1
|
Burke E, Gunstad J, Pavlenko O, Hamrick P. Distinguishable features of spontaneous speech in Alzheimer's clinical syndrome and healthy controls. NEUROPSYCHOLOGY, DEVELOPMENT, AND COGNITION. SECTION B, AGING, NEUROPSYCHOLOGY AND COGNITION 2024; 31:575-586. [PMID: 37272884 PMCID: PMC10696129 DOI: 10.1080/13825585.2023.2221020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/29/2023] [Indexed: 06/06/2023]
Abstract
There is growing evidence that subtle changes in spontaneous speech may reflect early pathological changes in cognitive function. Recent work has found that lexical-semantic features of spontaneous speech predict cognitive dysfunction in individuals with mild cognitive impairment (MCI). The current study assessed whether Ostrand and Gunstad's (OG) lexical-semantic features extend to predicting cognitive status in a sample of individuals with Alzheimer's clinical syndrome (ACS) and healthy controls. Four additional (New) speech indices shown to be important in language processing research were also explored in this sample to extend prior work. Speech transcripts of the Cookie Theft Task from 81 individuals with ACS (Mage = 72.7 years, SD = 8.80, 70.4% female) and 61 healthy controls (HC) (Mage = 63.9 years, SD = 8.52, 62.3% female) from Dementia Bank were analyzed. Random forest and logistic machine learning techniques examined whether subject-level lexical-semantic features could be used to accurately discriminate those with ACS from HC. Results showed that logistic models with the New lexical-semantic features obtained good classification accuracy (78.4%), but the OG features had wider success across machine learning model types. In terms of sensitivity and specificity, the random forest model trained on the OG features was the most balanced. Findings from the current study suggest that features of spontaneous speech used to predict MCI may also distinguish between individuals with ACS and healthy controls. Future work should evaluate these lexical-semantic features in pre-clinical persons to further explore their potential to assist with early detection through speech analysis.
Collapse
Affiliation(s)
- Erin Burke
- Department of Psychological Sciences, Kent State University
| | - John Gunstad
- Department of Psychological Sciences, Kent State University
| | | | | |
Collapse
|
2
|
Young CB, Smith V, Karjadi C, Grogan S, Ang TFA, Insel PS, Henderson VW, Sumner M, Poston KL, Au R, Mormino EC. Speech patterns during memory recall relates to early tau burden across adulthood. Alzheimers Dement 2024; 20:2552-2563. [PMID: 38348772 PMCID: PMC11032578 DOI: 10.1002/alz.13731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 04/22/2024]
Abstract
INTRODUCTION Early cognitive decline may manifest in subtle differences in speech. METHODS We examined 238 cognitively unimpaired adults from the Framingham Heart Study (32-75 years) who completed amyloid and tau PET imaging. Speech patterns during delayed recall of a story memory task were quantified via five speech markers, and their associations with global amyloid status and regional tau signal were examined. RESULTS Total utterance time, number of between-utterance pauses, speech rate, and percentage of unique words significantly correlated with delayed recall score although the shared variance was low (2%-15%). Delayed recall score was not significantly different between β-amyoid-positive (Aβ+) and -negative (Aβ-) groups and was not associated with regional tau signal. However, longer and more between-utterance pauses, and slower speech rate were associated with increased tau signal across medial temporal and early neocortical regions. DISCUSSION Subtle speech changes during memory recall may reflect cognitive impairment associated with early Alzheimer's disease pathology. HIGHLIGHTS Speech during delayed memory recall relates to tau PET signal across adulthood. Delayed memory recall score was not associated with tau PET signal. Speech shows greater sensitivity to detecting subtle cognitive changes associated with early tau accumulation. Our cohort spans adulthood, while most PET imaging studies focus on older adults.
Collapse
Affiliation(s)
- Christina B. Young
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Viktorija Smith
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Cody Karjadi
- Department of Anatomy & Neurobiology and Framingham Heart StudyBoston University Chobanian and Avedisian School of MedicineBostonMassachusettsUSA
| | - Selah‐Marie Grogan
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
| | - Ting Fang Alvin Ang
- Department of Anatomy & Neurobiology and Framingham Heart StudyBoston University Chobanian and Avedisian School of MedicineBostonMassachusettsUSA
| | - Philip S. Insel
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Victor W. Henderson
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Department of Epidemiology and Population HealthStanford UniversityStanfordCaliforniaUSA
| | - Meghan Sumner
- Department of LinguisticsStanford UniversityStanfordCaliforniaUSA
| | - Kathleen L. Poston
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Wu Tsai Neuroscience InstituteStanford UniversityStanfordCaliforniaUSA
| | - Rhoda Au
- Department of Anatomy & Neurobiology and Framingham Heart StudyBoston University Chobanian and Avedisian School of MedicineBostonMassachusettsUSA
| | - Elizabeth C. Mormino
- Department of Neurology and Neurological SciencesStanford University School of MedicineStanfordCaliforniaUSA
- Wu Tsai Neuroscience InstituteStanford UniversityStanfordCaliforniaUSA
| |
Collapse
|
3
|
Varlokosta S, Fragkopoulou K, Arfani D, Manouilidou C. Methodologies for assessing morphosyntactic ability in people with Alzheimer's disease. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:38-57. [PMID: 36840629 DOI: 10.1111/1460-6984.12862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 01/27/2023] [Indexed: 05/26/2023]
Abstract
BACKGROUND The detection and description of language impairments in neurodegenerative diseases like Alzheimer's Disease (AD) play an important role in research, clinical diagnosis and intervention. Various methodological protocols have been implemented for the assessment of morphosyntactic abilities in AD; narrative discourse elicitation tasks and structured experimental tasks for production, offline and online structured experimental tasks for comprehension. Very few studies implement and compare different methodological protocols; thus, little is known about the advantages and disadvantages of each methodology. AIMS To discuss and compare the main behavioral methodological approaches and tasks that have been used in psycholinguistic research to assess different aspects of morphosyntactic production and comprehension in individuals with AD at the word and sentence levels. METHODS A narrative review was conducted through searches in the scientific databases Google Scholar, Scopus, Science Direct, MITCogNet, PubMed. Only studies written in English, that reported quantitative data and were published in peer-reviewed journals were considered with respect to their methodological protocol. Moreover, we considered studies that reported research on all stages of the disease and we included only studies that also reported results of a healthy control group. Studies that implemented standardized assessment tools were not considered in this review. OUTCOMES & RESULTS The main narrative discourse elicitation tasks implemented for the assessment of morphosyntactic production include interviews, picture-description and story narration, whereas the main structured experimental tasks include sentence completion, constrained sentence production, sentence repetition and naming. Morphosyntactic comprehension in AD has been assessed with the use of structured experimental tasks, both offline (sentence-picture matching, grammaticality judgment) and online (cross-modal naming,speeded sentence acceptability judgment, auditory moving window, word detection, reading). For each task we considered studies that reported results from different morphosyntactic structures and phenomena in as many different languages as possible. CONCLUSIONS & IMPLICATIONS Our review revealed strengths and weaknesses of these methods but also directions for future research. Narrative discourse elicitation tasks as well as structured experimental tasks have been used in a variety of languages, and have uncovered preserved morphosyntactic production but also deficits in people with AD. A combination of narrative discourse elicitation and structured production tasks for the assessment of the same morphosyntactic structure has been rarely used. Regarding comprehension, offline tasks have been implemented in various languages, whereas online tasks have been mainly used in English. Offline and online experimental paradigms have often produced contradictory results even within the same study. The discrepancy between the two paradigms has been attributed to the different working memory demands they impose to the comprehender or to the different parsing processes they tap. Strengths and shortcomings of each methodology are summarized in the paper, and comparisons between different tasks are attempted when this is possible. Thus, the paper may serve as a methodological guide for the study of morphosyntax in AD and possibly in other neurodegenerative diseases. WHAT THIS PAPER ADDS What is already known on this subject For the assessment of morphosyntactic abilities in AD, various methodological paradigms have been implemented: narrative discourse elicitation tasks and structured experimental tasks for production, and offline and online structured experimental tasks for comprehension. Very few studies implement and compare different methodological protocols; thus, little is known about the advantages and disadvantages of each methodology. What this paper adds to existing knowledge The paper presents an overview of methodologies that have been used to assess morphosyntactic production and comprehension of people with AD at the word and sentence levels. The paper summarizes the strengths and shortcomings of each methodology, providing both the researcher and the clinician with some directions in their endeavour of investigating language in AD. Also, the paper highlights the need for further research that will implement carefully scrutinized tasks from various experimental paradigms and will explore distinct aspects of the AD patients' morphosyntactic abilities in typologically different languages. What are the potential or actual clinical implications of this work? The paper may serve as a reference point for (psycho-)linguists who wish to study morphosyntactic abilities in AD, and for speech and language therapists who might need to apply morphosyntactic protocols to their patients in order to assess them or design appropriate therapeutic interventions for production and comprehension deficits.
Collapse
Affiliation(s)
- Spyridoula Varlokosta
- Department of Linguistics, Faculty of Philology, National and Kapodistrian University of Athens, Athens, Greece
| | - Katerina Fragkopoulou
- Department of Linguistics, Faculty of Philology, National and Kapodistrian University of Athens, Athens, Greece
| | - Dimitra Arfani
- Department of Linguistics, Faculty of Philology, National and Kapodistrian University of Athens, Athens, Greece
| | - Christina Manouilidou
- Department of Comparative and General Linguistics, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| |
Collapse
|
4
|
Ivanova O, Martínez-Nicolás I, Meilán JJG. Speech changes in old age: Methodological considerations for speech-based discrimination of healthy ageing and Alzheimer's disease. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:13-37. [PMID: 37140204 DOI: 10.1111/1460-6984.12888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 04/03/2023] [Indexed: 05/05/2023]
Abstract
BACKGROUND Recent evidence suggests that speech substantially changes in ageing. As a complex neurophysiological process, it can accurately reflect changes in the motor and cognitive systems underpinning human speech. Since healthy ageing is not always easily discriminable from early stages of dementia based on cognitive and behavioural hallmarks, speech is explored as a preclinical biomarker of pathological itineraries in old age. A greater and more specific impairment of neuromuscular activation, as well as a specific cognitive and linguistic impairment in dementia, unchain discriminating changes in speech. Yet, there is no consensus on such discriminatory speech parameters, neither on how they should be elicited and assessed. AIMS To provide a state-of-the-art on speech parameters that allow for early discrimination between healthy and pathological ageing; the aetiology of these parameters; the effect of the type of experimental stimuli on speech elicitation and the predictive power of different speech parameters; and the most promising methods for speech analysis and their clinical implications. METHODS & PROCEDURES A scoping review methodology is used in accordance with the PRISMA model. Following a systematic search of PubMed, PsycINFO and CINAHL, 24 studies are included and analysed in the review. MAIN CONTRIBUTION The results of this review yield three key questions for the clinical assessment of speech in ageing. First, acoustic and temporal parameters are more sensitive to changes in pathological ageing and, of these two, temporal variables are more affected by cognitive impairment. Second, different types of stimuli can trigger speech parameters with different degree of accuracy for the discrimination of clinical groups. Tasks with higher cognitive load are more precise in eliciting higher levels of accuracy. Finally, automatic speech analysis for the discrimination of healthy and pathological ageing should be improved for both research and clinical practice. CONCLUSIONS & IMPLICATIONS Speech analysis is a promising non-invasive tool for the preclinical screening of healthy and pathological ageing. The main current challenges of speech analysis in ageing are the automatization of its clinical assessment and the consideration of the speaker's cognitive background during evaluation. WHAT THIS PAPER ADDS What is already known on the subject Societal aging goes hand in hand with the rising incidence of ageing-related neurodegenerations, mainly Alzheimer's disease (AD). This is particularly noteworthy in countries with longer life expectancies. Healthy ageing and early stages of AD share a set of cognitive and behavioural characteristics. Since there is no cure for dementias, developing methods for accurate discrimination of healthy ageing and early AD is currently a priority. Speech has been described as one of the most significantly impaired features in AD. Neuropathological alterations in motor and cognitive systems would underlie specific speech impairment in dementia. Since speech can be evaluated quickly, non-invasively and inexpensively, its value for the clinical assessment of ageing itineraries may be particularly high. What this paper adds to existing knowledge Theoretical and experimental advances in the assessment of speech as a marker of AD have developed rapidly over the last decade. Yet, they are not always known to clinicians. Furthermore, there is a need to provide an updated state-of-the-art on which speech features are discriminatory to AD, how they can be assessed, what kind of results they can yield, and how such results should be interpreted. This article provides an updated overview of speech profiling, methods of speech measurement and analysis, and the clinical power of speech assessment for early discrimination of AD as the most common cause of dementia. What are the potential or actual clinical implications of this work? This article provides an overview of the predictive potential of different speech parameters in relation to AD cognitive impairment. In addition, it discusses the effect that the cognitive state, the type of elicitation task and the type of assessment method may have on the results of the speech-based analysis in ageing.
