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Franz L, Viljoen M, Askew S, Brown M, Dawson G, Di Martino JM, Sapiro G, Sebolai K, Seris N, Shabalala N, Stahmer A, Turner EL, de Vries PJ. Autism Caregiver Coaching in Africa (ACACIA): Protocol for a type 1-hybrid effectiveness-implementation trial. PLoS One 2024; 19:e0291883. [PMID: 38215154 PMCID: PMC10786379 DOI: 10.1371/journal.pone.0291883] [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: 09/18/2023] [Accepted: 09/28/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND While early autism intervention can significantly improve outcomes, gaps in implementation exist globally. These gaps are clearest in Africa, where forty percent of the world's children will live by 2050. Task-sharing early intervention to non-specialists is a key implementation strategy, given the lack of specialists in Africa. Naturalistic Developmental Behavioral Interventions (NDBI) are a class of early autism intervention that can be delivered by caregivers. As a foundational step to address the early autism intervention gap, we adapted a non-specialist delivered caregiver coaching NDBI for the South African context, and pre-piloted this cascaded task-sharing approach in an existing system of care. OBJECTIVES First, we will test the effectiveness of the caregiver coaching NDBI compared to usual care. Second, we will describe coaching implementation factors within the Western Cape Department of Education in South Africa. METHODS This is a type 1 effectiveness-implementation hybrid design; assessor-blinded, group randomized controlled trial. Participants include 150 autistic children (18-72 months) and their caregivers who live in Cape Town, South Africa, and those involved in intervention implementation. Early Childhood Development practitioners, employed by the Department of Education, will deliver 12, one hour, coaching sessions to the intervention group. The control group will receive usual care. Distal co-primary outcomes include the Communication Domain Standard Score (Vineland Adaptive Behavior Scales, Third Edition) and the Language and Communication Developmental Quotient (Griffiths Scales of Child Development, Third Edition). Proximal secondary outcome include caregiver strategies measured by the sum of five items from the Joint Engagement Rating Inventory. We will describe key implementation determinants. RESULTS Participant enrolment started in April 2023. Estimated primary completion date is March 2027. CONCLUSION The ACACIA trial will determine whether a cascaded task-sharing intervention delivered in an educational setting leads to meaningful improvements in communication abilities of autistic children, and identify implementation barriers and facilitators. TRIAL REGISTRATION NCT05551728 in Clinical Trial Registry (https://clinicaltrials.gov).
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Affiliation(s)
- Lauren Franz
- Duke Center for Autism and Brain Development, Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, United States of America
- Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Marisa Viljoen
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Sandy Askew
- Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
| | - Musaddiqah Brown
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, United States of America
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States of America
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States of America
| | - Katlego Sebolai
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Noleen Seris
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Nokuthula Shabalala
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Aubyn Stahmer
- Center for Excellence in Developmental Disabilities, MIND Institute, University of California, Davis, Davis, California, United States of America
| | - Elizabeth L Turner
- Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, United States of America
| | - Petrus J de Vries
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
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Wedasingha N, Samarasinghe P, Senevirathna L, Papandrea M, Puiatti A, Rankin D. Automated anomalous child repetitive head movement identification through transformer networks. Phys Eng Sci Med 2023; 46:1427-1445. [PMID: 37814077 DOI: 10.1007/s13246-023-01309-5] [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: 02/01/2023] [Accepted: 07/24/2023] [Indexed: 10/11/2023]
Abstract
The increasing prevalence of behavioral disorders in children is of growing concern within the medical community. Recognising the significance of early identification and intervention for atypical behaviors, there is a consensus on their pivotal role in improving outcomes. Due to inadequate facilities and a shortage of medical professionals with specialized expertise, traditional diagnostic methods have been unable to effectively address the rising incidence of behavioral disorders. Hence, there is a need to develop automated approaches for the diagnosis of behavioral disorders in children, to overcome the challenges with traditional methods. The purpose of this study is to develop an automated model capable of analyzing videos to differentiate between typical and atypical repetitive head movements in. To address problems resulting from the limited availability of child datasets, various learning methods are employed to mitigate these issues. In this work, we present a fusion of transformer networks, and Non-deterministic Finite Automata (NFA) techniques, which classify repetitive head movements of a child as typical or atypical based on an analysis of gender, age, and type of repetitive head movement, along with count, duration, and frequency of each repetitive head movement. Experimentation was carried out with different transfer learning methods to enhance the performance of the model. The experimental results on five datasets: NIR face dataset, Bosphorus 3D face dataset, ASD dataset, SSBD dataset, and the Head Movements in the Wild dataset, indicate that our proposed model has outperformed many state-of-the-art frameworks when distinguishing typical and atypical repetitive head movements in children.
