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Burke E, Gunstad J, Pavlenko O, Hamrick P. Distinguishable features of spontaneous speech in Alzheimer's clinical syndrome and healthy controls. NEUROPSYCHOLOGY, DEVELOPMENT, AND COGNITION. SECTION B, AGING, NEUROPSYCHOLOGY AND COGNITION 2024; 31:575-586. [PMID: 37272884 PMCID: PMC10696129 DOI: 10.1080/13825585.2023.2221020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 05/29/2023] [Indexed: 06/06/2023]
Abstract
There is growing evidence that subtle changes in spontaneous speech may reflect early pathological changes in cognitive function. Recent work has found that lexical-semantic features of spontaneous speech predict cognitive dysfunction in individuals with mild cognitive impairment (MCI). The current study assessed whether Ostrand and Gunstad's (OG) lexical-semantic features extend to predicting cognitive status in a sample of individuals with Alzheimer's clinical syndrome (ACS) and healthy controls. Four additional (New) speech indices shown to be important in language processing research were also explored in this sample to extend prior work. Speech transcripts of the Cookie Theft Task from 81 individuals with ACS (Mage = 72.7 years, SD = 8.80, 70.4% female) and 61 healthy controls (HC) (Mage = 63.9 years, SD = 8.52, 62.3% female) from Dementia Bank were analyzed. Random forest and logistic machine learning techniques examined whether subject-level lexical-semantic features could be used to accurately discriminate those with ACS from HC. Results showed that logistic models with the New lexical-semantic features obtained good classification accuracy (78.4%), but the OG features had wider success across machine learning model types. In terms of sensitivity and specificity, the random forest model trained on the OG features was the most balanced. Findings from the current study suggest that features of spontaneous speech used to predict MCI may also distinguish between individuals with ACS and healthy controls. Future work should evaluate these lexical-semantic features in pre-clinical persons to further explore their potential to assist with early detection through speech analysis.
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Affiliation(s)
- Erin Burke
- Department of Psychological Sciences, Kent State University
| | - John Gunstad
- Department of Psychological Sciences, Kent State University
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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 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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Lukic S, Fan Z, García AM, Welch AE, Ratnasiri BM, Wilson SM, Henry ML, Vonk J, Deleon J, Miller BL, Miller Z, Mandelli ML, Gorno-Tempini ML. Discriminating nonfluent/agrammatic and logopenic PPA variants with automatically extracted morphosyntactic measures from connected speech. Cortex 2024; 173:34-48. [PMID: 38359511 DOI: 10.1016/j.cortex.2023.12.013] [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: 05/24/2023] [Revised: 10/15/2023] [Accepted: 12/12/2023] [Indexed: 02/17/2024]
Abstract
Morphosyntactic assessments are important for characterizing individuals with nonfluent/agrammatic variant primary progressive aphasia (nfvPPA). Yet, standard tests are subject to examiner bias and often fail to differentiate between nfvPPA and logopenic variant PPA (lvPPA). Moreover, relevant neural signatures remain underexplored. Here, we leverage natural language processing tools to automatically capture morphosyntactic disturbances and their neuroanatomical correlates in 35 individuals with nfvPPA relative to 10 healthy controls (HC) and 26 individuals with lvPPA. Participants described a picture, and ensuing transcripts were analyzed via part-of-speech tagging to extract sentence-related features (e.g., subordinating and coordinating conjunctions), verbal-related features (e.g., tense markers), and nominal-related features (e.g., subjective and possessive pronouns). Gradient boosting machines were used to classify between groups using all features. We identified the most discriminant morphosyntactic marker via a feature importance algorithm and examined its neural correlates via voxel-based morphometry. Individuals with nfvPPA produced fewer morphosyntactic elements than the other two groups. Such features robustly discriminated them from both individuals with lvPPA and HCs with an AUC of .95 and .82, respectively. The most discriminatory feature corresponded to subordinating conjunctions was correlated with cortical atrophy within the left posterior inferior frontal gyrus across groups (pFWE < .05). Automated morphosyntactic analysis can efficiently differentiate nfvPPA from lvPPA. Also, the most sensitive morphosyntactic markers correlate with a core atrophy region of nfvPPA. Our approach, thus, can contribute to a key challenge in PPA diagnosis.
