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Soleimani L, Ouyang Y, Cho S, Kia A, Beeri MS, Lin H, Ravona‐Springer R, Ramsingh N, Liberman MY, Grossman M, Nevler N. Speech markers of depression dimensions across cognitive status. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12604. [PMID: 39092182 PMCID: PMC11292393 DOI: 10.1002/dad2.12604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 04/20/2024] [Accepted: 04/24/2024] [Indexed: 08/04/2024]
Abstract
Introduction Depression and its components significantly impact dementia prediction and severity, necessitating reliable objective measures for quantification. Methods We investigated associations between emotion-based speech measures (valence, arousal, and dominance) during picture descriptions and depression dimensions derived from the geriatric depression scale (GDS, dysphoria, withdrawal-apathy-vigor (WAV), anxiety, hopelessness, and subjective memory complaint). Results Higher WAV was associated with more negative valence (estimate = -0.133, p = 0.030). While interactions of apolipoprotein E (APOE) 4 status with depression dimensions on emotional valence did not reach significance, there was a trend for more negative valence with higher dysphoria in those with at least one APOE4 allele (estimate = -0.404, p = 0.0846). Associations were similar irrespective of dementia severity. Discussion Our study underscores the potential utility of speech biomarkers in characterizing depression dimensions. In future research, using emotionally charged stimuli may enhance emotional measure elicitation. The role of APOE on the interaction of speech markers and depression dimensions warrants further exploration with greater sample sizes. Highlights Participants reporting higher apathy used more negative words to describe a neutral picture.Those with higher dysphoria and at least one APOE4 allele also tended to use more negative words.Our results suggest the potential use of speech biomarkers in characterizing depression dimensions.
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Affiliation(s)
| | - Yuxia Ouyang
- Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Sunghye Cho
- Linguistic Data ConsortiumUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arash Kia
- Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | | | - Hung‐Mo Lin
- Department of AnesthesiologyYale School of MedicineNew HavenConnecticutUSA
| | - Ramit Ravona‐Springer
- The Joseph Sagol Neuroscience CenterSheba Medical CenterTel‐HashomerIsrael
- Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
| | - Nadia Ramsingh
- Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Mark Y Liberman
- Linguistic Data ConsortiumUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Murray Grossman
- Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Naomi Nevler
- Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Nepal S, Pillai A, Wang W, Griffin T, Collins AC, Heinz M, Lekkas D, Mirjafari S, Nemesure M, Price G, Jacobson NC, Campbell AT. MoodCapture: Depression Detection Using In-the-Wild Smartphone Images. PROCEEDINGS OF THE SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS. CHI CONFERENCE 2024; 2024:996. [PMID: 39100498 PMCID: PMC11296678 DOI: 10.1145/3613904.3642680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: "I have felt down, depressed, or hopeless". Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools.
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Casten LG, Koomar T, Elsadany M, McKone C, Tysseling B, Sasidharan M, Tomblin JB, Michaelson JJ. Lingo: an automated, web-based deep phenotyping platform for language ability. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.29.24305034. [PMID: 38585791 PMCID: PMC10996758 DOI: 10.1101/2024.03.29.24305034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Background Language and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit. Methods Here, we describe and showcase Lingo - a flexible online battery of language and nonverbal reasoning skills based on seven widely used tasks (COWAT, picture narration, vocal rhythm entrainment, rapid automatized naming, following directions, sentence repetition, and nonverbal reasoning). The current version of Lingo takes approximately 30 minutes to complete, is entirely open source, and allows for a wide variety of performance metrics to be extracted. We asked > 1,300 individuals from multiple samples to complete Lingo, then investigated the validity and utility of the resulting data. Results We conducted an exploratory factor analysis across 14 features derived from the seven assessments, identifying five factors. Four of the five factors showed acceptable test-retest reliability (Pearson's R > 0.7). Factor 2 showed the highest reliability (Pearson's R = 0.95) and loaded primarily on sentence repetition task performance. We validated Lingo with objective measures of language ability by comparing performance to gold-standard assessments: CELF-5 and the VABS-3. Factor 2 was significantly associated with the CELF-5 "core language ability" scale (Pearson's R = 0.77, p-value < 0.05) and the VABS-3 "communication" scale (Pearson's R = 0.74, p-value < 0.05). Factor 2 was positively associated with phenotypic and genetic measures of socieconomic status. Interestingly, we found the parents of children with language impairments had lower Factor 2 scores (p-value < 0.01). Finally, we found Lingo factor scores were significantly predictive of numerous psychiatric and neurodevelopmental conditions. Conclusions Together, these analyses support Lingo as a powerful platform for scalable deep phenotyping of language and other cognitive abilities. Additionally, exploratory analyses provide supporting evidence for the heritability of language ability and the complex relationship between mental health and language.
