المؤلفون: Sarah Gregory, John Harrison, Janna Herrmann, Matthew Hunter, Natalie Jenkins, Alexandra König, Nicklas Linz, Saturnino Luz, Elisa Mallick, Hannah Pullen, Miles Welstead, Stephen Ruhmel, Johannes Tröger, Craig W. Ritchie

مصطلحات موضوعية: Medicine, Neuroscience, Biomarkers, Health Care, Diseases, Geriatrics and Gerontology, Neurology and Neuromuscular Diseases, Aged Health Care, speech, Alzheimer's disease, cognition, acceptability, feasibility

الوصف: Introduction Digital cognitive assessments are gathering importance for the decentralized remote clinical trials of the future. Before including such assessments in clinical trials, they must be tested to confirm feasibility and acceptability with the intended participant group. This study presents usability and acceptability data from the Speech on the Phone Assessment (SPeAk) study. Methods Participants (N = 68, mean age 70.43 years, 52.9% male) provided demographic data and completed baseline and 3-month follow-up phone based assessments. The baseline visit was administered by a trained researcher and included a spontaneous speech assessment and a brief cognitive battery (immediate and delayed recall, digit span, and verbal fluency). The follow-up visit repeated the cognitive battery which was administered by an automatic phone bot. Participants were randomized to receive their cognitive test results acer the final or acer each study visit. Participants completed acceptability questionnaires electronically acer each study visit. Results There was excellent retention (98.5%), few technical issues (n = 5), and good interrater reliability. Participants rated the assessment as acceptable, confirming the ease of use of the technology and their comfort in completing cognitive tasks on the phone. Participants generally reported feeling happy to receive the results of their cognitive tests, and this disclosure did not cause participants to feel worried. Discussion The results from this usability and acceptability analysis suggest that completing this brief battery of cognitive tests via a telephone call is both acceptable and feasible in a midlife-to-older adult population in the United Kingdom, living at risk for Alzheimer's disease.

العلاقة: https://figshare.com/articles/dataset/Data_Sheet_1_Remote_data_collection_speech_analysis_in_people_at_risk_for_Alzheimer_s_disease_dementia_usability_and_acceptability_results_docx/24305929Test

الإتاحة: https://doi.org/10.3389/frdem.2023.1271156.s001Test
https://figshare.com/articles/dataset/Data_Sheet_1_Remote_data_collection_speech_analysis_in_people_at_risk_for_Alzheimer_s_disease_dementia_usability_and_acceptability_results_docx/24305929Test

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: Gabriel Robert, Alexandra König, Isabelle Corouge, Nicklas Linz, M. Guillery, Elise Bannier, L. Domain, Dominique Drapier

المصدر: French Journal of Psychiatry. 1:S49-S50

مصطلحات موضوعية: medicine.medical_specialty, medicine.diagnostic_test, business.industry, Cognition, Disease, Neuropsychological test, Audiology, medicine.disease, Correlation, medicine, Major depressive disorder, Verbal fluency test, Cluster analysis, business, Depression (differential diagnoses)

