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Skirrow C, Meepegama U, Weston J, Miller MJ, Nosheny RL, Albala B, Weiner MW, Fristed E. Storyteller in ADNI4: Application of an early Alzheimer's disease screening tool using brief, remote, and speech-based testing. Alzheimers Dement 2024. [PMID: 39234647 DOI: 10.1002/alz.14206] [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: 05/08/2024] [Revised: 07/22/2024] [Accepted: 07/27/2024] [Indexed: 09/06/2024]
Abstract
INTRODUCTION Speech-based testing shows promise for sensitive and scalable objective screening for Alzheimer's disease (AD), but research to date offers limited evidence of generalizability. METHODS Data were taken from the AMYPRED (Amyloid Prediction in Early Stage Alzheimer's Disease from Acoustic and Linguistic Patterns of Speech) studies (N = 101, N = 46 mild cognitive impairment [MCI]) and Alzheimer's Disease Neuroimaging Initiative 4 (ADNI4) remote digital (N = 426, N = 58 self-reported MCI, mild AD or dementia) and in-clinic (N = 57, N = 13 MCI) cohorts, in which participants provided audio-recorded responses to automated remote story recall tasks in the Storyteller test battery. Text similarity, lexical, temporal, and acoustic speech feature sets were extracted. Models predicting early AD were developed in AMYPRED and tested out of sample in the demographically more diverse cohorts in ADNI4 (> 33% from historically underrepresented populations). RESULTS Speech models generalized well to unseen data in ADNI4 remote and in-clinic cohorts. The best-performing models evaluated text-based metrics (text similarity, lexical features: area under the curve 0.71-0.84 across cohorts). DISCUSSION Speech-based predictions of early AD from Storyteller generalize across diverse samples. HIGHLIGHTS The Storyteller speech-based test is an objective digital prescreener for Alzheimer's Disease Neuroimaging Initiative 4 (ADNI4). Speech-based models predictive of Alzheimer's disease (AD) were developed in the AMYPRED (Amyloid Prediction in Early Stage Alzheimer's Disease from Acoustic and Linguistic Patterns of Speech) sample (N = 101). Models were tested out of sample in ADNI4 in-clinic (N = 57) and remote (N = 426) cohorts. Models showed good generalization out of sample. Models evaluating text matching and lexical features were most predictive of early AD.
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Affiliation(s)
| | | | | | - Melanie J Miller
- Northern California Institute for Research and Education (NCIRE), San Francisco, California, USA
- VA Advanced Imaging Research Center, Department of Veterans Affairs Medical Center, San Francisco, California, USA
| | - Rachel L Nosheny
- Northern California Institute for Research and Education (NCIRE), San Francisco, California, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Bruce Albala
- Department of Environmental & Occupational Health, Public Health, University of California Irvine, Irvine, California, USA
- Department of Neurology, University of California Irvine School of Medicine, Irvine, California, USA
- Department of Pharmaceutical Sciences, University of California Irvine School of Pharmacy & Pharmaceutical Sciences, Irvine, California, USA
- Research Service, Veterans Administration Long Beach Healthcare System, Long Beach, California, USA
| | - Michael W Weiner
- Northern California Institute for Research and Education (NCIRE), San Francisco, California, USA
- VA Advanced Imaging Research Center, Department of Veterans Affairs Medical Center, San Francisco, California, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
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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 2024; 38:880-901. [PMID: 37722818 DOI: 10.1080/02699206.2023.2254458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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.
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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
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Wilson SC, Teghipco A, Sayers S, Newman-Norlund R, Newman-Norlund S, Fridriksson J. Story Recall in Peer Conflict Resolution Discourse Task to Identify Older Adults Testing Within Range of Cognitive Impairment. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2024:1-17. [PMID: 39173074 DOI: 10.1044/2024_ajslp-24-00005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
PURPOSE The current study used behavioral measures of discourse complexity and story recall accuracy in an expository discourse task to distinguish older adults testing within range of cognitive impairment according to a standardized cognitive screening tool in a sample of self-reported healthy older adults. METHOD Seventy-three older adults who self-identified as healthy completed an expository discourse task and neuropsychological screener. Discourse data were used to classify participants testing within range of cognitive impairment using multiple machine learning algorithms and stability analysis for identifying reliably predictive features in an effort to maximize prediction accuracy. We hypothesized that a higher rate of pronoun use and lower scores on story recall would best classify older adults testing within range of cognitive impairment. RESULTS The highest classification accuracy exploited a single variable in a remarkably intuitive way: using 66% story recall as a cutoff for cognitive impairment. Forcing this decision tree model to use more features or increasing its complexity did not improve accuracy. Permutation testing confirmed that the 77% accuracy and 0.18 Brier skill score achieved by the model were statistically significant (p < .00001). CONCLUSIONS These results suggest that expository discourse tasks that place demands on executive functions, such as working memory, can be used to identify aging adults who test within range of cognitive impairment. Accurate representation of story elements in working memory is critical for coherent discourse. Our simple yet highly accurate predictive model of expository discourse provides a promising assessment for easy identification of cognitive impairment in older adults. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.26543824.
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Affiliation(s)
- Sarah C Wilson
- Linguistics Program, University of South Carolina, Columbia
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia
| | - Alex Teghipco
- Department of Psychology, University of South Carolina, Columbia
| | - Sara Sayers
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia
| | | | - Sarah Newman-Norlund
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia
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Kleiman MJ, Galvin JE. High frequency post-pause word choices and task-dependent speech behavior characterize connected speech in individuals with mild cognitive impairment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.25.24303329. [PMID: 38464237 PMCID: PMC10925339 DOI: 10.1101/2024.02.25.24303329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Background Alzheimer's disease (AD) is characterized by progressive cognitive decline, including impairments in speech production and fluency. Mild cognitive impairment (MCI), a prodrome of AD, has also been linked with changes in speech behavior but to a more subtle degree. Objective This study aimed to investigate whether speech behavior immediately following both filled and unfilled pauses (post-pause speech behavior) differs between individuals with MCI and healthy controls (HCs), and how these differences are influenced by the cognitive demands of various speech tasks. Methods Transcribed speech samples were analyzed from both groups across different tasks, including immediate and delayed narrative recall, picture descriptions, and free responses. Key metrics including lexical and syntactic complexity, lexical frequency and diversity, and part of speech usage, both overall and post-pause, were examined. Results Significant differences in pause usage were observed between groups, with a higher incidence and longer latencies following these pauses in the MCI group. Lexical frequency following filled pauses was higher among MCI participants in the free response task but not in other tasks, potentially due to the relative cognitive load of the tasks. The immediate recall task was most useful at differentiating between groups. Predictive analyses utilizing random forest classifiers demonstrated high specificity in using speech behavior metrics to differentiate between MCI and HCs. Conclusions Speech behavior following pauses differs between MCI participants and healthy controls, with these differences being influenced by the cognitive demands of the speech tasks. These post-pause speech metrics can be easily integrated into existing speech analysis paradigms.
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Affiliation(s)
- Michael J. Kleiman
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Boca Raton, FL 33433
| | - James E. Galvin
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Boca Raton, FL 33433
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Kaltsa M, Tsolaki A, Lazarou I, Mittas I, Papageorgiou M, Papadopoulou D, Tsimpli IM, Tsolaki M. Language Markers of Dementia and Their Role in Early Diagnosis of Alzheimer's Disease: Exploring Grammatical and Syntactic Competence via Sentence Repetition. J Alzheimers Dis Rep 2024; 8:1115-1132. [PMID: 39114543 PMCID: PMC11305841 DOI: 10.3233/adr-230204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 06/21/2024] [Indexed: 08/10/2024] Open
Abstract
Background Earlier research focuses primarily on the cognitive changes due to Alzheimer's disease (AD); however, little is known with regard to changes in language competence across the lifespan. Objective The present study aims to investigate the decline of language skills at the grammatical and syntactic levels due to changes in cognitive function. Methods We administered the Litmus Sentence Repetition Task (SRT) to 150 native speakers of Greek who fall into five groups: 1) young healthy speakers, 2) cognitively intact elder healthy speakers, 3) speakers with subjective cognitive impairment (SCI), 4) speakers with mild cognitive impairment (MCI); and 5) speakers with AD dementia at the mild/moderate stages. All participants underwent a physical and neurological examination and cognitive screening with a standardized neuropsychological battery to assess cognitive status comprehensively and evaluate aspects like working memory, executive function, attention and memory to appropriately classify them. Results The data analysis revealed that the SRT had high discriminatory value in the development of AD; specifically, both accuracy and grammaticality indices were related to cognitive decline. Additionally, syntax significantly affected the performance of speakers with structures such as clitics being particularly challenging and in most structures the performance of speakers with MCI drops significantly compared to speakers with SCI. Conclusions Linguistic indices revealed subtle early signs of cognitive decline that can be helpful in the early detection of AD, thus facilitating the clinical process offering support to language-based assessment tools such as sentence repetition, a non-invasive type of assessment to evaluate symptoms of AD.
