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Gomes V, Simón T, Lázaro M. "I don't know who you are": anomia for people's names in Alzheimer's disease. NEUROPSYCHOLOGY, DEVELOPMENT, AND COGNITION. SECTION B, AGING, NEUROPSYCHOLOGY AND COGNITION 2024; 31:956-986. [PMID: 38351719 DOI: 10.1080/13825585.2024.2315773] [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: 07/21/2023] [Accepted: 01/05/2024] [Indexed: 07/18/2024]
Abstract
It is well known that difficulty in the retrieval of people's names is an early symptom of Alzheimer's Disease Dementia (ADD), but there is a controversy about the nature of this deficit. In this study, we analyzed whether the nature of the difficulty in retrieving proper names in ADD reflects pre-semantic, semantic, or post-semantic difficulties. To do so, 85 older adults, 35 with ADD and 50 cognitively healthy (CH), completed a task with famous faces involving: recognition, naming, semantic questions, and naming with phonological cues. The ADD group scored lower than the CH group in all tasks. Both groups showed a greater capacity for recognition than naming, but this difference was more pronounced in the ADD group. Additionally, the ADD group showed significantly fewer semantic errors than the CH group. Overall results suggest that the difficulties people with ADD have in naming reflect a degradation at semantic level.
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Affiliation(s)
- Vanessa Gomes
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
| | - Teresa Simón
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
| | - Miguel Lázaro
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
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2
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van den Berg RL, de Boer C, Zwan MD, Jutten RJ, van Liere M, van de Glind MCABJ, Dubbelman MA, Schlüter LM, van Harten AC, Teunissen CE, van de Giessen E, Barkhof F, Collij LE, Robin J, Simpson W, Harrison JE, van der Flier WM, Sikkes SAM. Digital remote assessment of speech acoustics in cognitively unimpaired adults: feasibility, reliability and associations with amyloid pathology. Alzheimers Res Ther 2024; 16:176. [PMID: 39090738 PMCID: PMC11293000 DOI: 10.1186/s13195-024-01543-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024]
Abstract
BACKGROUND Digital speech assessment has potential relevance in the earliest, preclinical stages of Alzheimer's disease (AD). We evaluated the feasibility, test-retest reliability, and association with AD-related amyloid-beta (Aβ) pathology of speech acoustics measured over multiple assessments in a remote setting. METHODS Fifty cognitively unimpaired adults (Age 68 ± 6.2 years, 58% female, 46% Aβ-positive) completed remote, tablet-based speech assessments (i.e., picture description, journal-prompt storytelling, verbal fluency tasks) for five days. The testing paradigm was repeated after 2-3 weeks. Acoustic speech features were automatically extracted from the voice recordings, and mean scores were calculated over the 5-day period. We assessed feasibility by adherence rates and usability ratings on the System Usability Scale (SUS) questionnaire. Test-retest reliability was examined with intraclass correlation coefficients (ICCs). We investigated the associations between acoustic features and Aβ-pathology, using linear regression models, adjusted for age, sex and education. RESULTS The speech assessment was feasible, indicated by 91.6% adherence and usability scores of 86.0 ± 9.9. High reliability (ICC ≥ 0.75) was found across averaged speech samples. Aβ-positive individuals displayed a higher pause-to-word ratio in picture description (B = -0.05, p = 0.040) and journal-prompt storytelling (B = -0.07, p = 0.032) than Aβ-negative individuals, although this effect lost significance after correction for multiple testing. CONCLUSION Our findings support the feasibility and reliability of multi-day remote assessment of speech acoustics in cognitively unimpaired individuals with and without Aβ-pathology, which lays the foundation for the use of speech biomarkers in the context of early AD.
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Affiliation(s)
- Rosanne L van den Berg
- Alzheimer Center Amsterdam, Neurology, Amsterdam University Medical Center, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
- Department of Clinical, Neuro and Developmental Psychology, Faculty of Movement and Behavioral Sciences, VU University, Amsterdam, The Netherlands.
| | - Casper de Boer
- Alzheimer Center Amsterdam, Neurology, Amsterdam University Medical Center, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Marissa D Zwan
- Alzheimer Center Amsterdam, Neurology, Amsterdam University Medical Center, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Roos J Jutten
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mariska van Liere
- Alzheimer Center Amsterdam, Neurology, Amsterdam University Medical Center, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Marie-Christine A B J van de Glind
- Alzheimer Center Amsterdam, Neurology, Amsterdam University Medical Center, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Groningen, Department of Neurology, Department of Neuropsychology and Department of Internal Medicine, University Medical Center Groningen, Groningen, The Netherlands
- Alzheimer Center Erasmus MC and Department of Neurology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Mark A Dubbelman
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lisa Marie Schlüter
- Alzheimer Center Amsterdam, Neurology, Amsterdam University Medical Center, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Argonde C van Harten
- Alzheimer Center Amsterdam, Neurology, Amsterdam University Medical Center, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Charlotte E Teunissen
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Neurochemistry Laboratory and Biobank, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, The Netherlands
| | - Elsmarieke van de Giessen
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Lyduine E Collij
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences, Faculty of Medicine, Lund University, Malmö, Lund, Sweden
| | | | | | - John E Harrison
- Alzheimer Center Amsterdam, Neurology, Amsterdam University Medical Center, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands
- Scottish Brain Sciences, Edinburgh, UK
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Neurology, Amsterdam University Medical Center, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Sietske A M Sikkes
- Alzheimer Center Amsterdam, Neurology, Amsterdam University Medical Center, De Boelelaan 1118, Amsterdam, 1081 HZ, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Clinical, Neuro and Developmental Psychology, Faculty of Movement and Behavioral Sciences, VU University, Amsterdam, The Netherlands
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Sakaie K, Koenig K, Lerner A, Appleby B, Ogrocki P, Pillai JA, Rao S, Leverenz JB, Lowe MJ. Multi-shell diffusion MRI of the fornix as a biomarker for cognition in Alzheimer's disease. Magn Reson Imaging 2024; 109:221-226. [PMID: 38521367 DOI: 10.1016/j.mri.2024.03.030] [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: 07/25/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND AND PURPOSE A substantial fraction of those who had Alzheimer's Disease (AD) pathology on autopsy did not have dementia in life. While biomarkers for AD pathology are well-developed, biomarkers specific to cognitive domains affected by early AD are lagging. Diffusion MRI (dMRI) of the fornix is a candidate biomarker for early AD-related cognitive changes but is susceptible to bias due to partial volume averaging (PVA) with cerebrospinal fluid. The purpose of this work is to leverage multi-shell dMRI to correct for PVA and to evaluate PVA-corrected dMRI measures in fornix as a biomarker for cognition in AD. METHODS Thirty-three participants in the Cleveland Alzheimer's Disease Research Center (CADRC) (19 with normal cognition (NC), 10 with mild cognitive impairment (MCI), 4 with dementia due to AD) were enrolled in this study. Multi-shell dMRI was acquired, and voxelwise fits were performed with two models: 1) diffusion tensor imaging (DTI) that was corrected for PVA and 2) neurite orientation dispersion and density imaging (NODDI). Values of tissue integrity in fornix were correlated with neuropsychological scores taken from the Uniform Data Set (UDS), including the UDS Global Composite 5 score (UDSGC5). RESULTS Statistically significant correlations were found between the UDSGC5 and PVA-corrected measure of mean diffusivity (MDc, r = -0.35, p < 0.05) from DTI and the intracelluar volume fraction (ficvf, r = 0.37, p < 0.04) from NODDI. A sensitivity analysis showed that the relationship to MDc was driven by episodic memory, which is often affected early in AD, and language. CONCLUSION This cross-sectional study suggests that multi-shell dMRI of the fornix that has been corrected for PVA is a potential biomarker for early cognitive domain changes in AD. A longitudinal study will be necessary to determine if the imaging measure can predict cognitive decline.
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Affiliation(s)
- Ken Sakaie
- Imaging Institute, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-15, Cleveland, OH 44195, USA.
| | - Katherine Koenig
- Imaging Institute, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-15, Cleveland, OH 44195, USA
| | - Alan Lerner
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Brian Appleby
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Paula Ogrocki
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Jagan A Pillai
- Lou Ruvo Center for Brain Health, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-10, Cleveland, OH 44195, USA
| | - Stephen Rao
- Lou Ruvo Center for Brain Health, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-10, Cleveland, OH 44195, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-10, Cleveland, OH 44195, USA
| | - Mark J Lowe
- Imaging Institute, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-15, Cleveland, OH 44195, USA
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Yan Z, Dube V, Heselton J, Johnson K, Yan C, Jones V, Blaskewicz Boron J, Shade M. Understanding older people's voice interactions with smart voice assistants: a new modified rule-based natural language processing model with human input. Front Digit Health 2024; 6:1329910. [PMID: 38812806 PMCID: PMC11135128 DOI: 10.3389/fdgth.2024.1329910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 05/06/2024] [Indexed: 05/31/2024] Open
Abstract
The COVID-19 pandemic has expedited the integration of Smart Voice Assistants (SVA) among older people. The qualitative data derived from user commands on SVA is pivotal for elucidating the engagement patterns of older individuals with such systems. However, the sheer volume of user-generated voice interaction data presents a formidable challenge for manual coding. Compounding this issue, age-related cognitive decline and alterations in speech patterns further complicate the interpretation of older users' SVA voice interactions. Conventional dictionary-based textual analysis tools, which count word frequencies, are inadequate in capturing the evolving and communicative essence of these interactions that unfold over a series of dialogues and modify with time. To address these challenges, our study introduces a novel, modified rule-based Natural Language Processing (MR-NLP) model augmented with human input. This reproducible approach capitalizes on human-derived insights to establish a lexicon of critical keywords and to formulate rules for the iterative refinement of the NLP model. English speakers, aged 50 or older and residing alone, were enlisted to engage with Amazon Alexa™ via predefined daily routines for a minimum of 30 min daily spanning three months (N = 35, mean age = 77). We amassed time-stamped, textual data comprising participants' user commands and responses from Alexa™. Initially, a subset constituting 20% of the data (1,020 instances) underwent manual coding by human coder, predicated on keywords and commands. Separately, a rule-based Natural Language Processing (NLP) methodology was employed to code the identical subset. Discrepancies arising between human coder and the NLP model programmer were deliberated upon and reconciled to refine the rule-based NLP coding framework for the entire dataset. The modified rule-based NLP approach demonstrated notable enhancements in efficiency and scalability and reduced susceptibility to inadvertent errors in comparison to manual coding. Furthermore, human input was instrumental in augmenting the NLP model, yielding insights germane to the aging adult demographic, such as recurring speech patterns or ambiguities. By disseminating this innovative software solution to the scientific community, we endeavor to advance research and innovation in NLP model formulation, subsequently contributing to the understanding of older people's interactions with SVA and other AI-powered systems.