الوصف: Background Major depressive disorder (MDD) is a highly disabling disease of which is related to chronic course. Currently, there is no robust prognosis biomarker. Cognitive dysfunction associated with the disease has been shown to predict treatment outcome. However, its cerebral substrate is poorly known. Verbal fluency (VF), which are broadly used neuropsychological test, assess the production of words according to a required criterion within a constrained time frame. During this task, subjects produce words within subcategories or “clusters” and occasionally “switch” to other clusters. The measurement of the cluster size and number of switches are relevant cognitive markers. Deep learning-based computational analysis method have been shown to improve the reproducibility and scalability of this measurement. The main objective of the current study was to characterize the cortical thickness (CT) surface-based morphometry (SBM) correlates of the switching and clustering scores in patients with MDD using an artificial intelligence empowered speech analysis system. The secondary objective was to compare those cognitive scores with the ones of the control group. Method The study included 26 depressed women and 25 matched in age and education level healthy females’ controls. All subjects underwent VF tests and had a structural Magnetic Resonance Imaging (MRI). The clustering and switching scores were assessed using the computational analysis method introduced by Linz et al ( https://ki-elements.deTest ). SBM was performed using SPM and CAT12 toolbox. Results In the depressed group, we observed a positive correlation between the switching score in semantic VF test and the CT, in few cortical areas. The main significant correlation was found in the left lingual gyrus (P = 0.003). The switching scores were lower in the depressed group in each VF task (P = 0.019 and P = 0.007). There was no difference between the two groups concerning the cluster size. Conclusion Our study revealed the relationship existing between the number of switches produced by depressed subjects in a semantic VF task and the CT in prefrontal regions and in more posterior areas. These results possibly suggest that switching and clustering scores assessment in VF tasks, and its cerebral correlates, may constitute potential prognosis biomarkers for depression. Although much work is still required, the establishment of such biomarkers has substantial implications for reducing the burden of the disease.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::6c46ec8be376898cbb0c7f35484c6ca1Test
https://doi.org/10.1016/j.fjpsy.2019.10.142Test

المؤلفون: Johannes Tröger, Nicklas Linz, Philippe Robert, Alexandra König, Maria Wolters, Jan Alexandersson

المساهمون: Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI), 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), 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), University of Edinburgh, Université Nice Sophia Antipolis (... - 2019) (UNS)

المصدر: ICDM 2017-IEEE International Conference on Data Mining, Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living
ICDM 2017-IEEE International Conference on Data Mining, Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living, Nov 2017, New Orleans, United States. pp.719-728, ⟨10.1109/ICDMW.2017.100⟩
ICDM Workshops
Linz, N, Troeger, J, Alexandersson, J, Koenig, A, Robert, P & Wolters, M 2017, Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance . in First Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living . Institute of Electrical and Electronics Engineers (IEEE), 2017 IEEE International Conference on Data Mining Workshops, New Orleans, Louisiana, United States, 18/11/17 . https://doi.org/10.1109/ICDMW.2017.100Test

مصطلحات موضوعية: Mini–Mental State Examination, medicine.diagnostic_test, Clinical Dementia Rating, 05 social sciences, Regression analysis, Cognition, medicine.disease, 050105 experimental psychology, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Cognitive test, 03 medical and health sciences, 0302 clinical medicine, mental disorders, medicine, Verbal fluency test, Dementia, 0501 psychology and cognitive sciences, Set (psychology), Psychology, 030217 neurology & neurosurgery, Clinical psychology

الوصف: International audience; The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as the Semantic Verbal Fluency (SVF), demand little time. With this as a starting point, we investigate the relation between SVF results and MMSE/CDR-SOB scores. We use regression models to predict scores based on persons' SVF performance. Over a set of 179 patients with different degree of dementia, we achieve a mean absolute error of of 2.2 for MMSE (range 0–30) and 1.7 for CDR-SOB (range 0–18). True and predicted scores agree with a Cohen's κ of 0.76 for MMSE and 0.52 for CDR-SOB. We conclude that our approach has potential to serve as a cheap dementia screening, possibly even in non-clinical settings.

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea4a62da9cbde6bab99072d432da428cTest
https://inria.hal.science/hal-01672590Test

المؤلفون: Jan Alexandersson, Nicklas Linz, Philippe Robert, Kathleen C. Fraser, Francois Bremond, Alexandra König, Maria Wolters, Liam D. Kaufman, Johannes Tröger, Frank Rudzicz

المصدر: Alzheimer's & Dementia. 13

مصطلحات موضوعية: 030214 geriatrics, Basis (linear algebra), Epidemiology, Health Policy, Early detection, Cognition, medicine.disease, 03 medical and health sciences, Psychiatry and Mental health, Cellular and Molecular Neuroscience, 0302 clinical medicine, Developmental Neuroscience, medicine, Dementia, Neurology (clinical), Geriatrics and Gerontology, Psychology, 030217 neurology & neurosurgery, Cross linguistic, Cognitive psychology

