, Psychology, 030217 neurology & neurosurgery, Cognitive psychology

الوصف: International audience; Contents lists available atScienceDirectNeuropsychologiajournal homepage:www.elsevier.com/locate/neuropsychologiaExploitation vs. exploration—computational temporal and semantic analysisexplains semantic verbalfluency impairment in Alzheimer's diseaseJohannes Trögera,∗, Nicklas Linza, Alexandra Königb, Philippe Robertb, Jan Alexanderssona,Jessica Peterc, Jutta KraydaGerman Research Center for Artificial Intelligence (DFKI), GermanybMemory Center, CoBTeK, IA CHU Université Côte d’Azur, FrancecUniversity Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, SwitzerlanddChair for Development of Language, Learning & Action, University of Saarland, GermanyARTICLE INFOKeywords:Alzheimer's diseaseMCI (mild cognitive impairment)Semantic speech analysisTemporal analysisABSTRACTImpaired Semantic Verbal Fluency (SVF) in dementia due to Alzheimer's Disease (AD) and its precursor MildCognitive Impairment (MCI) is well known. Yet, it remains open whether this impairment mirrors the break-down of semantic memory retrieval processes or executive control processes. Therefore, qualitative analysis ofthe SVF has been proposed but is limited in terms of methodology and feasibility in clinical practice.Consequently, research draws no conclusive picture which of these afore-mentioned processes drives the SVFimpairment in AD and MCI. This study uses a qualitative computational approach—combining temporal andsemantic information—to investigate exploitation and exploration patterns as indicators for semantic memoryretrieval and executive control processes. Audio SVF recordings of 20 controls (C, 66–81 years), 55 MCI (57–94years) and 20 AD subjects (66–82 years) were assessed while groups were matched according to age and edu-cation. All groups produced, on average, the same amount of semantically related items in rapid successionwithin word clusters. Conversely, towards AD, there was a clear decline in semantic as well as temporal ex-ploration patterns between clusters. Results strongly point towards preserved exploitation—semantic memoryretrieval processes—and hampered exploration—executive control processes—in AD and potentially in MCI.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b94614d2fe916c6cf12bab8784723924Test