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Affiliation(s)
- Maria Kaltsa
- Department of Theoretical & Applied Linguistics, School of English, Faculty of Philosophy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anthoula Tsolaki
- School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Greek Alzheimer’s Association and Related Disorders (GAARD), Thessaloniki, Greece
| | - Ioulietta Lazarou
- Greek Alzheimer’s Association and Related Disorders (GAARD), Thessaloniki, Greece
- Centre for Research and Technology-Hellas, Thessaloniki, Greece
| | - Ilias Mittas
- Department of Linguistics, School of Philology, Faculty of Philosophy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Mairi Papageorgiou
- Department of Linguistics, School of Philology, Faculty of Philosophy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Despina Papadopoulou
- Department of Linguistics, School of Philology, Faculty of Philosophy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ianthi Maria Tsimpli
- Faculty of Modern and Medieval Languages and Linguistics, University of Cambridge, Cambridge, UK
| | - Magda Tsolaki
- Greek Alzheimer’s Association and Related Disorders (GAARD), Thessaloniki, Greece
- First Department of Neurology, AHEPA University Hospital, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI – AUTh), Balkan Center, Aristotle University of Thessaloniki, Thessaloniki, Greece
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He R, Al-Tamimi J, Sánchez-Benavides G, Montaña-Valverde G, Domingo Gispert J, Grau-Rivera O, Suárez-Calvet M, Minguillon C, Fauria K, Navarro A, Hinzen W. Atypical cortical hierarchy in Aβ-positive older adults and its reflection in spontaneous speech. Brain Res 2024; 1830:148806. [PMID: 38365129 DOI: 10.1016/j.brainres.2024.148806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 02/07/2024] [Indexed: 02/18/2024]
Abstract
Abnormal deposition of Aβ amyloid is an early neuropathological marker of Alzheimer's disease (AD), arising long ahead of clinical symptoms. Non-invasive measures of associated early neurofunctional changes, together with easily accessible behavioral readouts of these changes, could be of great clinical benefit. We pursued this aim by investigating large-scale cortical gradients of functional connectivity with functional MRI, which capture the hierarchical integration of cortical functions, together with acoustic-prosodic features from spontaneous speech, in cognitively unimpaired older adults with and without Aβ positivity (total N = 188). We predicted distortions of the cortical hierarchy associated with prosodic changes in the Aβ + group. Results confirmed substantially altered cortical hierarchies and less variability in these in the Aβ + group, together with an increase in quantitative prosodic measures, which correlated with gradient variability as well as digit span test scores. Overall, these findings confirm that long before the clinical stage and objective cognitive impairment, increased risk of cognitive decline as indexed by Aβ accumulation is marked by neurofunctional changes in the cortical hierarchy, which are related to automatically extractable speech patterns and alterations in working memory functions.
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Affiliation(s)
- Rui He
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, 08018 Barcelona, Spain.
| | - Jalal Al-Tamimi
- Université Paris Cité, Laboratoire de Linguistique Formelle (LLF), CNRS, 75013 Paris, France
| | - Gonzalo Sánchez-Benavides
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Department of Medicine and Life Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Oriol Grau-Rivera
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain; Servei de Neurologia, Hospital del Mar, 08003 Barcelona, Spain
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain; Servei de Neurologia, Hospital del Mar, 08003 Barcelona, Spain
| | - Carolina Minguillon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Neurosciences Department, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Karine Fauria
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Arcadi Navarro
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain; Catalan Institution of Research and Advanced Studies (ICREA), 08010 Barcelona, Spain; Department of Medicine and Life Sciences, Institute of Evolutionary Biology (UPF-CSIC), Universitat Pompeu Fabra, 08003 Barcelona, Spain; CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), 08003 Barcelona, Spain
| | - Wolfram Hinzen
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, 08018 Barcelona, Spain; Catalan Institution of Research and Advanced Studies (ICREA), 08010 Barcelona, Spain
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Ambrosini E, Giangregorio C, Lomurno E, Moccia S, Milis M, Loizou C, Azzolino D, Cesari M, Cid Gala M, Galán de Isla C, Gomez-Raja J, Borghese NA, Matteucci M, Ferrante S. Automatic Spontaneous Speech Analysis for the Detection of Cognitive Functional Decline in Older Adults: Multilanguage Cross-Sectional Study. JMIR Aging 2024; 7:e50537. [PMID: 38386279 DOI: 10.2196/50537] [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: 07/05/2023] [Revised: 12/18/2023] [Accepted: 02/12/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND The rise in life expectancy is associated with an increase in long-term and gradual cognitive decline. Treatment effectiveness is enhanced at the early stage of the disease. Therefore, there is a need to find low-cost and ecological solutions for mass screening of community-dwelling older adults. OBJECTIVE This work aims to exploit automatic analysis of free speech to identify signs of cognitive function decline. METHODS A sample of 266 participants older than 65 years were recruited in Italy and Spain and were divided into 3 groups according to their Mini-Mental Status Examination (MMSE) scores. People were asked to tell a story and describe a picture, and voice recordings were used to extract high-level features on different time scales automatically. Based on these features, machine learning algorithms were trained to solve binary and multiclass classification problems by using both mono- and cross-lingual approaches. The algorithms were enriched using Shapley Additive Explanations for model explainability. RESULTS In the Italian data set, healthy participants (MMSE score≥27) were automatically discriminated from participants with mildly impaired cognitive function (20≤MMSE score≤26) and from those with moderate to severe impairment of cognitive function (11≤MMSE score≤19) with accuracy of 80% and 86%, respectively. Slightly lower performance was achieved in the Spanish and multilanguage data sets. CONCLUSIONS This work proposes a transparent and unobtrusive assessment method, which might be included in a mobile app for large-scale monitoring of cognitive functionality in older adults. Voice is confirmed to be an important biomarker of cognitive decline due to its noninvasive and easily accessible nature.
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Affiliation(s)
- Emilia Ambrosini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Chiara Giangregorio
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Eugenio Lomurno
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Sara Moccia
- BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | | | - Christos Loizou
- Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus
| | - Domenico Azzolino
- Geriatric Unit, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy
| | - Matteo Cesari
- Ageing and Health Unit, Department of Maternal, Newborn, Child, Adolescent Health and Ageing, World Health Organization, Geneva, Switzerland
| | - Manuel Cid Gala
- Consejería de Sanidad y Servicios Sociales, Junta de Extremadura, Merida, Spain
| | | | - Jonathan Gomez-Raja
- Consejería de Sanidad y Servicios Sociales, Junta de Extremadura, Merida, Spain
| | | | - Matteo Matteucci
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Simona Ferrante
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
- Laboratory of E-Health Technologies and Artificial Intelligence Research in Neurology, Joint Research Platform, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milano, Italy
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García AM, Johann F, Echegoyen R, Calcaterra C, Riera P, Belloli L, Carrillo F. Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration. Behav Res Methods 2024; 56:2886-2900. [PMID: 37759106 PMCID: PMC11200269 DOI: 10.3758/s13428-023-02240-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL's current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here: https://demo.sci.tellapp.org/ .
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Affiliation(s)
- Adolfo M García
- Global Brain Health Institute, University of California, 505 Parnassus Ave, San Francisco, CA, 94143, USA.
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina.
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile.