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Affiliation(s)
- Zhengxu Yan
- College of Computing, Data Science and Society, University of California-Berkeley, Berkeley, CA, United States
| | - Victoria Dube
- Department of Gerontology, University of Nebraska-Omaha, Omaha, NE, United States
| | - Judith Heselton
- Department of Gerontology, University of Nebraska-Omaha, Omaha, NE, United States
| | - Kate Johnson
- College of Journalism and Mass Communications, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Changmin Yan
- College of Journalism and Mass Communications, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Valerie Jones
- College of Journalism and Mass Communications, University of Nebraska-Lincoln, Lincoln, NE, United States
| | | | - Marcia Shade
- College of Nursing, University of Nebraska Medical Center, Omaha, NE, United States
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Pl R, Ks G. Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:1110-1127. [PMID: 37971395 DOI: 10.1111/1460-6984.12973] [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: 04/20/2023] [Accepted: 09/18/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking. AIMS This research proposes an approach for assessing cognitive decline by analysing speech data, specifically focusing on speech relevance as a crucial indicator for memory recall. METHODS & PROCEDURES This is a cross-sectional, online, self-administered. The proposed method used deep learning architecture based on transformers, with BERT (Bidirectional Encoder Representations from Transformers) and Sentence-Transformer to derive encoded representations of speech transcripts. These representations provide contextually descriptive information that is used to analyse the relevance of sentences in their respective contexts. The encoded information is then compared using cosine similarity metrics to measure the relevance of uttered sequences of sentences. The study uses the Pitt Corpus Dementia dataset for experimentation, which consists of speech data from individuals with and without dementia. The accuracy of the proposed multi-QA-MPNet (Multi-Query Maximum Inner Product Search Pretraining) model is compared with other pretrained transformer models of Sentence-Transformer. OUTCOMES & RESULTS The results show that the proposed approach outperforms the other models in capturing context level information, particularly semantic memory. Additionally, the study explores the suitability of different similarity measures to evaluate the relevance of uttered sequences of sentences. The experimentation reveals that cosine similarity is the most appropriate measure for this task. CONCLUSIONS & IMPLICATIONS This finding has significant implications for the early warning signs of dementia, as it suggests that cosine similarity metrics can effectively capture the semantic relevance of spoken language. The persistent cognitive decline over time acts as one of the indicators for prevalence of dementia. Additionally early dementia could be recognised by analysis on other modalities like speech and brain images. WHAT THIS PAPER ADDS What is already known on this subject It is already known that speech- and language-based detection methods can be useful for dementia diagnosis, as language difficulties are often early signs of the disease. Additionally, deep learning algorithms have shown promise in detecting and diagnosing dementia through analysing large datasets, particularly in speech- and language-based detection methods. However, further research is needed to validate the performance of these algorithms on larger and more diverse datasets and to address potential biases and limitations. What this paper adds to existing knowledge This study presents a unique and effective approach for cognitive decline assessment through analysing speech data. The study provides valuable insights into the importance of context and semantic memory in accurately detecting the potential in dementia and demonstrates the applicability of deep learning models for this purpose. The findings of this study have important clinical implications and can inform future research and development in the field of dementia detection and care. What are the potential or actual clinical implications of this work? The proposed approach for cognitive decline assessment using speech data and deep learning models has significant clinical implications. It has the potential to improve the accuracy and efficiency of dementia diagnosis, leading to earlier detection and more effective treatments, which can improve patient outcomes and quality of life.
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Affiliation(s)
- Rini Pl
- Sri Sivasubramaniya Nadar College of Engineering, Tamil Nadu, India
| | - Gayathri Ks
- Sri Sivasubramaniya Nadar College of Engineering, Tamil Nadu, India
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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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Chen TY, Zhu JD, Tsai SJ, Yang AC. Exploring morphological similarity and randomness in Alzheimer's disease using adjacent grey matter voxel-based structural analysis. Alzheimers Res Ther 2024; 16:88. [PMID: 38654366 PMCID: PMC11036786 DOI: 10.1186/s13195-024-01448-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/01/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Alzheimer's disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer's disease. METHODS Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer's disease was assessed by stepwise regression. RESULTS Compared to cognitively normal participants, individuals with Alzheimer's disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer's disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer's disease. CONCLUSIONS Our study suggested that individuals with Alzheimer's disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer's disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer's disease.
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Affiliation(s)
- Ting-Yu Chen
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jun-Ding Zhu
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Albert C Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Kaser AN, Lacritz LH, Winiarski HR, Gabirondo P, Schaffert J, Coca AJ, Jiménez-Raboso J, Rojo T, Zaldua C, Honorato I, Gallego D, Nieves ER, Rosenstein LD, Cullum CM. A novel speech analysis algorithm to detect cognitive impairment in a Spanish population. Front Neurol 2024; 15:1342907. [PMID: 38638311 PMCID: PMC11024431 DOI: 10.3389/fneur.2024.1342907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
Objective Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population. Method Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic "F" fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores. Results Mean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively. Conclusion Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.
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Affiliation(s)
- Alyssa N. Kaser
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Laura H. Lacritz
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Holly R. Winiarski
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | | | - Jeff Schaffert
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Alberto J. Coca
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
- Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, United Kingdom
| | | | - Tomas Rojo
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
| | - Carla Zaldua
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
| | | | | | - Emmanuel Rosario Nieves
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Parkland Health and Hospital System Behavioral Health Clinic, Dallas, TX, United States
| | - Leslie D. Rosenstein
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Parkland Health and Hospital System Behavioral Health Clinic, Dallas, TX, United States
| | - C. Munro Cullum
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurological Surgery, The University of Texas Southwestern Medical Center, Dallas, TX, United States
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Antonsson M, Lundholm Fors K, Hartelius L. Disfluencies in spontaneous speech in persons with low-grade glioma before and after surgery. CLINICAL LINGUISTICS & PHONETICS 2024; 38:359-380. [PMID: 37357743 DOI: 10.1080/02699206.2023.2226305] [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: 03/09/2022] [Accepted: 06/09/2023] [Indexed: 06/27/2023]
Abstract
Impaired lexical retrieval is common in persons with low-grade glioma (LGG). Several studies have reported a discrepancy between subjective word-finding difficulties and results on formal tests. Analysis of spontaneous speech might be more sensitive to signs of word-finding difficulties, hence we aimed to explore disfluencies in a spontaneous-speech task performed by participants with presumed LGG before and after surgery. Further, we wanted to explore how the presence of disfluencies in spontaneous speech differed in the participants with and without objectively established lexical-retrieval impairment and with and without self-reported subjective experience of impaired language, speech and communication. Speech samples of 26 persons with presumed low-grade glioma were analysed with regard to disfluency features. The post-operative speech samples had a higher occurrence of fillers, implying more disfluent language production. The participants performed worse on two of the word fluency tests, and after surgery the number of participants who were assessed as having an impaired lexical retrieval had increased from 6 to 12. The number of participants who experienced a change in their language, speech or communication had increased from 9 to 12. Additional comparisons showed that those with impaired lexical retrieval had a higher proportion of false starts after surgery than those with normal lexical retrieval, and differences in articulation rate and speech rate, favouring those not having experienced any change in language, speech or communication. Taken together, the findings from this study strengthen the existing claim that temporal aspects of language and speech are important when assessing persons with gliomas.
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Affiliation(s)
- Malin Antonsson
- Institute of Neuroscience and Physiology, Speech and Language Pathology Unit, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Kristina Lundholm Fors
- Institute of Neuroscience and Physiology, Speech and Language Pathology Unit, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
- Centre for Teaching and Learning, Medical Faculty, Lund University, Lund, Sweden
| | - Lena Hartelius
- Institute of Neuroscience and Physiology, Speech and Language Pathology Unit, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
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Bayat S, Santai M, Panahi MM, Khodadadi A, Ghassimi M, Rezaei S, Besharat S, Mahboubi Z, Almasi M, Sanei Taheri M, Dickerson BC, Rezaii N. Language Abnormalities in Alzheimer's Disease Arise from Reduced Informativeness: A Cross-Linguistic Study in English and Persian. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304407. [PMID: 38562858 PMCID: PMC10984049 DOI: 10.1101/2024.03.19.24304407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
INTRODUCTION This research investigates the psycholinguistic origins of language impairments in Alzheimer's Disease (AD), questioning if these impairments result from language-specific structural disruptions or from a universal deficit in generating meaningful content. METHODS Cross-linguistic analysis was conducted on language samples from 184 English and 52 Persian speakers, comprising both AD patients and healthy controls, to extract various language features. Furthermore, we introduced a machine learning-based metric, Language Informativeness Index (LII), to quantify informativeness. RESULTS Indicators of AD in English were found to be highly predictive of AD in Persian, with a 92.3% classification accuracy. Additionally, we found robust correlations between the typical linguistic abnormalities of AD and language emptiness (low LII) across both languages. DISCUSSION Findings suggest AD linguistics impairments are attributed to a core universal difficulty in generating informative messages. Our approach underscores the importance of incorporating biocultural diversity into research, fostering the development of inclusive diagnostic tools.
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Malcorra BLC, García AO, Marcotte K, de Paz H, Schilling LP, da Silva Filho IG, Soder R, da Rosa Franco A, Loureiro F, Hübner LC. Exploring Spoken Discourse and Its Neural Correlates in Women With Alzheimer's Disease With Low Levels of Education and Socioeconomic Status. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2024; 33:893-911. [PMID: 38157526 DOI: 10.1044/2023_ajslp-23-00137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
PURPOSE Early impairments in spoken discourse abilities have been identified in Alzheimer's disease (AD). However, the impact of AD on spoken discourse and the associated neuroanatomical correlates have mainly been studied in populations with higher levels of education, although preliminary evidence seems to indicate that socioeconomic status (SES) and level of education have an impact on spoken discourse. The purpose of this study was to analyze microstructural variables in spoken discourse in people with AD with low-to-middle SES and low level of education and to study their association with gray matter (GM) density. METHOD Nine women with AD and 10 matched (age, SES, and education) women without brain injury (WWBI) underwent a neuropsychological assessment, which included two spoken discourse tasks, and structural magnetic resonance imaging. Microstructural variables were extracted from the discourse samples using NILC-Metrix software. Brain density, measured by voxel-based morphometry, was compared between groups and then correlated with the differentiating microstructural variables. RESULTS The AD group produced a lower diversity of verbal time moods and fewer words and sentences than WWBI but a greater diversity of pronouns, prepositions, and lexical richness. At the neural level, the AD group presented a lower GM density bilaterally in the hippocampus, the inferior temporal gyrus, and the anterior cingulate gyrus. Number of words and sentences produced were associated with GM density in the left parahippocampal gyrus, whereas the diversity of verbal moods was associated with the basal ganglia and the anterior cingulate gyrus bilaterally. CONCLUSIONS The present findings are mainly consistent with previous studies conducted in groups with higher levels of SES and education, but they suggest that atrophy in the left inferior temporal gyrus could be critical in AD in populations with lower levels of SES and education. This research provides evidence on the importance of pursuing further studies including people with various SES and education levels. WHAT IS ALREADY KNOWN ON THIS SUBJECT Spoken discourse has been shown to be affected in Alzheimer disease, but most studies have been conducted on individuals with middle-to-high SES and high educational levels. WHAT THIS STUDY ADDS The study reports on microstructural measures of spoken discourse in groups of women in the early stage of AD and healthy women, with low-to-middle SES and lower levels of education. CLINICAL IMPLICATIONS OF THIS STUDY This study highlights the importance of taking into consideration the SES and education level in spoken discourse analysis and in investigating the neural correlates of AD. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.24905046.