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Affiliation(s)
- Sladjana Lukic
- University of California, San Francisco Memory and Aging Center, CA, USA; Ruth S. Ammon College of Education and Health Sciences, Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY, USA.
| | - Zekai Fan
- Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California, San Francisco, CA, 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
| | - Ariane E Welch
- Ruth S. Ammon College of Education and Health Sciences, Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY, USA
| | | | - Stephen M Wilson
- School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, QLD, Australia
| | - Maya L Henry
- University of Texas at Austin Moody College of Communication, Austin, TX, USA
| | - Jet Vonk
- University of California, San Francisco Memory and Aging Center, CA, USA
| | - Jessica Deleon
- University of California, San Francisco Memory and Aging Center, CA, USA
| | - Bruce L Miller
- University of California, San Francisco Memory and Aging Center, CA, USA
| | - Zachary Miller
- University of California, San Francisco Memory and Aging Center, CA, USA
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Lü W, Zhang M, Yu W, Kuang W, Chen L, Zhang W, Yu J, Lü Y. Differentiating Alzheimer's disease from mild cognitive impairment: a quick screening tool based on machine learning. BMJ Open 2023; 13:e073011. [PMID: 38070931 PMCID: PMC10729043 DOI: 10.1136/bmjopen-2023-073011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder characterised by cognitive decline, behavioural and psychological symptoms of dementia (BPSD) and impairment of activities of daily living (ADL). Early differentiation of AD from mild cognitive impairment (MCI) is necessary. METHODS A total of 458 patients newly diagnosed with AD and MCI were included. Eleven batteries were used to evaluate ADL, BPSD and cognitive function (ABC). Machine learning approaches including XGboost, classification and regression tree, Bayes, support vector machines and logical regression were used to build and verify the new tool. RESULTS The Alzheimer's Disease Assessment Scale (ADAS-cog) word recognition task showed the best importance in judging AD and MCI, followed by correct numbers of auditory verbal learning test delay recall and ADAS-cog orientation. We also provided a selected ABC-Scale that covered ADL, BPSD and cognitive function with an estimated completion time of 18 min. The sensitivity was improved in the four models. CONCLUSION The quick screen ABC-Scale covers three dimensions of ADL, BPSD and cognitive function with good efficiency in differentiating AD from MCI.
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Affiliation(s)
- Wenqi Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Psychiatry, West China Hospital, Sichuan University, Chengdu, China
| | - Meiwei Zhang
- College of Electrical Engineering, Chongqing University, Chongqing, China
| | - Weihua Yu
- Institutes of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital, Sichuan University, Chengdu, China
| | - Lihua Chen
- Institutes of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Wenbo Zhang
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Juan Yu
- College of Electrical Engineering, Chongqing University, Chongqing, China
| | - Yang Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Dávila G, Torres-Prioris MJ, López-Barroso D, Berthier ML. Turning the Spotlight to Cholinergic Pharmacotherapy of the Human Language System. CNS Drugs 2023; 37:599-637. [PMID: 37341896 PMCID: PMC10374790 DOI: 10.1007/s40263-023-01017-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/24/2023] [Indexed: 06/22/2023]
Abstract
Even though language is essential in human communication, research on pharmacological therapies for language deficits in highly prevalent neurodegenerative and vascular brain diseases has received little attention. Emerging scientific evidence suggests that disruption of the cholinergic system may play an essential role in language deficits associated with Alzheimer's disease and vascular cognitive impairment, including post-stroke aphasia. Therefore, current models of cognitive processing are beginning to appraise the implications of the brain modulator acetylcholine in human language functions. Future work should be directed further to analyze the interplay between the cholinergic system and language, focusing on identifying brain regions receiving cholinergic innervation susceptible to modulation with pharmacotherapy to improve affected language domains. The evaluation of language deficits in pharmacological cholinergic trials for Alzheimer's disease and vascular cognitive impairment has thus far been limited to coarse-grained methods. More precise, fine-grained language testing is needed to refine patient selection for pharmacotherapy to detect subtle deficits in the initial phases of cognitive decline. Additionally, noninvasive biomarkers can help identify cholinergic depletion. However, despite the investigation of cholinergic treatment for language deficits in Alzheimer's disease and vascular cognitive impairment, data on its effectiveness are insufficient and controversial. In the case of post-stroke aphasia, cholinergic agents are showing promise, particularly when combined with speech-language therapy to promote trained-dependent neural plasticity. Future research should explore the potential benefits of cholinergic pharmacotherapy in language deficits and investigate optimal strategies for combining these agents with other therapeutic approaches.