Collapse
Affiliation(s)
- Olga Ivanova
- Spanish Language Department, Faculty of Philology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
| | - Israel Martínez-Nicolás
- Department of Basic Psychology, Psychobiology and Behavioral Science Methodology, Faculty of Psychology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
| | - Juan José García Meilán
- Department of Basic Psychology, Psychobiology and Behavioral Science Methodology, Faculty of Psychology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
| |
Collapse
|
5
|
Burke E, Gunstad J, Hamrick P. Comparing global and local semantic coherence of spontaneous speech in persons with Alzheimer's disease and healthy controls. APPLIED CORPUS LINGUISTICS 2023; 3:100064. [PMID: 37476646 PMCID: PMC10354704 DOI: 10.1016/j.acorp.2023.100064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Affiliation(s)
- Erin Burke
- Department of Psychological Sciences, Kent State University
| | - John Gunstad
- Department of Psychological Sciences, Kent State University
| | | |
Collapse
|
6
|
Walker G, Pevy N, O'Malley R, Mirheidari B, Reuber M, Christensen H, Blackburn DJ. Speech patterns in responses to questions asked by an intelligent virtual agent can help to distinguish between people with early stage neurodegenerative disorders and healthy controls. CLINICAL LINGUISTICS & PHONETICS 2023:1-22. [PMID: 37722818 DOI: 10.1080/02699206.2023.2254458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 08/28/2023] [Indexed: 09/20/2023]
Abstract
Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions. The study investigated whether measures of response length, speech rate and pausing in responses to questions asked by an IVA help to distinguish between healthy control participants and people diagnosed with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD). The study also considered whether those measures can further help to distinguish between people with MCI, people with AD, and healthy control participants (HC). There were 38 people with ND (31 people with MCI, 7 people with AD) and 26 HC. All interactions took place in English. People with MCI spoke fewer words compared to HC, and people with AD and people with MCI spoke for less time than HC. People with AD spoke at a slower rate than people with MCI and HC. There were significant differences across all three groups for the proportion of time spent pausing and the average pause duration: silent pauses make up the greatest proportion of responses from people with AD, who also have the longest average silent pause duration, followed by people with MCI then HC. Therefore, the study demonstrates the potential of an IVA as a method for collecting data showing patterns which can help to distinguish between diagnostic groups.
Collapse
Affiliation(s)
- Gareth Walker
- School of English, University of Sheffield, Sheffield, UK
| | - Nathan Pevy
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Ronan O'Malley
- Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Bahman Mirheidari
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Markus Reuber
- Academic Neurology Unit, Royal Hallamshire Hospital, University of Sheffield, Sheffield, UK
| | - Heidi Christensen
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | | |
Collapse
|
7
|
Triantafyllopoulos A, Kathan A, Baird A, Christ L, Gebhard A, Gerczuk M, Karas V, Hübner T, Jing X, Liu S, Mallol-Ragolta A, Milling M, Ottl S, Semertzidou A, Rajamani ST, Yan T, Yang Z, Dineley J, Amiriparian S, Bartl-Pokorny KD, Batliner A, Pokorny FB, Schuller BW. HEAR4Health: a blueprint for making computer audition a staple of modern healthcare. Front Digit Health 2023; 5:1196079. [PMID: 37767523 PMCID: PMC10520966 DOI: 10.3389/fdgth.2023.1196079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/01/2023] [Indexed: 09/29/2023] Open
Abstract
Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine. Thus, we provide an overview and perspective of HEAR4Health: the sketch of a modern, ubiquitous sensing system that can bring computer audition on par with other AI technologies in the strive for improved healthcare systems.
Collapse
Affiliation(s)
- Andreas Triantafyllopoulos
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Alexander Kathan
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Alice Baird
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Lukas Christ
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Alexander Gebhard
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Maurice Gerczuk
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Vincent Karas
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Tobias Hübner
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Xin Jing
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Shuo Liu
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Adria Mallol-Ragolta
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Centre for Interdisciplinary Health Research, University of Augsburg, Augsburg, Germany
| | - Manuel Milling
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Sandra Ottl
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Anastasia Semertzidou
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | | | - Tianhao Yan
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Zijiang Yang
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Judith Dineley
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Shahin Amiriparian
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Katrin D. Bartl-Pokorny
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Anton Batliner
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Florian B. Pokorny
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Division of Phoniatrics, Medical University of Graz, Graz, Austria
- Centre for Interdisciplinary Health Research, University of Augsburg, Augsburg, Germany
| | - Björn W. Schuller
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Centre for Interdisciplinary Health Research, University of Augsburg, Augsburg, Germany
- GLAM – Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
| |
Collapse
|
8
|
Qi X, Zhou Q, Dong J, Bao W. Noninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review. Front Aging Neurosci 2023; 15:1224723. [PMID: 37693647 PMCID: PMC10484224 DOI: 10.3389/fnagi.2023.1224723] [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: 05/18/2023] [Accepted: 08/04/2023] [Indexed: 09/12/2023] Open
Abstract
Alzheimer's disease (AD) is considered as one of the leading causes of death among people over the age of 70 that is characterized by memory degradation and language impairment. Due to language dysfunction observed in individuals with AD patients, the speech-based methods offer non-invasive, convenient, and cost-effective solutions for the automatic detection of AD. This paper systematically reviews the technologies to detect the onset of AD from spontaneous speech, including data collection, feature extraction and classification. First the paper formulates the task of automatic detection of AD and describes the process of data collection. Then, feature extractors from speech data and transcripts are reviewed, which mainly contains acoustic features from speech and linguistic features from text. Especially, general handcrafted features and deep embedding features are organized from different modalities. Additionally, this paper summarizes optimization strategies for AD detection systems. Finally, the paper addresses challenges related to data size, model explainability, reliability and multimodality fusion, and discusses potential research directions based on these challenges.
Collapse
Affiliation(s)
- Xiaoke Qi
- School of Information Management for Law, China University of Political Science and Law, Beijing, China
| | | | - Jian Dong
- Information Technology Research Center, China Electronics Standardization Institute, Beijing, China
| | - Wei Bao
- Information Technology Research Center, China Electronics Standardization Institute, Beijing, China
| |
Collapse
|
9
|
Gray R, Shahin M, Valenzuela M, Ahmed B. Predicting Memory Score Using Paralinguistic Features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082578 DOI: 10.1109/embc40787.2023.10340939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
An automated method of assessing short term memory can act as a dementia risk predictor, as poor short-term memory is strongly linked to early signs of dementia. While previous works show the feasibility of using speech to predict healthy and diagnosed dementia participants, there are still gaps in predicting 'dementia risk' and clear difficulties distinguishing early dementia with regular ageing. We extracted paralinguistic features from audio of individuals completing an over the phone episodic memory test, LOGOS. These paralinguistic features were used to discriminate between those with strong and poor short term memory performance. This work also explored various feature selection methods and tested this method across multiple datasets. Our best result was achieved using a Support Vector Machine (SVM) classifier, obtaining accuracy of 84% per audio recording.Clinical relevance- This work establishes the efficacy of using speech from older participants completing the LOGOS episodic memory test to estimate risk of dementia.
Collapse
|
10
|
Vandersteen C, Plonka A, Manera V, Sawchuk K, Lafontaine C, Galery K, Rouaud O, Bengaied N, Launay C, Guérin O, Robert P, Allali G, Beauchet O, Gros A. Alzheimer's early detection in post-acute COVID-19 syndrome: a systematic review and expert consensus on preclinical assessments. Front Aging Neurosci 2023; 15:1206123. [PMID: 37416323 PMCID: PMC10320294 DOI: 10.3389/fnagi.2023.1206123] [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: 04/14/2023] [Accepted: 05/31/2023] [Indexed: 07/08/2023] Open
Abstract
Introduction The risk of developing Alzheimer's disease (AD) in older adults increasingly is being discussed in the literature on Post-Acute COVID-19 Syndrome (PACS). Remote digital Assessments for Preclinical AD (RAPAs) are becoming more important in screening for early AD, and should always be available for PACS patients, especially for patients at risk of AD. This systematic review examines the potential for using RAPA to identify impairments in PACS patients, scrutinizes the supporting evidence, and describes the recommendations of experts regarding their use. Methods We conducted a thorough search using the PubMed and Embase databases. Systematic reviews (with or without meta-analysis), narrative reviews, and observational studies that assessed patients with PACS on specific RAPAs were included. The RAPAs that were identified looked for impairments in olfactory, eye-tracking, graphical, speech and language, central auditory, or spatial navigation abilities. The recommendations' final grades were determined by evaluating the strength of the evidence and by having a consensus discussion about the results of the Delphi rounds among an international Delphi consensus panel called IMPACT, sponsored by the French National Research Agency. The consensus panel included 11 international experts from France, Switzerland, and Canada. Results Based on the available evidence, olfaction is the most long-lasting impairment found in PACS patients. However, while olfaction is the most prevalent impairment, expert consensus statements recommend that AD olfactory screening should not be used on patients with a history of PACS at this point in time. Experts recommend that olfactory screenings can only be recommended once those under study have reported full recovery. This is particularly important for the deployment of the olfactory identification subdimension. The expert assessment that more long-term studies are needed after a period of full recovery, suggests that this consensus statement requires an update in a few years. Conclusion Based on available evidence, olfaction could be long-lasting in PACS patients. However, according to expert consensus statements, AD olfactory screening is not recommended for patients with a history of PACS until complete recovery has been confirmed in the literature, particularly for the identification sub-dimension. This consensus statement may require an update in a few years.