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Affiliation(s)
- Bárbara Luzia Covatti Malcorra
- Department of Linguistics, School of Humanities, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Alberto Osa García
- Centre de recherche du Centre intégré universitaire de santé et de services sociaux du Nord-de-l'Île-de-Montréal, Hôpital du Sacré-Cœur de Montréal, Québec, Canada
- École d'orthophonie et d'audiologie, Faculté de médecine, Université de Montréal, Québec, Canada
| | - Karine Marcotte
- Centre de recherche du Centre intégré universitaire de santé et de services sociaux du Nord-de-l'Île-de-Montréal, Hôpital du Sacré-Cœur de Montréal, Québec, Canada
- École d'orthophonie et d'audiologie, Faculté de médecine, Université de Montréal, Québec, Canada
| | - Hanna de Paz
- Centre de recherche du Centre intégré universitaire de santé et de services sociaux du Nord-de-l'Île-de-Montréal, Hôpital du Sacré-Cœur de Montréal, Québec, Canada
- École d'orthophonie et d'audiologie, Faculté de médecine, Université de Montréal, Québec, Canada
| | - Lucas Porcello Schilling
- Graduate Course in Medicine and Healthy Sciences, School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
- Graduate Course in Biomedical Gerontology, School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
- Brain Institute of Rio Grande do Sul (InsCer)Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
- Institute of Geriatrics and Gerontology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Irênio Gomes da Silva Filho
- Graduate Course in Biomedical Gerontology, School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Ricardo Soder
- Graduate Course in Medicine and Healthy Sciences, School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
- Brain Institute of Rio Grande do Sul (InsCer)Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Alexandre da Rosa Franco
- Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline for Psychiatric Research, Orangeburg, NY
- Center for the Developing Brain, Child Mind Institute, New York, NY
- Department of Psychiatry, NYU Grossman School of Medicine, New York
| | - Fernanda Loureiro
- Graduate Course in Biomedical Gerontology, School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Lilian Cristine Hübner
- Department of Linguistics, School of Humanities, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
- Institute of Geriatrics and Gerontology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, RS, Brazil
- National Council for Scientific and Technological Development (CNPq), Brasília, DF, Brazil
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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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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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Aziz D, Dávid S. Multitask and Transfer Learning Approach for Joint Classification and Severity Estimation of Dysphonia. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 12:233-244. [PMID: 38196819 PMCID: PMC10776101 DOI: 10.1109/jtehm.2023.3340345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 01/11/2024]
Abstract
OBJECTIVE Despite speech being the primary communication medium, it carries valuable information about a speaker's health, emotions, and identity. Various conditions can affect the vocal organs, leading to speech difficulties. Extensive research has been conducted by voice clinicians and academia in speech analysis. Previous approaches primarily focused on one particular task, such as differentiating between normal and dysphonic speech, classifying different voice disorders, or estimating the severity of voice disorders. METHODS AND PROCEDURES This study proposes an approach that combines transfer learning and multitask learning (MTL) to simultaneously perform dysphonia classification and severity estimation. Both tasks use a shared representation; network is learned from these shared features. We employed five computer vision models and changed their architecture to support multitask learning. Additionally, we conducted binary 'healthy vs. dysphonia' and multiclass 'healthy vs. organic and functional dysphonia' classification using multitask learning, with the speaker's sex as an auxiliary task. RESULTS The proposed method achieved improved performance across all classification metrics compared to single-task learning (STL), which only performs classification or severity estimation. Specifically, the model achieved F1 scores of 93% and 90% in MTL and STL, respectively. Moreover, we observed considerable improvements in both classification tasks by evaluating beta values associated with the weight assigned to the sex-predicting auxiliary task. MTL achieved an accuracy of 77% compared to the STL score of 73.2%. However, the performance of severity estimation in MTL was comparable to STL. CONCLUSION Our goal is to improve how voice pathologists and clinicians understand patients' conditions, make it easier to track their progress, and enhance the monitoring of vocal quality and treatment procedures. Clinical and Translational Impact Statement: By integrating both classification and severity estimation of dysphonia using multitask learning, we aim to enable clinicians to gain a better understanding of the patient's situation, effectively monitor their progress and voice quality.
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Affiliation(s)
- Dosti Aziz
- Department of Telecommunications and Media InformaticsBudapest University of Technology and Economics1117BudapestHungary
| | - Sztahó Dávid
- Department of Telecommunications and Media InformaticsBudapest University of Technology and Economics1117BudapestHungary
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Kong APH, Cheung RTH, Wong GHY, Choy JCP, Dai R, Spector A. Spoken discourse in episodic autobiographical and verbal short-term memory in Chinese people with dementia: the roles of global coherence and informativeness. Front Psychol 2023; 14:1124477. [PMID: 38022958 PMCID: PMC10643863 DOI: 10.3389/fpsyg.2023.1124477] [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: 12/15/2022] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Memory and discourse production are closely related in healthy populations. A few studies in people with amnestic mild cognitive impairment and people with dementia (PWD) suggested similar links, although empirical evidence is insufficient to inform emerging intervention design and natural language processing research. Fine-grained discourse assessment is needed to understand their complex relationship in PWD. Methods Spoken samples from 104 PWD were elicited using personal narrative and sequential picture description and assessed using Main Concept Analysis and other content-based analytic methods. Discourse and memory performance data were analyzed in bivariate correlation and linear multiple regression models to determine the relationship between discourse production and episodic autobiographical memory and verbal short-term memory (vSTM). Results Global coherence was a significant predictor of episodic autobiographical memory, explaining over half of the variance. Both episodic autobiographical memory and vSTM were positively correlated with global coherence and informativeness, and negatively with empty speech indices. Discussion Coherence in personal narrative may be supported by episodic autobiographical memory and vice versa, suggesting potential mechanism of interventions targeting personhood through conversation. Indices of global coherence, informativeness, and empty speech can be used as markers of memory functions in PWD.
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Affiliation(s)
- Anthony Pak-Hin Kong
- Academic Unit of Human Communication, Development, and Information Sciences, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Aphasia Research and Therapy (ART) Laboratory, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Ryan Tsz Him Cheung
- Academic Unit of Human Communication, Development, and Information Sciences, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Gloria H. Y. Wong
- Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
- Department of Health Service and Population Research, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
| | - Jacky C. P. Choy
- Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Ruizhi Dai
- Department of Psychology, Guangzhou University, Guangzhou, China
| | - Aimee Spector
- Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom
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Zupan Z. Cognitive performance outcomes: considerations for drug development. J Patient Rep Outcomes 2023; 7:102. [PMID: 37855938 PMCID: PMC10587033 DOI: 10.1186/s41687-023-00644-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/08/2023] [Indexed: 10/20/2023] Open
Abstract
Ensuring that cognitive assessments are fit for purpose is critical in the evaluation of the clinical benefit of new therapeutic interventions. Although guidelines for Clinical Outcome Assessments (COA) are available, performance outcome (PerfO) assessments, and in particular those assessing cognition (Cog-PerfOs) are more complex and have additional requirements that need to be considered. I outline three areas where further discussion around validation methods for Cog-PerfOs and best practices is warranted: (1) content validity (2) ecological validity, and (3) construct validity in multinational contexts. I conclude with a discussion of several potential avenues for the improvement of validation of Cog-PerfOs used to evaluate the efficacy of medical products that target cognitive symptomatology. These include the involvement of cognitive psychologists in establishing content validity of Cog-PerfOs, evaluating the congruence of laypeople's and expert understanding of cognitive concepts, supplementing qualitative with quantitative evidence when establishing content validity, demonstrating ecological validity, and ensuring normative data are available in multinational contexts.
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Affiliation(s)
- Zorana Zupan
- Institute of Psychology, Faculty of Philosophy, University of Belgrade, Cika Ljubina 18/20, Belgrade, 11000, Serbia.
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Meulemans C, Leijten M, De Maeyer S. The influence of age and verb transitivity on written sentence production. CLINICAL LINGUISTICS & PHONETICS 2023; 37:958-977. [PMID: 36124559 DOI: 10.1080/02699206.2022.2109992] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 07/25/2022] [Accepted: 08/01/2022] [Indexed: 06/15/2023]
Abstract
In this paper we explore the influences of normal ageing and verb transitivity on sentence production. The underlying aim is to provide a foundation for further research on sentence production in Alzheimer's disease (AD). We used a computer-based written sentence production task, designed to elicit intransitive, monotransitive and ditransitive sentences. Data was collected using keystroke logging, a technique to capture the entire typing process. Data of ninety healthy elderly was analysed focusing on the following writing process variables: time on task, production time and pause times. Results show that age influences time on task, pause time before sentences and within words. Verb transitivity influences time on task, production time and pause time between words. For pause time before sentences and between words, an interaction effect between age and verb transitivity was found as well. These results indicate that a follow-up study with AD patients should not attribute a slowdown in one of these variables to the disease in its entirety but should instead be compared with the slowdown in age-matched healthy peers.