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Affiliation(s)
- Guadalupe Dávila
- Cognitive Neurology and Aphasia Unit, Centro de Investigaciones Médico-Sanitarias, University of Malaga, Marqués de Beccaria 3, 29010, Malaga, Spain
- Instituto de Investigación Biomédica de Malaga-IBIMA, Malaga, Spain
- Department of Psychobiology and Methodology of Behavioral Sciences, Faculty of Psychology and Speech Therapy, University of Malaga, Malaga, Spain
- Language Neuroscience Research Laboratory, Faculty of Psychology and Speech Therapy, University of Malaga, Malaga, Spain
| | - María José Torres-Prioris
- Cognitive Neurology and Aphasia Unit, Centro de Investigaciones Médico-Sanitarias, University of Malaga, Marqués de Beccaria 3, 29010, Malaga, Spain
- Instituto de Investigación Biomédica de Malaga-IBIMA, Malaga, Spain
- Department of Psychobiology and Methodology of Behavioral Sciences, Faculty of Psychology and Speech Therapy, University of Malaga, Malaga, Spain
- Language Neuroscience Research Laboratory, Faculty of Psychology and Speech Therapy, University of Malaga, Malaga, Spain
| | - Diana López-Barroso
- Cognitive Neurology and Aphasia Unit, Centro de Investigaciones Médico-Sanitarias, University of Malaga, Marqués de Beccaria 3, 29010, Malaga, Spain
- Instituto de Investigación Biomédica de Malaga-IBIMA, Malaga, Spain
- Department of Psychobiology and Methodology of Behavioral Sciences, Faculty of Psychology and Speech Therapy, University of Malaga, Malaga, Spain
- Language Neuroscience Research Laboratory, Faculty of Psychology and Speech Therapy, University of Malaga, Malaga, Spain
| | - Marcelo L Berthier
- Cognitive Neurology and Aphasia Unit, Centro de Investigaciones Médico-Sanitarias, University of Malaga, Marqués de Beccaria 3, 29010, Malaga, Spain.
- Instituto de Investigación Biomédica de Malaga-IBIMA, Malaga, Spain.
- Language Neuroscience Research Laboratory, Faculty of Psychology and Speech Therapy, University of Malaga, Malaga, Spain.
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Nasrolahzadeh M, Rahnamayan S, Haddadnia J. Alzheimer’s disease diagnosis using genetic programming based on higher order spectra features. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2021.100225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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7
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Sanz C, Carrillo F, Slachevsky A, Forno G, Gorno Tempini ML, Villagra R, Ibáñez A, Tagliazucchi E, García AM. Automated text-level semantic markers of Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12276. [PMID: 35059492 PMCID: PMC8759093 DOI: 10.1002/dad2.12276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 11/04/2021] [Accepted: 11/15/2021] [Indexed: 11/18/2022]
Abstract
INTRODUCTION Automated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer's disease dementia (ADD). Yet, most research is undermined by low interpretability and specificity. METHODS Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate ADD patients from healthy controls (HCs) based on automated measures of domains typically affected in ADD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson's disease (PD) patients. RESULTS Relative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly discriminated between ADD patients and HC, while yielding near-chance classification between PD patients and HCs. DISCUSSION Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.
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Affiliation(s)
- Camila Sanz
- Departamento de FísicaUniversidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA‐CONICET)Pabellón ICiudad Universitaria (1428)CABABuenos AiresArgentina
| | - Facundo Carrillo
- Applied Artificial Intelligence Lab (ICC‐CONICET)Pabellón ICiudad Universitaria (1428)CABABuenos AiresArgentina
| | - Andrea Slachevsky
- Memory and Neuropsychiatric Clinic, Neurology Department, Hospital del Salvador (7500000), SSMO & Faculty of Medicine (8380000)University of ChileSantiagoChile
- Center for Brain Health and Metabolism (GERO) (7500922)SantiagoChile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department, Institute of Biomedical Sciences (ICBM), Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile (7500922)University of ChileSantiagoChile
- Servicio de Neurología, Departamento de MedicinaClínica Alemana‐Universidad del Desarrollo (7550000)SantiagoChile
- East Neuroscience Department, Faculty of Medicine (7650567)University of ChileSantiagoChile
| | - Gonzalo Forno
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department, Institute of Biomedical Sciences (ICBM), Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile (7500922)University of ChileSantiagoChile
- School of PsychologyUniversidad de los Andes (7550000)SantiagoChile
- Alzheimer's and other cognitive disorders groupInstitute of Neurosciences (08035)University of BarcelonaBarcelonaSpain
| | - Maria Luisa Gorno Tempini
- Memory and Aging CenterDepartment of Neurology (94143)University of CaliforniaSan FranciscoCaliforniaUSA
| | - Roque Villagra
- Center for Brain Health and Metabolism (GERO) (7500922)SantiagoChile
- East Neuroscience Department, Faculty of Medicine (7650567)University of ChileSantiagoChile
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat) (7550000)Universidad Adolfo IbáñezSantiagoChile
- Cognitive Neuroscience Center (1644)Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (1425)Buenos AiresArgentina
- Global Brain Health Institute (94143)University of California‐San Francisco, San Francisco, California, USA; and Trinity College Dublin (D02), Dublin, Ireland
| | - Enzo Tagliazucchi
- Departamento de FísicaUniversidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA‐CONICET)Pabellón ICiudad Universitaria (1428)CABABuenos AiresArgentina
- Latin American Brain Health Institute (BrainLat) (7550000)Universidad Adolfo IbáñezSantiagoChile
| | - Adolfo M. García
- Cognitive Neuroscience Center (1644)Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (1425)Buenos AiresArgentina
- Global Brain Health Institute (94143)University of California‐San Francisco, San Francisco, California, USA; and Trinity College Dublin (D02), Dublin, Ireland
- Departamento de Lingüística y LiteraturaFacultad de Humanidades (9160000)Universidad de Santiago de ChileSantiagoChile
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Li Z, Jiang X, Wang Y, Kim Y. Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data. Emerg Top Life Sci 2021; 5:765-777. [PMID: 34881778 PMCID: PMC8786302 DOI: 10.1042/etls20210249] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/05/2021] [Accepted: 11/17/2021] [Indexed: 01/26/2023]
Abstract
Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.
