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Nussinov R, Yavuz BR, Jang H. Single cell spatial biology over developmental time can decipher pediatric brain pathologies. Neurobiol Dis 2024; 199:106597. [PMID: 38992777 DOI: 10.1016/j.nbd.2024.106597] [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: 03/27/2024] [Revised: 06/18/2024] [Accepted: 07/07/2024] [Indexed: 07/13/2024] Open
Abstract
Pediatric low grade brain tumors and neurodevelopmental disorders share proteins, signaling pathways, and networks. They also share germline mutations and an impaired prenatal differentiation origin. They may differ in the timing of the events and proliferation. We suggest that their pivotal distinct, albeit partially overlapping, outcomes relate to the cell states, which depend on their spatial location, and timing of gene expression during brain development. These attributes are crucial as the brain develops sequentially, and single-cell spatial organization influences cell state, thus function. Our underlying premise is that the root cause in neurodevelopmental disorders and pediatric tumors is impaired prenatal differentiation. Data related to pediatric brain tumors, neurodevelopmental disorders, brain cell (sub)types, locations, and timing of expression in the developing brain are scant. However, emerging single cell technologies, including transcriptomic, spatial biology, spatial high-resolution imaging performed over the brain developmental time, could be transformational in deciphering brain pathologies thereby pharmacology.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Bengi Ruken Yavuz
- Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Cancer Innovation Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA
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2
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Guerra M, Medici V, La Sala G, Farini D. Unravelling the Cerebellar Involvement in Autism Spectrum Disorders: Insights into Genetic Mechanisms and Developmental Pathways. Cells 2024; 13:1176. [PMID: 39056758 PMCID: PMC11275240 DOI: 10.3390/cells13141176] [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: 05/30/2024] [Revised: 07/05/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Autism spectrum disorders (ASDs) are complex neurodevelopmental conditions characterized by deficits in social interaction and communication, as well as repetitive behaviors. Although the etiology of ASD is multifactorial, with both genetic and environmental factors contributing to its development, a strong genetic basis is widely recognized. Recent research has identified numerous genetic mutations and genomic rearrangements associated with ASD-characterizing genes involved in brain development. Alterations in developmental programs are particularly harmful during critical periods of brain development. Notably, studies have indicated that genetic disruptions occurring during the second trimester of pregnancy affect cortical development, while disturbances in the perinatal and early postnatal period affect cerebellar development. The developmental defects must be viewed in the context of the role of the cerebellum in cognitive processes, which is now well established. The present review emphasizes the genetic complexity and neuropathological mechanisms underlying ASD and aims to provide insights into the cerebellar involvement in the disorder, focusing on recent advances in the molecular landscape governing its development in humans. Furthermore, we highlight when and in which cerebellar neurons the ASD-associated genes may play a role in the development of cortico-cerebellar circuits. Finally, we discuss improvements in protocols for generating cerebellar organoids to recapitulate the long period of development and maturation of this organ. These models, if generated from patient-induced pluripotent stem cells (iPSC), could provide a valuable approach to elucidate the contribution of defective genes to ASD pathology and inform diagnostic and therapeutic strategies.
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Affiliation(s)
- Marika Guerra
- Department of Neuroscience, Section of Human Anatomy, Catholic University of the Sacred Hearth, 00168 Rome, Italy; (M.G.); (V.M.)
| | - Vanessa Medici
- Department of Neuroscience, Section of Human Anatomy, Catholic University of the Sacred Hearth, 00168 Rome, Italy; (M.G.); (V.M.)
| | - Gina La Sala
- Institute of Biochemistry and Cell Biology, Italian National Research Council (CNR), 00015 Monterotondo Scalo, Italy
| | - Donatella Farini
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy
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3
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Nussinov R, Yavuz BR, Demirel HC, Arici MK, Jang H, Tuncbag N. Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide. Front Cell Dev Biol 2024; 12:1376639. [PMID: 39015651 PMCID: PMC11249571 DOI: 10.3389/fcell.2024.1376639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/10/2024] [Indexed: 07/18/2024] Open
Abstract
The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Bengi Ruken Yavuz
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | | | - M. Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | - Nurcan Tuncbag
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Türkiye
- School of Medicine, Koc University, Istanbul, Türkiye
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
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Ben-Sasson A, Guedalia J, Ilan K, Shefer G, Cohen R, Gabis LV. Early developmental milestone clusters of autistic children based on electronic health records. Autism Res 2024. [PMID: 38932567 DOI: 10.1002/aur.3177] [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: 01/15/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
Autistic children vary in symptoms, co-morbidities, and response to interventions. This study aimed to identify clusters of autistic children with a distinct pattern of attaining early developmental milestones (EDMs). The clustering of 5836 autistic children was based on the attainment of 43 gross motor, fine motor, language, and social developmental milestones during the first 3 years of life as recorded in baby wellness visits. K-means cluster analysis detected four EDM clusters: mild (n = 1686); moderate (n = 1691); severe (n = 2265); and global (n = 194). The most prominent cluster differences were in the language domain. The global cluster showed earlier and greater developmental delay across domains, unique early gross motor delays, and more were born preterm via cesarean section. The severe cluster had poor language development prominently in the second year of life, and later fine motor delays. Moderate cluster had mainly language delays in the third year of life. The mild cluster mostly passed milestones. EDM clusters differed demographically, with higher socioeconomic status in mild cluster and lowest in global cluster. However, the severe cluster had more immigrant and non-Jewish mothers followed by the moderate cluster. The rates of parental concerns and provider developmental referrals were significantly higher in the global, followed by the severe, moderate, and mild EDM clusters. Autistic children's language and motor delay in the first 3 years can be grouped by common magnitude and onset profiles as distinct groups that may link to specific etiologies (like prematurity or genetics) and specific intervention programs. Early autism screening should be tailored to these different developmental profiles.
