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Hegedűs É, Pákáski M, Gosztolya G, Hoffmann I, Kovács I, Kálmán J. Speech-related parameters are sensitive measures of acetylcholinesterase inhibitor therapy in mild Alzheimer's disease. PLoS One 2024; 19:e0308409. [PMID: 39163290 PMCID: PMC11335108 DOI: 10.1371/journal.pone.0308409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 07/23/2024] [Indexed: 08/22/2024] Open
Abstract
Our aim was to find out whether speech-related temporal parameters (SRTPs) are sensitive indicators of the clinical outcome in acetylcholinesterase (AChE) inhibitor therapy with donepezil, compared to the standard cognitive Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) used in clinical trials. In this 24-week-long, naturalistic, self-control, open-labeled, prospective pilot study with 10 mg donepezil on 20 mild AD patients, cognitive functions were evaluated using 15 different SRTPs analyzed by automatic speech recognition in the Speech-Gap Test® compared to ADAS-Cog test results. Among the SRTPs, the filled pause duration ratio significantly improved after 12 weeks of donepezil treatment. During the 24-week follow-up, additional SRTPs such as the filled pause count ratio and the filled pause frequency showed significant benefits. ADAS-Cog total scores showed a slight but not significant improvement compared to baseline after 12 and 24 weeks of donepezil treatment. Among the ADAS-Cog subtests, only orientation improved significantly after 24 weeks of donepezil treatment. Our results indicate that subtle changes in SRTPs measured by the Speech-Gap Test® could be considered as sensitive indicators of the efficacy of the pharmacotherapy in mild AD. According to our data, other cognitive domains did not show improvement in response to donepezil therapy rating by ADAS-Cog. Based on all of this, it is likely that examining and evaluating speech parameters may play an important role in determining the effects of pharmacological treatment of mild AD. The novelty of our study is that it applies the measurement of linguistic parameters as primary outcomes during a drug trial of mild AD in scientific research for the first time.
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Affiliation(s)
- Éva Hegedűs
- Department of Psychiatry, University of Szeged, Szeged, Hungary
| | | | - Gábor Gosztolya
- MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary
| | - Ildikó Hoffmann
- Department of Psychiatry, University of Szeged, Szeged, Hungary
| | - Ildikó Kovács
- Department of Psychiatry, University of Szeged, Szeged, Hungary
| | - János Kálmán
- Department of Psychiatry, University of Szeged, Szeged, Hungary
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He R, Al-Tamimi J, Sánchez-Benavides G, Montaña-Valverde G, Domingo Gispert J, Grau-Rivera O, Suárez-Calvet M, Minguillon C, Fauria K, Navarro A, Hinzen W. Atypical cortical hierarchy in Aβ-positive older adults and its reflection in spontaneous speech. Brain Res 2024; 1830:148806. [PMID: 38365129 DOI: 10.1016/j.brainres.2024.148806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 02/07/2024] [Indexed: 02/18/2024]
Abstract
Abnormal deposition of Aβ amyloid is an early neuropathological marker of Alzheimer's disease (AD), arising long ahead of clinical symptoms. Non-invasive measures of associated early neurofunctional changes, together with easily accessible behavioral readouts of these changes, could be of great clinical benefit. We pursued this aim by investigating large-scale cortical gradients of functional connectivity with functional MRI, which capture the hierarchical integration of cortical functions, together with acoustic-prosodic features from spontaneous speech, in cognitively unimpaired older adults with and without Aβ positivity (total N = 188). We predicted distortions of the cortical hierarchy associated with prosodic changes in the Aβ + group. Results confirmed substantially altered cortical hierarchies and less variability in these in the Aβ + group, together with an increase in quantitative prosodic measures, which correlated with gradient variability as well as digit span test scores. Overall, these findings confirm that long before the clinical stage and objective cognitive impairment, increased risk of cognitive decline as indexed by Aβ accumulation is marked by neurofunctional changes in the cortical hierarchy, which are related to automatically extractable speech patterns and alterations in working memory functions.
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Affiliation(s)
- Rui He
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, 08018 Barcelona, Spain.
| | - Jalal Al-Tamimi
- Université Paris Cité, Laboratoire de Linguistique Formelle (LLF), CNRS, 75013 Paris, France
| | - Gonzalo Sánchez-Benavides
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Department of Medicine and Life Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain; Servei de Neurologia, Hospital del Mar, 08003 Barcelona, Spain
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain; Servei de Neurologia, Hospital del Mar, 08003 Barcelona, Spain
| | - Carolina Minguillon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Arcadi Navarro
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Catalan Institution of Research and Advanced Studies (ICREA), 08010 Barcelona, Spain; Department of Medicine and Life Sciences, Institute of Evolutionary Biology (UPF-CSIC), Universitat Pompeu Fabra, 08003 Barcelona, Spain; CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), 08003 Barcelona, Spain
| | - Wolfram Hinzen
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, 08018 Barcelona, Spain; Catalan Institution of Research and Advanced Studies (ICREA), 08010 Barcelona, Spain
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DuBord AY, Paolillo EW, Staffaroni AM. Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases. J Diabetes Sci Technol 2023:19322968231171399. [PMID: 37102472 DOI: 10.1177/19322968231171399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.