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Affiliation(s)
- Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Xiaoqian Jiang
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, U.S.A
| | - Yizhuo Wang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A
| | - Yejin Kim
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, U.S.A
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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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Zhou D, Yuan J, Si J. Health issue identification in social media based on multi-task hierarchical neural networks with topic attention. Artif Intell Med 2021; 118:102119. [PMID: 34412842 DOI: 10.1016/j.artmed.2021.102119] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE Health issue identification in social media is to predict whether the writers have a disease based on their posts. Numerous posts and comments are shared on social media by users. Certain posts may reflect writers' health condition, which can be employed for health issue identification. Usually, the health issue identification problem is formulated as a classification task. METHODS AND MATERIAL In this paper, we propose novel multi-task hierarchical neural networks with topic attention for identifying health issue based on posts collected from the social media platforms. Specifically, the model incorporates the hierarchical relationship among the document, sentences, and words via bidirectional gated recurrent units (BiGRUs). The global topic information shared across posts is incorporated with the hidden states of BiGRUs to obtain the topic-enhanced attention weights for words. In addition, tasks of predicting whether the writers suffer from a disease (health issue identification) and predicting the specific domain of the posts (domain category classification) are learned jointly in multi-task mechanism. RESULTS The proposed method is evaluated on two datasets: dementia issue dataset and depression issue dataset. The proposed approach achieves 98.03% and 88.28% F-1 score on two datasets, outperforming the state-of-the-art approach by 0.73% and 0.4% respectively. Further experimental analysis shows the effectiveness of incorporating both the multi-task learning framework and topic attention mechanism.
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Affiliation(s)
- Deyu Zhou
- School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, Jiangsu Province 210096, China.
| | - Jiale Yuan
- School of Cyber Science and Engineering, Southeast University, Nanjing, Jiangsu Province 210096, China..
| | - Jiasheng Si
- School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, Jiangsu Province 210096, China.
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Roshanzamir A, Aghajan H, Soleymani Baghshah M. Transformer-based deep neural network language models for Alzheimer's disease risk assessment from targeted speech. BMC Med Inform Decis Mak 2021; 21:92. [PMID: 33750385 PMCID: PMC7971114 DOI: 10.1186/s12911-021-01456-3] [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: 07/31/2020] [Accepted: 02/23/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We developed transformer-based deep learning models based on natural language processing for early risk assessment of Alzheimer's disease from the picture description test. METHODS The lack of large datasets poses the most important limitation for using complex models that do not require feature engineering. Transformer-based pre-trained deep language models have recently made a large leap in NLP research and application. These models are pre-trained on available large datasets to understand natural language texts appropriately, and are shown to subsequently perform well on classification tasks with small training sets. The overall classification model is a simple classifier on top of the pre-trained deep language model. RESULTS The models are evaluated on picture description test transcripts of the Pitt corpus, which contains data of 170 AD patients with 257 interviews and 99 healthy controls with 243 interviews. The large bidirectional encoder representations from transformers (BERTLarge) embedding with logistic regression classifier achieves classification accuracy of 88.08%, which improves the state-of-the-art by 2.48%. CONCLUSIONS Using pre-trained language models can improve AD prediction. This not only solves the problem of lack of sufficiently large datasets, but also reduces the need for expert-defined features.