- TELL Toolkit SA, Beethovenstraat, Netherlands.
| | - Fernando Johann
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- TELL Toolkit SA, Beethovenstraat, Netherlands
| | - Raúl Echegoyen
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- TELL Toolkit SA, Beethovenstraat, Netherlands
| | - Cecilia Calcaterra
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- TELL Toolkit SA, Beethovenstraat, Netherlands
| | - Pablo Riera
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Laouen Belloli
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Facundo Carrillo
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
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Gagliardi G. Natural language processing techniques for studying language in pathological ageing: A scoping review. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:110-122. [PMID: 36960885 DOI: 10.1111/1460-6984.12870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria). AIMS To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach. METHODS & PROCEDURES We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews. MAIN CONTRIBUTION The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders). CONCLUSIONS & IMPLICATIONS Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice. WHAT THIS PAPER ADDS What is already known on this subject Speech and languages abilities change due to non-pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers-DLBs). What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.
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Affiliation(s)
- Gloria Gagliardi
- Department of Classical Philology and Italian Studies, University of Bologna, Bologna, Italy
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Al-Hammadi M, Fleyeh H, Åberg AC, Halvorsen K, Thomas I. Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review. J Alzheimers Dis 2024; 100:1-27. [PMID: 38848181 PMCID: PMC11307068 DOI: 10.3233/jad-231459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2024] [Indexed: 06/09/2024]
Abstract
Background Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection. Objective Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods. Methods A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review. Results The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results. Conclusions The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection.
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Affiliation(s)
- Mustafa Al-Hammadi
- School of Information and Engineering, Dalarna University, Falun, Sweden
| | - Hasan Fleyeh
- School of Information and Engineering, Dalarna University, Falun, Sweden
| | - Anna Cristina Åberg
- School of Health and Welfare, Dalarna University, Falun, Sweden
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | | | - Ilias Thomas
- School of Information and Engineering, Dalarna University, Falun, Sweden
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11
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Fromm D, Dalton SG, Brick A, Olaiya G, Hill S, Greenhouse J, MacWhinney B. The Case of the Cookie Jar: Differences in Typical Language Use in Dementia. J Alzheimers Dis 2024; 100:1417-1434. [PMID: 38995772 PMCID: PMC11380261 DOI: 10.3233/jad-230844] [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: 07/14/2024]
Abstract
Background Findings from language sample analyses can provide efficient and effective indicators of cognitive impairment in older adults. Objective This study used newly automated core lexicon analyses of Cookie Theft picture descriptions to assess differences in typical use across three groups. Methods Participants included adults without diagnosed cognitive impairments (Control), adults diagnosed with Alzheimer's disease (ProbableAD), and adults diagnosed with mild cognitive impairment (MCI). Cookie Theft picture descriptions were transcribed and analyzed using CLAN. Results Results showed that the ProbableAD group used significantly fewer core lexicon words overall than the MCI and Control groups. For core lexicon content words (nouns, verbs), however, both the MCI and ProbableAD groups produced significantly fewer words than the Control group. The groups did not differ in their use of core lexicon function words. The ProbableAD group was also slower to produce most of the core lexicon words than the MCI and Control groups. The MCI group was slower than the Control group for only two of the core lexicon content words. All groups mentioned a core lexicon word in the top left quadrant of the picture early in the description. The ProbableAD group was then significantly slower than the other groups to mention a core lexicon word in the other quadrants. Conclusions This standard and simple-to-administer task reveals group differences in overall core lexicon scores and the amount of time until the speaker produces the key items. Clinicians and researchers can use these tools for both early assessment and measurement of change over time.
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Affiliation(s)
- Davida Fromm
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Sarah Grace Dalton
- Department of Speech Pathology and Audiology, Marquette University, Milwaukee, WI, USA
| | - Alexander Brick
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Gbenuola Olaiya
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Sophia Hill
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Joel Greenhouse
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Brian MacWhinney
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA
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12
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Reilly J, Finley AM, Litovsky CP, Kenett YN. Bigram semantic distance as an index of continuous semantic flow in natural language: Theory, tools, and applications. J Exp Psychol Gen 2023; 152:2578-2590. [PMID: 37079833 PMCID: PMC10790181 DOI: 10.1037/xge0001389] [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] [Indexed: 04/22/2023]
Abstract
Much of our understanding of word meaning has been informed through studies of single words. High-dimensional semantic space models have recently proven instrumental in elucidating connections between words. Here we show how bigram semantic distance can yield novel insights into conceptual cohesion and topic flow when computed over continuous language samples. For example, "Cats drink milk" is comprised of an ordered vector of bigrams (cat-drink, drink-milk). Each of these bigrams has a unique semantic distance. These distances in turn may provide a metric of dispersion or the flow of concepts as language unfolds. We offer an R-package ("semdistflow") that transforms any user-specified language transcript into a vector of ordered bigrams, appending two metrics of semantic distance to each pair. We validated these distance metrics on a continuous stream of simulated verbal fluency data assigning predicted switch markers between alternating semantic clusters (animals, musical instruments, fruit). We then generated bigram distance norms on a large sample of text and demonstrated applications of the technique to a classic work of short fiction, To Build a Fire (London, 1908). In one application, we showed that bigrams spanning sentence boundaries are punctuated by jumps in the semantic distance. We discuss the promise of this technique for characterizing semantic processing in real-world narratives and for bridging findings at the single word level with macroscale discourse analyses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Jamie Reilly
- Eleanor M. Saffran Center for Cognitive Neuroscience
- Department of Communication Sciences and Disorders, Temple University, Philadelphia, Pennsylvania USA
| | - Ann Marie Finley
- Eleanor M. Saffran Center for Cognitive Neuroscience
- Department of Communication Sciences and Disorders, Temple University, Philadelphia, Pennsylvania USA
| | - Celia P. Litovsky
- Eleanor M. Saffran Center for Cognitive Neuroscience
- Department of Communication Sciences and Disorders, Temple University, Philadelphia, Pennsylvania USA
| | - Yoed N. Kenett
- Faculty of Faculty of Data and Decision Sciences, Technion Israel Institute of Technology, Haifa, Israel
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13
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Quek LJ, Heikkonen MR, Lau Y. Use of artificial intelligence techniques for detection of mild cognitive impairment: A systematic scoping review. J Clin Nurs 2023; 32:5752-5762. [PMID: 37032649 DOI: 10.1111/jocn.16699] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 12/10/2022] [Accepted: 02/28/2023] [Indexed: 04/11/2023]
Abstract
AIMS AND OBJECTIVES The objective of this scoping review is to explore the types and mechanisms of Artificial intelligence (AI) techniques for detecting mild cognitive impairment (MCI). BACKGROUND Early detection of MCI is crucial because it may progress to Alzheimer's disease. DESIGN A systematic scoping review. METHODS Five-step framework of Arksey and O'Malley was used following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews checklist. A total of 11 databases (PubMed, EMBASE, CINAHL, Cochrane Library, Scopus, Web of Science, IEEE Explore, Science.gov, ACM digital library, arXIV and ProQuest) was used to search from inception till 17th December 2021. Grey literature and reference list were searched. Articles screening and data charting were conducted by two independent reviewers. RESULTS There were a total of 70 articles included from 2011 to 2022 across 16 countries. Four types of AI techniques were found, namely machine learning (ML), deep learning (DL), fuzzy logic (FL) and technique combinations. Herein, ML detects similar pattern within preselected data to classify subjects into non-MCI or MCI groups. Meanwhile, DL performs classification based on data patterns and data analyses are performed by themselves. Furthermore, FL utilises human-defined rules to decide the degree to which a person has MCI. A combination of AI techniques enhances the feature preparation phase for ML or DL to perform accurate classification. CONCLUSION Although AI-based MCI detection tool is critical for healthcare decision-making, clinical utility and risks remain underexplored. Hopefully, this review equips clinicians with background AI knowledge to address these clinical concerns. Hence, future research should explore more techniques and representative datasets to improve AI development. RELEVANCE TO CLINICAL PRACTICE Results of this review can increase the knowledge of AI-based MCI detection tools. REVIEW REGISTRATION This study protocol was registered in the Open Science Framework Registries (https://osf.io/45rdt).