Collapse
Affiliation(s)
- Clair Vandersteen
- Institut Universitaire de la Face et du Cou, ENT Department, Centre Hospitalier Universitaire, Nice, France
- Laboratoire CoBTeK, Université Côte d'Azur, Nice, France
| | - Alexandra Plonka
- Laboratoire CoBTeK, Université Côte d'Azur, Nice, France
- Centre Hospitalier Universitaire de Nice, Service Clinique Gériatrique du Cerveau et du Mouvement, Nice, France
- Département d'Orthophonie, UFR Médecine, Université Côte d'Azur, Nice, France
- Institut NeuroMod, Université Côte d'Azur, Sophia Antipolis, France
| | - Valeria Manera
- Laboratoire CoBTeK, Université Côte d'Azur, Nice, France
- Département d'Orthophonie, UFR Médecine, Université Côte d'Azur, Nice, France
- Institut NeuroMod, Université Côte d'Azur, Sophia Antipolis, France
| | - Kim Sawchuk
- ACTLab, engAGE: Centre for Research on Aging, Concordia University Montreal, Montreal, QC, Canada
| | - Constance Lafontaine
- ACTLab, engAGE: Centre for Research on Aging, Concordia University Montreal, Montreal, QC, Canada
| | - Kevin Galery
- Research Centre of the Geriatric University Institute of Montreal, Montreal, QC, Canada
| | - Olivier Rouaud
- Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nouha Bengaied
- Federation of Quebec Alzheimer Societies, Montreal, QC, Canada
| | - Cyrille Launay
- Mc Gill University Jewish General Hospital, Montreal, QC, Canada
| | - Olivier Guérin
- Centre Hospitalier Universitaire de Nice, Service Clinique Gériatrique du Cerveau et du Mouvement, Nice, France
- Université Côte d'Azur, CNRS UMR 7284/INSERM U108, Institute for Research on Cancer and Aging Nice, UFR de Médecine, Nice, France
| | - Philippe Robert
- Laboratoire CoBTeK, Université Côte d'Azur, Nice, France
- Centre Hospitalier Universitaire de Nice, Service Clinique Gériatrique du Cerveau et du Mouvement, Nice, France
- Département d'Orthophonie, UFR Médecine, Université Côte d'Azur, Nice, France
| | - Gilles Allali
- Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Olivier Beauchet
- Research Centre of the Geriatric University Institute of Montreal, Montreal, QC, Canada
- Mc Gill University Jewish General Hospital, Montreal, QC, Canada
- Departments of Medicine and Geriatric, University of Montreal, Montreal, QC, Canada
| | - Auriane Gros
- Laboratoire CoBTeK, Université Côte d'Azur, Nice, France
- Centre Hospitalier Universitaire de Nice, Service Clinique Gériatrique du Cerveau et du Mouvement, Nice, France
- Département d'Orthophonie, UFR Médecine, Université Côte d'Azur, Nice, France
| |
Collapse
|
11
|
Martínez-Nicolás I, Martínez-Sánchez F, Ivanova O, Meilán JJG. Reading and lexical-semantic retrieval tasks outperforms single task speech analysis in the screening of mild cognitive impairment and Alzheimer's disease. Sci Rep 2023; 13:9728. [PMID: 37322073 PMCID: PMC10272227 DOI: 10.1038/s41598-023-36804-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
Age-related cognitive impairment have increased dramatically in recent years, which has risen the interes in developing screening tools for mild cognitive impairment and Alzheimer's disease. Speech analysis allows to exploit the behavioral consequences of cognitive deficits on the patient's vocal performance so that it is possible to identify pathologies affecting speech production such as dementia. Previous studies have further shown that the speech task used determines how the speech parameters are altered. We aim to combine the impairments in several speech production tasks in order to improve the accuracy of screening through speech analysis. The sample consists of 72 participants divided into three equal groups of healthy older adults, people with mild cognitive impairment, or Alzheimer's disease, matched by age and education. A complete neuropsychological assessment and two voice recordings were performed. The tasks required the participants to read a text, and complete a sentence with semantic information. A stepwise linear discriminant analysis was performed to select speech parameters with discriminative power. The discriminative functions obtained an accuracy of 83.3% in simultaneous classifications of several levels of cognitive impairment. It would therefore be a promising screening tool for dementia.
Collapse
Affiliation(s)
| | | | - Olga Ivanova
- Faculty of Philology, University of Salamanca, 37008, Salamanca, Spain
| | - Juan J G Meilán
- Faculty of Psychology, University of Salamanca, 37008, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, 37007, Salamanca, Spain
| |
Collapse
|
12
|
Bae M, Seo MG, Ko H, Ham H, Kim KY, Lee JY. The efficacy of memory load on speech-based detection of Alzheimer's disease. Front Aging Neurosci 2023; 15:1186786. [PMID: 37333455 PMCID: PMC10272350 DOI: 10.3389/fnagi.2023.1186786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/16/2023] [Indexed: 06/20/2023] Open
Abstract
Introduction The study aims to test whether an increase in memory load could improve the efficacy in detection of Alzheimer's disease and prediction of the Mini-Mental State Examination (MMSE) score. Methods Speech from 45 mild-to-moderate Alzheimer's disease patients and 44 healthy older adults were collected using three speech tasks with varying memory loads. We investigated and compared speech characteristics of Alzheimer's disease across speech tasks to examine the effect of memory load on speech characteristics. Finally, we built Alzheimer's disease classification models and MMSE prediction models to assess the diagnostic value of speech tasks. Results The speech characteristics of Alzheimer's disease in pitch, loudness, and speech rate were observed and the high-memory-load task intensified such characteristics. The high-memory-load task outperformed in AD classification with an accuracy of 81.4% and MMSE prediction with a mean absolute error of 4.62. Discussion The high-memory-load recall task is an effective method for speech-based Alzheimer's disease detection.
Collapse
Affiliation(s)
- Minju Bae
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Republic of Korea
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Myo-Gyeong Seo
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyunwoong Ko
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Republic of Korea
- Samsung Medical Center, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hyunsun Ham
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Republic of Korea
| | - Keun You Kim
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jun-Young Lee
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Republic of Korea
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
13
|
Petti U, Baker S, Korhonen A, Robin J. The Generalizability of Longitudinal Changes in Speech Before Alzheimer's Disease Diagnosis. J Alzheimers Dis 2023; 92:547-564. [PMID: 36776053 DOI: 10.3233/jad-220847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
BACKGROUND Language impairment in Alzheimer's disease (AD) has been widely studied but due to limited data availability, relatively few studies have focused on the longitudinal change in language in the individuals who later develop AD. Significant differences in speech have previously been found by comparing the press conference transcripts of President Bush and President Reagan, who was later diagnosed with AD. OBJECTIVE In the current study, we explored whether the patterns previously established in the single AD-healthy control (HC) participant pair apply to a larger group of individuals who later receive AD diagnosis. METHODS We replicated previous methods on two larger corpora of longitudinal spontaneous speech samples of public figures, consisting of 10 and 9 AD-HC participant pairs. As we failed to find generalizable patterns of language change using previous methodology, we proposed alternative methods for data analysis, investigating the benefits of using different language features and their change with age, and compiling the single features into aggregate scores. RESULTS The single features that showed the strongest results were moving average type:token ratio (MATTR) and pronoun-related features. The aggregate scores performed better than the single features, with lexical diversity capturing a similar change in two-thirds of the participants. CONCLUSION Capturing universal patterns of language change prior to AD can be challenging, but the decline in lexical diversity and changes in MATTR and pronoun-related features act as promising measures that reflect the cognitive changes in many participants.
Collapse
Affiliation(s)
- Ulla Petti
- University of Cambridge, Language Technology Lab, Cambridge, UK
| | - Simon Baker
- University of Cambridge, Language Technology Lab, Cambridge, UK
| | - Anna Korhonen
- University of Cambridge, Language Technology Lab, Cambridge, UK
| | | |
Collapse
|
14
|
Patel S, Grabowski C, Dayalu V, Testa AJ. Speech error rates after a sports-related concussion. Front Psychol 2023; 14:1135441. [PMID: 36960009 PMCID: PMC10027790 DOI: 10.3389/fpsyg.2023.1135441] [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: 12/31/2022] [Accepted: 02/13/2023] [Indexed: 03/09/2023] Open
Abstract
Background Alterations in speech have long been identified as indicators of various neurologic conditions including traumatic brain injury, neurodegenerative diseases, and stroke. The extent to which speech errors occur in milder brain injuries, such as sports-related concussions, is unknown. The present study examined speech error rates in student athletes after a sports-related concussion compared to pre-injury speech performance in order to determine the presence and relevant characteristics of changes in speech production in this less easily detected neurologic condition. Methods A within-subjects pre/post-injury design was used. A total of 359 Division I student athletes participated in pre-season baseline speech testing. Of these, 27 athletes (18-22 years) who sustained a concussion also participated in speech testing in the days immediately following diagnosis of concussion. Picture description tasks were utilized to prompt connected speech samples. These samples were recorded and then transcribed for identification of errors and disfluencies. These were coded by two trained raters using a 6-category system that included 14 types of error metrics. Results Repeated measures analysis of variance was used to compare the difference in error rates at baseline and post-concussion. Results revealed significant increases in the speech error categories of pauses and time fillers (interjections/fillers). Additionally, regression analysis showed that a different pattern of errors and disfluencies occur after a sports-related concussion (primarily time fillers) compared to pre-injury (primarily pauses). Conclusion Results demonstrate that speech error rates increase following even mild head injuries, in particular, sports-related concussion. Furthermore, the speech error patterns driving this increase in speech errors, rate of pauses and interjections, are distinct features of this neurological injury, which is in contrast with more severe injuries that are marked by articulation errors and an overall reduction in verbal output. Future studies should consider speech as a diagnostic tool for concussion.
Collapse
Affiliation(s)
- Sona Patel
- Department of Speech-Language Pathology, Seton Hall University, Nutley, NJ, United States
- Department of Medical Sciences, Hackensack Meridian School of Medicine, Nutley, NJ, United States
- *Correspondence: Sona Patel,
| | - Caryn Grabowski
- Department of Speech-Language Pathology, Seton Hall University, Nutley, NJ, United States
| | - Vikram Dayalu
- Department of Speech-Language Pathology, Seton Hall University, Nutley, NJ, United States
| | - Anthony J. Testa
- Center for Sports Medicine, Seton Hall University, South Orange, NJ, United States
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
Yang Q, Li X, Ding X, Xu F, Ling Z. Deep learning-based speech analysis for Alzheimer's disease detection: a literature review. Alzheimers Res Ther 2022; 14:186. [PMID: 36517837 PMCID: PMC9749308 DOI: 10.1186/s13195-022-01131-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Alzheimer's disease has become one of the most common neurodegenerative diseases worldwide, which seriously affects the health of the elderly. Early detection and intervention are the most effective prevention methods currently. Compared with traditional detection methods such as traditional scale tests, electroencephalograms, and magnetic resonance imaging, speech analysis is more convenient for automatic large-scale Alzheimer's disease detection and has attracted extensive attention from researchers. In particular, deep learning-based speech analysis and language processing techniques for Alzheimer's disease detection have been studied and achieved impressive results. METHODS To integrate the latest research progresses, hundreds of relevant papers from ACM, DBLP, IEEE, PubMed, Scopus, Web of Science electronic databases, and other sources were retrieved. We used these keywords for paper search: (Alzheimer OR dementia OR cognitive impairment) AND (speech OR voice OR audio) AND (deep learning OR neural network). CONCLUSIONS Fifty-two papers were finally retained after screening. We reviewed and presented the speech databases, deep learning methods, and model performances of these studies. In the end, we pointed out the mainstreams and limitations in the current studies and provided a direction for future research.