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Affiliation(s)
- Alireza Roshanzamir
- Department of Computer Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran
| | - Hamid Aghajan
- Department of Electrical Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran
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Themistocleous C, Webster K, Afthinos A, Tsapkini K. Part of Speech Production in Patients With Primary Progressive Aphasia: An Analysis Based on Natural Language Processing. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2021; 30:466-480. [PMID: 32697669 PMCID: PMC8702871 DOI: 10.1044/2020_ajslp-19-00114] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 02/14/2020] [Accepted: 05/13/2020] [Indexed: 06/11/2023]
Abstract
Background Primary progressive aphasia (PPA) is a neurodegenerative disorder characterized by a progressive decline of language functions. Its symptoms are grouped into three PPA variants: nonfluent PPA, logopenic PPA, and semantic PPA. Grammatical deficiencies differ depending on the PPA variant. Aims This study aims to determine the differences between PPA variants with respect to part of speech (POS) production and to identify morphological markers that classify PPA variants using machine learning. By fulfilling these aims, the overarching goal is to provide objective measures that can facilitate clinical diagnosis, evaluation, and prognosis. Method and Procedure Connected speech productions from PPA patients produced in a picture description task were transcribed, and the POS class of each word was estimated using natural language processing, namely, POS tagging. We then implemented a twofold analysis: (a) linear regression to determine how patients with nonfluent PPA, semantic PPA, and logopenic PPA variants differ in their POS productions and (b) a supervised classification analysis based on POS using machine learning models (i.e., random forests, decision trees, and support vector machines) to subtype PPA variants and generate feature importance (FI). Outcome and Results Using an automated analysis of a short picture description task, this study showed that content versus function words can distinguish patients with nonfluent PPA, semantic PPA, and logopenic PPA variants. Verbs were less important as distinguishing features of patients with different PPA variants than earlier thought. Finally, the study showed that among the most important distinguishing features of PPA variants were elaborative speech elements, such as adjectives and adverbs.
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Affiliation(s)
| | - Kimberly Webster
- Department of Otolaryngology, Johns Hopkins Medicine, Baltimore MD
| | | | - Kyrana Tsapkini
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Cognitive Science, Johns Hopkins University, Baltimore MD
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13
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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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14
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Jarrold W, Rofes A, Wilson S, Pressman P, Stabler E, Gorno-Tempini M. A "Verbal Thermometer" for Assessing Neurodegenerative Disease: Automated Measurement of Pronoun and Verb Ratio from Speech. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5831-5837. [PMID: 33019300 PMCID: PMC7959106 DOI: 10.1109/embc44109.2020.9176185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Clinicians often use speech to characterize neurodegenerative disorders. Such characterizations require clinical judgment, which is subjective and can require extensive training. Quantitative Production Analysis (QPA) can be used to obtain objective quantifiable assessments of patient functioning. However, such human-based analyses of speech are costly and time consuming. Inexpensive off-the-shelf technologies such as speech recognition and part of speech taggers may avoid these problems. This study evaluates the ability of an automatic speech to text transcription system and a part of speech tagger to assist with measuring pronoun and verb ratios, measures based on QPA. Five participant groups provided spontaneous speech samples. One group consisted of healthy controls, while the remaining groups represented four subtypes of frontotemporal dementia. Findings indicated measurement of pronoun and verb ratio was robust despite errors introduced by automatic transcription and the tagger and despite these off-the-shelf products not having been trained on the language obtained from speech of the included population.
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15
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Slegers A, Filiou RP, Montembeault M, Brambati SM. Connected Speech Features from Picture Description in Alzheimer's Disease: A Systematic Review. J Alzheimers Dis 2019; 65:519-542. [PMID: 30103314 DOI: 10.3233/jad-170881] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The language changes that occur over the course of Alzheimer's disease (AD) can impact communication abilities and have profound functional consequences. Picture description tasks can be used to approximate everyday communication abilities of AD patients. As various methods and variables have been studied over the years, current knowledge about the most affected features of AD discourse in the context of picture descriptions is difficult to summarize. This systematic review aims to provide researchers with an overview of the most common areas of impairment in AD discourse as they appear in picture description tasks. Based on the 44 articles fulfilling inclusion criteria, our findings reflect a multidimensional pattern of changes in the production (speech rate), syntactic (length of utterance), lexical (word-frequency and use of pronouns), fluency (repetitions and word-finding difficulties), semantic (information units), and discourse (efficiency) domains. We discuss our findings in the light of current research and point to potential scientific and clinical uses of picture description tasks in the context of AD.