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Affiliation(s)
- Li JuanVivian Quek
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore city, Singapore
| | - Maria Rosaliini Heikkonen
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore city, Singapore
| | - Ying Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore city, Singapore
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14
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Mizuguchi D, Yamamoto T, Omiya Y, Endo K, Tano K, Oya M, Takano S. Novel Screening Tool Using Non-linguistic Voice Features Derived from Simple Phrases to Detect Mild Cognitive Impairment and Dementia. JAR LIFE 2023; 12:72-76. [PMID: 37637273 PMCID: PMC10450207 DOI: 10.14283/jarlife.2023.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/13/2023] [Indexed: 08/29/2023]
Abstract
Appropriate intervention and care in detecting cognitive impairment early are essential to effectively prevent the progression of cognitive deterioration. Diagnostic voice analysis is a noninvasive and inexpensive screening method that could be useful for detecting cognitive deterioration at earlier stages such as mild cognitive impairment. We aimed to distinguish between patients with dementia or mild cognitive impairment and healthy controls by using purely acoustic features (i.e., nonlinguistic features) extracted from two simple phrases. Voice was analyzed on 195 recordings from 150 patients (age, 45-95 years). We applied a machine learning algorithm (LightGBM; Microsoft, Redmond, WA, USA) to test whether the healthy control, mild cognitive impairment, and dementia groups could be accurately classified, based on acoustic features. Our algorithm performed well: area under the curve was 0.81 and accuracy, 66.7% for the 3-class classification. Thus, our vocal biomarker is useful for automated assistance in diagnosing early cognitive deterioration.
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Affiliation(s)
| | | | | | - K Endo
- PST Inc., Yokohama, Japan
| | - K Tano
- Takeyama Hospital, Yokohama, Japan
| | - M Oya
- Takeyama Hospital, Yokohama, Japan
| | - S Takano
- Honjo Kodama Hospital, Honjo, Japan
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15
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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.
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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
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16
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Lanzi AM, Saylor AK, Fromm D, Liu H, MacWhinney B, Cohen ML. DementiaBank: Theoretical Rationale, Protocol, and Illustrative Analyses. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2023; 32:426-438. [PMID: 36791255 PMCID: PMC10171844 DOI: 10.1044/2022_ajslp-22-00281] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/01/2022] [Accepted: 11/25/2022] [Indexed: 05/12/2023]
Abstract
PURPOSE Dementia from Alzheimer's disease (AD) is characterized primarily by a significant decline in memory abilities; however, language abilities are also commonly affected and may precede the decline of other cognitive abilities. To study the progression of language, there is a need for open-access databases that can be used to build algorithms to produce translational models sensitive enough to detect early declines in language abilities. DementiaBank is an open-access repository of transcribed video/audio data from communicative interactions from people with dementia, mild cognitive impairment (MCI), and controls. The aims of this tutorial are to (a) describe the newly established standardized DementiaBank discourse protocol, (b) describe the Delaware corpus data, and (c) provide examples of automated linguistic analyses that can be conducted with the Delaware corpus data and describe additional DementiaBank resources. METHOD The DementiaBank discourse protocol elicits four types of discourse: picture description, story narrative, procedural, and personal narrative. The Delaware corpus currently includes data from 20 neurotypical adults and 33 adults with MCI from possible AD who completed the DementiaBank discourse protocol and a cognitive-linguistic battery. Language samples were video- and audio-recorded, transcribed, coded, and uploaded to DementiaBank. The protocol materials and transcription programs can be accessed for free via the DementiaBank website. RESULTS Illustrative analyses show the potential of the Delaware corpus data to help understand discourse metrics at the individual and group levels. In addition, they highlight analyses that could be used across TalkBank's other clinical banks (e.g., AphasiaBank). Information is also included on manual and automatic speech recognition transcription methods. CONCLUSIONS DementiaBank is a shared online database that can facilitate research efforts to address the gaps in knowledge about language changes associated with MCI and dementia from AD. Identifying early language markers could lead to improved assessment and treatment approaches for adults at risk for dementia.
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Affiliation(s)
- Alyssa M. Lanzi
- Department of Communication Sciences and Disorders, University of Delaware, Newark
- Delaware Center for Cognitive Aging Research, University of Delaware, Newark
| | - Anna K. Saylor
- Department of Communication Sciences and Disorders, University of Delaware, Newark
| | - Davida Fromm
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA
| | | | - Brian MacWhinney
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA
| | - Matthew L. Cohen
- Department of Communication Sciences and Disorders, University of Delaware, Newark
- Delaware Center for Cognitive Aging Research, University of Delaware, Newark
- Center for Health Assessment Research and Translation, University of Delaware, Newark
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17
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Mefford JA, Zhao Z, Heilier L, Xu M, Zhou G, Mace R, Sloane KL, Sheppard SM, Glenn S. Varied performance of picture description task as a screening tool across MCI subtypes. PLOS DIGITAL HEALTH 2023; 2:e0000197. [PMID: 36913425 PMCID: PMC10010512 DOI: 10.1371/journal.pdig.0000197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
A picture description task is a component of Miro Health's platform for self-administration of neurobehavioral assessments. Picture description has been used as a screening tool for identification of individuals with Alzheimer's disease and mild cognitive impairment (MCI), but currently requires in-person administration and scoring by someone with access to and familiarity with a scoring rubric. The Miro Health implementation allows broader use of this assessment through self-administration and automated processing, analysis, and scoring to deliver clinically useful quantifications of the users' speech production, vocal characteristics, and language. Picture description responses were collected from 62 healthy controls (HC), and 33 participants with MCI: 18 with amnestic MCI (aMCI) and 15 with non-amnestic MCI (naMCI). Speech and language features and contrasts between pairs of features were evaluated for differences in their distributions in the participant subgroups. Picture description features were selected and combined using penalized logistic regression to form risk scores for classification of HC versus MCI as well as HC versus specific MCI subtypes. A picture-description based risk score distinguishes MCI and HC with an area under the receiver operator curve (AUROC) of 0.74. When contrasting specific subtypes of MCI and HC, the classifiers have an AUROC of 0.88 for aMCI versus HC and and AUROC of 0.61 for naMCI versus HC. Tests of association of individual features or contrasts of pairs of features with HC versus aMCI identified 20 features with p-values below 5e-3 and False Discovery Rates (FDRs) at or below 0.113, and 61 contrasts with p-values below 5e-4 and FDRs at or below 0.132. Findings suggest that performance of picture description as a screening tool for MCI detection will vary greatly by MCI subtype or by the proportion of various subtypes in an undifferentiated MCI population.
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Affiliation(s)
- Joel A. Mefford
- Department of Neurology, University of California, Los Angeles, California, United States of America
| | - Zilong Zhao
- Miro Health, Inc., San Francisco, California, United States of America
| | - Leah Heilier
- Miro Health, Inc., San Francisco, California, United States of America
| | - Man Xu
- Miro Health, Inc., San Francisco, California, United States of America
| | - Guifeng Zhou
- Miro Health, Inc., San Francisco, California, United States of America
| | - Rachel Mace
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Kelly L. Sloane
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Shannon M. Sheppard
- Department of Communication Sciences & Disorders, Chapman University, Orange, California, United States of America
| | - Shenly Glenn
- Miro Health, Inc., San Francisco, California, United States of America
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18
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Liu G, Xue Z, Zhan L, Dodge HH, Zhou J. Detection of Mild Cognitive Impairment from Language Markers with Crossmodal Augmentation. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023; 28:7-18. [PMID: 36540960 PMCID: PMC9782729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Mild cognitive impairment is the prodromal stage of Alzheimer's disease. Its detection has been a critical task for establishing cohort studies and developing therapeutic interventions for Alzheimer's. Various types of markers have been developed for detection. For example, imaging markers from neuroimaging have shown great sensitivity, while its cost is still prohibitive for large-scale screening of early dementia. Recent advances from digital biomarkers, such as language markers, have provided an accessible and affordable alternative. While imaging markers give anatomical descriptions of the brain, language markers capture the behavior characteristics of early dementia subjects. Such differences suggest the benefits of auxiliary information from the imaging modality to improve the predictive power of unimodal predictive models based on language markers alone. However, one significant barrier to the joint analysis is that in typical cohorts, there are only very limited subjects that have both imaging and language modalities. To tackle this challenge, in this paper, we develop a novel crossmodal augmentation tool, which leverages auxiliary imaging information to improve the feature space of language markers so that a subject with only language markers can benefit from imaging information through the augmentation. Our experimental results show that the multi-modal predictive model trained with language markers and auxiliary imaging information significantly outperforms unimodal predictive models.
