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Kleiman MJ, Galvin JE. High frequency post-pause word choices and task-dependent speech behavior characterize connected speech in individuals with mild cognitive impairment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.25.24303329. [PMID: 38464237 PMCID: PMC10925339 DOI: 10.1101/2024.02.25.24303329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Background Alzheimer's disease (AD) is characterized by progressive cognitive decline, including impairments in speech production and fluency. Mild cognitive impairment (MCI), a prodrome of AD, has also been linked with changes in speech behavior but to a more subtle degree. Objective This study aimed to investigate whether speech behavior immediately following both filled and unfilled pauses (post-pause speech behavior) differs between individuals with MCI and healthy controls (HCs), and how these differences are influenced by the cognitive demands of various speech tasks. Methods Transcribed speech samples were analyzed from both groups across different tasks, including immediate and delayed narrative recall, picture descriptions, and free responses. Key metrics including lexical and syntactic complexity, lexical frequency and diversity, and part of speech usage, both overall and post-pause, were examined. Results Significant differences in pause usage were observed between groups, with a higher incidence and longer latencies following these pauses in the MCI group. Lexical frequency following filled pauses was higher among MCI participants in the free response task but not in other tasks, potentially due to the relative cognitive load of the tasks. The immediate recall task was most useful at differentiating between groups. Predictive analyses utilizing random forest classifiers demonstrated high specificity in using speech behavior metrics to differentiate between MCI and HCs. Conclusions Speech behavior following pauses differs between MCI participants and healthy controls, with these differences being influenced by the cognitive demands of the speech tasks. These post-pause speech metrics can be easily integrated into existing speech analysis paradigms.
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Affiliation(s)
- Michael J. Kleiman
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Boca Raton, FL 33433
| | - James E. Galvin
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Boca Raton, FL 33433
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Veneziani I, Marra A, Formica C, Grimaldi A, Marino S, Quartarone A, Maresca G. Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review. J Pers Med 2024; 14:113. [PMID: 38276235 PMCID: PMC10820741 DOI: 10.3390/jpm14010113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
In the context of advancing healthcare, the diagnosis and treatment of cognitive disorders, particularly Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), pose significant challenges. This review explores Artificial Intelligence (AI) and Machine Learning (ML) in neuropsychological assessment for the early detection and personalized treatment of MCI and AD. The review includes 37 articles that demonstrate that AI could be an useful instrument for optimizing diagnostic procedures, predicting cognitive decline, and outperforming traditional tests. Three main categories of applications are identified: (1) combining neuropsychological assessment with clinical data, (2) optimizing existing test batteries using ML techniques, and (3) employing virtual reality and games to overcome the limitations of traditional tests. Despite advancements, the review highlights a gap in developing tools that simplify the clinician's workflow and underscores the need for explainable AI in healthcare decision making. Future studies should bridge the gap between technical performance measures and practical clinical utility to yield accurate results and facilitate clinicians' roles. The successful integration of AI/ML in predicting dementia onset could reduce global healthcare costs and benefit aging societies.
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Affiliation(s)
- Isabella Veneziani
- Department of Nervous System and Behavioural Sciences, Psychology Section, University of Pavia, Piazza Botta, 11, 27100 Pavia, Italy;
| | - Angela Marra
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
| | - Caterina Formica
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
| | - Alessandro Grimaldi
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
| | - Silvia Marino
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
| | - Angelo Quartarone
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
| | - Giuseppa Maresca
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
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Tseng YT, Chang YL, Chiu YS. Assessment of Language Function in Older Mandarin-Speaking Adults with Mild Cognitive Impairment using Multifaceted Language Tests. J Alzheimers Dis 2024; 97:1189-1209. [PMID: 38217600 PMCID: PMC10836557 DOI: 10.3233/jad-230871] [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] [Accepted: 11/15/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND Individuals with amnestic mild cognitive impairment (aMCI), especially for those with multidomain cognitive deficits, should be clinically examined for determining risk of developing Alzheimer's disease. English-speakers with aMCI exhibit language impairments mostly at the lexical-semantic level. Given that the language processing of Mandarin Chinese is different from that of alphabetic languages, whether previous findings for English-speakers with aMCI can be generalized to Mandarin Chinese speakers with aMCI remains unclear. OBJECTIVE This study examined the multifaceted language functions of Mandarin Chinese speakers with aMCI and compared them with those without cognitive impairment by using a newly developed language test battery. METHODS Twenty-three individuals with aMCI and 29 individuals without cognitive impairment were recruited. The new language test battery comprises five language domains (oral production, auditory and reading comprehension, reading aloud, repetition, and writing). RESULTS Compared with the controls, the individuals with aMCI exhibited poorer performance in the oral production and auditory and reading comprehension domains, especially on tests involving effortful lexical and semantic processing. Moreover, the aMCI group made more semantic naming errors compared with their counterparts and tended to experience difficulty in processing items belonging to the categories of living objects. CONCLUSIONS The pattern identified in the present study is similar to that of English-speaking individuals with aMCI across multiple language domains. Incorporating language tests involving lexical and semantic processing into clinical practice is essential and can help identify early language dysfunction in Mandarin Chinese speakers with aMCI.