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Affiliation(s)
- Antoine Slegers
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada
| | - Renée-Pier Filiou
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada
| | - Maxime Montembeault
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada
| | - Simona Maria Brambati
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada
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16
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Fraser KC, Lundholm Fors K, Kokkinakis D. Multilingual word embeddings for the assessment of narrative speech in mild cognitive impairment. COMPUT SPEECH LANG 2019. [DOI: 10.1016/j.csl.2018.07.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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17
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Kautzky A, Seiger R, Hahn A, Fischer P, Krampla W, Kasper S, Kovacs GG, Lanzenberger R. Prediction of Autopsy Verified Neuropathological Change of Alzheimer's Disease Using Machine Learning and MRI. Front Aging Neurosci 2018; 10:406. [PMID: 30618713 PMCID: PMC6295575 DOI: 10.3389/fnagi.2018.00406] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 11/26/2018] [Indexed: 12/29/2022] Open
Abstract
Background: Alzheimer’s disease (AD) is the most common form of dementia. While neuropathological changes pathognomonic for AD have been defined, early detection of AD prior to cognitive impairment in the clinical setting is still lacking. Pioneer studies applying machine learning to magnetic-resonance imaging (MRI) data to predict mild cognitive impairment (MCI) or AD have yielded high accuracies, however, an algorithm predicting neuropathological change is still lacking. The objective of this study was to compute a prediction model supporting a more distinct diagnostic criterium for AD compared to clinical presentation, allowing identification of hallmark changes even before symptoms occur. Methods: Autopsy verified neuropathological changes attributed to AD, as described by a combined score for Aβ-peptides, neurofibrillary tangles and neuritic plaques issued by the National Institute on Aging – Alzheimer’s Association (NIAA), the ABC score for AD, were predicted from structural MRI data with RandomForest (RF). MRI scans were performed at least 2 years prior to death. All subjects derive from the prospective Vienna Trans-Danube Aging (VITA) study that targeted all 1750 inhabitants of the age of 75 in the starting year of 2000 in two districts of Vienna and included irregular follow-ups until death, irrespective of clinical symptoms or diagnoses. For 68 subjects MRI as well as neuropathological data were available and 49 subjects (mean age at death: 82.8 ± 2.9, 29 female) with sufficient MRI data quality were enrolled for further statistical analysis using nested cross-validation (CV). The decoding data of the inner loop was used for variable selection and parameter optimization with a fivefold CV design, the new data of the outer loop was used for model validation with optimal settings in a fivefold CV design. The whole procedure was performed ten times and average accuracies with standard deviations were reported. Results: The most informative ROIs included caudal and rostral anterior cingulate gyrus, entorhinal, fusiform and insular cortex and the subcortical ROIs anterior corpus callosum and the left vessel, a ROI comprising lacunar alterations in inferior putamen and pallidum. The resulting prediction models achieved an average accuracy for a three leveled NIAA AD score of 0.62 within the decoding sets and of 0.61 for validation sets. Higher accuracies of 0.77 for both sets, respectively, were achieved when predicting presence or absence of neuropathological change. Conclusion: Computer-aided prediction of neuropathological change according to the categorical NIAA score in AD, that currently can only be assessed post-mortem, may facilitate a more distinct and definite categorization of AD dementia. Reliable detection of neuropathological hallmarks of AD would enable risk stratification at an earlier level than prediction of MCI or clinical AD symptoms and advance precision medicine in neuropsychiatry.
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Affiliation(s)
- Alexander Kautzky
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rene Seiger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Peter Fischer
- Department of Psychiatry, Danube Hospital, Medical Research Society Vienna D.C., Vienna, Austria
| | | | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Gabor G Kovacs
- Institute of Neurology, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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18
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Mueller KD, Koscik RL, Clark LR, Hermann BP, Johnson SC, Turkstra LS. The Latent Structure and Test-Retest Stability of Connected Language Measures in the Wisconsin Registry for Alzheimer's Prevention (WRAP). Arch Clin Neuropsychol 2018; 33:993-1005. [PMID: 29186313 PMCID: PMC6455482 DOI: 10.1093/arclin/acx116] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 10/02/2017] [Accepted: 11/07/2017] [Indexed: 11/14/2022] Open
Abstract
INTRODUCTION While it is well known that discourse-related language functions are impaired in the dementia phase of Alzheimer's Disease (AD), the presymptomatic temporal course of discourse dysfunction are not known earlier in the course of AD. To conduct discourse-related studies in this phase of AD, validated psychometric instruments are needed. This study investigates the latent structure, validity, and test-retest stability of discourse measures in a late-middle-aged normative group who are relatively free from sporadic AD risk factors. METHODS Using a normative sample of 399 participants (mean age = 61), exploratory factor analyses (EFA) and confirmatory factor analyses (CFA) were conducted on 18 measures of connected language derived from picture descriptions. Factor invariance across sex and family history and longitudinal test-retest stability measures were calculated. RESULTS The EFA revealed a four-factor solution, consisting of semantic, syntax, fluency, and lexical constructs. The CFA model substantiated the structure, and factors were invariant across sex and parental history of AD status. Test-retest stability measures were within acceptable ranges. CONCLUSIONS Results confirm a factor structure that is invariant across sex and parental AD history. The factor structure could be useful in similar cohorts designed to detect early language decline in investigations of preclinical or clinical AD or as outcome measures in clinical prevention trials.
