المؤلفون: Simona, Schäfer, Elisa, Mallick, Louisa, Schwed, Alexandra, König, Jian, Zhao, Nicklas, Linz, Timothy Hadarsson, Bodin, Johan, Skoog, Nina, Possemis, Daphne, Ter Huurne, Anna, Zettergren, Silke, Kern, Simona, Sacuiu, Inez, Ramakers, Ingmar, Skoog, Johannes, Tröger

المساهمون: RS: MHeNs - R3 - Neuroscience, Basic Neuroscience 2, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, Psychology 1, Psychology 2

المصدر: Journal of Alzheimer's Disease, 91(3), 1165-1171. IOS Press

مصطلحات موضوعية: Psychiatry and Mental health, Clinical Psychology, General Neuroscience, General Medicine, Geriatrics and Gerontology

الوصف: BACKGROUND: Modern prodromal Alzheimer's disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed.OBJECTIVE: Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations.METHODS: Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as across on the unrelated validation cohort.RESULTS: The algorithms achieved a performance of AUC 0.73 and AUC 0.77 in the respective training cohorts and AUC 0.81 in the unseen validation cohort.CONCLUSION: The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::309dee186fd0b3643ecb8375d6ea4d0fTest
https://doi.org/10.3233/jad-220762Test

المؤلفون: Daphne ter Huurne, Inez Ramakers, Nina Possemis, Leonie Banning, Angelique Gruters, Stephanie Van Asbroeck, Alexandra König, Nicklas Linz, Johannes Tröger, Kai Langel, Frans Verhey, Marjolein de Vugt

المصدر: Archives of Clinical Neuropsychology.

مصطلحات موضوعية: MILD COGNITIVE IMPAIRMENT, NORMATIVE DATA, EDUCATION, General Medicine, PERFORMANCE, ALZHEIMERS-DISEASE, Psychiatry and Mental health, Clinical Psychology, VERBAL FLUENCY, Neuropsychology and Physiological Psychology, AGE, Cognitive dysfunction, PARKINSONS-DISEASE, Neuropsychological tests, TESTS, Speech, PARTICIPANTS, Alzheimer disease

الوصف: Objective To investigate whether automatic analysis of the Semantic Verbal Fluency test (SVF) is reliable and can extract additional information that is of value for identifying neurocognitive disorders. In addition, the associations between the automatically derived speech and linguistic features and other cognitive domains were explored. Method We included 135 participants from the memory clinic of the Maastricht University Medical Center+ (with Subjective Cognitive Decline [SCD; N = 69] and Mild Cognitive Impairment [MCI]/dementia [N = 66]). The SVF task (one minute, category animals) was recorded and processed via a mobile application, and speech and linguistic features were automatically extracted. The diagnostic performance of the automatically derived features was investigated by training machine learning classifiers to differentiate SCD and MCI/dementia participants. Results The intraclass correlation for interrater reliability between the clinical total score (golden standard) and automatically derived total word count was 0.84. The full model including the total word count and the automatically derived speech and linguistic features had an Area Under the Curve (AUC) of 0.85 for differentiating between people with SCD and MCI/dementia. The model with total word count only and the model with total word count corrected for age showed an AUC of 0.75 and 0.81, respectively. Semantic switching correlated moderately with memory as well as executive functioning. Conclusion The one-minute SVF task with automatically derived speech and linguistic features was as reliable as the manual scoring and differentiated well between SCD and MCI/dementia. This can be considered as a valuable addition in the screening of neurocognitive disorders and in clinical practice.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::449c9b2306b9d3201b0e91f334358209Test
https://doi.org/10.1093/arclin/acac105Test

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