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Affiliation(s)
- Guangliang Liu
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
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19
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Wang R, Kuang C, Guo C, Chen Y, Li C, Matsumura Y, Ishimaru M, Van Pelt AJ, Chen F. Automatic Detection of Putative Mild Cognitive Impairment from Speech Acoustic Features in Mandarin-Speaking Elders. J Alzheimers Dis 2023; 95:901-914. [PMID: 37638439 DOI: 10.3233/jad-230373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
BACKGROUND To date, the reliable detection of mild cognitive impairment (MCI) remains a significant challenge for clinicians. Very few studies investigated the sensitivity of acoustic features in detecting Mandarin-speaking elders at risk for MCI, defined as "putative MCI" (pMCI). OBJECTIVE This study sought to investigate the possibility of using automatically extracted speech acoustic features to detect elderly people with pMCI and reveal the potential acoustic markers of cognitive decline at an early stage. METHODS Forty-one older adults with pMCI and 41 healthy elderly controls completed four reading tasks (syllable utterance, tongue twister, diadochokinesis, and short sentence reading), from which acoustic features were extracted automatically to train machine learning classifiers. Correlation analysis was employed to evaluate the relationship between classifier predictions and participants' cognitive ability measured by Mini-Mental State Examination 2. RESULTS Classification results revealed that some temporal features (e.g., speech rate, utterance duration, and the number of silent pauses), spectral features (e.g., variability of F1 and F2), and energy features (e.g., SD of peak intensity and SD of intensity range) were effective predictors of pMCI. The best classification result was achieved in the Random Forest classifier (accuracy = 0.81, AUC = 0.81). Correlation analysis uncovered a strong negative correlation between participants' cognitive test scores and the probability estimates of pMCI in the Random Forest classifier, and a modest negative correlation in the Support Vector Machine classifier. CONCLUSIONS The automatic acoustic analysis of speech could provide a promising non-invasive way to assess and monitor the early cognitive decline in Mandarin-speaking elders.
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Affiliation(s)
- Rumi Wang
- Rehabilitation Medicine Department, Speech and Language Pathology Therapy Section, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Chen Kuang
- School of Foreign Languages, Hunan University, Hunan, China
| | - Chengyu Guo
- School of Foreign Languages, Hunan University, Hunan, China
| | - Yong Chen
- Laboratory of Food Oral Processing, School of Food Science & Biotechnology, Zhejiang Gongshang University, Hangzhou, Zhejiang, China
| | - Canyang Li
- Rehabilitation Medicine Department, Speech and Language Pathology Therapy Section, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | | | | | - Alice J Van Pelt
- Section of Gastroenterology, Edward Hines, Jr. VA Hospital, Hines, IL, USA
- Division of Gastroenterology and Nutrition, Loyola University Stritch School of Medicine, Maywood, IL, USA
| | - Fei Chen
- School of Foreign Languages, Hunan University, Hunan, China
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20
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Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, Wang F, Cheng F, Luo Y. Multimodal machine learning in precision health: A scoping review. NPJ Digit Med 2022; 5:171. [PMID: 36344814 PMCID: PMC9640667 DOI: 10.1038/s41746-022-00712-8] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.
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Affiliation(s)
- Adrienne Kline
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Hanyin Wang
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Yikuan Li
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Saya Dennis
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Meghan Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Fei Wang
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Feixiong Cheng
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, 44195, OH, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA.
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Fristed E, Skirrow C, Meszaros M, Lenain R, Meepegama U, Cappa S, Aarsland D, Weston J. A remote speech-based AI system to screen for early Alzheimer's disease via smartphones. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12366. [PMID: 36348974 PMCID: PMC9632864 DOI: 10.1002/dad2.12366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/16/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022]
Abstract
Introduction Artificial intelligence (AI) systems leveraging speech and language changes could support timely detection of Alzheimer's disease (AD). Methods The AMYPRED study (NCT04828122) recruited 133 subjects with an established amyloid beta (Aβ) biomarker (66 Aβ+, 67 Aβ-) and clinical status (71 cognitively unimpaired [CU], 62 mild cognitive impairment [MCI] or mild AD). Daily story recall tasks were administered via smartphones and analyzed with an AI system to predict MCI/mild AD and Aβ positivity. Results Eighty-six percent of participants (115/133) completed remote assessments. The AI system predicted MCI/mild AD (area under the curve [AUC] = 0.85, ±0.07) but not Aβ (AUC = 0.62 ±0.11) in the full sample, and predicted Aβ in clinical subsamples (MCI/mild AD: AUC = 0.78 ±0.14; CU: AUC = 0.74 ±0.13) on short story variants (immediate recall). Long stories and delayed retellings delivered broadly similar results. Discussion Speech-based testing offers simple and accessible screening for early-stage AD.
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Affiliation(s)
| | | | | | | | | | - Stefano Cappa
- IUSS Cognitive Neuroscience (ICoN) CenterUniversity School for Advanced StudiesPaviaItaly
- IRCCS Mondino FoundationPaviaItaly
| | - Dag Aarsland
- Department of Old Age PsychiatryInstitute of PsychiatryPsychology & NeuroscienceKing's College LondonLondonUK
- Centre for Age‐Related DiseasesStavanger University HospitalStavangerNorway
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Soroski T, da Cunha Vasco T, Newton-Mason S, Granby S, Lewis C, Harisinghani A, Rizzo M, Conati C, Murray G, Carenini G, Field TS, Jang H. Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis. JMIR Aging 2022; 5:e33460. [PMID: 36129754 PMCID: PMC9536526 DOI: 10.2196/33460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 07/11/2022] [Accepted: 07/23/2022] [Indexed: 11/16/2022] Open
Abstract
Background Speech data for medical research can be collected noninvasively and in large volumes. Speech analysis has shown promise in diagnosing neurodegenerative disease. To effectively leverage speech data, transcription is important, as there is valuable information contained in lexical content. Manual transcription, while highly accurate, limits the potential scalability and cost savings associated with language-based screening. Objective To better understand the use of automatic transcription for classification of neurodegenerative disease, namely, Alzheimer disease (AD), mild cognitive impairment (MCI), or subjective memory complaints (SMC) versus healthy controls, we compared automatically generated transcripts against transcripts that went through manual correction. Methods We recruited individuals from a memory clinic (“patients”) with a diagnosis of mild-to-moderate AD, (n=44, 30%), MCI (n=20, 13%), SMC (n=8, 5%), as well as healthy controls (n=77, 52%) living in the community. Participants were asked to describe a standardized picture, read a paragraph, and recall a pleasant life experience. We compared transcripts generated using Google speech-to-text software to manually verified transcripts by examining transcription confidence scores, transcription error rates, and machine learning classification accuracy. For the classification tasks, logistic regression, Gaussian naive Bayes, and random forests were used. Results The transcription software showed higher confidence scores (P<.001) and lower error rates (P>.05) for speech from healthy controls compared with patients. Classification models using human-verified transcripts significantly (P<.001) outperformed automatically generated transcript models for both spontaneous speech tasks. This comparison showed no difference in the reading task. Manually adding pauses to transcripts had no impact on classification performance. However, manually correcting both spontaneous speech tasks led to significantly higher performances in the machine learning models. Conclusions We found that automatically transcribed speech data could be used to distinguish patients with a diagnosis of AD, MCI, or SMC from controls. We recommend a human verification step to improve the performance of automatic transcripts, especially for spontaneous tasks. Moreover, human verification can focus on correcting errors and adding punctuation to transcripts. However, manual addition of pauses is not needed, which can simplify the human verification step to more efficiently process large volumes of speech data.