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Affiliation(s)
| | | | | | - Galit Shefer
- TIMNA-Israel Ministry of Health's Big Data Platform, Jerusalem, Israel
| | - Roe Cohen
- TIMNA-Israel Ministry of Health's Big Data Platform, Jerusalem, Israel
| | - Lidia V Gabis
- Maccabi Healthcare Services, Tel-Aviv, Israel
- Tel-Aviv University, Tel-Aviv, Israel
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Nogay HS, Adeli H. Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning. J Med Syst 2024; 48:15. [PMID: 38252192 PMCID: PMC10803393 DOI: 10.1007/s10916-023-02032-0] [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: 08/29/2023] [Accepted: 12/31/2023] [Indexed: 01/23/2024]
Abstract
The fact that the rapid and definitive diagnosis of autism cannot be made today and that autism cannot be treated provides an impetus to look into novel technological solutions. To contribute to the resolution of this problem through multiple classifications by considering age and gender factors, in this study, two quadruple and one octal classifications were performed using a deep learning (DL) approach. Gender in one of the four classifications and age groups in the other were considered. In the octal classification, classes were created considering gender and age groups. In addition to the diagnosis of ASD (Autism Spectrum Disorders), another goal of this study is to find out the contribution of gender and age factors to the diagnosis of ASD by making multiple classifications based on age and gender for the first time. Brain structural MRI (sMRI) scans of participators with ASD and TD (Typical Development) were pre-processed in the system originally designed for this purpose. Using the Canny Edge Detection (CED) algorithm, the sMRI image data was cropped in the data pre-processing stage, and the data set was enlarged five times with the data augmentation (DA) techniques. The most optimal convolutional neural network (CNN) models were developed using the grid search optimization (GSO) algorism. The proposed DL prediction system was tested with the five-fold cross-validation technique. Three CNN models were designed to be used in the system. The first of these models is the quadruple classification model created by taking gender into account (model 1), the second is the quadruple classification model created by taking into account age (model 2), and the third is the eightfold classification model created by taking into account both gender and age (model 3). ). The accuracy rates obtained for all three designed models are 80.94, 85.42 and 67.94, respectively. These obtained accuracy rates were compared with pre-trained models by using the transfer learning approach. As a result, it was revealed that age and gender factors were effective in the diagnosis of ASD with the system developed for ASD multiple classifications, and higher accuracy rates were achieved compared to pre-trained models.
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Affiliation(s)
- Hidir Selcuk Nogay
- Electrical and Energy Department, Bursa Uludag University, Bursa, Turkey
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, College of Medicine, The Ohio State University Neurology, 370 W. 9th Avenue, Columbus, OH, 43210, USA.
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Ali MT, Gebreil A, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Sleman A, Giridharan GA, Barnes G, Elbaz AS. A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework. Sci Rep 2023; 13:17048. [PMID: 37813914 PMCID: PMC10562430 DOI: 10.1038/s41598-023-43478-z] [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: 03/09/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.
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Affiliation(s)
- Mohamed T Ali
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- UT Southwestern Medical Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ahmad Gebreil
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- UT Southwestern Medical Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ahmed Elnakib
- Electrical and Computer Engineering, Penn State Erie-The Behrend College, Erie, PA, 16563, USA
| | - Ahmed Shalaby
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Ahmed Sleman
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | | | - Gregory Barnes
- Department of Neurology and Pediatric Research Institute, University of Louisville, Louisville, KY, 40202, USA
| | - Ayman S Elbaz
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA.
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Pellicano E, Heyworth M. The Foundations of Autistic Flourishing. Curr Psychiatry Rep 2023; 25:419-427. [PMID: 37552401 PMCID: PMC10506917 DOI: 10.1007/s11920-023-01441-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/18/2023] [Indexed: 08/09/2023]
Abstract
PURPOSE OF REVIEW All people-including Autistic people-deserve to live flourishing lives. But what does a flourishing life look like for Autistic people? We suggest that the hidden biases, methodological errors, and key assumptions of autism science have obscured answers to this question. Here, we seek to initiate a broader discussion about what the foundations for a good Autistic life might be and how this discussion might be framed. RECENT FINDINGS We identify five ways in which autism science can help us all to secure those foundations, including by (1) giving Autistic well-being prominence in research, (2) amplifying Autistic autonomy, (3) attending better to everyday experiences, (4) acknowledging context, and (5) working in partnership with Autistic people and their families and allies to ensure that they are at the heart of research decision-making. Such an approach would direct the focus of autism research to help shape good Autistic lives.
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Affiliation(s)
- Elizabeth Pellicano
- Department of Clinical, Educational and Health Psychology, University College London, 26 Bedford Way, London, WC1H 0DS, UK.
- Macquarie School of Education, Macquarie University, 29 Wally's Walk, Sydney, Australia.
| | - Melanie Heyworth
- Macquarie School of Education, Macquarie University, 29 Wally's Walk, Sydney, Australia
- Reframing Autism, Warners Bay, NSW, Australia
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Li Y, Huang WC, Song PH. A face image classification method of autistic children based on the two-phase transfer learning. Front Psychol 2023; 14:1226470. [PMID: 37720633 PMCID: PMC10501480 DOI: 10.3389/fpsyg.2023.1226470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 07/17/2023] [Indexed: 09/19/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which seriously affects children's normal life. Screening potential autistic children before professional diagnose is helpful to early detection and early intervention. Autistic children have some different facial features from non-autistic children, so the potential autistic children can be screened by taking children's facial images and analyzing them with a mobile phone. The area under curve (AUC) is a more robust metrics than accuracy in evaluating the performance of a model used to carry out the two-category classification, and the AUC of the deep learning model suitable for the mobile terminal in the existing research can be further improved. Moreover, the size of an input image is large, which is not fit for a mobile phone. A deep transfer learning method is proposed in this research, which can use images with smaller size and improve the AUC of existing studies. The proposed transfer method uses the two-phase transfer learning mode and the multi-classifier integration mode. For MobileNetV2 and MobileNetV3-Large that are suitable for a mobile phone, the two-phase transfer learning mode is used to improve their classification performance, and then the multi-classifier integration mode is used to integrate them to further improve the classification performance. A multi-classifier integrating calculation method is also proposed to calculate the final classification results according to the classifying results of the participating models. The experimental results show that compared with the one-phase transfer learning, the two-phase transfer learning can significantly improve the classification performance of MobileNetV2 and MobileNetV3-Large, and the classification performance of the integrated classifier is better than that of any participating classifiers. The accuracy of the integrated classifier in this research is 90.5%, and the AUC is 96.32%, which is 3.51% greater than the AUC (92.81%) of the previous studies.