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Affiliation(s)
- Nushara Wedasingha
- Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe, 10115, Colombo, Sri Lanka.
| | - Pradeepa Samarasinghe
- Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe, 10115, Colombo, Sri Lanka
| | - Lasantha Senevirathna
- Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe, 10115, Colombo, Sri Lanka
| | - Michela Papandrea
- Information Systems and Networking Institute (ISIN), University of Applied Sciences and Arts of Southern Switzerland, Via Pobiette, Manno, 6928, Switzerland
| | - Alessandro Puiatti
- Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Via Pobiette, Manno, 6928, Switzerland
| | - Debbie Rankin
- School of Computing, Engineering and Intelligent Systems, Ulster University, Northland Road, Derry-Londonderry, BT48 7JL, Northern Ireland, UK
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Perochon S, Di Martino JM, Carpenter KLH, Compton S, Davis N, Eichner B, Espinosa S, Franz L, Krishnappa Babu PR, Sapiro G, Dawson G. Early detection of autism using digital behavioral phenotyping. Nat Med 2023; 29:2489-2497. [PMID: 37783967 PMCID: PMC10579093 DOI: 10.1038/s41591-023-02574-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/25/2023] [Indexed: 10/04/2023]
Abstract
Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17-36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning. An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.
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Affiliation(s)
- Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Supérieure Paris-Saclay, Gif-sur-Yvette, France
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Kimberly L H Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Brian Eichner
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | | | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Departments of Biomedical Engineering, Mathematics, and Computer Science, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA.
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Franz L, Viljoen M, Askew S, Brown M, Dawson G, Di Martino JM, Sapiro G, Sebolai K, Seris N, Shabalala N, Stahmer A, Turner EL, de Vries PJ. Autism Caregiver Coaching in Africa (ACACIA): Protocol for a type 1-hybrid effectiveness-implementation trial. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.10.23295331. [PMID: 37745535 PMCID: PMC10516098 DOI: 10.1101/2023.09.10.23295331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Background While early autism intervention can significantly improve outcomes, gaps in implementation exist globally. These gaps are clearest in Africa, where forty percent of the world's children will live by 2050. Task-sharing early intervention to non-specialists is a key implementation strategy, given the lack of specialists in Africa. Naturalistic Developmental Behavioral Interventions (NDBI) are a class of early autism intervention that can be delivered by caregivers. As a foundational step to address the early autism intervention gap, we adapted a non-specialist delivered caregiver coaching NDBI for the South African context, and pre-piloted this cascaded task-sharing approach in an existing system of care. Objectives First, we will test the effectiveness of the caregiver coaching NDBI compared to usual care. Second, we will describe coaching implementation factors within the Western Cape Department of Education in South Africa. Methods This is a type 1 effectiveness-implementation hybrid design; assessor-blinded, group randomized controlled trial. Participants include 150 autistic children (18-72 months) and their caregivers who live in Cape Town, South Africa, and those involved in intervention implementation. Early Childhood Development practitioners, employed by the Department of Education, will deliver 12, one hour, coaching sessions to the intervention group. The control group will receive usual care. Distal co-primary outcomes include the Communication Domain Standard Score (Vineland Adaptive Behavior Scales, Third Edition) and the Language and Communication Developmental Quotient (Griffiths Scales of Child Development, Third Edition). Proximal secondary outcome include caregiver strategies measured by the sum of five items from the Joint Engagement Rating Inventory. We will describe key implementation determinants. Results Participant enrolment started in April 2023. Estimated primary completion date is March 2027. Conclusion The ACACIA trial will determine whether a cascaded task-sharing intervention delivered in an educational setting leads to meaningful improvements in communication abilities of autistic children, and identify implementation barriers and facilitators.