Collapse
Affiliation(s)
- Qin Yang
- iFlytek Research, iFlytek Co.Ltd, Hefei, China
| | - Xin Li
- grid.59053.3a0000000121679639NELSLIP, University of Science and Technology of China, Hefei, China ,iFlytek Research, iFlytek Co.Ltd, Hefei, China
| | - Xinyun Ding
- iFlytek Research, iFlytek Co.Ltd, Hefei, China
| | - Feiyang Xu
- iFlytek Research, iFlytek Co.Ltd, Hefei, China
| | - Zhenhua Ling
- grid.59053.3a0000000121679639NELSLIP, University of Science and Technology of China, Hefei, China
| |
Collapse
|
17
|
Hason L, Krishnan S. Spontaneous speech feature analysis for alzheimer's disease screening using a random forest classifier. Front Digit Health 2022; 4:901419. [PMID: 36465088 PMCID: PMC9712439 DOI: 10.3389/fdgth.2022.901419] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 10/19/2022] [Indexed: 07/20/2023] Open
Abstract
Detecting Alzheimer's disease (AD) and disease progression based on the patient's speech not the patient's speech data can aid non-invasive, cost-effective, real-time early diagnostic and repetitive monitoring in minimum time and effort using machine learning (ML) classification approaches. This paper aims to predict early AD diagnosis and evaluate stages of AD through exploratory analysis of acoustic features, non-stationarity, and non-linearity testing, and applying data augmentation techniques on spontaneous speech signals collected from AD and cognitively normal (CN) subjects. Evaluation of the proposed AD prediction and AD stages classification models using Random Forest classifier yielded accuracy rates of 82.2% and 71.5%. This will enrich the Alzheimer's research community with further understanding of methods to improve models for AD classification and addressing non-stationarity and non-linearity properties on audio features to determine the best-suited acoustic features for AD monitoring.
Collapse
|
18
|
Xiu N, Vaxelaire B, Li L, Ling Z, Xu X, Huang L, Sun B, Huang L, Sock R. A Study on Voice Measures in Patients With Alzheimer's Disease. J Voice 2022:S0892-1997(22)00242-9. [PMID: 36150998 DOI: 10.1016/j.jvoice.2022.08.010] [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: 05/05/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE As Alzheimer's disease (AD) might provoke certain nerve disorders, patients with AD can acquire sensorimotor adaptation problems, and thus the acoustic characteristics of the speech they produce may differ from those of healthy subjects. This study aimed to (1) extract acoustic characteristics (relating to articulatory gestures) potentially useful for detecting AD and (2) examine whether these characteristics could help identify AD patients. METHODS A total of 50 individuals participated in the study, including the AD group (17 cases), the Neurologically Healthy (NH) group (13 cases), the Mild Cognitive Impairment (MCI) group (11 cases), and the Vascular Cognitive Impairment (VCI) group (9 cases). Voice samples involving three vowels (/i/, /a/, and /u/) and six consonants (/p/, /pʰ/, /t/, /tʰ/, /k/, and /kʰ/) were collected using a digital recorder (TASCAM DR40X). Microphone-to-mouth distance was maintained at 30 cm. Acoustic measures included F0, jitter, shimmer, HNR, F1, F2, F3, and VOT. RESULTS One-way ANOVA tests were carried out to compare the acoustic measures among the four groups. F3 of vowel /u/, F2 bandwidth of vowel /a/, VOT of consonant /t/, and male participants' F0 of three vowels (/a/, /i/, and /u/) were found significantly different, while no significant differences were found in the other measures. CONCLUSION Some acoustic characteristics can indeed help detect AD patients.
Collapse
Affiliation(s)
- Noé Xiu
- U.R. 1339 Linguistique, Langues et Parole (LiLPa) and Institut de Phonétique de Strasbourg (IPS) - Université de Strasbourg, France; Memory Clinic and Neurology Inpatient Department, Zigong First People's Hospital, China; Interdisciplinary Research Center for Linguistic Science, University of Science and Technology of China, China
| | - Béatrice Vaxelaire
- U.R. 1339 Linguistique, Langues et Parole (LiLPa) and Institut de Phonétique de Strasbourg (IPS) - Université de Strasbourg, France
| | - Lanlan Li
- Interdisciplinary Research Center for Linguistic Science, University of Science and Technology of China, China
| | - Zhenhua Ling
- Interdisciplinary Research Center for Linguistic Science, University of Science and Technology of China, China; National Engineering Research Center of Speech and Language Information Processing, University of Science and Technology of China, China
| | - Xiaoya Xu
- Memory Clinic and Neurology Inpatient Department, Zigong First People's Hospital, China
| | - Linming Huang
- Memory Clinic and Neurology Inpatient Department, Zigong First People's Hospital, China
| | - Bo Sun
- Interdisciplinary Research Center for Linguistic Science, University of Science and Technology of China, China.
| | - Lin Huang
- Memory Clinic and Neurology Inpatient Department, Zigong First People's Hospital, China.
| | - Rudolph Sock
- U.R. 1339 Linguistique, Langues et Parole (LiLPa) and Institut de Phonétique de Strasbourg (IPS) - Université de Strasbourg, France; Language, Information and Communication Laboratory - LICOLAB, Pavol Jozef Šafárik University, Košice, Slovakia
| |
Collapse
|
19
|
Egas-López JV, Balogh R, Imre N, Hoffmann I, Szabó MK, Tóth L, Pákáski M, Kálmán J, Gosztolya G. Automatic screening of mild cognitive impairment and Alzheimer’s disease by means of posterior-thresholding hesitation representation. COMPUT SPEECH LANG 2022. [DOI: 10.1016/j.csl.2022.101377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
20
|
Metarugcheep S, Punyabukkana P, Wanvarie D, Hemrungrojn S, Chunharas C, Pratanwanich PN. Selecting the Most Important Features for Predicting Mild Cognitive Impairment from Thai Verbal Fluency Assessments. SENSORS 2022; 22:s22155813. [PMID: 35957370 PMCID: PMC9370961 DOI: 10.3390/s22155813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/22/2022] [Accepted: 07/23/2022] [Indexed: 12/10/2022]
Abstract
Mild cognitive impairment (MCI) is an early stage of cognitive decline or memory loss, commonly found among the elderly. A phonemic verbal fluency (PVF) task is a standard cognitive test that participants are asked to produce words starting with given letters, such as “F” in English and “ก” /k/ in Thai. With state-of-the-art machine learning techniques, features extracted from the PVF data have been widely used to detect MCI. The PVF features, including acoustic features, semantic features, and word grouping, have been studied in many languages but not Thai. However, applying the same PVF feature extraction methods used in English to Thai yields unpleasant results due to different language characteristics. This study performs analytical feature extraction on Thai PVF data to classify MCI patients. In particular, we propose novel approaches to extract features based on phonemic clustering (ability to cluster words by phonemes) and switching (ability to shift between clusters) for the Thai PVF data. The comparison results of the three classifiers revealed that the support vector machine performed the best with an area under the receiver operating characteristic curve (AUC) of 0.733 (N = 100). Furthermore, our implemented guidelines extracted efficient features, which support the machine learning models regarding MCI detection on Thai PVF data.
Collapse
Affiliation(s)
- Suppat Metarugcheep
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand;
| | - Proadpran Punyabukkana
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand;
- Correspondence:
| | - Dittaya Wanvarie
- Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand; (D.W.); (P.N.P.)
| | - Solaphat Hemrungrojn
- Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand;
- Cognitive Fitness and Biopsychological Technology Research Unit, Chulalongkorn University, Bangkok 10330, Thailand
| | - Chaipat Chunharas
- Cognitive Clinical & Computational Neuroscience Research Unit, Department of Internal Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand;
- Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok 10330, Thailand
| | - Ploy N. Pratanwanich
- Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand; (D.W.); (P.N.P.)
- Chula Intelligent and Complex Systems Research Unit, Chulalongkorn University, Bangkok 10330, Thailand
| |
Collapse
|
21
|
AI-Atroshi C, Rene Beulah J, Singamaneni KK, Pretty Diana Cyril C, Neelakandan S, Velmurugan S. Automated speech based evaluation of mild cognitive impairment and Alzheimer’s disease detection using with deep belief network model. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2022. [DOI: 10.1080/20479700.2022.2097764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Chiai AI-Atroshi
- Department of Educational Counseling, College of Basic Education, University of Duhok, Dahuk, Iraq
| | - J. Rene Beulah
- Department of Computing Technologies, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, India
| | | | - C. Pretty Diana Cyril
- Department of Computing Technologies, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, India
| | - S. Neelakandan
- Department of CSE, R.M.K Engineering College, Chennai, India
| | - S. Velmurugan
- Department of CSE, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India
| |
Collapse
|
22
|
Ivanova O, Meilán JJG, Martínez-Sánchez F, Martínez-Nicolás I, Llorente TE, González NC. Discriminating speech traits of Alzheimer's disease assessed through a corpus of reading task for Spanish language. COMPUT SPEECH LANG 2022. [DOI: 10.1016/j.csl.2021.101341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
23
|
Kálmán J, Devanand DP, Gosztolya G, Balogh R, Imre N, Tóth L, Hoffmann I, Kovács I, Vincze V, Pákáski M. Temporal speech parameters detect mild cognitive impairment in different languages: validation and comparison of the Speech-GAP Test® in English and Hungarian. Curr Alzheimer Res 2022; 19:373-386. [PMID: 35440309 DOI: 10.2174/1567205019666220418155130] [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: 12/20/2021] [Revised: 02/08/2022] [Accepted: 02/17/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND The development of automatic speech recognition (ASR) technology allows the analysis of temporal (time-based) speech parameters characteristic of mild cognitive impairment (MCI). However, no information has been available on whether the analysis of spontaneous speech can be used with the same efficiency in different language environments. OBJECTIVE The main goal of this international pilot study is to address the question whether the Speech-Gap Test® (S-GAP Test®), previously tested in the Hungarian language, is appropriate for and applicable to the recognition of MCI in other languages such as English. METHOD After an initial screening of 88 individuals, English-speaking (n = 33) and Hungarian-speaking (n = 33) participants were classified as having MCI or as healthy controls (HC) based on Petersen's criteria. Speech of each participant was recorded via a spontaneous speech task. 15 temporal parameters were determined and calculated by means of ASR. RESULTS Seven temporal parameters in the English-speaking sample and 5 in the Hungarian-speaking sample showed significant differences between the MCI and the HC group. Receiver operating characteristics (ROC) analysis clearly distinguished the English-speaking MCI cases from the HC group based on speech tempo and articulation tempo with 100% sensitivity, and on three more temporal parameters with high sensitivity (85.7%). In the Hungarian-speaking sample, the ROC analysis showed similar sensitivity rates (92.3%). CONCLUSION The results of this study in different native-speaking populations suggest that changes in acoustic parameters detected by the S-GAP Test® might be present across different languages.
Collapse
Affiliation(s)
- János Kálmán
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - Davangere P Devanand
- Columbia University Medical Center, New York, NY.,New York State Psychiatric Institute, New York, NY
| | - Gábor Gosztolya
- MTA-SZTE Research Group on Artificial Intelligence, Faculty of Science and Informatics, University of Szeged, Szeged
| | - Réka Balogh
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - Nóra Imre
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - László Tóth
- Faculty of Science and Informatics, University of Szeged, Szeged
| | - Ildikó Hoffmann
- Faculty of Humanities and Social Sciences, University of Szeged, Szeged.,Hungarian Research Centre for Linguistics, Eötvös Loránd Research Network, Budapest
| | - Ildikó Kovács
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - Veronika Vincze
- MTA-SZTE Research Group on Artificial Intelligence, Faculty of Science and Informatics, University of Szeged, Szeged
| | - Magdolna Pákáski
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| |
Collapse
|
24
|
Imre N, Balogh R, Gosztolya G, Tóth L, Hoffmann I, Várkonyi T, Lengyel C, Pákáski M, Kálmán J. Temporal Speech Parameters Indicate Early Cognitive Decline in Elderly Patients With Type 2 Diabetes Mellitus. Alzheimer Dis Assoc Disord 2022; 36:148-155. [PMID: 35293378 PMCID: PMC9132238 DOI: 10.1097/wad.0000000000000492] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 12/28/2021] [Indexed: 12/02/2022]
Abstract
INTRODUCTION The earliest signs of cognitive decline include deficits in temporal (time-based) speech characteristics. Type 2 diabetes mellitus (T2DM) patients are more prone to mild cognitive impairment (MCI). The aim of this study was to compare the temporal speech characteristics of elderly (above 50 y) T2DM patients with age-matched nondiabetic subjects. MATERIALS AND METHODS A total of 160 individuals were screened, 100 of whom were eligible (T2DM: n=51; nondiabetic: n=49). Participants were classified either as having healthy cognition (HC) or showing signs of MCI. Speech recordings were collected through a phone call. Based on automatic speech recognition, 15 temporal parameters were calculated. RESULTS The HC with T2DM group showed significantly shorter utterance length, higher duration rate of silent pause and total pause, and higher average duration of silent pause and total pause compared with the HC without T2DM group. Regarding the MCI participants, parameters were similar between the T2DM and the nondiabetic subgroups. CONCLUSIONS Temporal speech characteristics of T2DM patients showed early signs of altered cognitive functioning, whereas neuropsychological tests did not detect deterioration. This method is useful for identifying the T2DM patients most at risk for manifest MCI, and could serve as a remote cognitive screening tool.