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Affiliation(s)
- Ying Li
- Guangxi Key Laboratory of Human-machine Interaction and Intelligent Decision, School of Logistics Management and Engineering, Nanning Normal University, Nanning, China
| | - Wen-Cong Huang
- Department of Sports and Health, Guangxi College for Preschool Education, Nanning, China
| | - Pei-Hua Song
- Guangxi Key Laboratory of Human-machine Interaction and Intelligent Decision, School of Logistics Management and Engineering, Nanning Normal University, Nanning, China
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Liu XQ, Ji XY, Weng X, Zhang YF. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World J Meta-Anal 2023; 11:79-91. [DOI: 10.13105/wjma.v11.i4.79] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.
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Affiliation(s)
- Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xin-Yu Ji
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xing Weng
- Huzhou Educational Science & Research Center, Huzhou 313000, Zhejiang Province, China
| | - Yi-Fan Zhang
- School of Education, Tianjin University, Tianjin 300350, China
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Goldway N, Eldar E, Shoval G, Hartley CA. Computational Mechanisms of Addiction and Anxiety: A Developmental Perspective. Biol Psychiatry 2023; 93:739-750. [PMID: 36775050 PMCID: PMC10038924 DOI: 10.1016/j.biopsych.2023.02.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 02/05/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023]
Abstract
A central goal of computational psychiatry is to identify systematic relationships between transdiagnostic dimensions of psychiatric symptomatology and the latent learning and decision-making computations that inform individuals' thoughts, feelings, and choices. Most psychiatric disorders emerge prior to adulthood, yet little work has extended these computational approaches to study the development of psychopathology. Here, we lay out a roadmap for future studies implementing this approach by developing empirically and theoretically informed hypotheses about how developmental changes in model-based control of action and Pavlovian learning processes may modulate vulnerability to anxiety and addiction. We highlight how insights from studies leveraging computational approaches to characterize the normative developmental trajectories of clinically relevant learning and decision-making processes may suggest promising avenues for future developmental computational psychiatry research.
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Affiliation(s)
- Noam Goldway
- Department of Psychology, New York University, New York, New York
| | - Eran Eldar
- Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel; Department of Cognitive and Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Gal Shoval
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey; Child and Adolescent Division, Geha Mental Health Center, Petah Tikva, Israel; Department of Psychiatry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Catherine A Hartley
- Department of Psychology, New York University, New York, New York; Center for Neural Science, New York University, New York, New York.
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Bertamini G, Perzolli S, Bentenuto A, Paolizzi E, Furlanello C, Venuti P. Child-therapist interaction features impact Autism treatment response trajectories. RESEARCH IN DEVELOPMENTAL DISABILITIES 2023; 135:104452. [PMID: 36796270 DOI: 10.1016/j.ridd.2023.104452] [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: 12/12/2022] [Revised: 01/18/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Identifying mechanisms of change in Autism treatment may help explain response variability and maximize efficacy. For this, the child-therapist interaction could have a key role as stressed by developmental models of intervention, but still remains under-investigated. AIMS The longitudinal study of treatment response trajectories considering both baseline and child-therapist interaction features by means of predictive modeling. METHODS AND PROCEDURES N = 25 preschool children were monitored for one year during Naturalistic Developmental Behavioral Intervention. N = 100 video-recorded sessions were annotated with an observational coding system at four time points, to extract quantitative interaction features. OUTCOMES AND RESULTS Baseline and interaction variables were combined to predict response trajectories at one year, and achieved the best predictive performance. The baseline developmental gap, therapist's efficacy in child engagement, respecting children's timing after fast behavioral synchronization, and modulating the interplay to prevent child withdrawal emerged as key factors. Further, changes in interaction patterns in the early phase of the intervention were predictive of the overall response to treatment. CONCLUSIONS AND IMPLICATIONS Clinical implications are discussed, stressing the importance of promoting emotional self-regulation during intervention and the possible relevance of the first period of intervention for later response.
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Affiliation(s)
- Giulio Bertamini
- Laboratory of Observation, Diagnosis, and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy; Department of Child and Adolescent Psychiatry, Pitie-Salpetriere University Hospital, Sorbonne University, 75013 Paris, France; Data Science for Health (DSH), Bruno Kessler Foundation, 38123 Trento, Italy; Institute for Ingelligent Systems and Robotics (ISIR), Sorbonne University, 75005 Paris, France.
| | - Silvia Perzolli
- Laboratory of Observation, Diagnosis, and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Arianna Bentenuto
- Laboratory of Observation, Diagnosis, and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Eleonora Paolizzi
- Laboratory of Observation, Diagnosis, and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Cesare Furlanello
- Orobix Life Sciences, 24121 Bergamo, Italy; HK3Lab, 38068 Rovereto, Italy
| | - Paola Venuti
- Laboratory of Observation, Diagnosis, and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
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Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach. J Autism Dev Disord 2023; 53:934-946. [PMID: 35913654 DOI: 10.1007/s10803-022-05685-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2022] [Indexed: 10/16/2022]
Abstract
This study segmented the time series of gaze behavior from nineteen children with autism spectrum disorder (ASD) and 20 children with typical development in a face-to-face conversation. A machine learning approach showed that behavior segments produced by these two groups of participants could be classified with the highest accuracy of 74.15%. These results were further used to classify children using a threshold classifier. A maximum classification accuracy of 87.18% was achieved, under the condition that a participant was considered as 'ASD' if over 46% of the child's 7-s behavior segments were classified as ASD-like behaviors. The idea of combining the behavior segmentation technique and the threshold classifier could maximally preserve participants' data, and promote the automatic screening of ASD.
