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Kim H, Hillis AE, Themistocleous C. Machine Learning Classification of Patients with Amnestic Mild Cognitive Impairment and Non-Amnestic Mild Cognitive Impairment from Written Picture Description Tasks. Brain Sci 2024; 14:652. [PMID: 39061392 PMCID: PMC11274603 DOI: 10.3390/brainsci14070652] [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: 06/10/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
Individuals with Mild Cognitive Impairment (MCI), a transitional stage between cognitively healthy aging and dementia, are characterized by subtle neurocognitive changes. Clinically, they can be grouped into two main variants, namely patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). The distinction of the two variants is known to be clinically significant as they exhibit different progression rates to dementia. However, it has been particularly challenging to classify the two variants robustly. Recent research indicates that linguistic changes may manifest as one of the early indicators of pathology. Therefore, we focused on MCI's discourse-level writing samples in this study. We hypothesized that a written picture description task can provide information that can be used as an ecological, cost-effective classification system between the two variants. We included one hundred sixty-nine individuals diagnosed with either aMCI or naMCI who received neurophysiological evaluations in addition to a short, written picture description task. Natural Language Processing (NLP) and a BERT pre-trained language model were utilized to analyze the writing samples. We showed that the written picture description task provided 90% overall classification accuracy for the best classification models, which performed better than cognitive measures. Written discourses analyzed by AI models can automatically assess individuals with aMCI and naMCI and facilitate diagnosis, prognosis, therapy planning, and evaluation.
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Affiliation(s)
- Hana Kim
- Department of Communication Sciences and Disorders, University of South Florida, Tampa, FL 33620, USA;
| | - Argye E. Hillis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA;
- Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD 21287, USA
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García-Gutiérrez F, Alegret M, Marquié M, Muñoz N, Ortega G, Cano A, De Rojas I, García-González P, Olivé C, Puerta R, García-Sanchez A, Capdevila-Bayo M, Montrreal L, Pytel V, Rosende-Roca M, Zaldua C, Gabirondo P, Tárraga L, Ruiz A, Boada M, Valero S. Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer's disease spectrum. Alzheimers Res Ther 2024; 16:26. [PMID: 38308366 PMCID: PMC10835990 DOI: 10.1186/s13195-024-01394-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: 11/07/2023] [Accepted: 01/18/2024] [Indexed: 02/04/2024]
Abstract
BACKGROUND Advancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS. Furthermore, for the first time, this study investigates the relationship between paralinguistic features from SS and cognitive function within the AD spectrum. METHODS Physical-acoustic features were extracted from voice recordings of patients evaluated in a memory unit who underwent a SS protocol. We implemented several ML models evaluated via cross-validation to identify individuals without cognitive impairment (subjective cognitive decline, SCD), with mild cognitive impairment (MCI), and with dementia due to AD (ADD). In addition, we established models capable of predicting cognitive domain performance based on a comprehensive neuropsychological battery from Fundació Ace (NBACE) using SS-derived information. RESULTS The results of this study showed that, based on a paralinguistic analysis of sound, it is possible to identify individuals with ADD (F1 = 0.92) and MCI (F1 = 0.84). Furthermore, our models, based on physical acoustic information, exhibited correlations greater than 0.5 for predicting the cognitive domains of attention, memory, executive functions, language, and visuospatial ability. CONCLUSIONS In this study, we show the potential of a brief and cost-effective SS protocol in distinguishing between different degrees of cognitive impairment and forecasting performance in cognitive domains commonly affected within the AD spectrum. Our results demonstrate a high correspondence with protocols traditionally used to assess cognitive function. Overall, it opens up novel prospects for developing screening tools and remote disease monitoring.
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Affiliation(s)
| | - Montserrat Alegret
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Marquié
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Nathalia Muñoz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Gemma Ortega
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Amanda Cano
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Itziar De Rojas
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Pablo García-González
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Clàudia Olivé
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Raquel Puerta
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Ainhoa García-Sanchez
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - María Capdevila-Bayo
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Laura Montrreal
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Vanesa Pytel
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Maitee Rosende-Roca
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | | | | | - Lluís Tárraga
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Agustín Ruiz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Mercè Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Sergi Valero
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain.
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain.
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García-Gutiérrez F, Marquié M, Muñoz N, Alegret M, Cano A, de Rojas I, García-González P, Olivé C, Puerta R, Orellana A, Montrreal L, Pytel V, Ricciardi M, Zaldua C, Gabirondo P, Hinzen W, Lleonart N, García-Sánchez A, Tárraga L, Ruiz A, Boada M, Valero S. Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment. Front Neurosci 2023; 17:1221401. [PMID: 37746151 PMCID: PMC10512723 DOI: 10.3389/fnins.2023.1221401] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/08/2023] [Indexed: 09/26/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of mild cognitive impairment (MCI) to enable early interventions. Among the neuropathological events associated with the onset of the disease is the accumulation of amyloid protein in the brain, which correlates with decreased levels of Aβ42 peptide in the cerebrospinal fluid (CSF). Consequently, the development of non-invasive, low-cost, and easy-to-administer proxies for detecting Aβ42 positivity in CSF becomes particularly valuable. A promising approach to achieve this is spontaneous speech analysis, which combined with machine learning (ML) techniques, has proven highly useful in AD. In this study, we examined the relationship between amyloid status in CSF and acoustic features derived from the description of the Cookie Theft picture in MCI patients from a memory clinic. The cohort consisted of fifty-two patients with MCI (mean age 73 years, 65% female, and 57% positive amyloid status). Eighty-eight acoustic parameters were extracted from voice recordings using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and several ML models were used to classify the amyloid status. Furthermore, interpretability techniques were employed to examine the influence of input variables on the determination of amyloid-positive status. The best model, based on acoustic variables, achieved an accuracy of 75% with an area under the curve (AUC) of 0.79 in the prediction of amyloid status evaluated by bootstrapping and Leave-One-Out Cross Validation (LOOCV), outperforming conventional neuropsychological tests (AUC = 0.66). Our results showed that the automated analysis of voice recordings derived from spontaneous speech tests offers valuable insights into AD biomarkers during the preclinical stages. These findings introduce novel possibilities for the use of digital biomarkers to identify subjects at high risk of developing AD.
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Affiliation(s)
| | - Marta Marquié
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Nathalia Muñoz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Montserrat Alegret
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Amanda Cano
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Itziar de Rojas
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Pablo García-González
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Clàudia Olivé
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Raquel Puerta
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Adelina Orellana
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Laura Montrreal
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Vanesa Pytel
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Mario Ricciardi
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | | | | | - Wolfram Hinzen
- Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Núria Lleonart
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Ainhoa García-Sánchez
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Lluís Tárraga
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Agustín Ruiz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Mercè Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Sergi Valero
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
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