Collapse
Affiliation(s)
| | | | - Gábor Gosztolya
- MTA-SZTE Research Group on Artificial Intelligence, University of Szeged, Szeged
| | - László Tóth
- MTA-SZTE Research Group on Artificial Intelligence, University of Szeged, Szeged
| | - Ildikó Hoffmann
- Hungarian Linguistics
- Hungarian Research Centre for Linguistics, Eötvös Loránd Research Network, Budapest, Hungary
| | | | | | | | | |
Collapse
|
25
|
Liu Z, Paek EJ, Yoon SO, Casenhiser D, Zhou W, Zhao X. Detecting Alzheimer's Disease Using Natural Language Processing of Referential Communication Task Transcripts. J Alzheimers Dis 2022; 86:1385-1398. [PMID: 35213368 DOI: 10.3233/jad-215137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
BACKGROUND People with Alzheimer's disease (AD) often demonstrate difficulties in discourse production. Referential communication tasks (RCTs) are used to examine a speaker's capability to select and verbally code the characteristics of an object in interactive conversation. OBJECTIVE In this study, we used contextualized word representations from Natural language processing (NLP) to evaluate how well RCTs are able to distinguish between people with AD and cognitively healthy older adults. METHODS We adapted machine learning techniques to analyze manually transcribed speech transcripts in an RCT from 28 older adults, including 12 with AD and 16 cognitively healthy older adults. Two approaches were applied to classify these speech transcript samples: 1) using clinically relevant linguistic features, 2) using machine learned representations derived by a state-of-art pretrained NLP transfer learning model, Bidirectional Encoder Representation from Transformer (BERT) based classification model. RESULTS The results demonstrated the superior performance of AD detection using a designed transfer learning NLP algorithm. Moreover, the analysis showed that transcripts of a single image yielded high accuracies in AD detection. CONCLUSION The results indicated that RCT may be useful as a diagnostic tool for AD, and that the task can be simplified to a subset of images without significant sacrifice to diagnostic accuracy, which can make RCT an easier and more practical tool for AD diagnosis. The results also demonstrate the potential of RCT as a tool to better understand cognitive deficits from the perspective of discourse production in people with AD.
Collapse
Affiliation(s)
- Ziming Liu
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA
| | - Eun Jin Paek
- Department of Audiology and Speech Pathology, College of Health Professions, University of Tennessee Health Science Center, Knoxville, TN, USA
| | - Si On Yoon
- Department of Communication Sciences and Disorder, University of Iowa, IA, USA
| | - Devin Casenhiser
- Department of Audiology and Speech Pathology, College of Health Professions, University of Tennessee Health Science Center, Knoxville, TN, USA
| | - Wenjun Zhou
- Department of Business Analytics and Statistics, University of Tennessee, Knoxville, TN, USA
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA
| |
Collapse
|
26
|
Speech- and Language-Based Classification of Alzheimer's Disease: A Systematic Review. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9010027. [PMID: 35049736 PMCID: PMC8772820 DOI: 10.3390/bioengineering9010027] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/25/2021] [Accepted: 11/30/2021] [Indexed: 12/04/2022]
Abstract
Background: Alzheimer’s disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure. Objectives: In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer’s Disease with the purpose of identifying the most effective algorithms and best practices. Methods: A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori. Results: We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported. Discussion: A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.
Collapse
|
27
|
A Comparison of Speech Features between Mild Cognitive Impairment and Healthy Aging Groups. Dement Neurocogn Disord 2021; 20:52-61. [PMID: 34795768 PMCID: PMC8585532 DOI: 10.12779/dnd.2021.20.4.52] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/11/2021] [Accepted: 09/13/2021] [Indexed: 12/02/2022] Open
Abstract
Background and Purpose Language dysfunction is a symptom common to patients with Alzheimer's disease (AD). Speech feature analysis may be a patient-friendly screening test for early-stage AD. We aimed to investigate the speech features of amnestic mild cognitive impairment (aMCI) compared to normal controls (NCs). Methods Spoken responses to test questions were recorded with a microphone placed 15 cm in front of each participant. Speech samples delivered in response to four spoken test prompts (free speech test, Mini-Mental State Examination [MMSE], picture description test, and sentence repetition test) were obtained from 98 patients with aMCI and 139 NCs. Each recording was transcribed, with speech features noted. The frequency of the ten speech features assessed was evaluated to compare speech abilities between the test groups. Results Among the ten speech features, the frequency of pauses (p=0.001) and mumbles (p=0.001) were significantly higher in patients with aMCI than in NCs. Moreover, MMSE score was found to negatively correlate with the frequency of pauses (r=−0.441, p<0.001) and mumbles (r=−0.341, p<0.001). Conclusions Frequent pauses and mumbles reflect cognitive decline in aMCI patients in episodic and semantic memory tests. Speech feature analysis may prove to be a speech-based biomarker for screening early-stage cognitive impairment.
Collapse
|
28
|
Sherman JC, Henderson CR, Flynn S, Gair JW, Lust B. Language Decline Characterizes Amnestic Mild Cognitive Impairment Independent of Cognitive Decline. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2021; 64:4287-4307. [PMID: 34699277 DOI: 10.1044/2021_jslhr-20-00503] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Purpose This research investigated the nature of cognitive decline in prodromal Alzheimer's disease (AD), particularly in mild cognitive impairment, amnestic type (aMCI). We assessed language in aMCI as compared with healthy aging (HA) and healthy young (HY) with new psycholinguistic assessment of complex sentences, and we tested the degree to which deficits on this language measure relate to performance in other general cognitive domains such as memory. Method Sixty-one individuals with aMCI were compared with 24 HA and 10 HY adults on a psycholinguistic measure of complex sentence production (relative clauses). In addition, HA, HY, and a subset of the aMCI participants (n = 22) were also tested on a multidomain cognitive screen, the Addenbrooke's Cognitive Examination-Revised (ACE-R), and on a verbal working memory Brown-Peterson (BP) test. General and generalized linear mixed models were used to test psycholinguistic results and to test whether ACE-R and BP performance predicted performance on the psycholinguistic test similarly in the aMCI and HA groups. Results On the psycholinguistic measure, sentence imitation was significantly deficited in aMCI in comparison with that in HA and HY. Experimental factorial designs revealed that individuals with aMCI had particular difficulty repeating sentences that especially challenged syntax-semantics integration. As expected, the aMCI group also performed significantly below the HY and HA groups on the ACE-R. Neither the ACE-R Memory subtest nor the BP total scores predicted performance on the psycholinguistic task for either the aMCI or the HA group. However, the ACE-R total score significantly predicted psycholinguistic task performance, with increased ACE-R performance predicting increased psycholinguistic task performance only for the HA group, not for the aMCI group. Conclusions Results suggest a selective deterioration in language in aMCI, specifically a weakening of syntax-semantics integration in complex sentence processing, and a general independence of this language deficit and memory decline. Results cohere with previous assessments of the nature of difficulty in complex sentence formation in aMCI. We argue that clinical screening for prodromal AD can be strengthened by supplementary testing of language, as well as memory, and extended evaluation of strength of their relation.
Collapse
Affiliation(s)
| | - Charles R Henderson
- Department of Psychology and Cognitive Science Cornell University, Ithaca, NY
| | - Suzanne Flynn
- Department of Linguistics and Philosophy, Massachusetts Institute of Technology, Cambridge
| | | | - Barbara Lust
- Department of Psychology and Cognitive Science Cornell University, Ithaca, NY
| |
Collapse
|
29
|
Yamada Y, Shinkawa K, Kobayashi M, Nishimura M, Nemoto M, Tsukada E, Ota M, Nemoto K, Arai T. Tablet-Based Automatic Assessment for Early Detection of Alzheimer's Disease Using Speech Responses to Daily Life Questions. Front Digit Health 2021; 3:653904. [PMID: 34713127 PMCID: PMC8521899 DOI: 10.3389/fdgth.2021.653904] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 02/22/2021] [Indexed: 01/09/2023] Open
Abstract
Health-monitoring technologies for automatically detecting the early signs of Alzheimer's disease (AD) have become increasingly important. Speech responses to neuropsychological tasks have been used for quantifying changes resulting from AD and differentiating AD and mild cognitive impairment (MCI) from cognitively normal (CN). However, whether and how other types of speech tasks with less burden on older adults could be used for detecting early signs of AD remains unexplored. In this study, we developed a tablet-based application and compared speech responses to daily life questions with those to neuropsychological tasks in terms of differentiating MCI from CN. We found that in daily life questions, around 80% of speech features showing significant differences between CN and MCI overlapped those showing significant differences in both our study and other studies using neuropsychological tasks, but the number of significantly different features as well as their effect sizes from life questions decreased compared with those from neuropsychological tasks. On the other hand, the results of classification models for detecting MCI by using the speech features showed that daily life questions could achieve high accuracy, i.e., 86.4%, comparable to neuropsychological tasks by using eight questions against all five neuropsychological tasks. Our results indicate that, while daily life questions may elicit weaker but statistically discernable differences in speech responses resulting from MCI than neuropsychological tasks, combining them could be useful for detecting MCI with comparable performance to using neuropsychological tasks, which could help develop health-monitoring technologies for early detection of AD in a less burdensome manner.
Collapse
Affiliation(s)
| | | | | | - Masafumi Nishimura
- Department of Informatics, Graduate School of Integrated Science and Technology, Shizuoka University, Shizuoka, Japan
| | - Miyuki Nemoto
- Department of Psychiatry, University of Tsukuba Hospital, Ibaraki, Japan
| | - Eriko Tsukada
- Department of Psychiatry, University of Tsukuba Hospital, Ibaraki, Japan
| | - Miho Ota
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Tetsuaki Arai
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| |
Collapse
|
30
|
DeSouza DD, Robin J, Gumus M, Yeung A. Natural Language Processing as an Emerging Tool to Detect Late-Life Depression. Front Psychiatry 2021; 12:719125. [PMID: 34552519 PMCID: PMC8450440 DOI: 10.3389/fpsyt.2021.719125] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/11/2021] [Indexed: 12/14/2022] Open
Abstract
Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.