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13
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Vandewouw MM, Brian J, Crosbie J, Schachar RJ, Iaboni A, Georgiades S, Nicolson R, Kelley E, Ayub M, Jones J, Taylor MJ, Lerch JP, Anagnostou E, Kushki A. Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data. JAMA Netw Open 2023; 6:e232066. [PMID: 36912839 PMCID: PMC10011941 DOI: 10.1001/jamanetworkopen.2023.2066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/14/2023] Open
Abstract
IMPORTANCE Neurodevelopmental conditions, such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), have highly heterogeneous and overlapping phenotypes and neurobiology. Data-driven approaches are beginning to identify homogeneous transdiagnostic subgroups of children; however, findings have yet to be replicated in independently collected data sets, a necessity for translation into clinical settings. OBJECTIVE To identify subgroups of children with and without neurodevelopmental conditions with shared functional brain characteristics using data from 2 large, independent data sets. DESIGN, SETTING, AND PARTICIPANTS This case-control study used data from the Province of Ontario Neurodevelopmental (POND) network (study recruitment began June 2012 and is ongoing; data were extracted April 2021) and the Healthy Brain Network (HBN; study recruitment began May 2015 and is ongoing; data were extracted November 2020). POND and HBN data are collected from institutions across Ontario and New York, respectively. Participants who had diagnoses of ASD, ADHD, and OCD or were typically developing (TD); were aged between 5 and 19 years; and successfully completed the resting-state and anatomical neuroimaging protocol were included in the current study. MAIN OUTCOMES AND MEASURES The analyses consisted of a data-driven clustering procedure on measures derived from each participant's resting-state functional connectome, performed independently on each data set. Differences between each pair of leaves in the resulting clustering decision trees in the demographic and clinical characteristics were tested. RESULTS Overall, 551 children and adolescents were included from each data set. POND included 164 participants with ADHD; 217 with ASD; 60 with OCD; and 110 with TD (median [IQR] age, 11.87 [9.51-14.76] years; 393 [71.2%] male participants; 20 [3.6%] Black, 28 [5.1%] Latino, and 299 [54.2%] White participants) and HBN included 374 participants with ADHD; 66 with ASD; 11 with OCD; and 100 with TD (median [IQR] age, 11.50 [9.22-14.20] years; 390 [70.8%] male participants; 82 [14.9%] Black, 57 [10.3%] Hispanic, and 257 [46.6%] White participants). In both data sets, subgroups with similar biology that differed significantly in intelligence as well as hyperactivity and impulsivity problems were identified, yet these groups showed no consistent alignment with current diagnostic categories. For example, there was a significant difference in Strengths and Weaknesses ADHD Symptoms and Normal Behavior Hyperactivity/Impulsivity subscale (SWAN-HI) between 2 subgroups in the POND data (C and D), with subgroup D having increased hyperactivity and impulsivity traits compared with subgroup C (median [IQR], 2.50 [0.00-7.00] vs 1.00 [0.00-5.00]; U = 1.19 × 104; P = .01; η2 = 0.02). A significant difference in SWAN-HI scores between subgroups g and d in the HBN data was also observed (median [IQR], 1.00 [0.00-4.00] vs 0.00 [0.00-2.00]; corrected P = .02). There were no differences in the proportion of each diagnosis between the subgroups in either data set. CONCLUSIONS AND RELEVANCE The findings of this study suggest that homogeneity in the neurobiology of neurodevelopmental conditions transcends diagnostic boundaries and is instead associated with behavioral characteristics. This work takes an important step toward translating neurobiological subgroups into clinical settings by being the first to replicate our findings in independently collected data sets.
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Affiliation(s)
- Marlee M. Vandewouw
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Jessica Brian
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Jennifer Crosbie
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Russell J. Schachar
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alana Iaboni
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Stelios Georgiades
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Robert Nicolson
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Muhammad Ayub
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Jessica Jones
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Margot J. Taylor
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Jason P. Lerch
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Evdokia Anagnostou
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Azadeh Kushki
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
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14
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Clark JE, Watson S. Modelling mood updating: a proof of principle study. Br J Psychiatry 2023; 222:125-134. [PMID: 36511113 PMCID: PMC9929713 DOI: 10.1192/bjp.2022.175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Recent developments in computational psychiatry have led to the hypothesis that mood represents an expectation (prior belief) on the likely interoceptive consequences of action (i.e. emotion). This stems from ideas about how the brain navigates its external world by minimising an upper bound on surprisal (free energy) of sensory information and echoes developments in other perceptual domains. AIMS In this paper we aim to present a simple partial observable Markov decision process that models mood updating in response to stressful or non-stressful environmental fluctuations while seeking to minimise surprisal in relation to prior beliefs about the likely interoceptive signals experienced with specific actions (attenuating or amplifying stress and pleasure signals). METHOD We examine how, by altering these prior beliefs we can model mood updating in depression, mania and anxiety. RESULTS We discuss how these models provide a computational account of mood and its related psychopathology and relate it to previous research in reward processing. CONCLUSIONS Models such as this can provide hypotheses for experimental work and also open up the potential modelling of predicted disease trajectories in individual patients.
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Affiliation(s)
- James E. Clark
- Translational and Clinical Research Institute, Newcastle University, UK
| | - Stuart Watson
- Translational and Clinical Research Institute, Newcastle University, UK; and Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, UK,Correspondence: Stuart Watson.
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15
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Pérez-Cano L, Azidane Chenlo S, Sabido-Vera R, Sirci F, Durham L, Guney E. Translating precision medicine for autism spectrum disorder: A pressing need. Drug Discov Today 2023; 28:103486. [PMID: 36623795 DOI: 10.1016/j.drudis.2023.103486] [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: 08/18/2022] [Revised: 12/01/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
Autism spectrum disorder (ASD) is a heterogenous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. Currently, ASD is diagnosed according to behavior-based criteria that overlook clinical and genomic heterogeneity, thus repeatedly resulting in failed clinical trials. Here, we summarize the scientific evidence pointing to the pressing need to create a precision medicine framework for ASD and other NDDs. We discuss the role of omics and systems biology to characterize more homogeneous disease subtypes with different underlying pathophysiological mechanisms and to determine corresponding tailored treatments. Finally, we provide recent initiatives towards tackling the complexity in NDDs for precision medicine and cost-effective drug discovery.
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Affiliation(s)
- Laura Pérez-Cano
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain
| | - Sara Azidane Chenlo
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain
| | - Rubén Sabido-Vera
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain
| | - Francesco Sirci
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain
| | - Lynn Durham
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain; Drug Development Unit (DDU), STALICLA SA, Avenue de Sécheron 15, 1202 Geneva, Switzerland.
| | - Emre Guney
- Discovery and Data Science (DDS) Unit, STALICLA SL, Moll de Barcelona, s/n, Edif Este, 08039 Barcelona, Spain.