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Affiliation(s)
- Kimberly D Mueller
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Department of Communication Sciences and Disorders, University of Wisconsin – Madison, Madison, WI, USA
| | - Rebecca L Koscik
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Lindsay R Clark
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, Wm.S. Middleton Veterans Hospital, Madison, WI, USA
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin – Madison, Madison, WI, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, Wm.S. Middleton Veterans Hospital, Madison, WI, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
| | - Lyn S Turkstra
- Department of Communication Sciences and Disorders, University of Wisconsin – Madison, Madison, WI, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
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19
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Mueller KD, Hermann B, Mecollari J, Turkstra LS. Connected speech and language in mild cognitive impairment and Alzheimer's disease: A review of picture description tasks. J Clin Exp Neuropsychol 2018; 40:917-939. [PMID: 29669461 PMCID: PMC6198327 DOI: 10.1080/13803395.2018.1446513] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
INTRODUCTION The neuropsychological profile of people with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia includes a history of decline in memory and other cognitive domains, including language. While language impairments have been well described in AD dementia, language features of MCI are less well understood. Connected speech and language analysis is the study of an individual's spoken discourse, usually elicited by a target stimulus, the results of which can facilitate understanding of how language deficits typical of MCI and AD dementia manifest in everyday communication. Among discourse genres, picture description is a constrained task that relies less on episodic memory and more on semantic knowledge and retrieval, within the cognitive demands of a communication context. Understanding the breadth of evidence across the continuum of cognitive decline will help to elucidate the areas of strength and need in terms of using this method as an evaluative tool for both cognitive changes and everyday functional communication. METHOD We performed an extensive literature search of peer-reviewed journal articles that focused on the use of picture description tasks for evaluating language in persons with MCI or AD dementia. We selected articles based on inclusion and exclusion criteria and described the measures assessed, the psychometric properties that were reported, the findings, and the limitations of the included studies. RESULTS 36 studies were selected and reviewed. Across all 36 studies, there were 1, 127 patients with AD dementia and 274 with MCI or early cognitive decline. Multiple measures were examined, including those describing semantic content, syntactic complexity, speech fluency, vocal parameters, and pragmatic language. Discriminant validity widely reported and distinct differences in language were observable between adults with dementia and controls; fewer studies were able to distinguish language differences between typically aging adults and those with MCI. DISCUSSION Our review shows that picture description tasks are useful tools for detecting differences in a wide variety of language and communicative measures. Future research should expand knowledge about subtle changes to language in preclinical AD and Mild Cognitive Impairment (MCI) which may improve the utility of this method as a clinically meaningful screening tool.
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Affiliation(s)
- Kimberly D. Mueller
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, USA
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, USA
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, USA
| | - Jonilda Mecollari
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, USA
| | - Lyn S. Turkstra
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, USA
- School of Rehabilitation Science, McMaster University, Canada
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20
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Boschi V, Catricalà E, Consonni M, Chesi C, Moro A, Cappa SF. Connected Speech in Neurodegenerative Language Disorders: A Review. Front Psychol 2017; 8:269. [PMID: 28321196 PMCID: PMC5337522 DOI: 10.3389/fpsyg.2017.00269] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 02/10/2017] [Indexed: 12/12/2022] Open
Abstract
Language assessment has a crucial role in the clinical diagnosis of several neurodegenerative diseases. The analysis of extended speech production is a precious source of information encompassing the phonetic, phonological, lexico-semantic, morpho-syntactic, and pragmatic levels of language organization. The knowledge about the distinctive linguistic variables identifying language deficits associated to different neurodegenerative diseases has progressively improved in the last years. However, the heterogeneity of such variables and of the way they are measured and classified limits any generalization and makes the comparison among studies difficult. Here we present an exhaustive review of the studies focusing on the linguistic variables derived from the analysis of connected speech samples, with the aim of characterizing the language disorders of the most prevalent neurodegenerative diseases, including primary progressive aphasia, Alzheimer's disease, movement disorders, and amyotrophic lateral sclerosis. A total of 61 studies have been included, considering only those reporting group analysis and comparisons with a group of healthy persons. This review first analyzes the differences in the tasks used to elicit connected speech, namely picture description, story narration, and interview, considering the possible different contributions to the assessment of different linguistic domains. This is followed by an analysis of the terminologies and of the methods of measurements of the variables, indicating the need for harmonization and standardization. The final section reviews the linguistic domains affected by each different neurodegenerative disease, indicating the variables most consistently impaired at each level and suggesting the key variables helping in the differential diagnosis among diseases. While a large amount of valuable information is already available, the review highlights the need of further work, including the development of automated methods, to take advantage of the richness of connected speech analysis for both research and clinical purposes.