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Affiliation(s)
- Catherine Meulemans
- Research Foundation Flanders, Antwerp, Belgium
- Department of Management, University of Antwerp, Antwerp, Belgium
| | - Mariëlle Leijten
- Department of Management, University of Antwerp, Antwerp, Belgium
| | - Sven De Maeyer
- Department of Training and Education Sciences, University of Antwerp, Antwerp, Belgium
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Chakrabarty M, Klooster N, Biswas A, Chatterjee A. The scope of using pragmatic language tests for early detection of dementia: A systematic review of investigations using figurative language. Alzheimers Dement 2023; 19:4705-4728. [PMID: 37534671 DOI: 10.1002/alz.13369] [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: 01/06/2023] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 08/04/2023]
Abstract
INTRODUCTION Dementia cases are expected to rise to 81.1 million in 2040. Efforts are underway to develop diagnostic methods to facilitate early detection of the disease. Herein we review research findings focusing on pragmatic dysfunction in patients with dementia and evaluate the usefulness of assessing dementia and its progress with a battery of tests assessing figurative language skills. METHODS A total of 74,778 article titles were identified from EMBASE, PubMed, and Google Scholar databases. After systematic screening, 51 journal articles were selected for the final review. RESULT The review suggests that impaired figurative language might be a marker for early cognitive decline. Different forms of figurative language may be impaired at different stages of the disease and in different types of dementia involving different neuropathologies. CONCLUSION The use of pragmatic tests in combination with the existing diagnostic protocols might increase the probability of early diagnosis. HIGHLIGHTS Pragmatic impairment could be a marker of early cognitive impairment. Figurative language-an important pragmatic aspect-is disrupted in mild cognitive impairment (MCI) and early Alzheimer's disease (AD). Figurative language impairment might precede literal language impairment. Pragmatic tests could be more sensitive than standard neuropsychological tests. Inclusion of pragmatic tests in diagnostic guidelines might bolster early detection.
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Affiliation(s)
- Madhushree Chakrabarty
- Department of Neurology, Institute of Post Graduate Medical Education & Research and Bangur Institute of Neurosciences, Kolkata, West Bengal, India
| | - Nathaniel Klooster
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Moss Rehabilitation Research Institute, Elkins Park, Pennsylvania, USA
- Hope College, Holland, Michigan, USA
| | - Atanu Biswas
- Department of Neurology, Institute of Post Graduate Medical Education & Research and Bangur Institute of Neurosciences, Kolkata, West Bengal, India
| | - Anjan Chatterjee
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Moss Rehabilitation Research Institute, Elkins Park, Pennsylvania, USA
- Penn Center for Neuroaesthetics, University of Pennsylvania, Goddard Laboratories, Philadelphia, Pennsylvania, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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20
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Parsapoor M. AI-based assessments of speech and language impairments in dementia. Alzheimers Dement 2023; 19:4675-4687. [PMID: 37578167 DOI: 10.1002/alz.13395] [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: 11/01/2022] [Revised: 06/03/2023] [Accepted: 06/05/2023] [Indexed: 08/15/2023]
Abstract
Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI-powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets.
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Affiliation(s)
- Mahboobeh Parsapoor
- Centre de Recherche Informatique de Montréal: CRIM, Montreal, Quebec, Canada
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21
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Zolnoori M, Zolnour A, Topaz M. ADscreen: A speech processing-based screening system for automatic identification of patients with Alzheimer's disease and related dementia. Artif Intell Med 2023; 143:102624. [PMID: 37673583 PMCID: PMC10483114 DOI: 10.1016/j.artmed.2023.102624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 06/22/2023] [Accepted: 07/08/2023] [Indexed: 09/08/2023]
Abstract
Alzheimer's disease and related dementias (ADRD) present a looming public health crisis, affecting roughly 5 million people and 11 % of older adults in the United States. Despite nationwide efforts for timely diagnosis of patients with ADRD, >50 % of them are not diagnosed and unaware of their disease. To address this challenge, we developed ADscreen, an innovative speech-processing based ADRD screening algorithm for the protective identification of patients with ADRD. ADscreen consists of five major components: (i) noise reduction for reducing background noises from the audio-recorded patient speech, (ii) modeling the patient's ability in phonetic motor planning using acoustic parameters of the patient's voice, (iii) modeling the patient's ability in semantic and syntactic levels of language organization using linguistic parameters of the patient speech, (iv) extracting vocal and semantic psycholinguistic cues from the patient speech, and (v) building and evaluating the screening algorithm. To identify important speech parameters (features) associated with ADRD, we used the Joint Mutual Information Maximization (JMIM), an effective feature selection method for high dimensional, small sample size datasets. Modeling the relationship between speech parameters and the outcome variable (presence/absence of ADRD) was conducted using three different machine learning (ML) architectures with the capability of joining informative acoustic and linguistic with contextual word embedding vectors obtained from the DistilBERT (Bidirectional Encoder Representations from Transformers). We evaluated the performance of the ADscreen on an audio-recorded patients' speech (verbal description) for the Cookie-Theft picture description task, which is publicly available in the dementia databank. The joint fusion of acoustic and linguistic parameters with contextual word embedding vectors of DistilBERT achieved F1-score = 84.64 (standard deviation [std] = ±3.58) and AUC-ROC = 92.53 (std = ±3.34) for training dataset, and F1-score = 89.55 and AUC-ROC = 93.89 for the test dataset. In summary, ADscreen has a strong potential to be integrated with clinical workflow to address the need for an ADRD screening tool so that patients with cognitive impairment can receive appropriate and timely care.
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Affiliation(s)
- Maryam Zolnoori
- Columbia University Medical Center, New York, NY, United States of America; School of Nursing, Columbia University, New York, NY, United States of America.
| | - Ali Zolnour
- School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | - Maxim Topaz
- Columbia University Medical Center, New York, NY, United States of America; School of Nursing, Columbia University, New York, NY, United States of America
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22
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Qi X, Zhou Q, Dong J, Bao W. Noninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review. Front Aging Neurosci 2023; 15:1224723. [PMID: 37693647 PMCID: PMC10484224 DOI: 10.3389/fnagi.2023.1224723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/04/2023] [Indexed: 09/12/2023] Open
Abstract
Alzheimer's disease (AD) is considered as one of the leading causes of death among people over the age of 70 that is characterized by memory degradation and language impairment. Due to language dysfunction observed in individuals with AD patients, the speech-based methods offer non-invasive, convenient, and cost-effective solutions for the automatic detection of AD. This paper systematically reviews the technologies to detect the onset of AD from spontaneous speech, including data collection, feature extraction and classification. First the paper formulates the task of automatic detection of AD and describes the process of data collection. Then, feature extractors from speech data and transcripts are reviewed, which mainly contains acoustic features from speech and linguistic features from text. Especially, general handcrafted features and deep embedding features are organized from different modalities. Additionally, this paper summarizes optimization strategies for AD detection systems. Finally, the paper addresses challenges related to data size, model explainability, reliability and multimodality fusion, and discusses potential research directions based on these challenges.
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Affiliation(s)
- Xiaoke Qi
- School of Information Management for Law, China University of Political Science and Law, Beijing, China
| | | | - Jian Dong
- Information Technology Research Center, China Electronics Standardization Institute, Beijing, China
| | - Wei Bao
- Information Technology Research Center, China Electronics Standardization Institute, Beijing, China
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23
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Nguyen TM, Leow AD, Ajilore O. A Review on Smartphone Keystroke Dynamics as a Digital Biomarker for Understanding Neurocognitive Functioning. Brain Sci 2023; 13:959. [PMID: 37371437 DOI: 10.3390/brainsci13060959] [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: 05/04/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
Can digital technologies provide a passive unobtrusive means to observe and study cognition outside of the laboratory? Previously, cognitive assessments and monitoring were conducted in a laboratory or clinical setting, allowing for a cross-sectional glimpse of cognitive states. In the last decade, researchers have been utilizing technological advances and devices to explore ways of assessing cognition in the real world. We propose that the virtual keyboard of smartphones, an increasingly ubiquitous digital device, can provide the ideal conduit for passive data collection to study cognition. Passive data collection occurs without the active engagement of a participant and allows for near-continuous, objective data collection. Most importantly, this data collection can occur in the real world, capturing authentic datapoints. This method of data collection and its analyses provide a more comprehensive and potentially more suitable insight into cognitive states, as intra-individual cognitive fluctuations over time have shown to be an early manifestation of cognitive decline. We review different ways passive data, centered around keystroke dynamics, collected from smartphones, have been used to assess and evaluate cognition. We also discuss gaps in the literature where future directions of utilizing passive data can continue to provide inferences into cognition and elaborate on the importance of digital data privacy and consent.
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Affiliation(s)
- Theresa M Nguyen
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Alex D Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
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Kim KX, Dale CL, Ranasinghe KG, Kothare H, Beagle AJ, Lerner H, Mizuiri D, Gorno-Tempini ML, Vossel K, Nagarajan SS, Houde JF. Impaired Speaking-Induced Suppression in Alzheimer's Disease. eNeuro 2023; 10:ENEURO.0056-23.2023. [PMID: 37221089 PMCID: PMC10249944 DOI: 10.1523/eneuro.0056-23.2023] [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: 02/17/2023] [Accepted: 04/04/2023] [Indexed: 05/25/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease involving cognitive impairment and abnormalities in speech and language. Here, we examine how AD affects the fidelity of auditory feedback predictions during speaking. We focus on the phenomenon of speaking-induced suppression (SIS), the auditory cortical responses' suppression during auditory feedback processing. SIS is determined by subtracting the magnitude of auditory cortical responses during speaking from listening to playback of the same speech. Our state feedback control (SFC) model of speech motor control explains SIS as arising from the onset of auditory feedback matching a prediction of that feedback onset during speaking, a prediction that is absent during passive listening to playback of the auditory feedback. Our model hypothesizes that the auditory cortical response to auditory feedback reflects the mismatch with the prediction: small during speaking, large during listening, with the difference being SIS. Normally, during speaking, auditory feedback matches its predictions, then SIS will be large. Any reductions in SIS will indicate inaccuracy in auditory feedback prediction not matching the actual feedback. We investigated SIS in AD patients [n = 20; mean (SD) age, 60.77 (10.04); female (%), 55.00] and healthy controls [n = 12; mean (SD) age, 63.68 (6.07); female (%), 83.33] through magnetoencephalography (MEG)-based functional imaging. We found a significant reduction in SIS at ∼100 ms in AD patients compared with healthy controls (linear mixed effects model, F (1,57.5) = 6.849, p = 0.011). The results suggest that AD patients generate inaccurate auditory feedback predictions, contributing to abnormalities in AD speech.