Collapse
Affiliation(s)
| | | | | | - Anthony Yeung
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
31
|
Ostrand R, Gunstad J. Using Automatic Assessment of Speech Production to Predict Current and Future Cognitive Function in Older Adults. J Geriatr Psychiatry Neurol 2021; 34:357-369. [PMID: 32723128 PMCID: PMC8326891 DOI: 10.1177/0891988720933358] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Neurodegenerative conditions like Alzheimer disease affect millions and have no known cure, making early detection important. In addition to memory impairments, dementia causes substantial changes in speech production, particularly lexical-semantic characteristics. Existing clinical tools for detecting change often require considerable expertise or time, and efficient methods for identifying persons at risk are needed. This study examined whether early stages of cognitive decline can be identified using an automated calculation of lexical-semantic features of participants' spontaneous speech. Unimpaired or mildly impaired older adults (N = 39, mean 81 years old) produced several monologues (picture descriptions and expository descriptions) and completed a neuropsychological battery, including the Modified Mini-Mental State Exam. Most participants (N = 30) returned one year later for follow-up. Lexical-semantic features of participants' speech (particularly lexical frequency) were significantly correlated with cognitive status at the same visit and also with cognitive status one year in the future. Thus, automated analysis of speech production is closely associated with current and future cognitive test performance and could provide a novel, scalable method for longitudinal tracking of cognitive health.
Collapse
Affiliation(s)
- Rachel Ostrand
- Department of Healthcare and Life Sciences, IBM Research, Yorktown Heights, NY, USA,Rachel Ostrand, Department of Healthcare and Life Sciences, IBM Research, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA.
| | - John Gunstad
- Department of Psychological Sciences & Brain Health Research Institute, Kent State University, Kent, OH, USA
| |
Collapse
|
32
|
Vincze V, Szatlóczki G, Tóth L, Gosztolya G, Pákáski M, Hoffmann I, Kálmán J. Telltale silence: temporal speech parameters discriminate between prodromal dementia and mild Alzheimer's disease. CLINICAL LINGUISTICS & PHONETICS 2021; 35:727-742. [PMID: 32993390 DOI: 10.1080/02699206.2020.1827043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 06/11/2023]
Abstract
This study presents a novel approach for the early detection of mild cognitive impairment (MCI) and mild Alzheimer's disease (mAD) in the elderly. Participants were 25 elderly controls (C), 25 clinically diagnosed MCI and 25 mAD patients, included after a clinical diagnosis validated by CT or MRI and cognitive tests. Our linguistic protocol involved three connected speech tasks that stimulate different memory systems, which were recorded, then analyzed linguistically by using the PRAAT software. The temporal speech-related parameters successfully differentiate MCI from mAD and C, such as speech rate, number and length of pauses, the rate of pause and signal. Parameters pauses/duration and silent pauses/duration linearly decreased among the groups, in other words, the percentage of pauses in the total duration of speech continuously grows as dementia progresses. Thus, the proposed approach may be an effective tool for screening MCI and mAD.
Collapse
Affiliation(s)
- Veronika Vincze
- MTA-SZTE Research Group on Artifical Intelligence, Szeged, Hungary
| | | | - László Tóth
- Institute of Informatics, University of Szeged, Szeged, Hungary
| | - Gábor Gosztolya
- MTA-SZTE Research Group on Artifical Intelligence, Szeged, Hungary
| | | | - Ildikó Hoffmann
- Department of Linguistics, University of Szeged, Szeged, Hungary
- Research Institute for Linguistics, Hungarian Academy of Sciences, Budapest, Hungary
| | - János Kálmán
- Department of Psychiatry, University of Szeged, Szeged, Hungary
| |
Collapse
|
33
|
Shimoda A, Li Y, Hayashi H, Kondo N. Dementia risks identified by vocal features via telephone conversations: A novel machine learning prediction model. PLoS One 2021; 16:e0253988. [PMID: 34260593 PMCID: PMC8279312 DOI: 10.1371/journal.pone.0253988] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/16/2021] [Indexed: 12/16/2022] Open
Abstract
Due to difficulty in early diagnosis of Alzheimer's disease (AD) related to cost and differentiated capability, it is necessary to identify low-cost, accessible, and reliable tools for identifying AD risk in the preclinical stage. We hypothesized that cognitive ability, as expressed in the vocal features in daily conversation, is associated with AD progression. Thus, we have developed a novel machine learning prediction model to identify AD risk by using the rich voice data collected from daily conversations, and evaluated its predictive performance in comparison with a classification method based on the Japanese version of the Telephone Interview for Cognitive Status (TICS-J). We used 1,465 audio data files from 99 Healthy controls (HC) and 151 audio data files recorded from 24 AD patients derived from a dementia prevention program conducted by Hachioji City, Tokyo, between March and May 2020. After extracting vocal features from each audio file, we developed machine-learning models based on extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), using each audio file as one observation. We evaluated the predictive performance of the developed models by describing the receiver operating characteristic (ROC) curve, calculating the areas under the curve (AUCs), sensitivity, and specificity. Further, we conducted classifications by considering each participant as one observation, computing the average of their audio files' predictive value, and making comparisons with the predictive performance of the TICS-J based questionnaire. Of 1,616 audio files in total, 1,308 (81.0%) were randomly allocated to the training data and 308 (19.1%) to the validation data. For audio file-based prediction, the AUCs for XGboost, RF, and LR were 0.863 (95% confidence interval [CI]: 0.794-0.931), 0.882 (95% CI: 0.840-0.924), and 0.893 (95%CI: 0.832-0.954), respectively. For participant-based prediction, the AUC for XGboost, RF, LR, and TICS-J were 1.000 (95%CI: 1.000-1.000), 1.000 (95%CI: 1.000-1.000), 0.972 (95%CI: 0.918-1.000) and 0.917 (95%CI: 0.918-1.000), respectively. There was difference in predictive accuracy of XGBoost and TICS-J with almost approached significance (p = 0.065). Our novel prediction model using the vocal features of daily conversations demonstrated the potential to be useful for the AD risk assessment.
Collapse
Affiliation(s)
- Akihiro Shimoda
- Department of Public Health, McCann Healthcare Worldwide Japan Inc., Tokyo, Japan
| | - Yue Li
- Department of Public Health, McCann Healthcare Worldwide Japan Inc., Tokyo, Japan
| | - Hana Hayashi
- Department of Public Health, McCann Healthcare Worldwide Japan Inc., Tokyo, Japan
- Department of Global Health Promotion, Tokyo Medical and Dental University, Tokyo, Japan
- Graduate School of Health Management, Keio University, Tokyo, Japan
| | - Naoki Kondo
- Department of Social Epidemiology and Global Health, Graduate School of Medicine and School of Public Health, Kyoto University, Kyoto, Japan
| |
Collapse
|
34
|
Nasreen S, Rohanian M, Hough J, Purver M. Alzheimer’s Dementia Recognition From Spontaneous Speech Using Disfluency and Interactional Features. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.640669] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder mainly characterized by memory loss with deficits in other cognitive domains, including language, visuospatial abilities, and changes in behavior. Detecting diagnostic biomarkers that are noninvasive and cost-effective is of great value not only for clinical assessments and diagnostics but also for research purposes. Several previous studies have investigated AD diagnosis via the acoustic, lexical, syntactic, and semantic aspects of speech and language. Other studies include approaches from conversation analysis that look at more interactional aspects, showing that disfluencies such as fillers and repairs, and purely nonverbal features such as inter-speaker silence, can be key features of AD conversations. These kinds of features, if useful for diagnosis, may have many advantages: They are simple to extract and relatively language-, topic-, and task-independent. This study aims to quantify the role and contribution of these features of interaction structure in predicting whether a dialogue participant has AD. We used a subset of the Carolinas Conversation Collection dataset of patients with AD at moderate stage within the age range 60–89 and similar-aged non-AD patients with other health conditions. Our feature analysis comprised two sets: disfluency features, including indicators such as self-repairs and fillers, and interactional features, including overlaps, turn-taking behavior, and distributions of different types of silence both within patient speech and between patient and interviewer speech. Statistical analysis showed significant differences between AD and non-AD groups for several disfluency features (edit terms, verbatim repeats, and substitutions) and interactional features (lapses, gaps, attributable silences, turn switches per minute, standardized phonation time, and turn length). For the classification of AD patient conversations vs. non-AD patient conversations, we achieved 83% accuracy with disfluency features, 83% accuracy with interactional features, and an overall accuracy of 90% when combining both feature sets using support vector machine classifiers. The discriminative power of these features, perhaps combined with more conventional linguistic features, therefore shows potential for integration into noninvasive clinical assessments for AD at advanced stages.
Collapse
|
35
|
Thomas JA, Burkhardt HA, Chaudhry S, Ngo AD, Sharma S, Zhang L, Au R, Hosseini Ghomi R. Assessing the Utility of Language and Voice Biomarkers to Predict Cognitive Impairment in the Framingham Heart Study Cognitive Aging Cohort Data. J Alzheimers Dis 2021; 76:905-922. [PMID: 32568190 DOI: 10.3233/jad-190783] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND There is a need for fast, accessible, low-cost, and accurate diagnostic methods for early detection of cognitive decline. Dementia diagnoses are usually made years after symptom onset, missing a window of opportunity for early intervention. OBJECTIVE To evaluate the use of recorded voice features as proxies for cognitive function by using neuropsychological test measures and existing dementia diagnoses. METHODS This study analyzed 170 audio recordings, transcripts, and paired neuropsychological test results from 135 participants selected from the Framingham Heart Study (FHS), which includes 97 recordings of cognitively normal participants and 73 recordings of cognitively impaired participants. Acoustic and linguistic features of the voice samples were correlated with cognitive performance measures to verify their association. RESULTS Language and voice features, when combined with demographic variables, performed with an AUC of 0.942 (95% CI 0.929-0.983) in predicting cognitive status. Features with good predictive power included the acoustic features mean spectral slope in the 500-1500 Hz band, variation in the F2 bandwidth, and variation in the Mel-Frequency Cepstral Coefficient (MFCC) 1; the demographic features employment, education, and age; and the text features of number of words, number of compound words, number of unique nouns, and number of proper names. CONCLUSION Several linguistic and acoustic biomarkers show correlations and predictive power with regard to neuropsychological testing results and cognitive impairment diagnoses, including dementia. This initial study paves the way for a follow-up comprehensive study incorporating the entire FHS cohort.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Rhoda Au
- Boston University, Boston, MA, USA
| | | |
Collapse
|
36
|
Zhu Y, Liang X, Batsis JA, Roth RM. Exploring Deep Transfer Learning Techniques for Alzheimer's Dementia Detection. FRONTIERS IN COMPUTER SCIENCE 2021; 3:624683. [PMID: 34046588 PMCID: PMC8153512 DOI: 10.3389/fcomp.2021.624683] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Examination of speech datasets for detecting dementia, collected via various speech tasks, has revealed links between speech and cognitive abilities. However, the speech dataset available for this research is extremely limited because the collection process of speech and baseline data from patients with dementia in clinical settings is expensive. In this paper, we study the spontaneous speech dataset from a recent ADReSS challenge, a Cookie Theft Picture (CTP) dataset with balanced groups of participants in age, gender, and cognitive status. We explore state-of-the-art deep transfer learning techniques from image, audio, speech, and language domains. We envision that one advantage of transfer learning is to eliminate the design of handcrafted features based on the tasks and datasets. Transfer learning further mitigates the limited dementia-relevant speech data problem by inheriting knowledge from similar but much larger datasets. Specifically, we built a variety of transfer learning models using commonly employed MobileNet (image), YAMNet (audio), Mockingjay (speech), and BERT (text) models. Results indicated that the transfer learning models of text data showed significantly better performance than those of audio data. Performance gains of the text models may be due to the high similarity between the pre-training text dataset and the CTP text dataset. Our multi-modal transfer learning introduced a slight improvement in accuracy, demonstrating that audio and text data provide limited complementary information. Multi-task transfer learning resulted in limited improvements in classification and a negative impact in regression. By analyzing the meaning behind the AD/non-AD labels and Mini-Mental State Examination (MMSE) scores, we observed that the inconsistency between labels and scores could limit the performance of the multi-task learning, especially when the outputs of the single-task models are highly consistent with the corresponding labels/scores. In sum, we conducted a large comparative analysis of varying transfer learning models focusing less on model customization but more on pre-trained models and pre-training datasets. We revealed insightful relations among models, data types, and data labels in this research area.