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Affiliation(s)
- Lucas G. Casten
- Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA
- Department of Psychiatry, University of Iowa, Iowa City, IA
| | - Tanner Koomar
- Department of Psychiatry, University of Iowa, Iowa City, IA
| | - Muhammad Elsadany
- Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA
- Department of Psychiatry, University of Iowa, Iowa City, IA
| | - Caleb McKone
- Department of Psychiatry, University of Iowa, Iowa City, IA
| | - Ben Tysseling
- Department of Psychiatry, University of Iowa, Iowa City, IA
| | | | - J. Bruce Tomblin
- Department of Communication Sciences and Disorders, University of Iowa, Iowa City, IA
| | - Jacob J. Michaelson
- Department of Psychiatry, University of Iowa, Iowa City, IA
- Department of Communication Sciences and Disorders, University of Iowa, Iowa City, IA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA
- Hawkeye Intellectual and Developmental Disabilities Research Center (Hawk-IDDRC), University of Iowa, Iowa City, IA
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Gumus M, Koo M, Studzinski CM, Bhan A, Robin J, Black SE. Linguistic changes in neurodegenerative diseases relate to clinical symptoms. Front Neurol 2024; 15:1373341. [PMID: 38590720 PMCID: PMC10999640 DOI: 10.3389/fneur.2024.1373341] [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: 01/19/2024] [Accepted: 03/07/2024] [Indexed: 04/10/2024] Open
Abstract
Background The detection and characterization of speech changes may help in the identification and monitoring of neurodegenerative diseases. However, there is limited research validating the relationship between speech changes and clinical symptoms across a wide range of neurodegenerative diseases. Method We analyzed speech recordings from 109 patients who were diagnosed with various neurodegenerative diseases, including Alzheimer's disease, Frontotemporal Dementia, and Vascular Cognitive Impairment, in a cognitive neurology memory clinic. Speech recordings of an open-ended picture description task were processed using the Winterlight speech analysis platform which generates >500 speech features, including the acoustics of speech and linguistic properties of spoken language. We investigated the relationship between the speech features and clinical assessments including the Mini Mental State Examination (MMSE), Mattis Dementia Rating Scale (DRS), Western Aphasia Battery (WAB), and Boston Naming Task (BNT) in a heterogeneous patient population. Result Linguistic features including lexical and syntactic features were significantly correlated with clinical assessments in patients, across diagnoses. Lower MMSE and DRS scores were associated with the use of shorter words and fewer prepositional phrases. Increased impairment on WAB and BNT was correlated with the use of fewer nouns but more pronouns. Patients also differed from healthy adults as their speech duration was significantly shorter with more pauses. Conclusion Linguistic changes such as the use of simpler vocabularies and syntax were detectable in patients with different neurodegenerative diseases and correlated with cognitive decline. Speech has the potential to be a sensitive measure for detecting cognitive impairments across various neurodegenerative diseases.