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Affiliation(s)
- Thomas Soroski
- Vancouver Stroke Program and Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Thiago da Cunha Vasco
- Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, BC, Canada
| | - Sally Newton-Mason
- Vancouver Stroke Program and Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Saffrin Granby
- Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, BC, Canada
| | - Caitlin Lewis
- Vancouver Stroke Program and Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Anuj Harisinghani
- Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, BC, Canada
| | - Matteo Rizzo
- Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, BC, Canada
| | - Cristina Conati
- Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, BC, Canada
| | - Gabriel Murray
- School of Computing, University of the Fraser Valley, Abbotsford, BC, Canada
| | - Giuseppe Carenini
- Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, BC, Canada
| | - Thalia S Field
- Vancouver Stroke Program and Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Hyeju Jang
- Department of Computer Science, Faculty of Science, University of British Columbia, Vancouver, BC, Canada
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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]
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24
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Database Oriented Big Data Analysis Engine Based on Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4500684. [PMID: 36093505 PMCID: PMC9452003 DOI: 10.1155/2022/4500684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/04/2022] [Accepted: 07/23/2022] [Indexed: 12/01/2022]
Abstract
In recent years, with the development of enterprises to the Internet, the demand for cloud database is also growing, especially how to capture data quickly and efficiently through the database. In order to improve the data structure at all levels in the process of database analysis engine, this paper realizes the accurate construction and rapid analysis of cloud database based on big data analysis engine technology and deep learning wolf pack greedy algorithm. Through the deep learning strategy, a big data analysis engine system based on the deep learning model is constructed. The functions of deep learning technology, wolf greedy algorithm, and data analysis strategy in the cloud database analysis engine system are analyzed, as well as the functions of the whole analysis engine system. Finally, the accuracy and response speed of the cloud database analysis engine system are tested according to the known clustering data. The results show that compared with the traditional data analysis engine system with character search as the core, the database oriented big data analysis engine system based on a deep learning model and wolf swarm greedy algorithm has faster response speed and intelligence. The research application is that the proposed engine system can significantly improve the effect of the analysis engine and greatly improve the retrieval accuracy and analysis efficiency of fixed-point data in the database.
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25
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Ilias L, Askounis D. Explainable Identification of Dementia from Transcripts using Transformer Networks. IEEE J Biomed Health Inform 2022; 26:4153-4164. [PMID: 35511841 DOI: 10.1109/jbhi.2022.3172479] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Alzheimers disease (AD) is the main cause of dementia which is accompanied by loss of memory and may lead to severe consequences in peoples everyday life if not diagnosed on time. Very few works have exploited transformer-based networks and despite the high accuracy achieved, little work has been done in terms of model interpretability. In addition, although Mini-Mental State Exam (MMSE) scores are inextricably linked with the identification of dementia, research works face the task of dementia identification and the task of the prediction of MMSE scores as two separate tasks. In order to address these limitations, we employ several transformer-based models, with BERT achieving the highest accuracy accounting for 87.50%. Concurrently, we propose an interpretable method to detect AD patients based on siamese networks reaching accuracy up to 83.75%. Next, we introduce two multi-task learning models, where the main task refers to the identification of dementia (binary classification), while the auxiliary one corresponds to the identification of the severity of dementia (multiclass classification). Our model obtains accuracy equal to 86.25% on the detection of AD patients in the multi-task learning setting. Finally, we present some new methods to identify the linguistic patterns used by AD patients and non-AD ones, including text statistics, vocabulary uniqueness, word usage, correlations via a detailed linguistic analysis, and explainability techniques (LIME). Findings indicate significant differences in language between AD and non-AD patients.
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26
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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]
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27
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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.
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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
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Bertini F, Allevi D, Lutero G, Calzà L, Montesi D. An automatic Alzheimer’s disease classifier based on spontaneous spoken English. COMPUT SPEECH LANG 2022. [DOI: 10.1016/j.csl.2021.101298] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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29
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Many Changes in Speech through Aging Are Actually a Consequence of Cognitive Changes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042137. [PMID: 35206323 PMCID: PMC8871602 DOI: 10.3390/ijerph19042137] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 01/21/2022] [Accepted: 02/08/2022] [Indexed: 11/16/2022]
Abstract
Background: During aging, changes in human speech may arise because of the neurophysiological deterioration associated with age, or as the result of an impairment in the cognitive processes underlying speech production. Some speech parameters show specific alterations under the presence of dementia. The objective of our study is to identify which of these parameters change because of age, cognitive state, or the interaction of both. Methods: The sample includes 400 people over 55 years old, who were divided into four groups, according to their age. The cognitive state of the participants was assessed through the MMSE test and three ranks were stablished. Gender was also considered in the analysis. Results: Certain temporal, fluency, rhythm, amplitude and voice quality parameters were found to be related to the cognitive state, while disturbance parameters changed due to age. Frequency parameters were exclusively influenced by gender. Conclusions: Understanding how speech parameters are specifically affected by age, cognitive state, or the interaction of both, is determinant to advance in the use of speech as a clinical marker for the detection of cognitive impairments.
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Tang F, Chen J, Dodge HH, Zhou J. The Joint Effects of Acoustic and Linguistic Markers for Early Identification of Mild Cognitive Impairment. Front Digit Health 2022; 3:702772. [PMID: 35224534 PMCID: PMC8878676 DOI: 10.3389/fdgth.2021.702772] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 12/27/2021] [Indexed: 12/14/2022] Open
Abstract
In recent years, behavioral markers such as spoken language and lexical preferences have been studied in the early detection of mild cognitive impairment (MCI) using conversations. While the combination of linguistic and acoustic signals have been shown to be effective in detecting MCI, they have generally been restricted to structured conversations in which the interviewee responds to fixed prompts. In this study, we show that linguistic and acoustic features can be combined synergistically to identify MCI in semi-structured conversations. Using conversational data from an on-going clinical trial (Clinicaltrials.gov: NCT02871921), we find that the combination of linguistic and acoustic features on semi-structured conversations achieves a mean AUC of 82.7, significantly (p < 0.01) out-performing linguistic-only (74.9 mean AUC) or acoustic-only (65.0 mean AUC) detections on hold-out data. Additionally, features (linguistic, acoustic and combination) obtained from semi-structured conversations outperform their counterparts obtained from structured weekly conversations in identifying MCI. Some linguistic categories are significantly better at predicting MCI status (e.g., death, home) than others.
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Affiliation(s)
- Fengyi Tang
- Department of Computer Science of Engineering, Michigan State University, East Lansing, MI, United States
| | - Jun Chen
- Department of Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Hiroko H. Dodge
- Department of Neurology, Layton Aging and Alzheimer's Disease Center, Oregon Health & Science University, Portland, OR, United States
| | - Jiayu Zhou
- Department of Computer Science of Engineering, Michigan State University, East Lansing, MI, United States
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31
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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: 12] [Impact Index Per Article: 6.0] [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.
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32
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Hemrungrojn S, Tangwongchai S, Charoenboon T, Panasawat M, Supasitthumrong T, Chaipresertsud P, Maleevach P, Likitjaroen Y, Phanthumchinda K, Maes M. Use of the Montreal Cognitive Assessment Thai Version to Discriminate Amnestic Mild Cognitive Impairment from Alzheimer's Disease and Healthy Controls: Machine Learning Results. Dement Geriatr Cogn Disord 2021; 50:183-194. [PMID: 34325427 DOI: 10.1159/000517822] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 06/11/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The Montreal Cognitive Assessment (MoCA) is an effective and applicable screening instrument to confirm the diagnosis of amnestic mild cognitive impairment (aMCI) from patients with Alzheimer's disease (AD) and healthy controls (HCs). OBJECTIVES This study aimed to determine the reliability and validity of the following: (a) Thai translation of the MoCA (MoCA-Thai) and (b) delineate the key features of aMCI based on the MoCA subdomains. METHODS This study included 60 HCs, 61 aMCI patients, and 60 AD patients. The MoCA-Thai shows adequate psychometric properties including internal consistency, concurrent validity, test-retest validity, and inter-rater reliability. RESULTS The MoCA-Thai may be employed as a diagnostic criterion to make the diagnosis of aMCI, whereby aMCI patients are discriminated from HC with an area under the receiver-operating characteristic (AUC-ROC) curve of 0.813 and from AD patients with an AUC-ROC curve of 0.938. The best cutoff scores of the MoCA-Thai to discriminate aMCI from HC is ≤24 and from AD > 16. Neural network analysis showed that (a) aberrations in recall was the most important feature of aMCI versus HC with impairments in language and orientation being the second and third most important features and (b) aberrations in visuospatial skills and executive functions were the most important features of AD versus aMCI and that impairments in recall, language, and orientation but not attention, concentration, and working memory, further discriminated AD from aMCI. CONCLUSIONS The MoCA-Thai is an appropriate cognitive assessment tool to be used in the Thai population for the diagnosis of aMCI and AD.
