المؤلفون: Jessica Peter, Nicklas Linz, Johannes Tröger, Hali Lindsay, Jutta Kray, Mario Magued Mina, Stefan Klöppel

مصطلحات موضوعية: medicine.medical_specialty, Neuropsychological Tests, Audiology, behavioral disciplines and activities, 050105 experimental psychology, Task (project management), Executive Function, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Semantic memory, Verbal fluency test, Cognitive Dysfunction, 0501 psychology and cognitive sciences, In patient, Latency (engineering), Control (linguistics), Set (psychology), Point (typography), General Neuroscience, 05 social sciences, Semantics, Psychiatry and Mental health, Clinical Psychology, 150 Psychologie, Neurology (clinical), Psychology, 610 Medizin und Gesundheit, 030217 neurology & neurosurgery

الوصف: Objective:Semantic verbal fluency (SVF) tasks require individuals to name items from a specified category within a fixed time. An impaired SVF performance is well documented in patients with amnestic Mild Cognitive Impairment (aMCI). The two leading theoretical views suggest either loss of semantic knowledge or impaired executive control to be responsible.Method:We assessed SVF 3 times on 2 consecutive days in 29 healthy controls (HC) and 29 patients with aMCI with the aim to answer the question which of the two views holds true.Results:When doing the task for the first time, patients with aMCI produced fewer and more common words with a shorter mean response latency. When tested repeatedly, only healthy volunteers increased performance. Likewise, only the performance of HC indicated two distinct retrieval processes: a prompt retrieval of readily available items at the beginning of the task and an active search through semantic space towards the end. With repeated assessment, the pool of readily available items became larger in HC, but not patients with aMCI.Conclusion:The production of fewer and more common words in aMCI points to a smaller search set and supports the loss of semantic knowledge view. The failure to improve performance as well as the lack of distinct retrieval processes point to an additional impairment in executive control. Our data did not clearly favour one theoretical view over the other, but rather indicates that the impairment of patients with aMCI in SVF is due to a combination of both.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96ce4e29fbe653cb195d5c036779cfb4Test

المؤلفون: Pascale Lemoine, Philippe Robert, Vincent Bultingaire, Alexandra König, Nicklas Linz, Radia Zeghari, Rachid Guerchouche, Inez H.G.B. Ramakers

المصدر: Alzheimer's & Dementia. 16

مصطلحات موضوعية: 0303 health sciences, Telemedicine, Epidemiology, business.industry, Remote patient monitoring, Health Policy, medicine.disease, 03 medical and health sciences, Psychiatry and Mental health, Cellular and Molecular Neuroscience, 0302 clinical medicine, Developmental Neuroscience, Medicine, Dementia, Neurology (clinical), Medical emergency, Geriatrics and Gerontology, business, 030217 neurology & neurosurgery, 030304 developmental biology

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::0cb638aff51460d2be335b03b85cdca4Test
https://doi.org/10.1002/alz.047345Test

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

الوصف: BACKGROUND During 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. OBJECTIVE This 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. METHODS Speech 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. RESULTS Certain 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). CONCLUSIONS Automatic 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_________::584d3fc5acd66fa8c3d2c056f655a0c8Test
https://doi.org/10.2196/preprints.24191Test

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: Jan Alexandersson, Johannes Prudlo, Johannes Tröger, Nicklas Linz, Hali Lindsay

المصدر: Proceedings of International Conferences of Experimental Linguistics.

مصطلحات موضوعية: medicine.medical_specialty, medicine, Language impairment, Audiology, Psychology

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::28550795d92e57c9b4b76b3dee9b540fTest
https://doi.org/10.36505/exling-2019/10/0033/000395Test

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

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: Radia Zeghari, Robert Philippe, Valeria Manera, Nicklas Linz, Rachid Guerchouche, Alexandra König

المصدر: Alzheimer's & Dementia. 15

مصطلحات موضوعية: Telemedicine, Multimedia, Epidemiology, Computer science, Health Policy, computer.software_genre, Cognitive test, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Automatic speech, Neurology (clinical), Geriatrics and Gerontology, Rural area, computer

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::b66d189ac1e50ce299187f6121fb3e5aTest
https://doi.org/10.1016/j.jalz.2019.09.064Test

المؤلفون: Dimitrios Kokkinakis, Nicklas Linz, Philippe Robert, Frank Rudzicz, Jan Alexandersson, Bai Li, Alexandra König, Kathleen C. Fraser, Kristina Lundholm Fors

المصدر: NAACL-HLT (1)

مصطلحات موضوعية: Domain adaptation, Training set, business.industry, Computer science, computer.software_genre, medicine.disease, Task (project management), 030507 speech-language pathology & audiology, 03 medical and health sciences, 0302 clinical medicine, medicine, Dementia, Language modelling, Language model, Artificial intelligence, 0305 other medical science, business, Set (psychology), computer, 030217 neurology & neurosurgery, Natural language processing

الوصف: There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets. Here, we compare several methods of domain adaptation to augment a small French dataset of picture descriptions (n = 57) with a much larger English dataset (n = 550), for the task of automatically distinguishing participants with dementia from controls. The first challenge is to identify a set of features that transfer across languages; in addition to previously used features based on information units, we introduce a new set of features to model the order in which information units are produced by dementia patients and controls. These concept-based language model features improve classification performance in both English and French separately, and the best result (AUC = 0.89) is achieved using the multilingual training set with a combination of information and language model features.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::f36e527a739665c9382b8af3ae241d45Test
https://doi.org/10.18653/v1/n19-1367Test

المؤلفون: Hali Lindsay, Alexandra König, Nicklas Linz, Kathleen C. Fraser

المساهمون: National Research Council of Canada (NRC), Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI), 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: Computational Linguistics and Clinical Psychology : From Keyboard to Clinic
CLPsyc 2019-Sixth Workshop on Computational Linguistics and Clinical Psychology
CLPsyc 2019-Sixth Workshop on Computational Linguistics and Clinical Psychology, Jun 2019, Minneapolis, Minnesota, United States. pp.55-61, ⟨10.18653/v1/W19-3007⟩

مصطلحات موضوعية: Data sharing, [SCCO]Cognitive science, Annotation, Research groups, Computer science, Position paper, Transcription (software), Data science

الوصف: Increased access to large datasets has driven progress in NLP. However, most computational studies of clinically-validated, patient-generated speech and language involve very few datapoints, as such data are difficult (and expensive) to collect. In this position paper, we argue that we must find ways to promote data sharing across research groups, in order to build datasets of a more appropriate size for NLP and machine learning analysis. We review the benefits and challenges of sharing clinical language data, and suggest several concrete actions by both clinical and NLP researchers to encourage multi-site and multi-disciplinary data sharing. We also propose the creation of a collaborative data sharing platform, to allow NLP researchers to take a more active responsibility for data transcription, annotation, and curation.
Sixth Workshop on Computational Linguistics and Clinical Psychology, June 6, 2019, Minneapolis, Minnesota, United States

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1eccc014d914a939893fc6cb7d053fc4Test
https://doi.org/10.18653/v1/w19-3007Test