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16
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Zhao Z, Tang H, Zhang X, Zhu Z, Xing J, Li W, Tao D, Qu X, Lu J. Characteristics of Visual Fixation in Chinese Children with Autism During Face-to-Face Conversations. J Autism Dev Disord 2023; 53:746-758. [PMID: 34105046 DOI: 10.1007/s10803-021-04985-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2021] [Indexed: 11/29/2022]
Abstract
Few eye tracking studies have examined how people with autism spectrum disorder (ASD) visually attend during live interpersonal interaction, and none with the Chinese population. This study used an eye tracker to record the gaze behavior in 20 Chinese children with ASD and 23 children with typical development (TD) when they were engaged in a structured conversation. Results demonstrated that children with ASD looked significantly less at the interlocutor's mouth and whole-face, and more at background. Additionally, gaze behavior was found to vary with the conversational topic. Given the great variability in eye tracking findings in existing literature, future explorations might consider investigating how fundamental factors (i.e., participant's characteristics, tasks, and context) influence the gaze behavior in people with ASD.
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Affiliation(s)
- Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Haiming Tang
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xiaobin Zhang
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China.,Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Zhipeng Zhu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Jiayi Xing
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Wenzhou Li
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Da Tao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Jianping Lu
- Department of Child Psychiatry of Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China.
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17
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Zhong NH, Grimm RP, Kanne SM, Mazurek MO. Measurement invariance of the Autism Impact Measure (AIM) across sex in children with autism spectrum disorder. Autism Res 2023; 16:154-163. [PMID: 36341720 PMCID: PMC10099563 DOI: 10.1002/aur.2845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 10/25/2022] [Indexed: 11/09/2022]
Abstract
Measurement invariance, or the degree to which an instrument measures constructs consistently across subgroups, is critical for appropriate interpretations of measures. Given sex differences in the phenotypic and clinical presentation of autism spectrum disorder (ASD), it is particularly important to examine measurement invariance in autism instruments to ensure that ASD measures are not biased toward the more common male ASD phenotype. This study represents an important preliminary investigation evaluating the measurement equivalence of the Autism Impact Measure (AIM) across children and adolescents with ASD. The results indicated that the AIM demonstrated measurement invariance at the configural, metric, and scalar levels across sex in all five domains, including Repetitive Behavior, Communication, Atypical Behavior, Social Reciprocity, and Peer Interaction. These results suggest that ASD core symptoms assessed by the AIM were similar among male and female groups. In addition, the latent means for all five factors were not statistically significantly different across sex groups, revealing no systematic differences on any of the AIM subscales for males and females. Overall, this study showed that the AIM detects core ASD symptoms across all five areas equivalently in males and females and is not biased toward males with ASD.
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Affiliation(s)
- Nicole H Zhong
- School of Education and Human Development, University of Virginia, Charlottesville, Virginia, USA
| | | | - Stephen M Kanne
- Weill Cornell Medical College and New York-Presbyterian Hospital, New York, USA
| | - Micah O Mazurek
- School of Education and Human Development, University of Virginia, Charlottesville, Virginia, USA
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18
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Selcuk Nogay H, Adeli H. Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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19
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Woodward AA, Urbanowicz RJ, Naj AC, Moore JH. Genetic heterogeneity: Challenges, impacts, and methods through an associative lens. Genet Epidemiol 2022; 46:555-571. [PMID: 35924480 PMCID: PMC9669229 DOI: 10.1002/gepi.22497] [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] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/06/2022] [Accepted: 07/19/2022] [Indexed: 01/07/2023]
Abstract
Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.
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Affiliation(s)
- Alexa A. Woodward
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ryan J. Urbanowicz
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Adam C. Naj
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jason H. Moore
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
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20
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Cluster Analysis of Short Sensory Profile Data Reveals Sensory-Based Subgroups in Autism Spectrum Disorder. Int J Mol Sci 2022; 23:ijms232113030. [PMID: 36361815 PMCID: PMC9655407 DOI: 10.3390/ijms232113030] [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: 07/09/2022] [Accepted: 10/24/2022] [Indexed: 01/27/2023] Open
Abstract
Autism spectrum disorder is a common, heterogeneous neurodevelopmental disorder lacking targeted treatments. Additional features include restricted, repetitive patterns of behaviors and differences in sensory processing. We hypothesized that detailed sensory features including modality specific hyper- and hypo-sensitivity could be used to identify clinically recognizable subgroups with unique underlying gene variants. Participants included 378 individuals with a clinical diagnosis of autism spectrum disorder who contributed Short Sensory Profile data assessing the frequency of sensory behaviors and whole genome sequencing results to the Autism Speaks' MSSNG database. Sensory phenotypes in this cohort were not randomly distributed with 10 patterns describing 43% (162/378) of participants. Cross comparison of two independent cluster analyses on sensory responses identified six distinct sensory-based subgroups. We then characterized subgroups by calculating the percent of patients in each subgroup who had variants with a Combined Annotation Dependent Depletion (CADD) score of 15 or greater in each of 24,896 genes. Each subgroup exhibited a unique pattern of genes with a high frequency of variants. These results support the use of sensory features to identify autism spectrum disorder subgroups with shared genetic variants.