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Affiliation(s)
- Solaphat Hemrungrojn
- Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand, .,Cognitive Fitness Research Group, Chulalongkorn University, Bangkok, Thailand,
| | | | - Thammanard Charoenboon
- Department of Clinical Epidemiology, Faculty of Medicine, Thammasat University, Prathumthani, Thailand
| | - Muthita Panasawat
- Department of Psychiatry, Faculty of Medicine, Thammasat University, Prathumthani, Thailand
| | | | | | | | - Yuttachai Likitjaroen
- Division of Neurology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Kammant Phanthumchinda
- Division of Neurology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Michael Maes
- Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.,Department of Psychiatry, Medical University of Plovdiv and Technological Center for Emergency Medicine, Plovdiv, Bulgaria.,IMPACT Strategic Research Centre, Deakin University, Geelong, Victoria, Australia
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33
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Readman MR, Polden M, Gibbs MC, Wareing L, Crawford TJ. The Potential of Naturalistic Eye Movement Tasks in the Diagnosis of Alzheimer's Disease: A Review. Brain Sci 2021; 11:brainsci11111503. [PMID: 34827502 PMCID: PMC8615459 DOI: 10.3390/brainsci11111503] [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] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/29/2021] [Accepted: 11/07/2021] [Indexed: 01/31/2023] Open
Abstract
Extensive research has demonstrated that eye-tracking tasks can effectively indicate cognitive impairment. For example, lab-based eye-tracking tasks, such as the antisaccade task, have robustly distinguished between people with Alzheimer’s disease (AD) and healthy older adults. Due to the neurodegeneration associated with AD, people with AD often display extended saccade latencies and increased error rates on eye-tracking tasks. Although the effectiveness of using eye tracking to identify cognitive impairment appears promising, research considering the utility of eye tracking during naturalistic tasks, such as reading, in identifying cognitive impairment is limited. The current review identified 39 articles assessing eye-tracking distinctions between people with AD, mild cognitive impairment (MCI), and healthy controls when completing naturalistic task (reading, real-life simulations, static image search) or a goal-directed task involving naturalistic stimuli. The results revealed that naturalistic tasks show promising biomarkers and distinctions between healthy older adults and AD participants, and therefore show potential to be used for diagnostic and monitoring purposes. However, only twelve articles included MCI participants and assessed the sensitivity of measures to detect cognitive impairment in preclinical stages. In addition, the review revealed inconsistencies within the literature, particularly when assessing reading tasks. We urge researchers to expand on the current literature in this area and strive to assess the robustness and sensitivity of eye-tracking measures in both AD and MCI populations on naturalistic tasks.
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Ntracha A, Iakovakis D, Hadjidimitriou S, Charisis VS, Tsolaki M, Hadjileontiadis LJ. Detection of Mild Cognitive Impairment Through Natural Language and Touchscreen Typing Processing. Front Digit Health 2021; 2:567158. [PMID: 34713039 PMCID: PMC8521910 DOI: 10.3389/fdgth.2020.567158] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 08/27/2020] [Indexed: 11/13/2022] Open
Abstract
Mild cognitive impairment (MCI), an identified prodromal stage of Alzheimer's Disease (AD), often evades detection in the early stages of the condition, when existing diagnostic methods are employed in the clinical setting. From an alternative perspective, smartphone interaction behavioral data, unobtrusively acquired in a non-clinical setting, can assist the screening and monitoring of MCI and its symptoms' progression. In this vein, the diagnostic ability of digital biomarkers, drawn from Fine Motor Impairment (FMI)- and Spontaneous Written Speech (SWS)-related data analysis, are examined here. In particular, keystroke dynamics derived from touchscreen typing activities, using Convolutional Neural Networks, along with linguistic features of SWS through Natural Language Processing (NLP), were used to distinguish amongst MCI patients and healthy controls (HC). Analytically, three indices of FMI (rigidity, bradykinesia and alternate finger tapping) and nine NLP features, related with lexical richness, grammatical, syntactical complexity, and word deficits, formed the feature space. The proposed approach was tested on two demographically matched groups of 11 MCI patients and 12 HC, having undergone the same neuropsychological tests, producing 4,930 typing sessions and 78 short texts, within 6 months, for analysis. A cascaded-classifier scheme was realized under three different feature combinations and validated via a Leave-One-Subject-Out cross-validation scheme. The acquired results have shown: (a) keystroke features with a k-NN classifier achieved an Area Under Curve (AUC) of 0.78 [95% confidence interval (CI):0.68-0.88; specificity/sensitivity (SP/SE): 0.64/0.92], (b) NLP features with a Logistic regression classifier achieved an AUC of 0.76 (95% CI: 0.65-0.85; SP/SE: 0.80/0.71), and (c) an ensemble model with the fusion of keystroke and NLP features resulted in AUC of 0.75 (95% CI:0.63-0.86; SP/SE 0.90/0.60). The current findings indicate the potentiality of new digital biomarkers to capture early stages of cognitive decline, providing a highly specific remote screening tool in-the-wild.
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Affiliation(s)
- Anastasia Ntracha
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stelios Hadjidimitriou
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileios S Charisis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Magda Tsolaki
- Third Department of Neurology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leontios J Hadjileontiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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Jang H, Soroski T, Rizzo M, Barral O, Harisinghani A, Newton-Mason S, Granby S, Stutz da Cunha Vasco TM, Lewis C, Tutt P, Carenini G, Conati C, Field TS. Classification of Alzheimer's Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data. Front Hum Neurosci 2021; 15:716670. [PMID: 34616282 PMCID: PMC8488259 DOI: 10.3389/fnhum.2021.716670] [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: 05/29/2021] [Accepted: 08/13/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification. The cohort includes data from two novel tasks not previously assessed in classification models for AD (pupil fixation and description of a pleasant past experience), as well as two established tasks (picture description and paragraph reading). Our dataset includes language and eye movement data from 79 memory clinic patients with diagnoses of mild-moderate AD, mild cognitive impairment (MCI), or subjective memory complaints (SMC), and 83 older adult controls. The analysis of the individual novel tasks showed similar classification accuracy when compared to established tasks, demonstrating their discriminative ability for memory clinic patients. Fusing the multimodal data across tasks yielded the highest overall AUC of 0.83 ± 0.01, indicating that the data from novel tasks are complementary to established tasks.
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Affiliation(s)
- Hyeju Jang
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Thomas Soroski
- Vancouver Stroke Program and Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Matteo Rizzo
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Oswald Barral
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Anuj Harisinghani
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Sally Newton-Mason
- Vancouver Stroke Program and Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Saffrin Granby
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | | | - Caitlin Lewis
- Vancouver Stroke Program and Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Pavan Tutt
- Vancouver Stroke Program and Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Giuseppe Carenini
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Cristina Conati
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Thalia S Field
- Vancouver Stroke Program and Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
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Slegers A, Chafouleas G, Montembeault M, Bedetti C, Welch AE, Rabinovici GD, Langlais P, Gorno-Tempini ML, Brambati SM. Connected speech markers of amyloid burden in primary progressive aphasia. Cortex 2021; 145:160-168. [PMID: 34731686 DOI: 10.1016/j.cortex.2021.09.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 05/16/2021] [Accepted: 09/26/2021] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Positron emission tomography (PET) amyloid imaging has become an important part of the diagnostic workup for patients with primary progressive aphasia (PPA) and uncertain underlying pathology. Here, we employ a semi-automated analysis of connected speech (CS) with a twofold objective. First, to determine if quantitative CS features can help select primary progressive aphasia (PPA) patients with a higher probability of a positive PET amyloid imaging result. Second, to examine the relevant group differences from a clinical perspective. METHODS 117 CS samples from a well-characterised cohort of PPA patients who underwent PET amyloid imaging were collected. Expert consensus established PET amyloid status for each patient, and 40% of the sample was amyloid positive. RESULTS Leave-one-out cross-validation yields 77% classification accuracy (sensitivity: 74%, specificity: 79%). DISCUSSION Our results confirm the potential of CS analysis as a screening tool. Discriminant CS features from lexical, syntactic, pragmatic, and semantic domains are discussed.
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Affiliation(s)
- Antoine Slegers
- Department of Psychology, Université de Montréal, Canada; Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Geneviève Chafouleas
- Department of Computer Science and Operational Research, Université de Montréal, Montréal, Canada
| | - Maxime Montembeault
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Christophe Bedetti
- Department of Psychology, Université de Montréal, Canada; Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Ariane E Welch
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Gil D Rabinovici
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Philippe Langlais
- Department of Computer Science and Operational Research, Université de Montréal, Montréal, Canada
| | - Maria L Gorno-Tempini
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Simona M Brambati
- Department of Psychology, Université de Montréal, Canada; Centre de Recherche de L'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada; Centre de recherche du Centre intégré universitaire de santé et de services sociaux du Nord-de-l'Île-de-Montréal, Montréal, Québec, Canada.