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::fe28c0202ff15f467f8bb8074f5d32f5Test
https://doi.org/10.1016/j.jalz.2017.06.311Test

المؤلفون: P. Robert, Alexandra König, Nicklas Linz, Johannes Tröger, Jan Alexandersson

المصدر: French Journal of Psychiatry. 1:S76

مصطلحات موضوعية: Computer science, business.industry, Word error rate, Cognition, computer.software_genre, Task (project management), Classifier (linguistics), Semantic memory, Verbal fluency test, Artificial intelligence, Cognitive decline, business, computer, Neurocognitive, Natural language processing

الوصف: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI) and seem to be highly sensitive to cognitive changes [1]. In this task, participants name as many items of a semantic category under a time constraint. SVF can be considered as a multifactorial task, comprising both semantic memory retrieval and executive control processes. However, clinicians measure task performance manually only by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic speech analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. Furthermore, we examined the technologic feasibility of automatically assessing the SVF task – via a telephone-based solution for potential remote frontline prescreening of cognitive impairments [2]. We will present SVF data which was collected from 95 older people with MCI (n=47), Alzheimer’s or related dementias (ADRD; n=24) and healthy controls (HC; n=24). Speech was recorded through a mobile tablet device using the built-in microphone. All data was annotated manually and automatically with the named words, clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI and ADRD. Afterwards to simulate telephone conditions, the recordings were downsampled to a lower sampling rate. Automatically extracted clusters and switches were highly correlated (r=0.9) with manually established values, and performed as well on the classification task separating healthy controls from persons with Alzheimer’s (AUC=0.939) and MCI (AUC=0.758) [3]. The downsampled quality obtained similar encouraging results. We observe a relatively low word error rate of 33% despite phone-quality speech samples. The automated classification pipeline performs equally well compared to the classifier trained on manual transcriptions of the same speech data. Our results indicate SVF as a prime candidate for inclusion into an automated telephone-screening system and that it is possible to automate fine-grained analyses of this task for the assessment of cognitive decline.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::f570a363489a16ac7a6174acf5b1a066Test
https://doi.org/10.1016/s2590-2415Test(19)30183-7

المؤلفون: Nicklas Linz, Philipp Müller, Johannes Tröger, Mario Magued Mina, Alexandra König, Hali Lindsay, Radia Zeghari

المصدر: Scopus-Elsevier

مصطلحات موضوعية: Dissociation (neuropsychology), Point (typography), Semantic similarity, medicine, Dementia, Verbal fluency test, Cognition, medicine.disease, Psychology, Neurocognitive, Cognitive psychology, Task (project management)

الوصف: Effective management of dementia hinges on timely detection and precise diagnosis of the underlying cause of the syndrome at an early mild cognitive impairment (MCI) stage. Verbal fluency tasks are among the most often applied tests for early dementia detection due to their efficiency and ease of use. In these tasks, participants are asked to produce as many words as possible belonging to either a semantic category (SVF task) or a phonemic category (PVF task). Even though both SVF and PVF share neurocognitive function profiles, the PVF is typically believed to be less sensitive to measure MCI-related cognitive impairment and recent research on fine-grained automatic evaluation of VF tasks has mainly focused on the SVF. Contrary to this belief, we show that by applying state-of-the-art semantic and phonemic distance metrics in automatic analysis of PVF word productions, in-depth conclusions about production strategy of MCI patients are possible. Our results reveal a dissociation between semantically- and phonemically-guided search processes in the PVF. Specifically, we show that subjects with MCI rely less on semantic- and more on phonemic processes to guide their word production as compared to healthy controls (HC). We further show that semantic similarity-based features improve automatic MCI versus HC classification by 29% over previous approaches for the PVF. As such, these results point towards the yet underexplored utility of the PVF for in-depth assessment of cognition in MCI.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d92a821e7c7405359b9ae5c9660a7379Test
http://www.scopus.com/inward/record.url?eid=2-s2.0-85123183333&partnerID=MN8TOARSTest