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21
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Quinde-Zlibut J, Munshi A, Biswas G, Cascio CJ. Identifying and describing subtypes of spontaneous empathic facial expression production in autistic adults. J Neurodev Disord 2022; 14:43. [PMID: 35915404 PMCID: PMC9342940 DOI: 10.1186/s11689-022-09451-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 07/08/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND It is unclear whether atypical patterns of facial expression production metrics in autism reflect the dynamic and nuanced nature of facial expressions across people or a true diagnostic difference. Furthermore, the heterogeneity observed across autism symptomatology suggests a need for more adaptive and personalized social skills programs. Towards this goal, it would be useful to have a more concrete and empirical understanding of the different expressiveness profiles within the autistic population and how they differ from neurotypicals. METHODS We used automated facial coding and an unsupervised clustering approach to limit inter-individual variability in facial expression production that may have otherwise obscured group differences in previous studies, allowing an "apples-to-apples" comparison between autistic and neurotypical adults. Specifically, we applied k-means clustering to identify subtypes of facial expressiveness in an autism group (N = 27) and a neurotypical control group (N = 57) separately. The two most stable clusters from these analyses were then further characterized and compared based on their expressiveness and emotive congruence to emotionally charged stimuli. RESULTS Our main finding was that a subset of autistic adults in our sample show heightened spontaneous facial expressions irrespective of image valence. We did not find evidence for greater incongruous (i.e., inappropriate) facial expressions in autism. Finally, we found a negative trend between expressiveness and emotion recognition within the autism group. CONCLUSION The results from our previous study on self-reported empathy and current expressivity findings point to a higher degree of facial expressions recruited for emotional resonance in autism that may not always be adaptive (e.g., experiencing similar emotional resonance regardless of valence). These findings also build on previous work indicating that facial expression intensity is not diminished in autism and suggest the need for intervention programs to focus on emotion recognition and social skills in the context of both negative and positive emotions.
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Affiliation(s)
- Jennifer Quinde-Zlibut
- Graduate Program in Neuroscience, Vanderbilt University, Nashville, USA. .,Frist Center for Autism and Innovation, Vanderbilt University, Nashville, USA.
| | - Anabil Munshi
- grid.152326.10000 0001 2264 7217Institute for Software Integrated Systems, Vanderbilt University, Nashville, USA
| | - Gautam Biswas
- grid.152326.10000 0001 2264 7217Institute for Software Integrated Systems, Vanderbilt University, Nashville, USA
| | - Carissa J. Cascio
- grid.412807.80000 0004 1936 9916Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
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22
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Abstract
PURPOSE OF REVIEW There are currently no approved medications for the core symptoms of autism spectrum disorder (ASD), and only limited data on the management of co-occurring mental health and behavioural symptoms. The purpose of this review is to synthesize recent trials on novel treatments in ASD, with a focus on research trends in the past 2 years. RECENT FINDINGS No new pharmacologic agents received regulatory approval for use in ASD. Several large randomized controlled trials (RCTs) had negative or ambiguous results (e.g. fluoxetine, oxytocin). A cross-over RCT of an oral cannabinoid suggested possible benefits for disruptive behaviours. Two large-scale multicentre trials of bumetanide were terminated early for lack of efficacy. Multicenter trials using repetitive transcranial magnetic stimulation are underway. Recent meta-analyses indicate that specific behavioural and psychological interventions can support social communication and treat anxiety. Numerous novel treatment targets informed by biological mechanisms are under investigation. SUMMARY Recent data support the use of behavioural and psychological interventions for social communication and anxiety in ASD; data are more limited regarding pharmacotherapy for core and associated symptoms. Next steps include replication of early findings, trials of new molecular targets, and the identification of novel biomarkers, including genetic predictors, of treatment response.
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Affiliation(s)
- Danielle Baribeau
- University of Toronto
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Jacob Vorstman
- University of Toronto
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Evdokia Anagnostou
- University of Toronto
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
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23
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Zhao Z, Tang H, Alviar C, Kello CT, Zhang X, Hu X, Qu X, Lu J. Excessive and less complex body movement in children with autism during face-to-face conversation: An objective approach to behavioral quantification. Autism Res 2021; 15:305-316. [PMID: 34837352 DOI: 10.1002/aur.2646] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/08/2021] [Accepted: 11/16/2021] [Indexed: 11/05/2022]
Abstract
The majority of existing studies investigating characteristics of overt social behavior in individuals with autism spectrum disorder (ASD) relied on informants' evaluation through questionnaires and behavioral coding techniques. As a novelty, this study aimed to quantify the complex movements produced during social interactions in order to test differences in ASD movement dynamics and their convergence, or lack thereof, during social interactions. Twenty children with ASD and twenty-three children with typical development (TD) were videotaped while engaged in a face-to-face conversation with an interviewer. An image differencing technique was utilized to extract the movement time series. Spectral analyses were conducted to quantify the average power of movement, and the fractal scaling of movement. The degree of complexity matching was calculated to capture the level of behavioral coordination between the interviewer and children. Results demonstrated that the average power was significantly higher (p < 0.01), and the fractal scaling was steeper (p < 0.05) in children with ASD, suggesting excessive and less complex movement as compared to the TD peers. Complexity matching occurred between children and interviewers, but there was no reliable difference in the strength of matching between the ASD and TD children. Descriptive trends in the interviewer's behavior suggest that her movements adapted to match both ASD and TD movements equally well. The findings of our study might shed light on seeking novel behavioral markers of ASD, and on developing automatic ASD screening techniques during daily social interactions.
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Affiliation(s)
- Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Haiming Tang
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Camila Alviar
- Cognitive and Information Sciences, University of California, Merced, USA
| | | | - Xiaobin Zhang
- Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Xinyao Hu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Jianping Lu
- Department of Child Psychiatry of Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
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24
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Zhao Z, Zhang X, Tang H, Hu X, Qu X, Lu J, Peng Q. Restricted Kinematics in Children With Autism in the Execution of Complex Oscillatory Arm Movements. Front Hum Neurosci 2021; 15:708969. [PMID: 34803630 PMCID: PMC8597710 DOI: 10.3389/fnhum.2021.708969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 10/04/2021] [Indexed: 11/30/2022] Open
Abstract
Restricted and repetitive behavior is a core symptom of autism spectrum disorder (ASD) characterized by features of restrictedness, repetition, rigidity, and invariance. Few studies have investigated how restrictedness is manifested in motor behavior. This study aimed to address this question by instructing participants to perform the utmost complex movement. Twenty children with ASD and 23 children with typical development (TD) performed one-dimensional, left-right arm oscillations by demonstrating varying amplitudes and frequencies. The entropy of amplitude and velocity was calculated as an index of kinematic complexity. Results showed that the velocity entropy, but not the amplitude entropy, was significantly lower in ASD than in TD (p < 0.01), suggesting restricted kinematics. Further analysis demonstrated that a significantly higher proportion of the velocity values was allocated at a low-speed level in the children with ASD (p < 0.01). A qualitative comparison of the complex movement with movement at preferred frequency suggested that the children with ASD might be less likely to shift away from the preferred movement. However, our study can be improved in terms of recruiting a larger sample of participants, measuring the level of motivation, and collecting both complex and preferred movements of the same participant.
