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

المصدر: Digital Biomarkers, Vol 7, Iss 1, Pp 115-123 (2023)

مصطلحات موضوعية: phone assessment, automated speech analysis, mild cognitive impairment, memory, fluency, Biology (General), QH301-705.5

الوصف: Introduction: We studied the accuracy of the automatic speech recognition (ASR) software by comparing ASR scores with manual scores from a verbal learning test (VLT) and a semantic verbal fluency (SVF) task in a semiautomated phone assessment in a memory clinic population. Furthermore, we examined the differentiating value of these tests between participants with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). We also investigated whether the automatically calculated speech and linguistic features had an additional value compared to the commonly used total scores in a semiautomated phone assessment. Methods: We included 94 participants from the memory clinic of the Maastricht University Medical Center+ (SCD N = 56 and MCI N = 38). The test leader guided the participant through a semiautomated phone assessment. The VLT and SVF were audio recorded and processed via a mobile application. The recall count and speech and linguistic features were automatically extracted. The diagnostic groups were classified by training machine learning classifiers to differentiate SCD and MCI participants. Results: The intraclass correlation for inter-rater reliability between the manual and the ASR total word count was 0.89 (95% CI 0.09–0.97) for the VLT immediate recall, 0.94 (95% CI 0.68–0.98) for the VLT delayed recall, and 0.93 (95% CI 0.56–0.97) for the SVF. The full model including the total word count and speech and linguistic features had an area under the curve of 0.81 and 0.77 for the VLT immediate and delayed recall, respectively, and 0.61 for the SVF. Conclusion: There was a high agreement between the ASR and manual scores, keeping the broad confidence intervals in mind. The phone-based VLT was able to differentiate between SCD and MCI and can have opportunities for clinical trial screening.

وصف الملف: electronic resource

العلاقة: https://beta.karger.com/Article/FullText/533188Test; https://doaj.org/toc/2504-110XTest

الوصول الحر: https://doaj.org/article/57b076a8e51c43649c2884ed3a575440Test

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

الوصول الحر: https://doaj.org/article/44177b145fef4cf18722e82b51e0e781Test

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

العلاقة: https://www.karger.com/Article/FullText/526471Test; https://doaj.org/toc/2504-110XTest

الوصول الحر: https://doaj.org/article/d715274c09c14aa5807f9200f2c82a8fTest

المؤلفون: Craig W Ritchie, Saturnino Luz, Sarah Gregory, John Harrison, Alexandra König, Nicklas Linz, Kai Langel, Hannah Pullen

المصدر: BMJ Open, Vol 12, Iss 3 (2022)

مصطلحات موضوعية: Medicine

وصف الملف: electronic resource

العلاقة: https://bmjopen.bmj.com/content/12/3/e052250.fullTest; https://doaj.org/toc/2044-6055Test

الوصول الحر: https://doaj.org/article/31a49e1fe8dc46a583813ab0f1bb8ffdTest

المؤلفون: Philippe Robert, Alexandra König, Radia Zeghari, Rachid Guerchouche, Minh Duc Tran, François Bremond, Nicklas Linz, Hali Lindsay, Kai Langel, Inez Ramakers, Pascale Lemoine, Vincent Bultingaire

المصدر: BMJ Open, Vol 11, Iss 9 (2021)

مصطلحات موضوعية: Medicine

وصف الملف: electronic resource

العلاقة: https://bmjopen.bmj.com/content/11/9/e047083.fullTest; https://doaj.org/toc/2044-6055Test

الوصول الحر: https://doaj.org/article/13f878cad9eb4100bfbbd5566152901aTest

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

الوصول الحر: https://doaj.org/article/fc5f4ceb4eeb4332bcf6d70161a6e643Test

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: Céline Labbé, Alexandra König, Hali Lindsay, Nicklas Linz, Johannes Tröger, Philippe Robert

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_________::58be6d34fa42c506653dbb6c3fbeee93Test
https://doi.org/10.1002/alz.064486Test

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