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Affiliation(s)
- Yun-Ting Tseng
- Department of Psychology, College of Science, National Taiwan University, Taipei, Taiwan
- Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan, Taiwan
| | - Yu-Ling Chang
- Department of Psychology, College of Science, National Taiwan University, Taipei, Taiwan
- Department of Neurology, College of Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan
- Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan
- Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan
| | - Yen-Shiang Chiu
- Department of Psychology, College of Science, National Taiwan University, Taipei, Taiwan
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Idrisoglu A, Dallora AL, Anderberg P, Berglund JS. Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. J Med Internet Res 2023; 25:e46105. [PMID: 37467031 PMCID: PMC10398366 DOI: 10.2196/46105] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/26/2023] [Accepted: 05/23/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. OBJECTIVE This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. METHODS This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. RESULTS In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. CONCLUSIONS This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.
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Affiliation(s)
- Alper Idrisoglu
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
- School of Health Sciences, University of Skövde, Skövde, Sweden
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Xiang C, Ai W, Zhang Y. Language dysfunction correlates with cognitive impairments in older adults without dementia mediated by amyloid pathology. Front Neurol 2023; 14:1051382. [PMID: 37265466 PMCID: PMC10230042 DOI: 10.3389/fneur.2023.1051382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/05/2023] [Indexed: 06/03/2023] Open
Abstract
Background Previous studies have explored the application of non-invasive biomarkers of language dysfunction for the early detection of Alzheimer's disease (AD). However, language dysfunction over time may be quite heterogeneous within different diagnostic groups. Method Patient demographics and clinical data were retrieved from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database for the participants without dementia who had measures of cerebrospinal fluid (CSF) biomarkers and language dysfunction. We analyzed the effect of longitudinal neuropathological and clinical correlates in the pathological process of semantic fluency and confrontation naming. The mediation effects of AD biomarkers were also explored by the mediation analysis. Result There were 272 subjects without dementia included in this analysis. Higher rates of decline in semantic fluency and confrontation naming were associated with a higher risk of progression to MCI or AD, and a greater decline in cognitive abilities. Moreover, the rate of change in semantic fluency was significantly associated with Aβ deposition, while confrontation naming was significantly associated with both amyloidosis and tau burden. Mediation analyses revealed that both confrontation naming and semantic fluency were partially mediated by the Aβ aggregation. Conclusion In conclusion, the changes in language dysfunction may partly stem from the Aβ deposition, while confrontation naming can also partly originate from the increase in tau burden. Therefore, this study sheds light on how language dysfunction is partly constitutive of mild cognitive impairment and dementia and therefore is an important clinical predictor.
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Affiliation(s)
- Chunchen Xiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Weiping Ai
- Department of Neurology, Zhangjiakou First Hospital, Zhangjiakou, China
| | - Yumei Zhang
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Center of Stroke, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, 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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Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of Artificial Intelligence to aid detection of dementia: a scoping review on current capabilities and future directions. J Biomed Inform 2022; 127:104030. [DOI: 10.1016/j.jbi.2022.104030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/21/2022] [Accepted: 02/12/2022] [Indexed: 12/17/2022]
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