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Affiliation(s)
- Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xiaobin Zhang
- Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Haiming Tang
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xinyao Hu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Jianping Lu
- Department of Child Psychiatry of Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Qiongling Peng
- Developmental Behavioral Pediatric Department, Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, China
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25
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Nomura J, Mardo M, Takumi T. Molecular signatures from multi-omics of autism spectrum disorders and schizophrenia. J Neurochem 2021; 159:647-659. [PMID: 34537986 DOI: 10.1111/jnc.15514] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 08/11/2021] [Accepted: 09/07/2021] [Indexed: 01/25/2023]
Abstract
The genetic and phenotypic heterogeneity of autism spectrum disorder (ASD) impedes the unification of multiple biological hypotheses in an attempt to explain the complex features of ASD, such as impaired social communication, social interaction deficits, and restricted and repetitive patterns of behavior. However, recent psychiatric genetic studies have identified numerous risk genes and chromosome loci (copy number variation: CNV) which enable us to analyze at the single gene level and utilize system-level approaches. In this review, we focus on ASD as a major neurodevelopmental disorder and review recent findings mainly from the bioinformatics of omics studies. Additionally, by comparing these data with other major psychiatric disorders, including schizophrenia (SCZ), we identify unique characteristics of both diseases from multiple enrichment, pathway, and protein-protein interaction networks (PPIs) analyses using susceptible genes found in recent large-scale genetic studies. These unified, systematic approaches highlight unique characteristics of both disorders from multiple aspects and demonstrate how convergent pathways can contribute to an understanding of the complex etiology of such neurodevelopmental and neuropsychiatric disorders.
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Affiliation(s)
- Jun Nomura
- Department of Physiology and Cell Biology, Kobe University School of Medicine, Kobe, Japan
| | - Matthew Mardo
- Neuroscience concentration, Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Toru Takumi
- Department of Physiology and Cell Biology, Kobe University School of Medicine, Kobe, Japan
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Zhao Z, Tang H, Zhang X, Qu X, Hu X, Lu J. Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation. J Med Internet Res 2021; 23:e29328. [PMID: 34435957 PMCID: PMC8440949 DOI: 10.2196/29328] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/30/2021] [Accepted: 07/05/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Previous studies have shown promising results in identifying individuals with autism spectrum disorder (ASD) by applying machine learning (ML) to eye-tracking data collected while participants viewed varying images (ie, pictures, videos, and web pages). Although gaze behavior is known to differ between face-to-face interaction and image-viewing tasks, no study has investigated whether eye-tracking data from face-to-face conversations can also accurately identify individuals with ASD. OBJECTIVE The objective of this study was to examine whether eye-tracking data from face-to-face conversations could classify children with ASD and typical development (TD). We further investigated whether combining features on visual fixation and length of conversation would achieve better classification performance. METHODS Eye tracking was performed on children with ASD and TD while they were engaged in face-to-face conversations (including 4 conversational sessions) with an interviewer. By implementing forward feature selection, four ML classifiers were used to determine the maximum classification accuracy and the corresponding features: support vector machine (SVM), linear discriminant analysis, decision tree, and random forest. RESULTS A maximum classification accuracy of 92.31% was achieved with the SVM classifier by combining features on both visual fixation and session length. The classification accuracy of combined features was higher than that obtained using visual fixation features (maximum classification accuracy 84.62%) or session length (maximum classification accuracy 84.62%) alone. CONCLUSIONS Eye-tracking data from face-to-face conversations could accurately classify children with ASD and TD, suggesting that ASD might be objectively screened in everyday social interactions. However, these results will need to be validated with a larger sample of individuals with ASD (varying in severity and balanced sex ratio) using data collected from different modalities (eg, eye tracking, kinematic, electroencephalogram, and neuroimaging). In addition, individuals with other clinical conditions (eg, developmental delay and attention deficit hyperactivity disorder) should be included in similar ML studies for detecting ASD.
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Affiliation(s)
- Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Haiming Tang
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xiaobin Zhang
- Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xinyao Hu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Jianping Lu
- Department of Child Psychiatry of Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
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27
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Moreau CA, Raznahan A, Bellec P, Chakravarty M, Thompson PM, Jacquemont S. Dissecting autism and schizophrenia through neuroimaging genomics. Brain 2021; 144:1943-1957. [PMID: 33704401 PMCID: PMC8370419 DOI: 10.1093/brain/awab096] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 12/24/2020] [Accepted: 01/08/2021] [Indexed: 12/23/2022] Open
Abstract
Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a 'top-down' approach, beginning with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In this review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioural and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions' phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes.
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Affiliation(s)
- Clara A Moreau
- Sainte Justine Research Center, University of Montréal, Montréal, Québec H3T 1C5, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, Québec H3W 1W5, Canada
- Human Genetics and Cognitive Functions, CNRS UMR 3571, Université de Paris, Institut Pasteur, Paris, France
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Bethesda, MD 20892, USA
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, Québec H3W 1W5, Canada
| | - Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Hospital Mental Health University Institute, Verdun, Québec H4H 1R3, Canada
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, USC Keck School of Medicine, Marina del Rey, CA 90033, USA
| | - Sebastien Jacquemont
- Sainte Justine Research Center, University of Montréal, Montréal, Québec H3T 1C5, Canada
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28
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Zhao Z, Zhu Z, Zhang X, Tang H, Xing J, Hu X, Lu J, Qu X. Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms. J Autism Dev Disord 2021; 52:3038-3049. [PMID: 34250557 DOI: 10.1007/s10803-021-05179-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2021] [Indexed: 11/27/2022]
Abstract
Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes-no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD.
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Affiliation(s)
- Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Zhipeng Zhu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Xiaobin Zhang
- Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Haiming Tang
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Jiayi Xing
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Xinyao Hu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China
| | - Jianping Lu
- Department of Child Psychiatry of Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai, Avenue, Shenzhen City, Guangdong Province, China.