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Affiliation(s)
- Kyunghee X Kim
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA 94117
| | - Corby L Dale
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94117
| | - Kamalini G Ranasinghe
- Department of Neurology, University of California San Francisco, San Francisco, CA 94158
| | - Hardik Kothare
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94117
| | - Alexander J Beagle
- Department of Neurology, University of California San Francisco, San Francisco, CA 94158
| | - Hannah Lerner
- Department of Neurology, University of California San Francisco, San Francisco, CA 94158
| | - Danielle Mizuiri
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94117
| | | | - Keith Vossel
- Department of Neurology, University of California San Francisco, San Francisco, CA 94158
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94117
| | - John F Houde
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA 94117
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25
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Liampas I, Folia V, Morfakidou R, Siokas V, Yannakoulia M, Sakka P, Scarmeas N, Hadjigeorgiou G, Dardiotis E, Kosmidis MH. Language Differences Among Individuals with Normal Cognition, Amnestic and Non-Amnestic MCI, and Alzheimer's Disease. Arch Clin Neuropsychol 2023; 38:525-536. [PMID: 36244060 PMCID: PMC10202551 DOI: 10.1093/arclin/acac080] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2022] [Indexed: 10/29/2023] Open
Abstract
OBJECTIVE To investigate differences in language performance among older adults with normal cognition (CN), mild cognitive impairment (MCI), and Alzheimer's disease (ad). Owing to the conflicting literature concerning MCI, discrepancies between amnestic (aMCI) and non-amnestic MCI (naMCI) were explored in greater detail. METHOD The study sample was drawn from the older (>64 years) HELIAD cohort. Language performance was assessed via semantic and phonemic fluency, confrontation naming, verbal comprehension, verbal repetition as well as a composite language index. Age, sex, and education adjusted general linear models were used to quantify potential pairwise differences in language performance. RESULTS The present analysis involved 1607 participants with CN, 146 with aMCI [46 single and 100 multi-domain aMCI], 92 with naMCI [41 single and 51 multi-domain naMCI], and 79 with ad. The mean age and education of our predominantly female (60%) participants were 73.82 (±5.43) and 7.98 (±4.93) years, respectively. MCI individuals performed between those with CN and ad, whereas participants with aMCI performed worse compared to those with naMCI, especially in the semantic fluency and verbal comprehension tasks. Discrepancies between the aMCI and naMCI groups were driven by the exquisitely poor performance of multi-domain aMCI subgroup. CONCLUSIONS Overall, individuals could be hierarchically arranged in a continuum of language impairment with the CN individuals constituting the healthy reference and naMCI, aMCI, ad patients representing gradually declining classes in terms of language performance. Exploration of language performance via separation of single from multi-domain naMCI provided a potential explanation for the conflicting evidence of previous research.
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Affiliation(s)
- Ioannis Liampas
- Department of Neurology, University Hospital of Larissa, School of Medicine, University of Thessaly, Larissa, Greece
| | - Vasiliki Folia
- Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Greece
| | - Renia Morfakidou
- Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Greece
| | - Vasileios Siokas
- Department of Neurology, University Hospital of Larissa, School of Medicine, University of Thessaly, Larissa, Greece
| | - Mary Yannakoulia
- Department of Nutrition and Dietetics, Harokopio University, Athens, Greece
| | - Paraskevi Sakka
- Association of Alzheimer's Disease and Related Disorders, Marousi, Athens, Greece
| | - Nikolaos Scarmeas
- First Department of Neurology, Aeginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
- Taub Institute for Research in Alzheimer's Disease and the Aging Brain, The Gertrude H. Sergievsky Center, Department of Neurology, Columbia University, New York, USA
| | - Georgios Hadjigeorgiou
- Department of Neurology, University Hospital of Larissa, School of Medicine, University of Thessaly, Larissa, Greece
- Department of Neurology, Medical School, University of Cyprus, Nicosia, Cyprus
| | - Efthimios Dardiotis
- Department of Neurology, University Hospital of Larissa, School of Medicine, University of Thessaly, Larissa, Greece
| | - Mary H Kosmidis
- Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Greece
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Petti U, Baker S, Korhonen A, Robin J. The Generalizability of Longitudinal Changes in Speech Before Alzheimer's Disease Diagnosis. J Alzheimers Dis 2023; 92:547-564. [PMID: 36776053 DOI: 10.3233/jad-220847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
BACKGROUND Language impairment in Alzheimer's disease (AD) has been widely studied but due to limited data availability, relatively few studies have focused on the longitudinal change in language in the individuals who later develop AD. Significant differences in speech have previously been found by comparing the press conference transcripts of President Bush and President Reagan, who was later diagnosed with AD. OBJECTIVE In the current study, we explored whether the patterns previously established in the single AD-healthy control (HC) participant pair apply to a larger group of individuals who later receive AD diagnosis. METHODS We replicated previous methods on two larger corpora of longitudinal spontaneous speech samples of public figures, consisting of 10 and 9 AD-HC participant pairs. As we failed to find generalizable patterns of language change using previous methodology, we proposed alternative methods for data analysis, investigating the benefits of using different language features and their change with age, and compiling the single features into aggregate scores. RESULTS The single features that showed the strongest results were moving average type:token ratio (MATTR) and pronoun-related features. The aggregate scores performed better than the single features, with lexical diversity capturing a similar change in two-thirds of the participants. CONCLUSION Capturing universal patterns of language change prior to AD can be challenging, but the decline in lexical diversity and changes in MATTR and pronoun-related features act as promising measures that reflect the cognitive changes in many participants.
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Affiliation(s)
- Ulla Petti
- University of Cambridge, Language Technology Lab, Cambridge, UK
| | - Simon Baker
- University of Cambridge, Language Technology Lab, Cambridge, UK
| | - Anna Korhonen
- University of Cambridge, Language Technology Lab, Cambridge, UK
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Spinosa V, Vitulli A, Logroscino G, Brattico E. A Review on Music Interventions for Frontotemporal Aphasia and a Proposal for Alternative Treatments. Biomedicines 2022; 11:biomedicines11010084. [PMID: 36672592 PMCID: PMC9855720 DOI: 10.3390/biomedicines11010084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/08/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022] Open
Abstract
Frontotemporal dementia (FTD) is a rare neurodegenerative disease, characterized by behavioral and language impairments. Primary progressive aphasia (PPA) is the linguistic variant of this heterogeneous disorder. To date, there is a lack of consensus about which interventions are effective in these patients. However, several studies show that music-based interventions are beneficial in neurological diseases. This study aims, primarily, to establish the state of the art of music-based interventions designed for PPA due to FTD and, secondarily, to inform the planning of PPA-dedicated future interventions for Italian neurological institutions. The first aim is fulfilled by a review which critically screens the neurological studies examining the effects of music- and/or rhythm-based interventions, especially, on language rehabilitation in aphasic FTD. We found that only two papers fulfilled our criteria and concerned specifically aphasic patients due to FTD. Of those, one paper reported a study conducted in an Italian institution. Most of the reviewed studies focused, instead, on aphasia in post-stroke patients. The results of our review invite further studies to investigate the role of music as a valuable support in the therapy for neurodegenerative patients with language problems and in particular to PPA due to FTD. Moreover, based on this initial work, we can delineate new music-based interventions dedicated to PPA for Italian institutions.
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Affiliation(s)
- Vittoria Spinosa
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari “Aldo Moro”, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari “Aldo Moro”, 70121 Bari, Italy
| | - Alessandra Vitulli
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari “Aldo Moro”, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari “Aldo Moro”, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari “Aldo Moro”, 70121 Bari, Italy
| | - Elvira Brattico
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, 8000 Aarhus, Denmark
- Department of Education, Psychology, Communication, University of Bari “Aldo Moro”, 70121 Bari, Italy
- Correspondence:
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Hason L, Krishnan S. Spontaneous speech feature analysis for alzheimer's disease screening using a random forest classifier. Front Digit Health 2022; 4:901419. [PMID: 36465088 PMCID: PMC9712439 DOI: 10.3389/fdgth.2022.901419] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 10/19/2022] [Indexed: 07/20/2023] Open
Abstract
Detecting Alzheimer's disease (AD) and disease progression based on the patient's speech not the patient's speech data can aid non-invasive, cost-effective, real-time early diagnostic and repetitive monitoring in minimum time and effort using machine learning (ML) classification approaches. This paper aims to predict early AD diagnosis and evaluate stages of AD through exploratory analysis of acoustic features, non-stationarity, and non-linearity testing, and applying data augmentation techniques on spontaneous speech signals collected from AD and cognitively normal (CN) subjects. Evaluation of the proposed AD prediction and AD stages classification models using Random Forest classifier yielded accuracy rates of 82.2% and 71.5%. This will enrich the Alzheimer's research community with further understanding of methods to improve models for AD classification and addressing non-stationarity and non-linearity properties on audio features to determine the best-suited acoustic features for AD monitoring.
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29
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Nasiri M, Moayedfar S, Purmohammad M, Ghasisin L. Investigating sentence processing and working memory in patients with mild Alzheimer and elderly people. PLoS One 2022; 17:e0266552. [PMID: 36318545 PMCID: PMC9624401 DOI: 10.1371/journal.pone.0266552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 03/22/2022] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION Linguistic disorders are one of the common problems in Alzheimer's disease, which in recent years has been considered as one of the key parameters in the diagnosis of Alzheimer (AD). Given that changes in sentence processing and working memory and the relationship between these two activities may be a diagnostic parameter in the early and preclinical stages of AD, the present study examines the comprehension and production of sentences and working memory in AD patients and healthy aged people. METHODS Twenty-five people with mild Alzheimer's and 25 healthy elderly people participated in the study. In this study, we used the digit span to evaluate working memory. Syntactic priming and sentence completion tasks in canonical and non-canonical conditions were used for evaluating sentence production. We administered sentence picture matching and cross-modal naming tasks to assess sentence comprehension. RESULTS The results of the present study revealed that healthy elderly people and patients with mild Alzheimer's disease have a significant difference in comprehension of relative clause sentences (P <0.05). There was no significant difference between the two groups in comprehension of simple active, simple active with noun phrase and passive sentences (P> 0.05). They had a significant difference in auditory and visual reaction time (P <0.05). Also there was a significant difference between the two groups in syntactic priming and sentence completion tasks. However, in non-canonical condition of sentence completion, the difference between the two groups was not significant (P> 0.05). CONCLUSION The results of the present study showed that the mean scores related to comprehension, production and working memory in people with mild Alzheimer's were lower than healthy aged people, which indicate sentence processing problems at this level of the disease. People with Alzheimer have difficulty comprehending and producing complex syntactic structures and have poorer performance in tasks that required more memory demands. It seems that the processing problems of these people are due to both working memory and language problems, which are not separate from each other and both are involved in.