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Affiliation(s)
- Ashley Y DuBord
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Diabetes Technology Society, Burlingame, CA, USA
| | - Emily W Paolillo
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Adam M Staffaroni
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
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Liu J, Fu F, Li L, Yu J, Zhong D, Zhu S, Zhou Y, Liu B, Li J. Efficient Pause Extraction and Encode Strategy for Alzheimer's Disease Detection Using Only Acoustic Features from Spontaneous Speech. Brain Sci 2023; 13:477. [PMID: 36979287 PMCID: PMC10046767 DOI: 10.3390/brainsci13030477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
Clinical studies have shown that speech pauses can reflect the cognitive function differences between Alzheimer's Disease (AD) and non-AD patients, while the value of pause information in AD detection has not been fully explored. Herein, we propose a speech pause feature extraction and encoding strategy for only acoustic-signal-based AD detection. First, a voice activity detection (VAD) method was constructed to detect pause/non-pause feature and encode it to binary pause sequences that are easier to calculate. Then, an ensemble machine-learning-based approach was proposed for the classification of AD from the participants' spontaneous speech, based on the VAD Pause feature sequence and common acoustic feature sets (ComParE and eGeMAPS). The proposed pause feature sequence was verified in five machine-learning models. The validation data included two public challenge datasets (ADReSS and ADReSSo, English voice) and a local dataset (10 audio recordings containing five patients and five controls, Chinese voice). Results showed that the VAD Pause feature was more effective than common feature sets (ComParE: 6373 features and eGeMAPS: 88 features) for AD classification, and that the ensemble method improved the accuracy by more than 5% compared to several baseline methods (8% on the ADReSS dataset; 5.9% on the ADReSSo dataset). Moreover, the pause-sequence-based AD detection method could achieve 80% accuracy on the local dataset. Our study further demonstrated the potential of pause information in speech-based AD detection, and also contributed to a more accessible and general pause feature extraction and encoding method for AD detection.
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Affiliation(s)
- Jiamin Liu
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Fan Fu
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Liang Li
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Junxiao Yu
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Dacheng Zhong
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Songsheng Zhu
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Yuxuan Zhou
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Bin Liu
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Jianqing Li
- Jiangsu Province Engineering Research Center of Smart Wearable and Rehabilitation Devices, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 211166, China
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Wang R, Kuang C, Guo C, Chen Y, Li C, Matsumura Y, Ishimaru M, Van Pelt AJ, Chen F. Automatic Detection of Putative Mild Cognitive Impairment from Speech Acoustic Features in Mandarin-Speaking Elders. J Alzheimers Dis 2023; 95:901-914. [PMID: 37638439 DOI: 10.3233/jad-230373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
BACKGROUND To date, the reliable detection of mild cognitive impairment (MCI) remains a significant challenge for clinicians. Very few studies investigated the sensitivity of acoustic features in detecting Mandarin-speaking elders at risk for MCI, defined as "putative MCI" (pMCI). OBJECTIVE This study sought to investigate the possibility of using automatically extracted speech acoustic features to detect elderly people with pMCI and reveal the potential acoustic markers of cognitive decline at an early stage. METHODS Forty-one older adults with pMCI and 41 healthy elderly controls completed four reading tasks (syllable utterance, tongue twister, diadochokinesis, and short sentence reading), from which acoustic features were extracted automatically to train machine learning classifiers. Correlation analysis was employed to evaluate the relationship between classifier predictions and participants' cognitive ability measured by Mini-Mental State Examination 2. RESULTS Classification results revealed that some temporal features (e.g., speech rate, utterance duration, and the number of silent pauses), spectral features (e.g., variability of F1 and F2), and energy features (e.g., SD of peak intensity and SD of intensity range) were effective predictors of pMCI. The best classification result was achieved in the Random Forest classifier (accuracy = 0.81, AUC = 0.81). Correlation analysis uncovered a strong negative correlation between participants' cognitive test scores and the probability estimates of pMCI in the Random Forest classifier, and a modest negative correlation in the Support Vector Machine classifier. CONCLUSIONS The automatic acoustic analysis of speech could provide a promising non-invasive way to assess and monitor the early cognitive decline in Mandarin-speaking elders.
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Affiliation(s)
- Rumi Wang
- Rehabilitation Medicine Department, Speech and Language Pathology Therapy Section, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Chen Kuang
- School of Foreign Languages, Hunan University, Hunan, China
| | - Chengyu Guo
- School of Foreign Languages, Hunan University, Hunan, China
| | - Yong Chen
- Laboratory of Food Oral Processing, School of Food Science & Biotechnology, Zhejiang Gongshang University, Hangzhou, Zhejiang, China
| | - Canyang Li
- Rehabilitation Medicine Department, Speech and Language Pathology Therapy Section, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | | | | | - Alice J Van Pelt
- Section of Gastroenterology, Edward Hines, Jr. VA Hospital, Hines, IL, USA
- Division of Gastroenterology and Nutrition, Loyola University Stritch School of Medicine, Maywood, IL, USA
| | - Fei Chen
- School of Foreign Languages, Hunan University, Hunan, China
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