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Affiliation(s)
- Lauren Franz
- Duke Center for Autism and Brain Development, Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Marisa Viljoen
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Sandy Askew
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
| | - Musaddiqah Brown
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Katlego Sebolai
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Noleen Seris
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Nokuthula Shabalala
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Aubyn Stahmer
- Center for Excellence in Developmental Disabilities, MIND Institute, University of California Davis, California, USA
| | - Elizabeth L Turner
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA
| | - Petrus J de Vries
- Centre for Autism Research in Africa (CARA), Division of Child & Adolescent Psychiatry, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
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Lyall K. What are quantitative traits and how can they be used in autism research? Autism Res 2023; 16:1289-1298. [PMID: 37212172 PMCID: PMC10524676 DOI: 10.1002/aur.2937] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/28/2023] [Indexed: 05/23/2023]
Abstract
Quantitative traits are measurable characteristics distributed along a continuous scale thought to relate to underlying biology. There is growing interest in the use of quantitative traits in behavioral and psychiatric research, particularly in research on conditions diagnosed based on reports of behaviors, including autism. This brief commentary describes quantitative traits, including defining what they are, how we can measure them, and key considerations for their use in autism research. Examples of measures include behavioral report scales like the Social Responsiveness Scale and Broader Autism Phenotype Questionnaire, as well as biological measurements, like certain neuroimaging metrics; such measures can capture quantitative traits or constructs like the broader autism phenotype, social communication, and social cognition. Quantitative trait measures align with the Research Domain Criteria (RDoC) approach and can be used in autism research to help gain a better understanding of causal pathways and biological processes. They can also be used to aid identification of genetic and environmental factors involved in such pathways, and thereby lead to an understanding of influences on traits across the entire population. Finally, in some cases, they may be used to gauge treatment response, and assist screening and clinical characterization of phenotype. In addition, practical benefits of quantitative trait measures include improved statistical power relative to categorical classifications and (for some measures) efficiency. Ultimately, research across autism fields may benefit from incorporating quantitative trait measures as a complement to categorical diagnosis to advance understanding of autism and neurodevelopment.
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Affiliation(s)
- Kristen Lyall
- AJ Drexel Autism Institute, Drexel University, 3020 Market St, Suite 560, Philadelphia PA 19104
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Coffman M, Di Martino JM, Aiello R, Carpenter KL, Chang Z, Compton S, Eichner B, Espinosa S, Flowers J, Franz L, Perochon S, Krishnappa Babu PR, Sapiro G, Dawson G. Relationship between quantitative digital behavioral features and clinical profiles in young autistic children. Autism Res 2023; 16:1360-1374. [PMID: 37259909 PMCID: PMC10524806 DOI: 10.1002/aur.2955] [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: 08/19/2022] [Accepted: 05/06/2023] [Indexed: 06/02/2023]
Abstract
Early behavioral markers for autism include differences in social attention and orienting in response to one's name when called, and differences in body movements and motor abilities. More efficient, scalable, objective, and reliable measures of these behaviors could improve early screening for autism. This study evaluated whether objective and quantitative measures of autism-related behaviors elicited from an app (SenseToKnow) administered on a smartphone or tablet and measured via computer vision analysis (CVA) are correlated with standardized caregiver-report and clinician administered measures of autism-related behaviors and cognitive, language, and motor abilities. This is an essential step in establishing the concurrent validity of a digital phenotyping approach. In a sample of 485 toddlers, 43 of whom were diagnosed with autism, we found that CVA-based gaze variables related to social attention were associated with the level of autism-related behaviors. Two language-related behaviors measured via the app, attention to people during a conversation and responding to one's name being called, were associated with children's language skills. Finally, performance during a bubble popping game was associated with fine motor skills. These findings provide initial support for the concurrent validity of the SenseToKnow app and its potential utility in identifying clinical profiles associated with autism. Future research is needed to determine whether the app can be used as an autism screening tool, can reliably stratify autism-related behaviors, and measure changes in autism-related behaviors over time.