Collapse
Affiliation(s)
- Youxiang Zhu
- Computer Science, University of Massachusetts Boston, Boston, MA, USA
| | - Xiaohui Liang
- Computer Science, University of Massachusetts Boston, Boston, MA, USA
| | - John A. Batsis
- School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Robert M. Roth
- Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| |
Collapse
|
37
|
De Looze C, Dehsarvi A, Crosby L, Vourdanou A, Coen RF, Lawlor BA, Reilly RB. Cognitive and Structural Correlates of Conversational Speech Timing in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer's Disease: Relevance for Early Detection Approaches. Front Aging Neurosci 2021; 13:637404. [PMID: 33986656 PMCID: PMC8110716 DOI: 10.3389/fnagi.2021.637404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 03/31/2021] [Indexed: 11/19/2022] Open
Abstract
Background: Increasing efforts have focused on the establishment of novel biomarkers for the early detection of Alzheimer’s disease (AD) and prediction of Mild Cognitive Impairment (MCI)-to-AD conversion. Behavioral changes over the course of healthy ageing, at disease onset and during disease progression, have been recently put forward as promising markers for the detection of MCI and AD. The present study examines whether the temporal characteristics of speech in a collaborative referencing task are associated with cognitive function and the volumes of brain regions involved in speech production and known to be reduced in MCI and AD pathology. We then explore the discriminative ability of the temporal speech measures for the classification of MCI and AD. Method: Individuals with MCI, mild-to-moderate AD and healthy controls (HCs) underwent a structural MRI scan and a battery of neuropsychological tests. They also engaged in a collaborative referencing task with a caregiver. The associations between the conversational speech timing features, cognitive function (domain-specific) and regional brain volumes were examined by means of linear mixed-effect modeling. Genetic programming was used to explore the discriminative ability of the conversational speech features. Results: MCI and mild-to-moderate AD are characterized by a general slowness of speech, attributed to slower speech rate and slower turn-taking in conversational settings. The speech characteristics appear to be reflective of episodic, lexico-semantic, executive functioning and visuospatial deficits and underlying volume reductions in frontal, temporal and cerebellar areas. Conclusion: The implementation of conversational speech timing-based technologies in clinical and community settings may provide additional markers for the early detection of cognitive deficits and structural changes associated with MCI and AD.
Collapse
Affiliation(s)
- Céline De Looze
- Trinity Centre for Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland
| | - Amir Dehsarvi
- Trinity Centre for Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland
| | - Lisa Crosby
- Mercer's Institute for Successful Ageing, St James's Hospital, Dublin, Ireland
| | - Aisling Vourdanou
- Trinity Centre for Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland
| | - Robert F Coen
- Mercer's Institute for Successful Ageing, St James's Hospital, Dublin, Ireland
| | - Brian A Lawlor
- Mercer's Institute for Successful Ageing, St James's Hospital, Dublin, Ireland.,Institute of Neuroscience, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Richard B Reilly
- Trinity Centre for Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland.,Institute of Neuroscience, School of Medicine, Trinity College Dublin, Dublin, Ireland
| |
Collapse
|
38
|
Martínez-Nicolás I, Llorente TE, Martínez-Sánchez F, Meilán JJG. Ten Years of Research on Automatic Voice and Speech Analysis of People With Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review Article. Front Psychol 2021; 12:620251. [PMID: 33833713 PMCID: PMC8021952 DOI: 10.3389/fpsyg.2021.620251] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 02/15/2021] [Indexed: 11/25/2022] Open
Abstract
Background: The field of voice and speech analysis has become increasingly popular over the last 10 years, and articles on its use in detecting neurodegenerative diseases have proliferated. Many studies have identified characteristic speech features that can be used to draw an accurate distinction between healthy aging among older people and those with mild cognitive impairment and Alzheimer's disease. Speech analysis has been singled out as a cost-effective and reliable method for detecting the presence of both conditions. In this research, a systematic review was conducted to determine these features and their diagnostic accuracy. Methods: Peer-reviewed literature was located across multiple databases, involving studies that apply new procedures of automatic speech analysis to collect behavioral evidence of linguistic impairments along with their diagnostic accuracy on Alzheimer's disease and mild cognitive impairment. The risk of bias was assessed by using JBI and QUADAS-2 checklists. Results: Thirty-five papers met the inclusion criteria; of these, 11 were descriptive studies that either identified voice features or explored their cognitive correlates, and the rest were diagnostic studies. Overall, the studies were of good quality and presented solid evidence of the usefulness of this technique. The distinctive acoustic and rhythmic features found are gathered. Most studies record a diagnostic accuracy over 88% for Alzheimer's and 80% for mild cognitive impairment. Conclusion: Automatic speech analysis is a promising tool for diagnosing mild cognitive impairment and Alzheimer's disease. The reported features seem to be indicators of the cognitive changes in older people. The specific features and the cognitive changes involved could be the subject of further research.
Collapse
Affiliation(s)
- Israel Martínez-Nicolás
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
| | - Thide E Llorente
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
| | | | - Juan José G Meilán
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
| |
Collapse
|
39
|
Yamada Y, Shinkawa K, Kobayashi M, Takagi H, Nemoto M, Nemoto K, Arai T. Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study. J Med Internet Res 2021; 23:e27667. [PMID: 33830066 PMCID: PMC8063093 DOI: 10.2196/27667] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/08/2021] [Accepted: 03/15/2021] [Indexed: 01/27/2023] Open
Abstract
Background With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. Objective The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. Methods At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data—neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)—from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. Results We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. Conclusions Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function.
Collapse
Affiliation(s)
| | | | | | | | - Miyuki Nemoto
- Department of Psychiatry, University of Tsukuba Hospital, Ibaraki, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Tetsuaki Arai
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| |
Collapse
|
40
|
Walker G, Morris LA, Christensen H, Mirheidari B, Reuber M, Blackburn DJ. Characterising spoken responses to an intelligent virtual agent by persons with mild cognitive impairment. CLINICAL LINGUISTICS & PHONETICS 2021; 35:237-252. [PMID: 32552087 DOI: 10.1080/02699206.2020.1777586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/27/2020] [Accepted: 05/31/2020] [Indexed: 06/11/2023]
Abstract
The diagnosis of Mild Cognitive Impairment (MCI) characterises patients at risk of dementia and may provide an opportunity for disease-modifying interventions. Identifying persons with MCI (PwMCI) from adults of a similar age without cognitive complaints is a significant challenge. The main aims of this study were to determine whether generic speech differences were evident between PwMCI and healthy controls (HC), whether such differences were identifiable in responses to recent or remote memory questions, and to determine which speech variables showed the clearest between-group differences. This study analysed recordings of 8 PwMCI (5 females, 3 males) and 14 HC of a similar age (8 females, 6 males). Participants were recorded interacting with an intelligent virtual agent: a computer-generated talking head on a computer screen which asks pre-recorded questions when prompted by the interviewee through pressing the next key on a computer keyboard. Responses to recent and remote memory questions were analysed. Mann-Whitney U tests were used to test for statistically significant differences between PwMCI and HC on each of 12 speech variables, relating to temporal characteristics, number of words produced and pitch. It was found that compared to HC, PwMCI produce speech for less time and in shorter chunks, they pause more often and for longer, take longer to begin speaking and produce fewer words in their answers. It was also found that the PwMCI and HC were more alike when responding to remote memory questions than when responding to recent memory questions. These findings show great promise and suggest that detailed speech analysis can make an important contribution to diagnostic and stratification systems in patients with memory complaints.
Collapse
Affiliation(s)
- Gareth Walker
- School of English, University of Sheffield , Sheffield, UK
| | - Lee-Anne Morris
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield , Sheffield, UK
| | - Heidi Christensen
- Department of Computer Science, University of Sheffield , Sheffield, UK
| | - Bahman Mirheidari
- Department of Computer Science, University of Sheffield , Sheffield, UK
| | - Markus Reuber
- Academic Neurology Unit, Royal Hallamshire Hospital, University of Sheffield , Sheffield, UK
| | - Daniel J Blackburn
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield , Sheffield, UK
| |
Collapse
|
41
|
Abe MS, Otake-Matsuura M. Scaling laws in natural conversations among elderly people. PLoS One 2021; 16:e0246884. [PMID: 33606774 PMCID: PMC7894956 DOI: 10.1371/journal.pone.0246884] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 01/27/2021] [Indexed: 11/18/2022] Open
Abstract
Language is a result of brain function; thus, impairment in cognitive function can result in language disorders. Understanding the aging of brain functions in terms of language processing is crucial for modern aging societies. Previous studies have shown that language characteristics, such as verbal fluency, are associated with cognitive functions. However, the scaling laws in language in elderly people remain poorly understood. In the current study, we recorded large-scale data of one million words from group conversations among healthy elderly people and analyzed the relationship between spoken language and cognitive functions in terms of scaling laws, namely, Zipf's law and Heaps' law. We found that word patterns followed these scaling laws irrespective of cognitive function, and that the variations in Heaps' exponents were associated with cognitive function. Moreover, variations in Heaps' exponents were associated with the ratio of new words taken from the other participants' speech. These results indicate that the exponents of scaling laws in language are related to cognitive processes.
Collapse
Affiliation(s)
- Masato S. Abe
- Center for Advanced Intelligence Project, RIKEN, Chuo-ku, Tokyo, Japan
| | | |
Collapse
|
42
|
Shah Z, Sawalha J, Tasnim M, Qi SA, Stroulia E, Greiner R. Learning Language and Acoustic Models for Identifying Alzheimer’s Dementia From Speech. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.624659] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s dementia (AD) is a chronic neurodegenerative illness that manifests in a gradual decline of cognitive function. Early identification of AD is essential for managing the ensuing cognitive deficits, which may lead to a better prognostic outcome. Speech data can serve as a window into cognitive functioning and can be used to screen for early signs of AD. This paper describes methods for learning models using speech samples from the DementiaBank database, for identifying which subjects have Alzheimer’s dementia. We consider two machine learning tasks: 1) binary classification to distinguish patients from healthy controls, and 2) regression to estimate each subject’s Mini-Mental State Examination (MMSE) score. To develop models that can use acoustic and/or language features, we explore a variety of dimension reduction techniques, training algorithms, and fusion strategies. Our best performing classification model, using language features with dimension reduction and regularized logistic regression, achieves an accuracy of 85.4% on a held-out test set. On the regression task, a linear regression model trained on a reduced set of language features achieves a root mean square error (RMSE) of 5.62 on the test set. These results demonstrate the promise of using machine learning for detecting cognitive decline from speech in AD patients.
