-
1دورية أكاديمية
المؤلفون: Alexandra König, Johannes Tröger, Elisa Mallick, Mario Mina, Nicklas Linz, Carole Wagnon, Julia Karbach, Caroline Kuhn, Jessica Peter
المصدر: BMC Psychiatry, Vol 22, Iss 1, Pp 1-8 (2022)
مصطلحات موضوعية: Depressive symptoms, Automated speech analysis, Acoustic features, Textual features, Machine learning, Psychiatry, RC435-571
الوصف: Abstract Background 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. Methods 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. Results 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. Conclusions 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.
وصف الملف: electronic resource
العلاقة: https://doaj.org/toc/1471-244XTest
-
2دورية أكاديمية
المؤلفون: Johannes Tröger, Ebru Baykara, Jian Zhao, Daphne ter Huurne, Nina Possemis, Elisa Mallick, Simona Schäfer, Louisa Schwed, Mario Mina, Nicklas Linz, Inez Ramakers, Craig Ritchie
المصدر: Digital Biomarkers, Vol 6, Iss 3, Pp 107-116 (2022)
مصطلحات موضوعية: mild cognitive impairment, digital biomarker, speech biomarker, dementia, speech analysis, clinical trials, Biology (General), QH301-705.5
الوصف: Introduction: Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer’s disease. While most well-established measures for cognition might not fit tomorrow’s decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society’s V3 framework: verification, analytical validation, and clinical validation. Methods: Evaluation was done in two independent clinical samples: the Dutch DeepSpA (N = 69 subjective cognitive impairment [SCI], N = 52 mild cognitive impairment [MCI], and N = 13 dementia) and the Scottish SPeAk datasets (N = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. Results: Verification: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. Analytical Validation: In both languages, the SB-C was strongly correlated with MMSE scores. Clinical Validation: The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline. Conclusion: Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials.
وصف الملف: electronic resource
-
3
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 -
4دورية أكاديمية
المؤلفون: Alexandra König, Elisa Mallick, Johannes Tröger, Nicklas Linz, Radia Zeghari, Valeria Manera, Philippe Robert
المصدر: European Psychiatry, Vol 64 (2021)
مصطلحات موضوعية: apathy, depression, mild neurocognitive disorders, neuropsychiatric symptoms, speech analysis, vocal parameters, Psychiatry, RC435-571
الوصف: Abstract Background Certain neuropsychiatric symptoms (NPS), namely apathy, depression, and anxiety demonstrated great value in predicting dementia progression, representing eventually an opportunity window for timely diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing. Therefore, the present study aims to investigate the association between automatically extracted speech features and NPS in patients with mild neurocognitive disorders. Methods Speech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. NPS were assessed by the neuropsychiatric inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers. Results Different speech variables are associated with specific NPS; apathy correlates with temporal aspects, and anxiety with voice quality—and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores. Conclusions Different NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS in patients with cognitive impairment. This could have great implications for the design of future clinical trials as this cost-effective method could allow more continuous and even remote monitoring of symptoms.
وصف الملف: electronic resource
العلاقة: https://www.cambridge.org/core/product/identifier/S0924933821022367/type/journal_articleTest; https://doaj.org/toc/0924-9338Test; https://doaj.org/toc/1778-3585Test
-
5
المؤلفون: Johan Skoog, Elisa Mallick, Timothy Hadarsson Bodin, Mario Mina, Nicklas Linz, Johannes Tröger, Ingmar Skoog
المصدر: Alzheimer's & Dementia. 18
مصطلحات موضوعية: Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::9e50e4564a3b9190a4c6b5b8e57b2222Test
https://doi.org/10.1002/alz.067821Test -
6
المؤلفون: Nicklas Linz, Elisa Mallick, Mario Mina, Nina Possemis, Alexandra König, Daphne B.G. ter Huurne, Inez H.G.B. Ramakers, Johannes Tröger
المصدر: Alzheimer's & Dementia. 18
مصطلحات موضوعية: Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::d76b16caf1c911123fd79b740922f60eTest
https://doi.org/10.1002/alz.061600Test -
7
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.
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