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, 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, Johannes, Tröger, Elisa, Mallick, Mario, Mina, Nicklas, Linz, Carole, Wagnon, Julia, Karbach, Caroline, Kuhn, Jessica, Peter

المصدر: BMC psychiatry. 22(1)

مصطلحات موضوعية: Adult, Male, Young Adult, Depressive Disorder, Major, Depression, Surveys and Questionnaires, Humans, Speech, Female, Longitudinal Studies

الوصف: Automated speech analysis has gained increasing attention to help diagnosing depression. Most previous studies, however, focused on comparing speech in patients with major depressive disorder to that in healthy volunteers. An alternative may be to associate speech with depressive symptoms in a non-clinical sample as this may help to find early and sensitive markers in those at risk of depression.We included n = 118 healthy young adults (mean age: 23.5 ± 3.7 years; 77% women) and asked them to talk about a positive and a negative event in their life. Then, we assessed the level of depressive symptoms with a self-report questionnaire, with scores ranging from 0-60. We transcribed speech data and extracted acoustic as well as linguistic features. Then, we tested whether individuals below or above the cut-off of clinically relevant depressive symptoms differed in speech features. Next, we predicted whether someone would be below or above that cut-off as well as the individual scores on the depression questionnaire. Since depression is associated with cognitive slowing or attentional deficits, we finally correlated depression scores with performance in the Trail Making Test.In our sample, n = 93 individuals scored below and n = 25 scored above cut-off for clinically relevant depressive symptoms. Most speech features did not differ significantly between both groups, but individuals above cut-off spoke more than those below that cut-off in the positive and the negative story. In addition, higher depression scores in that group were associated with slower completion time of the Trail Making Test. We were able to predict with 93% accuracy who would be below or above cut-off. In addition, we were able to predict the individual depression scores with low mean absolute error (3.90), with best performance achieved by a support vector machine.Our results indicate that even in a sample without a clinical diagnosis of depression, changes in speech relate to higher depression scores. This should be investigated in more detail in the future. In a longitudinal study, it may be tested whether speech features found in our study represent early and sensitive markers for subsequent depression in individuals at risk.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=pmid________::3e6ace8f78688c41e4856af38a2962ddTest
https://pubmed.ncbi.nlm.nih.gov/36575442Test

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

المؤلفون: Hali Lindsay, Philippe Robert, Nicklas Linz, Julia Elbaum, Kevin Riviere, Alexandre Derreumaux, Roxane Fabre, Alexandra König

المساهمون: Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria), Spatio-Temporal Activity Recognition Systems (STARS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre Hospitalier Universitaire de Nice (CHU Nice), ki:elements [Saarbrücken], 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)

المصدر: Journal of Medical Internet Research, Vol 23, Iss 4, p e24191 (2021)
Journal of Medical Internet Research
Journal of Medical Internet Research, 2021, 23 (4), pp.e24191. ⟨10.2196/24191⟩
Journal of Medical Internet Research, JMIR Publications, 2021, 23 (4), pp.e24191. ⟨10.2196/24191⟩

مصطلحات موضوعية: Adult, Male, Health Personnel, speech, Computer applications to medicine. Medical informatics, 0206 medical engineering, R858-859.7, Pilot Projects, Health Informatics, 02 engineering and technology, Anxiety, Speech Acoustics, Voice analysis, phone monitoring, [SCCO]Cognitive science, 03 medical and health sciences, 0302 clinical medicine, Phone, Surveys and Questionnaires, Health care, medicine, Humans, Stress measures, Burnout, Professional, Pandemics, Original Paper, computer linguistics, SARS-CoV-2, business.industry, COVID-19, 020601 biomedical engineering, Telephone, 3. Good health, voice analysis, Female, Observational study, Public aspects of medicine, RA1-1270, medicine.symptom, business, Psychology, Psychosocial, 030217 neurology & neurosurgery, stress detection, Clinical psychology

الوصف: BackgroundDuring the COVID-19 pandemic, health professionals have been directly confronted with the suffering of patients and their families. By making them main actors in the management of this health crisis, they have been exposed to various psychosocial risks (stress, trauma, fatigue, etc). Paradoxically, stress-related symptoms are often underreported in this vulnerable population but are potentially detectable through passive monitoring of changes in speech behavior.ObjectiveThis study aims to investigate the use of rapid and remote measures of stress levels in health professionals working during the COVID-19 outbreak. This was done through the analysis of participants’ speech behavior during a short phone call conversation and, in particular, via positive, negative, and neutral storytelling tasks.MethodsSpeech samples from 89 health care professionals were collected over the phone during positive, negative, and neutral storytelling tasks; various voice features were extracted and compared with classical stress measures via standard questionnaires. Additionally, a regression analysis was performed.ResultsCertain speech characteristics correlated with stress levels in both genders; mainly, spectral (ie, formant) features, such as the mel-frequency cepstral coefficient, and prosodic characteristics, such as the fundamental frequency, appeared to be sensitive to stress. Overall, for both male and female participants, using vocal features from the positive tasks for regression yielded the most accurate prediction results of stress scores (mean absolute error 5.31).ConclusionsAutomatic speech analysis could help with early detection of subtle signs of stress in vulnerable populations over the phone. By combining the use of this technology with timely intervention strategies, it could contribute to the prevention of burnout and the development of comorbidities, such as depression or anxiety.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c2dca6c3a053b0e42769a679c9562ebeTest
https://doi.org/10.2196/24191Test