Collapse
|
43
|
Linguistic features and automatic classifiers for identifying mild cognitive impairment and dementia. COMPUT SPEECH LANG 2021. [DOI: 10.1016/j.csl.2020.101113] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
|
44
|
Guo Z, Ling Z, Li Y. Detecting Alzheimer's Disease from Continuous Speech Using Language Models. J Alzheimers Dis 2020; 70:1163-1174. [PMID: 31322577 DOI: 10.3233/jad-190452] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Recently, many studies have been carried out to detect Alzheimer's disease (AD) from continuous speech by linguistic analysis and modeling. However, few of them utilize language models (LMs) to extract linguistic features and to investigate the lexical-level differences between AD and healthy speech. OBJECTIVE Our goals include obtaining state-of-art performance of automatic AD detection, emphasizing N-gram LMs as powerful tools for distinguishing AD patients' narratives from those of healthy controls, and discovering the differences of lexical usages between AD patients and healthy people. METHOD We utilize a subset of the DementiaBank corpus, including 242 control samples from 99 control participants and 256 AD samples from 169 "PossibleAD" or "ProbableAD" participants. Baseline models are built through area under curve-based feature selection and using five machine learning algorithms for comparison. Perplexity features are extracted using LMs to build enhanced detection models. Finally, the differences of lexical usages between AD patients and healthy people are investigated by a proportion test based on unigram probabilities. RESULTS Our baseline model obtains a detection accuracy of 80.7%. This accuracy increases to 85.4% after integrating the perplexity features derived from LMs. Further investigations show that AD patients tend to use more general, less informative, and less accurate words to describe characters and actions than healthy controls. CONCLUSION The perplexity features extracted by LMs can benefit the automatic AD detection from continuous speech. There exist lexical-level differences between AD and healthy speech that can be captured by statistical N-gram LMs.
Collapse
Affiliation(s)
- Zhiqiang Guo
- National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China
| | - Zhenhua Ling
- National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, China
| | - Yunxia Li
- Department of Neurology, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| |
Collapse
|
45
|
Fu Z, Haider F, Luz S. Predicting Mini-Mental Status Examination Scores through Paralinguistic Acoustic Features of Spontaneous Speech. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5548-5552. [PMID: 33019235 DOI: 10.1109/embc44109.2020.9175379] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Speech analysis could provide an indicator of cognitive health and help develop clinical tools for automatically detecting and monitoring cognitive health progression. The Mini Mental Status Examination (MMSE) is the most widely used screening tool for cognitive health. But the manual operation of MMSE restricts its screening within primary care facilities. An automatic screening tool has the potential to remedy this situation. This study aims to assess the association between acoustic features of spontaneous speech and assess whether acoustic features can be used to automatically predict MMSE score. We assessed the effectiveness of paralinguistic feature set for MMSE score prediction on a balanced sample of DementiaBank's Pitt spontaneous speech dataset, with patients matched by gender and age. Linear regression analysis shows that fusion of acoustic features, age, sex and years of education provides better results (mean absolute error, MAE = 4.97, and R2 = 0.261) than acoustic features alone (MAE = 5.66 and R2 =0.125) and age, gender and education level alone (MAE of 5.36 and R2 =0.17). This suggests that the acoustic features of spontaneous speech are an important part of an automatic screening tool for cognitive impairment detection.Clinical relevance- We hereby present a method for automatic screening of cognitive health. It is based on acoustic information of speech, a ubiquitous source of data, therefore being cost-efficient, non-invasive and with little infrastructure required.
Collapse
|
46
|
Mazzon G, Ajčević M, Cattaruzza T, Menichelli A, Guerriero M, Capitanio S, Pesavento V, Dore F, Sorbi S, Manganotti P, Marini A. Connected Speech Deficit as an Early Hallmark of CSF-defined Alzheimer's Disease and Correlation with Cerebral Hypoperfusion Pattern. Curr Alzheimer Res 2020; 16:483-494. [PMID: 31057108 DOI: 10.2174/1567205016666190506141733] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 04/10/2019] [Accepted: 04/30/2019] [Indexed: 12/12/2022]
Abstract
BACKGROUND Diagnosis of prodromal Alzheimer's disease (AD) still represents a hot topic and there is a growing interest for the detection of early and non-invasive biomarkers. Although progressive episodic memory impairment is the typical predominant feature of AD, communicative difficulties can be already present at the early stages of the disease. OBJECTIVE This study investigated the narrative discourse production deficit as a hallmark of CSFdefined prodromal AD and its correlation with cerebral hypoperfusion pattern. METHODS Narrative assessment with a multilevel procedure for discourse analysis was conducted on 28 subjects with Mild Cognitive Impairment (15 MCI due to AD; 13 MCI non-AD) and 28 healthy controls. The diagnostic workup included CSF AD biomarkers. Cerebral hypoperfusion pattern was identified by SPECT image processing. RESULTS The results showed that the discourse analysis of global coherence and lexical informativeness indexes allowed to identify MCI due to AD from MCI non-AD and healthy subjects. These findings allow to hypothesize that the loss of narrative efficacy could be a possible early clinical hallmark of Alzheimer's disease. Furthermore, a significant correlation of global coherence and lexical informativeness reduction with the SPECT hypoperfusion was found in the dorsal aspect of the anterior part of the left inferior frontal gyrus, supporting the hypothesis that this area has a significant role in communicative efficacy, and in particular, in semantic selection executive control. CONCLUSION This study contributes to the understanding of the neural networks for language processing and their involvement in prodromal Alzheimer's disease. It also suggests an easy and sensitive tool for clinical practice that can help identifying individuals with prodromal Alzheimer's disease.
Collapse
Affiliation(s)
- Giulia Mazzon
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, University Hospital and Health Services of Trieste, University of Trieste, Trieste, Italy
| | - Miloš Ajčević
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, University Hospital and Health Services of Trieste, University of Trieste, Trieste, Italy.,NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Tatiana Cattaruzza
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, University Hospital and Health Services of Trieste, University of Trieste, Trieste, Italy
| | - Alina Menichelli
- Department of Rehabilitation Medicine, Neuropsychology Unit, University Hospital and Health Services of Trieste, University of Trieste, Trieste, Italy
| | - Michele Guerriero
- Department of Rehabilitation Medicine, Neuropsychology Unit, University Hospital and Health Services of Trieste, University of Trieste, Trieste, Italy
| | - Selene Capitanio
- Unit of Nuclear Medicine, University Hospital and Health Services of Trieste, University of Trieste, Trieste, Italy
| | - Valentina Pesavento
- Department of Rehabilitation Medicine, Neuropsychology Unit, University Hospital and Health Services of Trieste, University of Trieste, Trieste, Italy
| | - Franca Dore
- Unit of Nuclear Medicine, University Hospital and Health Services of Trieste, University of Trieste, Trieste, Italy
| | - Sandro Sorbi
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy.,IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Paolo Manganotti
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, University Hospital and Health Services of Trieste, University of Trieste, Trieste, Italy
| | - Andrea Marini
- Department of Language and Literatures, Communication, Education and Society, University of Udine, Udine, Italy.,Claudiana - Landesfachhochschule für Gesundheitsberufe, Bozen, Italy
| |
Collapse
|
47
|
Radjenovic S, Voracek M, Adler G. [Validity Study of the Cookie Theft Picture Test - Early Detection of Dementia Based on Linguistic Abnormalities]. PSYCHIATRISCHE PRAXIS 2020; 48:149-155. [PMID: 32869219 DOI: 10.1055/a-1207-1255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
OBJECTIVE Previous studies have provided inconsistent results regarding discriminatory language variables between subjects with dementia and healthy controls. In this study, using the Cookie Theft Picture Test (CTP), selected language variables are tested for predicting actual diagnoses. METHODS 24 healthy subjects and 24 subjects with mild dementia were included in the present study. RESULTS All language variables except repetitions, word finding difficulties and paraphasias showed significant differences between the groups. The variables pause length and clues increase significantly the likelihood of AD, while the variable sentence length decreases it. CONCLUSION Due to the small sample size and insufficient standardization, the study can only be interpreted to a limited extent. Nevertheless, the results indicate that the CTP appears to be suitable for practical use.
Collapse
Affiliation(s)
- Sonja Radjenovic
- Institut für Psychologische Grundlagenforschung und Forschungsmethoden, Fakultät für Psychologie, Universität Wien, Österreich
| | - Martin Voracek
- Institut für Psychologische Grundlagenforschung und Forschungsmethoden, Fakultät für Psychologie, Universität Wien, Österreich
| | - Georg Adler
- Institut für Studien zur Psychischen Gesundheit (ISPG), Mannheim, Deutschland
| |
Collapse
|
48
|
Themistocleous C, Eckerström M, Kokkinakis D. Voice quality and speech fluency distinguish individuals with Mild Cognitive Impairment from Healthy Controls. PLoS One 2020; 15:e0236009. [PMID: 32658934 PMCID: PMC7357785 DOI: 10.1371/journal.pone.0236009] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 06/27/2020] [Indexed: 11/19/2022] Open
Abstract
Mild Cognitive Impairment (MCI) is a syndrome characterized by cognitive decline greater than expected for an individual's age and education level. This study aims to determine whether voice quality and speech fluency distinguish patients with MCI from healthy individuals to improve diagnosis of patients with MCI. We analyzed recordings of the Cookie Theft picture description task produced by 26 patients with MCI and 29 healthy controls from Sweden and calculated measures of voice quality and speech fluency. The results show that patients with MCI differ significantly from HC with respect to acoustic aspects of voice quality, namely H1-A3, cepstral peak prominence, center of gravity, and shimmer; and speech fluency, namely articulation rate and averaged speaking time. The method proposed along with the obtainability of connected speech productions can enable quick and easy analysis of speech fluency and voice quality, providing accessible and objective diagnostic markers of patients with MCI.
Collapse
Affiliation(s)
| | - Marie Eckerström
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Dimitrios Kokkinakis
- Department of Swedish, University of Gothenburg, Gothenburg, Sweden
- Center of Ageing and Health—AgeCap, University of Gothenburg, Gothenburg, Sweden
| |
Collapse
|
49
|
Wisler AA, Fletcher AR, McAuliffe MJ. Predicting Montreal Cognitive Assessment Scores From Measures of Speech and Language. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2020; 63:1752-1761. [PMID: 32459131 DOI: 10.1044/2020_jslhr-19-00183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Purpose This study examined the relationship between measurements derived from spontaneous speech and participants' scores on the Montreal Cognitive Assessment. Method Participants (N = 521) aged between 64 and 97 years completed the cognitive assessment and were prompted to describe an early childhood memory. A range of acoustic and linguistic measures was extracted from the resulting speech sample. A least absolute shrinkage and selection operator approach was used to model the relationship between acoustic, lexical, and demographic information and participants' scores on the cognitive assessment. Results Using the covariance test statistic, four important variables were identified, which, together, explained 16.52% of the variance in participants' cognitive scores. Conclusions The degree to which cognition can be accurately predicted through spontaneously produced speech samples is limited. Statistically significant relationships were found between specific measurements of lexical variation, participants' speaking rate, and their scores on the Montreal Cognitive Assessment.
Collapse
Affiliation(s)
- Alan A Wisler
- New Zealand Institute of Language, Brain and Behaviour, Christchurch, New Zealand
| | - Annalise R Fletcher
- Department of Audiology and Speech-Language Pathology, University of North Texas, Denton
| | - Megan J McAuliffe
- Department of Communication Disorders, University of Canterbury, Christchurch, New Zealand
| |
Collapse
|
50
|
Qiao Y, Xie XY, Lin GZ, Zou Y, Chen SD, Ren RJ, Wang G. Computer-Assisted Speech Analysis in Mild Cognitive Impairment and Alzheimer’s Disease: A Pilot Study from Shanghai, China. J Alzheimers Dis 2020; 75:211-221. [PMID: 32250297 DOI: 10.3233/jad-191056] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Yuan Qiao
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin-Yi Xie
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guo-Zhen Lin
- Department of Psychiatry, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Zou
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sheng-Di Chen
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ru-Jing Ren
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gang Wang
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|