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Affiliation(s)
- Maryam Nasiri
- Student Research Committee, School of Rehabilitation, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Saeideh Moayedfar
- Department of speech therapy, School of Rehabilitation Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Purmohammad
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Leila Ghasisin
- Communication Disorders Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
- * E-mail: ,
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Pigliautile M, Colombo M, Pizzuti T, Procopio N, Stillo M, Curia R, Mecocci P. DMapp: a developing promising approach to monitor symptoms progression and stimulate memory in Italian people with cognitive impairments. Aging Clin Exp Res 2022; 34:2721-2731. [PMID: 36036304 DOI: 10.1007/s40520-022-02219-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 07/31/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND Based on recent researches on the use of natural language processing techniques for very early detection of cognitive decline and the benefits of cognitive stimulation for people with cognitive impairments, the Dementia Monitoring application (DMapp) is developed inside the Memento project. AIMS The aims of this work are: (1) to present DMapp; (2) to report the results of two preliminary studies on DMapp; (3) to describe the clinical and experimental potentiality of DMapp. METHODS Italian people with the diagnosis of mild cognitive impairment due to Alzheimer's disease or dementia due to Alzheimer's Disease with a Mini-Mental-State-Examination between 24 and 28 (inclusive) were involved in the DMapp development prototype during the Lab Trial (4 subjects) and Filed Trial (5 subjects) of the Memento project. Qualitative and quantitative data were collected to evaluate participants' opinions, the DMapp ability to perform the automatic analysis of the speech and participants' visible emotional state effective. Ad hoc interviews, the Observed Emotion Rating Scale and performance metrics to solve different tasks were used. The relation between cognitive measures (global cognitive measures) and linguistic indexes values was considered using Euclidean distances between the participants. RESULTS Linguistic indexes were calculated and seemed to classify the participants' performance as expected from cognitive measures. The DMapp was appreciated by people with cognitive impairment. Positive emotions were present. CONCLUSION DMapp seems an interesting approach to monitor dementia symptoms progression and stimulate memory. Possible developments and open questions are discussed.
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Affiliation(s)
- Martina Pigliautile
- Institute of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, Piazzale Gambuli, 1, 06132, Perugia, Italy.
| | - Matteo Colombo
- Institute of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, Piazzale Gambuli, 1, 06132, Perugia, Italy
| | | | | | - Maria Stillo
- Innovation Lab, Integris S.P.A, Rende and Pisa, Italy
| | - Rosario Curia
- Innovation Lab, Integris S.P.A, Rende and Pisa, Italy
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, Piazzale Gambuli, 1, 06132, Perugia, Italy.,Division of Clinical Geriatrics NVS Department Karolinska Institutet, Stockholm, Sweden
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31
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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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Diaz-Asper M, Holmlund TB, Chandler C, Diaz-Asper C, Foltz PW, Cohen AS, Elvevåg B. Using automated syllable counting to detect missing information in speech transcripts from clinical settings. Psychiatry Res 2022; 315:114712. [PMID: 35839638 PMCID: PMC9378537 DOI: 10.1016/j.psychres.2022.114712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/01/2022] [Accepted: 07/02/2022] [Indexed: 11/19/2022]
Abstract
Speech rate and quantity reflect clinical state; thus automated transcription holds potential clinical applications. We describe two datasets where recording quality and speaker characteristics affected transcription accuracy. Transcripts of low-quality recordings omitted significant portions of speech. An automated syllable counter estimated actual speech output and quantified the amount of missing information. The efficacy of this method differed by audio quality: the correlation between missing syllables and word error rate was only significant when quality was low. Automatically counting syllables could be useful to measure and flag transcription omissions in clinical contexts where speaker characteristics and recording quality are problematic.
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Affiliation(s)
| | - Terje B Holmlund
- Department of Clinical Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway
| | - Chelsea Chandler
- Department of Computer Science, University of Colorado Boulder, CO, United States
| | | | - Peter W Foltz
- Institute of Cognitive Science, University of Colorado Boulder, CO, United States
| | - Alex S Cohen
- Department of Psychology, Louisiana State University, LA, United States
| | - Brita Elvevåg
- Department of Clinical Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway; Norwegian Center for eHealth Research, University Hospital of North Norway, Tromsø, Norway.
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Alorfi NM. Public Awareness of Alzheimer’s Disease: A Cross-Sectional Study from Saudi Arabia. Int J Gen Med 2022; 15:7535-7546. [PMID: 36199585 PMCID: PMC9527696 DOI: 10.2147/ijgm.s373447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/12/2022] [Indexed: 12/04/2022] Open
Abstract
Background Alzheimer’s disease is considered the most common neurodegenerative and progressive illness. It is also a common type of dementia characterized by brain atrophy, neuronal tissue loss, and the formation of amyloid plaques. Mild memory loss is a commonly expected start of the disease, which can progress to loss of capacity to carry on a conversation and react to certain situations. Objective This study aimed to measure knowledge about Alzheimer’s disease in Saudi Arabia through the use of the Alzheimer’s Disease Knowledge Scale (ADKS) and measure the association between the ADKS with relevant demographic variables. Methods A pre-validated questionnaire containing 30 questions was distributed electronically to anyone older than 18 years old living in Saudi Arabia. Items regarding socio-demographic characteristics and the Alzheimer’s Disease Knowledge Scale (ADKS) were also included. Results Participants did not have a high enough mean score to be regarded as appropriately knowledgeable (mean = 17.35). Higher knowledge scores on Life impact, Risk factors, Assessment and diagnosis, Caregiving, Treatment and management, and ADKS were associated with the female gender. Higher knowledge of caregiving was associated with a postgraduate academic qualification. Higher knowledge on Assessment and Diagnosis was associated with higher age. Higher knowledge on risk factors was associated with having relatives diagnosed with Alzheimer’s disease. Higher knowledge on life impact was associated with having newspaper and journal articles as the source of medical information. Conclusion National awareness campaigns for the community and continuing education courses for caregivers must be placed to aid in increasing awareness regarding Alzheimer’s disease.
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Affiliation(s)
- Nasser M Alorfi
- Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
- Correspondence: Nasser M Alorfi, Department of Pharmacology and Toxicology, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia, Tel +966500644261, Email
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Jones GB, Wright JM. The economic imperatives for technology enabled wellness centered healthcare. J Public Health Policy 2022; 43:456-468. [PMID: 35922479 PMCID: PMC9362427 DOI: 10.1057/s41271-022-00356-8] [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] [Accepted: 07/05/2022] [Indexed: 11/28/2022]
Abstract
A 2020 World Health Organization report underscored the impact of rising healthcare spending globally and questioned the long-term economic sustainability of current funding models. Increases in costs associated with care of late-stage irreversible diseases and the increasing prevalence of debilitating neurodegenerative disorders, coupled with increases in life expectancy are likely to overload the healthcare systems in many nations within the next decade if not addressed. One option for sustainability of the healthcare system is a change in emphasis from illness to wellness centered care. An attractive model is the P4 (Predictive, Preventative, Personalized and Participatory) medicine approach. Recent advances in connected health technology can help accelerate this transition; they offer prediction, diagnosis, and monitoring of health-related parameters. We explain how to integrate such technologies with conventional approaches and guide public health policy toward wellness-based care models and strategies to relieve the escalating economic burdens of managed care.
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Affiliation(s)
- Graham B Jones
- Connected Health Program, Global Drug Development, Novartis Pharmaceuticals, 1 Health Plaza, East Hanover, NJ, 07936, USA. .,Clinical and Translational Science Institute, Tufts University Medical Center, 800 Washington Street, Boston, MA, 02111, USA.
| | - Justin M Wright
- Connected Health Program, Global Drug Development, Novartis Pharmaceuticals, 1 Health Plaza, East Hanover, NJ, 07936, USA
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Li L, Wang W, Lian T, Guo P, He M, Zhang W, Li J, Guan H, Luo D, Zhang W, Zhang W. The Influence of 24-h Ambulatory Blood Pressure on Cognitive Function and Neuropathological Biomarker in Patients With Alzheimer's Disease. Front Aging Neurosci 2022; 14:909582. [PMID: 35813940 PMCID: PMC9257169 DOI: 10.3389/fnagi.2022.909582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThis study aimed to investigate the influence of 24-h ambulatory blood pressure (BP) on cognitive function and neuropathological biomarkers in patients with Alzheimer's disease (AD) at the stages of mild cognitive impairment (MCI) and dementia.MethodsThe patients with AD were divided into the MCI (AD-MCI) group and the dementia (AD-D) group. Notably, 24-h BP variables, including BP level, coefficient of variation (CV) of BP, and pulse pressure, were collected and compared between the two groups. The correlations between 24-h BP variables and the scores of cognitive domains were analyzed. The independent influencing factors of cognitive domains of patients with AD were investigated. The levels of neuropathological biomarkers of AD, including β amyloid (Aβ)1−42, phosphorylated tau (P-tau), and total tau (T-tau), in cerebrospinal fluid (CSF) were measured and compared between the two groups, and the correlations between 24-h BP variables and the levels of neuropathological biomarkers of AD were analyzed.ResultsDaytime CV of systolic BP (SBP) was significantly increased in the AD-D group compared to that in the AD-MCI group. The 24-h and daytime CV of SBP and ambulatory pulse pressure were significantly and negatively correlated with memory score. The average 24-h and average daytime SBP level and CV of SBP, daytime CV of diastolic BP (DBP), and 24-h, daytime, and night-time ambulatory pulse pressure were significantly and negatively correlated with language score. The average 24-h SBP level, daytime CV of SBP, and 24-h, daytime, and night-time ambulatory pulse pressure were significantly and negatively correlated with attention score. Further analysis indicated that daytime CV of SBP as well as age and course of disease were the independent influencing factors of language. Age was also the independent influencing factor of memory and attention of patients with AD. T-tau level in CSF in the AD-D group was significantly higher than that in the AD-MCI group, but the levels of Aβ1−42, P-tau, and T-tau in CSF were not correlated with 24-h ambulatory BP variables.ConclusionDaytime CV of SBP was the independent influencing factor of language in patients with AD. The AD-D patients had significantly severe neurodegeneration than AD-MCI patients, which was, however, not through the influence of 24-h ambulatory BP variables on neuropathological biomarkers of AD.