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Affiliation(s)
- Marika Coffman
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - J. Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Rachel Aiello
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Kimberly L.H. Carpenter
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Scott Compton
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Brian Eichner
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Steve Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Jacqueline Flowers
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Lauren Franz
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Sam Perochon
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Ecole Normale Superieure Paris-Saclay, Gif-Sur-Yvette, France
| | | | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Department of Psychiatric and Behavioral Sciences, Duke University, Durham, NC, USA
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Krishnappa Babu PR, Aikat V, Di Martino JM, Chang Z, Perochon S, Espinosa S, Aiello R, L H Carpenter K, Compton S, Davis N, Eichner B, Flowers J, Franz L, Dawson G, Sapiro G. Blink rate and facial orientation reveal distinctive patterns of attentional engagement in autistic toddlers: a digital phenotyping approach. Sci Rep 2023; 13:7158. [PMID: 37137954 PMCID: PMC10156751 DOI: 10.1038/s41598-023-34293-7] [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: 10/27/2022] [Accepted: 04/27/2023] [Indexed: 05/05/2023] Open
Abstract
Differences in social attention are well-documented in autistic individuals, representing one of the earliest signs of autism. Spontaneous blink rate has been used to index attentional engagement, with lower blink rates reflecting increased engagement. We evaluated novel methods using computer vision analysis (CVA) for automatically quantifying patterns of attentional engagement in young autistic children, based on facial orientation and blink rate, which were captured via mobile devices. Participants were 474 children (17-36 months old), 43 of whom were diagnosed with autism. Movies containing social or nonsocial content were presented via an iPad app, and simultaneously, the device's camera recorded the children's behavior while they watched the movies. CVA was used to extract the duration of time the child oriented towards the screen and their blink rate as indices of attentional engagement. Overall, autistic children spent less time facing the screen and had a higher mean blink rate compared to neurotypical children. Neurotypical children faced the screen more often and blinked at a lower rate during the social movies compared to the nonsocial movies. In contrast, autistic children faced the screen less often during social movies than during nonsocial movies and showed no differential blink rate to social versus nonsocial movies.
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Affiliation(s)
| | - Vikram Aikat
- Department of Computer Science, Duke University, Durham, NC, USA
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Zhuoqing Chang
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Sam Perochon
- Ecole Normale Supérieure Paris-Saclay, Gif-Sur-Yvette, France
| | - Steven Espinosa
- Office of Information Technology, Duke University, Durham, NC, USA
| | - Rachel Aiello
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Kimberly L H Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Scott Compton
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Naomi Davis
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Brian Eichner
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Jacqueline Flowers
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
| | - Lauren Franz
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Geraldine Dawson
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
- Duke Center for Autism and Brain Development, Duke University, Durham, NC, USA.
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
- Departments of Biomedical Engineering, Mathematics, and Computer Science, Duke University, Durham, NC, USA.
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McDonald DQ, DeJardin E, Sariyanidi E, Herrington JD, Tunç B, Zampella CJ, Schultz RT. Predicting Autism from Head Movement Patterns during Naturalistic Social Interactions. PROCEEDINGS OF THE 2023 7TH INTERNATIONAL CONFERENCE ON MEDICAL AND HEALTH INFORMATICS (ICMHI 2023) : MAY 12-14, 2023, KYOTO, JAPAN. INTERNATIONAL CONFERENCE ON MEDICAL AND HEALTH INFORMATICS (7TH : 2023 : KYOTO, JAPAN) 2023; 2023:55-60. [PMID: 38699395 PMCID: PMC11064057 DOI: 10.1145/3608298.3608309] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized in part by difficulties in verbal and nonverbal social communication. Evidence indicates that autistic people, compared to neurotypical peers, exhibit differences in head movements, a key form of nonverbal communication. Despite the crucial role of head movements in social communication, research on this nonverbal cue is relatively scarce compared to other forms of nonverbal communication, such as facial expressions and gestures. There is a need for scalable, reliable, and accurate instruments for measuring head movements directly within the context of social interactions. In this study, we used computer vision and machine learning to examine the head movement patterns of neurotypical and autistic individuals during naturalistic, face-to-face conversations, at both the individual (monadic) and interpersonal (dyadic) levels. Our model predicts diagnostic status using dyadic head movement data with an accuracy of 80%, highlighting the value of head movement as a marker of social communication. The monadic data pipeline had lower accuracy (69.2%) compared to the dyadic approach, emphasizing the importance of studying back-and-forth social communication cues within a true social context. The proposed classifier is not intended for diagnostic purposes, and future research should replicate our findings in larger, more representative samples.