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Affiliation(s)
- Veronica Boschi
- NETS, Center for Neurocognition, Epistemology and Theoretical Syntax, Institute for Advanced Study-Pavia Pavia, Italy
| | - Eleonora Catricalà
- NETS, Center for Neurocognition, Epistemology and Theoretical Syntax, Institute for Advanced Study-Pavia Pavia, Italy
| | - Monica Consonni
- Third Neurology Unit and Motor Neuron Diseases Center, IRCCS Foundation "Carlo Besta" Neurological Institute Milan, Italy
| | - Cristiano Chesi
- NETS, Center for Neurocognition, Epistemology and Theoretical Syntax, Institute for Advanced Study-Pavia Pavia, Italy
| | - Andrea Moro
- NETS, Center for Neurocognition, Epistemology and Theoretical Syntax, Institute for Advanced Study-Pavia Pavia, Italy
| | - Stefano F Cappa
- NETS, Center for Neurocognition, Epistemology and Theoretical Syntax, Institute for Advanced Study-PaviaPavia, Italy; IRCCS S. Giovanni di Dio FatebenefratelliBrescia, Italy
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Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study. Behav Neurol 2017; 2017:1850909. [PMID: 28255200 PMCID: PMC5307249 DOI: 10.1155/2017/1850909] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Revised: 12/07/2016] [Accepted: 12/21/2016] [Indexed: 12/12/2022] Open
Abstract
Subjects with Alzheimer's disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their daily life and that of their families and caregivers. A neuropsychological assessment plays a crucial role in detecting such changes from normal conditions. However, despite the existence of clinical measures that are used to classify and diagnose AD, a large amount of subjectivity continues to exist. Our aim was to assess the potential of machine learning in quantifying this process and optimizing or even reducing the amount of neuropsychological tests used to classify AD patients, also at an early stage of impairment. We investigated the role of twelve state-of-the-art neuropsychological tests in the automatic classification of subjects with none, mild, or severe impairment as measured by the clinical dementia rating (CDR). Data were obtained from the ADNI database. In the groups of measures used as features, we included measures of both cognitive domains and subdomains. Our findings show that some tests are more frequently best predictors for the automatic classification, namely, LM, ADAS-Cog, AVLT, and FAQ, with a major role of the ADAS-Cog measures of delayed and immediate memory and the FAQ measure of financial competency.
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22
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Fraser KC, Meltzer JA, Rudzicz F. Linguistic Features Identify Alzheimer's Disease in Narrative Speech. J Alzheimers Dis 2016; 49:407-22. [PMID: 26484921 DOI: 10.3233/jad-150520] [Citation(s) in RCA: 231] [Impact Index Per Article: 28.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Although memory impairment is the main symptom of Alzheimer's disease (AD), language impairment can be an important marker. Relatively few studies of language in AD quantify the impairments in connected speech using computational techniques. OBJECTIVE We aim to demonstrate state-of-the-art accuracy in automatically identifying Alzheimer's disease from short narrative samples elicited with a picture description task, and to uncover the salient linguistic factors with a statistical factor analysis. METHODS Data are derived from the DementiaBank corpus, from which 167 patients diagnosed with "possible" or "probable" AD provide 240 narrative samples, and 97 controls provide an additional 233. We compute a number of linguistic variables from the transcripts, and acoustic variables from the associated audio files, and use these variables to train a machine learning classifier to distinguish between participants with AD and healthy controls. To examine the degree of heterogeneity of linguistic impairments in AD, we follow an exploratory factor analysis on these measures of speech and language with an oblique promax rotation, and provide interpretation for the resulting factors. RESULTS We obtain state-of-the-art classification accuracies of over 81% in distinguishing individuals with AD from those without based on short samples of their language on a picture description task. Four clear factors emerge: semantic impairment, acoustic abnormality, syntactic impairment, and information impairment. CONCLUSION Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD.
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Affiliation(s)
- Kathleen C Fraser
- Department of Computer Science, University of Toronto, Toronto, Canada
| | | | - Frank Rudzicz
- Department of Computer Science, University of Toronto, Toronto, Canada.,Toronto Rehabilitation Institute-UHN, Toronto, Canada
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