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Affiliation(s)
- Lixia Li
- Department of Internal Medicine in International Medical Services, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Weijia Wang
- Department of Internal Medicine in International Medical Services, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tenghong Lian
- Center for Cognitive Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Peng Guo
- Center for Cognitive Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mingyue He
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Weijiao Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jinghui Li
- Center for Cognitive Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Huiying Guan
- Center for Cognitive Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Dongmei Luo
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Weijia Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Zhang
- Center for Cognitive Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
- Beijing Key Laboratory on Parkinson's Disease, Beijing, China
- *Correspondence: Wei Zhang
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Lofgren M, Hinzen W. Breaking the flow of thought: Increase of empty pauses in the connected speech of people with mild and moderate Alzheimer's disease. JOURNAL OF COMMUNICATION DISORDERS 2022; 97:106214. [PMID: 35397387 DOI: 10.1016/j.jcomdis.2022.106214] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/03/2022] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
INTRODUCTION The profile of spontaneous speech in Alzheimer's disease (AD) includes increased pausing as a window into cognitive decline. We here aimed to further characterize the pausing profile of AD by linking pauses to the syntactic positions in which they appear and disease progression. METHODS Speech was obtained through a picture description task, thus minimizing demands on episodic memory (EM), from a group of mild (N = 21) and moderate AD (N = 19), and healthy elderly controls (N = 40). Pauses were sub-indexed according to whether they occurred within-clauses, clause-initially, or utterance-initially, and whether they preceded nouns, verbs, or adjectives/adverbs, when occurring within-clauses. Additionally, relations to verbal fluency (VF) measures at the single-word level were explored. RESULTS Pause rate but not duration distinguished controls from both AD groups, while fillers did not distinguish any groups. The analysis by syntactic position revealed a highly differentiated picture, with largest effect sizes of significant group differences seen in the utterance-initial pause rate. The two AD groups patterned differently when compared to controls, while none of the measures differentiated the AD groups. Specifically, moderate but not mild AD differed from controls in clause-initial pauses, while mild but not moderate AD differed from controls in within-clause positions. At the within-clause level, the effect dividing controls from mild-AD was specifically driven by pauses ahead of nouns. A significant negative correlation emerged between pausing rate in spontaneous speech and VF measures in the mild-AD group only. CONCLUSIONS Increased empty (non-filled) pauses in AD are not confined to pauses in within-clause positions, which are most directly related to problems in the retrieval of words. Even in early disease stages, where these within-clause pause effects are seen, they are confined to nouns, revealing a grammatically specific problem possibly related to the referencing of objects. At all disease stages, pauses increase in utterance-sized units of structure, indicating progressive problems in the creative configuration of complete thoughts.
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Affiliation(s)
- Mary Lofgren
- Dept. Translation & Language Sciences, Universitat Pompeu Fabra, Carrer Roc Boronat, 138, Barcelona 08018, Spain.
| | - Wolfram Hinzen
- Dept. Translation & Language Sciences, Universitat Pompeu Fabra, Carrer Roc Boronat, 138, Barcelona 08018, Spain; Intitut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain, Passeig de Lluís Companys, 23, 08010 Barcelona, Spain
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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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Imre N, Balogh R, Gosztolya G, Tóth L, Hoffmann I, Várkonyi T, Lengyel C, Pákáski M, Kálmán J. Temporal Speech Parameters Indicate Early Cognitive Decline in Elderly Patients With Type 2 Diabetes Mellitus. Alzheimer Dis Assoc Disord 2022; 36:148-155. [PMID: 35293378 PMCID: PMC9132238 DOI: 10.1097/wad.0000000000000492] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 12/28/2021] [Indexed: 12/02/2022]
Abstract
INTRODUCTION The earliest signs of cognitive decline include deficits in temporal (time-based) speech characteristics. Type 2 diabetes mellitus (T2DM) patients are more prone to mild cognitive impairment (MCI). The aim of this study was to compare the temporal speech characteristics of elderly (above 50 y) T2DM patients with age-matched nondiabetic subjects. MATERIALS AND METHODS A total of 160 individuals were screened, 100 of whom were eligible (T2DM: n=51; nondiabetic: n=49). Participants were classified either as having healthy cognition (HC) or showing signs of MCI. Speech recordings were collected through a phone call. Based on automatic speech recognition, 15 temporal parameters were calculated. RESULTS The HC with T2DM group showed significantly shorter utterance length, higher duration rate of silent pause and total pause, and higher average duration of silent pause and total pause compared with the HC without T2DM group. Regarding the MCI participants, parameters were similar between the T2DM and the nondiabetic subgroups. CONCLUSIONS Temporal speech characteristics of T2DM patients showed early signs of altered cognitive functioning, whereas neuropsychological tests did not detect deterioration. This method is useful for identifying the T2DM patients most at risk for manifest MCI, and could serve as a remote cognitive screening tool.
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Affiliation(s)
| | | | - Gábor Gosztolya
- MTA-SZTE Research Group on Artificial Intelligence, University of Szeged, Szeged
| | - László Tóth
- MTA-SZTE Research Group on Artificial Intelligence, University of Szeged, Szeged
| | - Ildikó Hoffmann
- Hungarian Linguistics
- Hungarian Research Centre for Linguistics, Eötvös Loránd Research Network, Budapest, Hungary
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Determinants of Adherence to a “GRADIOR” Computer-Based Cognitive Training Program in People with Mild Cognitive Impairment (MCI) and Mild Dementia. J Clin Med 2022; 11:jcm11061714. [PMID: 35330040 PMCID: PMC8955227 DOI: 10.3390/jcm11061714] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 02/04/2023] Open
Abstract
Background: Computer-based programs have been implemented from a psychosocial approach for the care of people with dementia (PwD). However, several factors may determine adherence of older PwD to this type of treatment. The aim of this paper was to identify the sociodemographic, cognitive, psychological, and physical-health determinants that helped predict adherence or not to a “GRADIOR” computerized cognitive training (CCT) program in people with mild cognitive impairment (MCI) and mild dementia. Method: This study was part of a randomized clinical trial (RCT) (ISRCTN: 15742788). However, this study will only focus on the experimental group (n = 43) included in the RCT. This group was divided into adherent people (compliance: ≥60% of the sessions and persistence in treatment up to 4 months) and non-adherent. The participants were 60–90 age and diagnosed with MCI and mild dementia. We selected from the evaluation protocol for the RCT, tests that evaluated cognitive aspects (memory and executive functioning), psychological and physical health. The CCT with GRADIOR consisted of attending 2–3 weekly sessions for 4 months with a duration of 30 min Data analysis: Phi and Biserial-point correlations, a multiple logical regression analysis was obtained to find the adherence model and U Mann–Whitney was used. Results: The adherence model was made up of the Digit Symbol and Arithmetic of Wechsler Adult Intelligence Scale (WAIS-III) and Lexical Verbal Fluency (LVF) -R tests. This model had 90% sensitivity, 50% specificity and 75% precision. The goodness-of-fit p-value of the model was 0.02. Conclusions: good executive functioning in attention, working memory (WM), phonological verbal fluency and cognitive flexibility predicted a greater probability that a person would be adherent.
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Santander-Cruz Y, Salazar-Colores S, Paredes-García WJ, Guendulain-Arenas H, Tovar-Arriaga S. Semantic Feature Extraction Using SBERT for Dementia Detection. Brain Sci 2022; 12:brainsci12020270. [PMID: 35204032 PMCID: PMC8870383 DOI: 10.3390/brainsci12020270] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/28/2022] [Accepted: 01/29/2022] [Indexed: 01/27/2023] Open
Abstract
Dementia is a neurodegenerative disease that leads to the development of cognitive deficits, such as aphasia, apraxia, and agnosia. It is currently considered one of the most significant major medical problems worldwide, primarily affecting the elderly. This condition gradually impairs the patient’s cognition, eventually leading to the inability to perform everyday tasks without assistance. Since dementia is an incurable disease, early detection plays an important role in delaying its progression. Because of this, tools and methods have been developed to help accurately diagnose patients in their early stages. State-of-the-art methods have shown that the use of syntactic-type linguistic features provides a sensitive and noninvasive tool for detecting dementia in its early stages. However, these methods lack relevant semantic information. In this work, we propose a novel methodology, based on the semantic features approach, by using sentence embeddings computed by Siamese BERT networks (SBERT), along with support vector machine (SVM), K-nearest neighbors (KNN), random forest, and an artificial neural network (ANN) as classifiers. Our methodology extracted 17 features that provide demographic, lexical, syntactic, and semantic information from 550 oral production samples of elderly controls and people with Alzheimer’s disease, provided by the DementiaBank Pitt Corpus database. To quantify the relevance of the extracted features for the dementia classification task, we calculated the mutual information score, which demonstrates a dependence between our features and the MMSE score. The experimental classification performance metrics, such as the accuracy, precision, recall, and F1 score (77, 80, 80, and 80%, respectively), validate that our methodology performs better than syntax-based methods and the BERT approach when only the linguistic features are used.
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Affiliation(s)
- Yamanki Santander-Cruz
- Facultad de Ingeniería, Universidad Autónoma de Querétaro, Queretaro C.P. 76010, Mexico; (Y.S.-C.); (W.J.P.-G.)
| | | | | | | | - Saúl Tovar-Arriaga
- Facultad de Ingeniería, Universidad Autónoma de Querétaro, Queretaro C.P. 76010, Mexico; (Y.S.-C.); (W.J.P.-G.)
- Correspondence:
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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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Detecting Alzheimer’s Disease Based on Acoustic Features Extracted from Pre-trained Models. ARTIF INTELL 2022. [DOI: 10.1007/978-3-031-20503-3_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Sherman JC, Henderson CR, Flynn S, Gair JW, Lust B. Language Decline Characterizes Amnestic Mild Cognitive Impairment Independent of Cognitive Decline. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2021; 64:4287-4307. [PMID: 34699277 DOI: 10.1044/2021_jslhr-20-00503] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Purpose This research investigated the nature of cognitive decline in prodromal Alzheimer's disease (AD), particularly in mild cognitive impairment, amnestic type (aMCI). We assessed language in aMCI as compared with healthy aging (HA) and healthy young (HY) with new psycholinguistic assessment of complex sentences, and we tested the degree to which deficits on this language measure relate to performance in other general cognitive domains such as memory. Method Sixty-one individuals with aMCI were compared with 24 HA and 10 HY adults on a psycholinguistic measure of complex sentence production (relative clauses). In addition, HA, HY, and a subset of the aMCI participants (n = 22) were also tested on a multidomain cognitive screen, the Addenbrooke's Cognitive Examination-Revised (ACE-R), and on a verbal working memory Brown-Peterson (BP) test. General and generalized linear mixed models were used to test psycholinguistic results and to test whether ACE-R and BP performance predicted performance on the psycholinguistic test similarly in the aMCI and HA groups. Results On the psycholinguistic measure, sentence imitation was significantly deficited in aMCI in comparison with that in HA and HY. Experimental factorial designs revealed that individuals with aMCI had particular difficulty repeating sentences that especially challenged syntax-semantics integration. As expected, the aMCI group also performed significantly below the HY and HA groups on the ACE-R. Neither the ACE-R Memory subtest nor the BP total scores predicted performance on the psycholinguistic task for either the aMCI or the HA group. However, the ACE-R total score significantly predicted psycholinguistic task performance, with increased ACE-R performance predicting increased psycholinguistic task performance only for the HA group, not for the aMCI group. Conclusions Results suggest a selective deterioration in language in aMCI, specifically a weakening of syntax-semantics integration in complex sentence processing, and a general independence of this language deficit and memory decline. Results cohere with previous assessments of the nature of difficulty in complex sentence formation in aMCI. We argue that clinical screening for prodromal AD can be strengthened by supplementary testing of language, as well as memory, and extended evaluation of strength of their relation.