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Gosztolya G, Balogh R, Imre N, Egas-López JV, Hoffmann I, Vincze V, Tóth L, Devanand DP, Pákáski M, Kálmán J. Cross-lingual detection of mild cognitive impairment based on temporal parameters of spontaneous speech. COMPUT SPEECH LANG 2021. [DOI: 10.1016/j.csl.2021.101215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Clarke N, Barrick TR, Garrard P. A Comparison of Connected Speech Tasks for Detecting Early Alzheimer’s Disease and Mild Cognitive Impairment Using Natural Language Processing and Machine Learning. FRONTIERS IN COMPUTER SCIENCE 2021. [DOI: 10.3389/fcomp.2021.634360] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Alzheimer’s disease (AD) has a long pre-clinical period, and so there is a crucial need for early detection, including of Mild Cognitive Impairment (MCI). Computational analysis of connected speech using Natural Language Processing and machine learning has been found to indicate disease and could be utilized as a rapid, scalable test for early diagnosis. However, there has been a focus on the Cookie Theft picture description task, which has been criticized. Fifty participants were recruited – 25 healthy controls (HC), 25 mild AD or MCI (AD+MCI) – and these completed five connected speech tasks: picture description, a conversational map reading task, recall of an overlearned narrative, procedural recall and narration of a wordless picture book. A high-dimensional set of linguistic features were automatically extracted from each transcript and used to train Support Vector Machines to classify groups. Performance varied, with accuracy for HC vs. AD+MCI classification ranging from 62% using picture book narration to 78% using overlearned narrative features. This study shows that, importantly, the conditions of the speech task have an impact on the discourse produced, which influences accuracy in detection of AD beyond the length of the sample. Further, we report the features important for classification using different tasks, showing that a focus on the Cookie Theft picture description task may narrow the understanding of how early AD pathology impacts speech.
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Wang T, Hong Y, Wang Q, Su R, Ng ML, Xu J, Wang L, Yan N. Identification of Mild Cognitive Impairment Among Chinese Based on Multiple Spoken Tasks. J Alzheimers Dis 2021; 82:185-204. [PMID: 33998535 DOI: 10.3233/jad-201387] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Previous studies explored the use of noninvasive biomarkers of speech and language for the detection of mild cognitive impairment (MCI). Yet, most of them employed single task which might not have adequately captured all aspects of their cognitive functions. OBJECTIVE The present study aimed to achieve the state-of-the-art accuracy in detecting individuals with MCI using multiple spoken tasks and uncover task-specific contributions with a tentative interpretation of features. METHODS Fifty patients clinically diagnosed with MCI and 60 healthy controls completed three spoken tasks (picture description, semantic fluency, and sentence repetition), from which multidimensional features were extracted to train machine learning classifiers. With a late-fusion configuration, predictions from multiple tasks were combined and correlated with the participants' cognitive ability assessed using the Montreal Cognitive Assessment (MoCA). Statistical analyses on pre-defined features were carried out to explore their association with the diagnosis. RESULTS The late-fusion configuration could effectively boost the final classification result (SVM: F1 = 0.95; RF: F1 = 0.96; LR: F1 = 0.93), outperforming each individual task classifier. Besides, the probability estimates of MCI were strongly correlated with the MoCA scores (SVM: -0.74; RF: -0.71; LR: -0.72). CONCLUSION Each single task tapped more dominantly to distinct cognitive processes and have specific contributions to the prediction of MCI. Specifically, picture description task characterized communications at the discourse level, while semantic fluency task was more specific to the controlled lexical retrieval processes. With greater demands on working memory load, sentence repetition task uncovered memory deficits through modified speech patterns in the reproduced sentences.
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Affiliation(s)
- Tianqi Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China.,Speech Science Laboratory, The University of Hong Kong, Hong Kong, China
| | - Yin Hong
- Health Management Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Quanyi Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China
| | - Rongfeng Su
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China
| | - Manwa Lawrence Ng
- Speech Science Laboratory, The University of Hong Kong, Hong Kong, China
| | - Jun Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lan Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China
| | - Nan Yan
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China
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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: 58] [Impact Index Per Article: 19.3] [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.
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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
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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]
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S AA, Ranjan U, Sharma M, Dutt S. Identification of Patterns of Cognitive Impairment for Early Detection of Dementia. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5498-5501. [PMID: 33019224 DOI: 10.1109/embc44109.2020.9175495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Early detection of dementia is crucial to devise effective interventions. Comprehensive cognitive tests, while being the most accurate means of diagnosis, are long and tedious, thus limiting their applicability to a large population, especially when periodic assessments are needed. The problem is compounded by the fact that people have differing patterns of cognitive impairment as they progress to different forms of dementia. This paper presents a novel scheme by which individual-specific patterns of impairment can be identified and used to devise personalized tests for periodic follow-up. Patterns of cognitive impairment are initially learned from a population cluster of combined normals and cognitively impaired subjects, using a set of standardized cognitive tests. Impairment patterns in the population are identified using a 2-step procedure involving an ensemble wrapper feature selection followed by cluster identification and analysis. These patterns have been shown to correspond to clinically accepted variants of Mild Cognitive Impairment (MCI), a prodrome of dementia. The learned clusters of patterns can subsequently be used to identify the most likely route of cognitive impairment, even for pre-symptomatic and apparently normal people. Baseline data of 24,000 subjects from the NACC database was used for the study.
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Clarke N, Foltz P, Garrard P. How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease. Cortex 2020; 129:446-463. [PMID: 32622173 DOI: 10.1016/j.cortex.2020.05.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 01/30/2020] [Accepted: 05/07/2020] [Indexed: 12/28/2022]
Abstract
Natural Language Processing (NLP) is an ever-growing field of computational science that aims to model natural human language. Combined with advances in machine learning, which learns patterns in data, it offers practical capabilities including automated language analysis. These approaches have garnered interest from clinical researchers seeking to understand the breakdown of language due to pathological changes in the brain, offering fast, replicable and objective methods. The study of Alzheimer's disease (AD), and preclinical Mild Cognitive Impairment (MCI), suggests that changes in discourse (connected speech or writing) may be key to early detection of disease. There is currently no disease-modifying treatment for AD, the leading cause of dementia in people over the age of 65, but detection of those at risk of developing the disease could help with the identification and testing of medications which can take effect before the underlying pathology has irreversibly spread. We outline important components of natural language, as well as NLP tools and approaches with which they can be extracted, analysed and used for disease identification and risk prediction. We review literature using these tools to model discourse across the spectrum of AD, including the contribution of machine learning approaches and Automatic Speech Recognition (ASR). We conclude that NLP and machine learning techniques are starting to greatly enhance research in the field, with measurable and quantifiable language components showing promise for early detection of disease, but there remain research and practical challenges for clinical implementation of these approaches. Challenges discussed include the availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability.
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Affiliation(s)
- Natasha Clarke
- Neurosciences Research Centre, Molecular & Clinical Sciences Research Institute, St George's, University of London, Cranmer Terrace, London, UK.
| | - Peter Foltz
- Institute of Cognitive Science, University of Colorado, Boulder, USA.
| | - Peter Garrard
- Neurosciences Research Centre, Molecular & Clinical Sciences Research Institute, St George's, University of London, Cranmer Terrace, London, UK.
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de la Fuente Garcia S, Ritchie CW, Luz S. Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review. J Alzheimers Dis 2020; 78:1547-1574. [PMID: 33185605 PMCID: PMC7836050 DOI: 10.3233/jad-200888] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND Language is a valuable source of clinical information in Alzheimer's disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. OBJECTIVE Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer's disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. METHODS Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. RESULTS From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). CONCLUSION Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.
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Affiliation(s)
| | - Craig W. Ritchie
- Centre for Clinical Brain Sciences, The University of Edinburgh, Scotland, UK
| | - Saturnino Luz
- Usher Institute, Edinburgh Medical School, The University of Edinburgh, Scotland, UK
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