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Affiliation(s)
- Melisa Gumus
- Winterlight Labs, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Morgan Koo
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | | | - Aparna Bhan
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
| | | | - Sandra E. Black
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
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Simmatis LER, Robin J, Spilka MJ, Yunusova Y. Detecting bulbar amyotrophic lateral sclerosis (ALS) using automatic acoustic analysis. Biomed Eng Online 2024; 23:15. [PMID: 38311731 PMCID: PMC10838438 DOI: 10.1186/s12938-023-01174-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 11/19/2023] [Indexed: 02/06/2024] Open
Abstract
Automatic speech assessments have the potential to dramatically improve ALS clinical practice and facilitate patient stratification for ALS clinical trials. Acoustic speech analysis has demonstrated the ability to capture a variety of relevant speech motor impairments, but implementation has been hindered by both the nature of lab-based assessments (requiring travel and time for patients) and also by the opacity of some acoustic feature analysis methods. These challenges and others have obscured the ability to distinguish different ALS disease stages/severities. Validation of automated acoustic analysis tools could enable detection of early signs of ALS, and these tools could be deployed to screen and monitor patients without requiring clinic visits. Here, we sought to determine whether acoustic features gathered using an automated assessment app could detect ALS as well as different levels of speech impairment severity resulting from ALS. Speech samples (readings of a standardized, 99-word passage) from 119 ALS patients with varying degrees of disease severity as well as 22 neurologically healthy participants were analyzed, and 53 acoustic features were extracted. Patients were stratified into early and late stages of disease (ALS-early/ALS-E and ALS-late/ALS-L) based on the ALS Functional Ratings Scale-Revised bulbar score (FRS-bulb) (median [interquartile range] of FRS-bulbar scores: 11[3]). The data were analyzed using a sparse Bayesian logistic regression classifier. It was determined that the current relatively small set of acoustic features could distinguish between ALS and controls well (area under receiver-operating characteristic curve/AUROC = 0.85), that the ALS-E patients could be separated well from control participants (AUROC = 0.78), and that ALS-E and ALS-L patients could be reasonably separated (AUROC = 0.70). These results highlight the potential for automated acoustic analyses to detect and stratify ALS.
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Affiliation(s)
- Leif E R Simmatis
- KITE-Toronto Rehabilitation Institute, UHN, Toronto, ON, Canada.
- Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada.
- Sunnybrook Research Institute, Toronto, ON, Canada.
| | | | | | - Yana Yunusova
- KITE-Toronto Rehabilitation Institute, UHN, Toronto, ON, Canada
- Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Toronto, ON, Canada
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Simmatis L, Robin J, Spilka M, Yunusova Y. Detecting bulbar amyotrophic lateral sclerosis (ALS) using automatic acoustic analysis. RESEARCH SQUARE 2023:rs.3.rs-3306951. [PMID: 37720012 PMCID: PMC10503837 DOI: 10.21203/rs.3.rs-3306951/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Home-based speech assessments have the potential to dramatically improve ALS clinical practice and facilitate patient stratification for ALS clinical trials. Acoustic speech analysis has demonstrated the ability to capture a variety of relevant speech motor impairments, but implementation has been hindered by both the nature of lab-based assessments (requiring travel and time for patients) and also by the opacity of some acoustic feature analysis methods. Furthermore, these challenges and others have obscured the ability to distinguish different ALS disease stages/severities. Validation of remote-capable acoustic analysis tools could enable detection of early signs of ALS, and these tools could be deployed to screen and monitor patients without requiring clinic visits. Here, we sought to determine whether acoustic features gathered using a remote-capable assessment app could detect ALS as well as different levels of speech impairment severity resulting from ALS. Speech samples (readings of a standardized, 99-word passage) from 119 ALS patients with varying degrees of disease severity as well as 22 neurologically healthy participants were analyzed, and 53 acoustic features were extracted. Patients were stratified into early and late stages of disease (ALS-early/ALS-E and ALS-late/ALS-L) based on the ALS Functional Ratings Scale - Revised bulbar score (FRS-bulb). Data were analyzed using a sparse Bayesian logistic regression classifier. It was determined that the current relatively small set of acoustic features could distinguish between ALS and controls well (area under receiver operating characteristic curve/AUROC = 0.85), that the ALS-E patients could be separated well from control participants (AUROC = 0.78), and that ALS-E and ALS-L patients could be reasonably separated (AUROC = 0.70). These results highlight the potential for remote acoustic analyses to detect and stratify ALS.
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