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Affiliation(s)
| | - Ellis DeJardin
- Children's Hospital of Philadelphia Philadelphia, PA, USA
| | | | - John D Herrington
- Children's Hospital of Philadelphia Philadelphia, PA, USA
- University of Pennsylvania Philadelphia, PA, USA
| | - Birkan Tunç
- Children's Hospital of Philadelphia Philadelphia, PA, USA
- University of Pennsylvania Philadelphia, PA, USA
| | | | - Robert T Schultz
- Children's Hospital of Philadelphia Philadelphia, PA, USA
- University of Pennsylvania Philadelphia, PA, USA
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Dawson G, Rieder AD, Johnson MH. Prediction of autism in infants: progress and challenges. Lancet Neurol 2023; 22:244-254. [PMID: 36427512 PMCID: PMC10100853 DOI: 10.1016/s1474-4422(22)00407-0] [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: 05/27/2022] [Revised: 09/17/2022] [Accepted: 09/27/2022] [Indexed: 11/24/2022]
Abstract
Autism spectrum disorder (henceforth autism) is a neurodevelopmental condition that can be reliably diagnosed in children by age 18-24 months. Prospective longitudinal studies of infants aged 1 year and younger who are later diagnosed with autism are elucidating the early developmental course of autism and identifying ways of predicting autism before diagnosis is possible. Studies that use MRI, EEG, and near-infrared spectroscopy have identified differences in brain development in infants later diagnosed with autism compared with infants without autism. Retrospective studies of infants younger than 1 year who received a later diagnosis of autism have also showed an increased prevalence of health conditions, such as sleep disorders, gastrointestinal disorders, and vision problems. Behavioural features of infants later diagnosed with autism include differences in attention, vocalisations, gestures, affect, temperament, social engagement, sensory processing, and motor abilities. Although research findings offer insight on promising screening approaches for predicting autism in infants, individual-level predictions remain a future goal. Multiple scientific challenges and ethical questions remain to be addressed to translate research on early brain-based and behavioural predictors of autism into feasible and reliable screening tools for clinical practice.
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Affiliation(s)
- Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
| | - Amber D Rieder
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Mark H Johnson
- Department of Psychology, University of Cambridge, Cambridge, UK; Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
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McDonald DQ, Zampella CJ, Sariyanidi E, Manakiwala A, DeJardin E, Herrington JD, Schultz RT, Tunç B. Head Movement Patterns during Face-to-Face Conversations Vary with Age. ICMI'22 COMPANION : COMPANION PUBLICATION OF THE 2022 INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION : NOVEMBER 7-11, 2022, BANGALORE, INDIA. ICMI (CONFERENCE) (2022 : BANGALORE, INDIA) 2022; 2022:185-195. [PMID: 37975062 PMCID: PMC10652276 DOI: 10.1145/3536220.3563366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Advances in computational behavior analysis have the potential to increase our understanding of behavioral patterns and developmental trajectories in neurotypical individuals, as well as in individuals with mental health conditions marked by motor, social, and emotional difficulties. This study focuses on investigating how head movement patterns during face-to-face conversations vary with age from childhood through adulthood. We rely on computer vision techniques due to their suitability for analysis of social behaviors in naturalistic settings, since video data capture can be unobtrusively embedded within conversations between two social partners. The methods in this work include unsupervised learning for movement pattern clustering, and supervised classification and regression as a function of age. The results demonstrate that 3-minute video recordings of head movements during conversations show patterns that distinguish between participants that are younger vs. older than 12 years with 78% accuracy. Additionally, we extract relevant patterns of head movement upon which the age distinction was determined by our models.
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Affiliation(s)
| | | | | | - Aashvi Manakiwala
- University of Pennsylvania, Philadelphia, PA, USA
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ellis DeJardin
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - John D Herrington
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
- University of Pennsylvania, Philadelphia, PA, USA
| | - Robert T Schultz
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
- University of Pennsylvania, Philadelphia, PA, USA
| | - Birkan Tunç
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
- University of Pennsylvania, Philadelphia, PA, USA
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