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Affiliation(s)
| | - Charles R Henderson
- Department of Psychology and Cognitive Science Cornell University, Ithaca, NY
| | - Suzanne Flynn
- Department of Linguistics and Philosophy, Massachusetts Institute of Technology, Cambridge
| | | | - Barbara Lust
- Department of Psychology and Cognitive Science Cornell University, Ithaca, NY
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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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Plonka A, Mouton A, Macoir J, Tran TM, Derremaux A, Robert P, Manera V, Gros A. Primary Progressive Aphasia: Use of Graphical Markers for an Early and Differential Diagnosis. Brain Sci 2021; 11:1198. [PMID: 34573219 PMCID: PMC8464890 DOI: 10.3390/brainsci11091198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 09/07/2021] [Accepted: 09/09/2021] [Indexed: 11/25/2022] Open
Abstract
Primary progressive aphasia (PPA) brings together neurodegenerative pathologies whose main characteristic is to start with a progressive language disorder. PPA diagnosis is often delayed in non-specialised clinical settings. With the technologies' development, new writing parameters can be extracted, such as the writing pressure on a touch pad. Despite some studies having highlighted differences between patients with typical Alzheimer's disease (AD) and healthy controls, writing parameters in PPAs are understudied. The objective was to verify if the writing pressure in different linguistic and non-linguistic tasks can differentiate patients with PPA from patients with AD and healthy subjects. Patients with PPA (n = 32), patients with AD (n = 22) and healthy controls (n = 26) were included in this study. They performed a set of handwriting tasks on an iPad® digital tablet, including linguistic, cognitive non-linguistic, and non-cognitive non-linguistic tasks. Average and maximum writing pressures were extracted for each task. We found significant differences in writing pressure, between healthy controls and patients with PPA, and between patients with PPA and AD. However, the classification of performances was dependent on the nature of the tasks. These results suggest that measuring writing pressure in graphical tasks may improve the early diagnosis of PPA, and the differential diagnosis between PPA and AD.
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Affiliation(s)
- Alexandra Plonka
- Département d’Orthophonie de Nice, Faculté de Médecine, Université Côte d’Azur, 06000 Nice, France; (A.M.); (P.R.); (A.G.)
- Laboratoire CoBTeK (Cognition Behaviour Technology), Université Côte d’Azur, 06000 Nice, France; (A.D.); (V.M.)
- Institut NeuroMod, Université Côte d’Azur, 06902 Sophia-Antipolis, France
| | - Aurélie Mouton
- Département d’Orthophonie de Nice, Faculté de Médecine, Université Côte d’Azur, 06000 Nice, France; (A.M.); (P.R.); (A.G.)
- Laboratoire CoBTeK (Cognition Behaviour Technology), Université Côte d’Azur, 06000 Nice, France; (A.D.); (V.M.)
- Service Clinique Gériatrique du Cerveau et du Mouvement, CMRR, Centre Hospitalier Universitaire, 06000 Nice, France
| | - Joël Macoir
- Département de Réadaptation, Faculté de Médecine, Université Laval, Québec, QC G1V 0A6, Canada;
- Centre de Recherche CERVO (CERVO Brain Research Centre), Québec, QC G1J 2G3, Canada
| | - Thi-Mai Tran
- Laboratoire STL, UMR 8163, Département d‘Orthophonie, UFR3S, Université de Lille, 59000 Lille, France;
| | - Alexandre Derremaux
- Laboratoire CoBTeK (Cognition Behaviour Technology), Université Côte d’Azur, 06000 Nice, France; (A.D.); (V.M.)
| | - Philippe Robert
- Département d’Orthophonie de Nice, Faculté de Médecine, Université Côte d’Azur, 06000 Nice, France; (A.M.); (P.R.); (A.G.)
- Laboratoire CoBTeK (Cognition Behaviour Technology), Université Côte d’Azur, 06000 Nice, France; (A.D.); (V.M.)
- Service Clinique Gériatrique du Cerveau et du Mouvement, CMRR, Centre Hospitalier Universitaire, 06000 Nice, France
| | - Valeria Manera
- Laboratoire CoBTeK (Cognition Behaviour Technology), Université Côte d’Azur, 06000 Nice, France; (A.D.); (V.M.)
| | - Auriane Gros
- Département d’Orthophonie de Nice, Faculté de Médecine, Université Côte d’Azur, 06000 Nice, France; (A.M.); (P.R.); (A.G.)
- Laboratoire CoBTeK (Cognition Behaviour Technology), Université Côte d’Azur, 06000 Nice, France; (A.D.); (V.M.)
- Service Clinique Gériatrique du Cerveau et du Mouvement, CMRR, Centre Hospitalier Universitaire, 06000 Nice, France
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DeSouza DD, Robin J, Gumus M, Yeung A. Natural Language Processing as an Emerging Tool to Detect Late-Life Depression. Front Psychiatry 2021; 12:719125. [PMID: 34552519 PMCID: PMC8450440 DOI: 10.3389/fpsyt.2021.719125] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/11/2021] [Indexed: 12/14/2022] Open
Abstract
Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.
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Affiliation(s)
| | | | | | - Anthony Yeung
- Department of Psychiatry, University of Toronto, Toronto, ON, 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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Vincze V, Szatlóczki G, Tóth L, Gosztolya G, Pákáski M, Hoffmann I, Kálmán J. Telltale silence: temporal speech parameters discriminate between prodromal dementia and mild Alzheimer's disease. CLINICAL LINGUISTICS & PHONETICS 2021; 35:727-742. [PMID: 32993390 DOI: 10.1080/02699206.2020.1827043] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 06/11/2023]
Abstract
This study presents a novel approach for the early detection of mild cognitive impairment (MCI) and mild Alzheimer's disease (mAD) in the elderly. Participants were 25 elderly controls (C), 25 clinically diagnosed MCI and 25 mAD patients, included after a clinical diagnosis validated by CT or MRI and cognitive tests. Our linguistic protocol involved three connected speech tasks that stimulate different memory systems, which were recorded, then analyzed linguistically by using the PRAAT software. The temporal speech-related parameters successfully differentiate MCI from mAD and C, such as speech rate, number and length of pauses, the rate of pause and signal. Parameters pauses/duration and silent pauses/duration linearly decreased among the groups, in other words, the percentage of pauses in the total duration of speech continuously grows as dementia progresses. Thus, the proposed approach may be an effective tool for screening MCI and mAD.
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Affiliation(s)
- Veronika Vincze
- MTA-SZTE Research Group on Artifical Intelligence, Szeged, Hungary
| | | | - László Tóth
- Institute of Informatics, University of Szeged, Szeged, Hungary
| | - Gábor Gosztolya
- MTA-SZTE Research Group on Artifical Intelligence, Szeged, Hungary
| | | | - Ildikó Hoffmann
- Department of Linguistics, University of Szeged, Szeged, Hungary
- Research Institute for Linguistics, Hungarian Academy of Sciences, Budapest, Hungary
| | - János Kálmán
- Department of Psychiatry, University of Szeged, Szeged, Hungary
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Applying Attention-Based Models for Detecting Cognitive Processes and Mental Health Conditions. Cognit Comput 2021; 13:1154-1171. [PMID: 34306241 PMCID: PMC8286051 DOI: 10.1007/s12559-021-09901-1] [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: 10/07/2020] [Accepted: 06/23/2021] [Indexed: 11/27/2022]
Abstract
According to the psychological literature, implicit motives allow for the characterization of behavior, subsequent success, and long-term development. Contrary to personality traits, implicit motives are often deemed to be rather stable personality characteristics. Normally, implicit motives are obtained by Operant Motives, unconscious intrinsic desires measured by the Operant Motive Test (OMT). The OMT test requires participants to write freely descriptions associated with a set of provided images and questions. In this work, we explore different recent machine learning techniques and various text representation techniques for facing the problem of the OMT classification task. We focused on advanced language representations (e.g, BERT, XLM, and DistilBERT) and deep Supervised Autoencoders for solving the OMT task.
We performed an exhaustive analysis and compared their performance against fully connected neural networks and traditional support vector classifiers. Our comparative study highlights the importance of BERT which outperforms the traditional machine learning techniques by a relative improvement of 7.9%. In addition, we performed an analysis of how the BERT attention mechanism is being modified. Our findings indicate that the writing style features acquire higher importance at the moment of accurately identifying the different OMT categories. This is the first time that a study to determine the performance of different transformer-based architectures in the OMT task is performed. Similarly, our work propose, for the first time, using deep supervised autoencoders in the OMT classification task. Our experiments demonstrate that transformer-based methods exhibit the best empirical results, obtaining a relative improvement of 7.9% over the competitive baseline suggested as part of the GermEval 2020 challenge. Additionally, we show that features associated with the writing style are more important than content-based words. Some of these findings show strong connections to previously reported behavioral research on the implicit psychometrics theory.
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Alves GÂDS, Coêlho JF, Leitão MM. Coreferential processing in elderly with and without Alzheimer's disease. Codas 2021; 33:e20200127. [PMID: 34231668 DOI: 10.1590/2317-1782/20202020127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 08/31/2020] [Indexed: 11/22/2022] Open
Abstract
PURPOSE To compare coreferential processing in elderly people with and without Alzheimer's disease in Brazilian Portuguese. METHODS Twelve elderly people without Alzheimer's (EA) and six elderly people with Alzheimer's disease (EWA) participated in the study. The Mini-Mental State Examination was used for cognitive screening of participants. Two experiments were performed using the self-monitored reading technique to analyze coreference processing. Each contained eight experimental phrases and 24 distracting phrases, one of them using repeated pronouns and names and the other using hyponyms and hypernyms. After reading, questions were asked related to the content of the sentences. The main variable of interest was reading time, measured after the presentation of anaphoric resuming. RESULTS In the first experiment, there were statistically significant results. The EA group processed the pronouns more quickly than repeated names. The volunteers of the EWA group were quicker in resuming repeated names. In the second experiment, the results show that the EA group showed preference for hypernyms in anaphoric resumption, whereas the EWA group did not present significant differences between conditions. CONCLUSION Elderly people without pathology processed pronouns and hypernyms more quickly compared to retrievals with repeated names and hyponyms, respectively, due to the smaller amount of semantic traits necessary to identify the antecedents in those conditions, as well as syntactic and discursive prominence. Elderly people with AD read names more readily than pronouns. There was no difference in anaphoric processing involving hyponyms and hypernyms, which may result from impaired working memory.
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Affiliation(s)
| | - Julyane Feitoza Coêlho
- Programa de Pós-graduação em Linguística, Universidade Federal da Paraíba - UFPB - João Pessoa (PB), Brasil
| | - Márcio Martins Leitão
- Departamento de Línguas Clássicas e Vernáculas, Universidade Federal da Paraíba - UFPB - João Pessoa (PB), Brasil
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