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29
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Constantino JN, Charman T, Jones EJH. Clinical and Translational Implications of an Emerging Developmental Substructure for Autism. Annu Rev Clin Psychol 2021; 17:365-389. [PMID: 33577349 PMCID: PMC9014692 DOI: 10.1146/annurev-clinpsy-081219-110503] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A vast share of the population-attributable risk for autism relates to inherited polygenic risk. A growing number of studies in the past five years have indicated that inherited susceptibility may operate through a finite number of early developmental liabilities that, in various permutations and combinations, jointly predict familial recurrence of the convergent syndrome of social communication disability that defines the condition. Here, we synthesize this body of research to derive evidence for a novel developmental substructure for autism, which has profound implications for ongoing discovery efforts to elucidate its neurobiological causes, and to inform future clinical and biomarker studies, early interventions, and personalized approaches to therapy.
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Affiliation(s)
- John N Constantino
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri 63110, USA;
| | - Tony Charman
- Department of Psychology, King's College London Institute of Psychiatry, Psychology & Neuroscience, London SE5 8AF, United Kingdom
| | - Emily J H Jones
- Centre for Brain & Cognitive Development, Birkbeck, University of London, London WC1E 7HX, United Kingdom
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30
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Kitchin JL, Karlin NJ. Awareness and Stigma of Autism Spectrum Disorders in Undergraduate Students. Psychol Rep 2021; 125:2069-2087. [PMID: 33947290 DOI: 10.1177/00332941211014144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Social stigma is a barrier for students with autism on college campuses. The purpose of this study is to examine the relationship between autism knowledge and autism stigma endorsement. METHOD 144 college undergraduate students were asked to complete the Autism Stigma and Knowledge Questionnaire as well as a brief demographic questionnaire. The relationship between stigma endorsement and ASD knowledge in the areas of diagnosis and symptoms, etiology, and treatment were evaluated using a multiple linear regression. Two independent-sample t-tests were conducted to investigate group differences between participants who know someone with autism and those who do not as well as between male and female participants. RESULTS A significant regression equation was found (F(3,140) = 51.35, p = .000), with an R2 of .52. While Treatment and Etiology subscale scores significantly predicted Stigma subscale scores, Diagnosis/Symptom subscale score was not. In terms of knowing someone with an autism diagnosis, there was a significant difference in ASD diagnosis and symptom knowledge (t(142) = 4.16, p = .000), etiology knowledge (t(142) = 3.51, p = .001), treatment knowledge (t(62.99) = 3.54, p = .001), and stigma endorsement (t(142) = 3.03, p = .003). No significant differences were found between male and female participants. CONCLUSIONS Contrary to past studies, gender was not associated with ASD knowledge or stigma endorsement. This study suggests that an intervention designed to increase ASD knowledge, particularly in the areas of etiology and treatment, and to increase contact with students diagnosed with autism would be effective in reducing ASD stigma.
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Affiliation(s)
- Jannessa L Kitchin
- Department of Applied Psychology and Counseling Education, The 3604University of Northern Colorado, Greeley, CO, USA
| | - Nancy J Karlin
- Department of Applied Psychology and Counseling Education, The 3604University of Northern Colorado, Greeley, CO, USA
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31
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Alvari G, Furlanello C, Venuti P. Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD. J Clin Med 2021; 10:1776. [PMID: 33921756 PMCID: PMC8073678 DOI: 10.3390/jcm10081776] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 01/01/2023] Open
Abstract
Time is a key factor to consider in Autism Spectrum Disorder. Detecting the condition as early as possible is crucial in terms of treatment success. Despite advances in the literature, it is still difficult to identify early markers able to effectively forecast the manifestation of symptoms. Artificial intelligence (AI) provides effective alternatives for behavior screening. To this end, we investigated facial expressions in 18 autistic and 15 typical infants during their first ecological interactions, between 6 and 12 months of age. We employed Openface, an AI-based software designed to systematically analyze facial micro-movements in images in order to extract the subtle dynamics of Social Smiles in unconstrained Home Videos. Reduced frequency and activation intensity of Social Smiles was computed for children with autism. Machine Learning models enabled us to map facial behavior consistently, exposing early differences hardly detectable by non-expert naked eye. This outcome contributes to enhancing the potential of AI as a supportive tool for the clinical framework.
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Affiliation(s)
- Gianpaolo Alvari
- Department of Psychology and Cognitive Sciences, University of Trento, 38068 Rovereto, Italy;
- Data Science for Health (DSH) Research Unit, Bruno Kessler Foundation (FBK), 38123 Trento, Italy
| | | | - Paola Venuti
- Department of Psychology and Cognitive Sciences, University of Trento, 38068 Rovereto, Italy;
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32
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Bertamini G, Bentenuto A, Perzolli S, Paolizzi E, Furlanello C, Venuti P. Quantifying the Child-Therapist Interaction in ASD Intervention: An Observational Coding System. Brain Sci 2021; 11:366. [PMID: 33805630 PMCID: PMC7998397 DOI: 10.3390/brainsci11030366] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/07/2021] [Accepted: 03/10/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Observational research plays an important part in developmental research due to its noninvasiveness. However, it has been hardly applied to investigate efficacy of the child-therapist interaction in the context of naturalistic developmental behavioral interventions (NDBI). In particular, the characteristics of child-therapist interplay are thought to have a significant impact in NDBIs in children with autism spectrum disorder (ASD). Quantitative approaches may help to identify the key features of interaction during therapy and could be translated as instruments to monitor early interventions. METHODS n = 24 children with autism spectrum disorder (ASD) were monitored from the time of the diagnosis (T0) and after about one year of early intervention (T1). A novel observational coding system was applied to video recorded sessions of intervention to extract quantitative behavioral descriptors. We explored the coding scheme reliability together with its convergent and predictive validity. Further, we applied computational techniques to investigate changes and associations between interaction profiles and developmental outcomes. RESULTS Significant changes in interaction variables emerged with time, suggesting that a favorable outcome is associated with interactions characterized by increased synchrony, better therapist's strategies to successfully engage the child and scaffold longer, more complex and engaging interchanges. Interestingly, data models linked interaction profiles, outcome measures and response trajectories. CONCLUSION Current research stresses the need for process measures to understand the hows and the whys of ASD early intervention. Combining observational techniques with computational approaches may help in explaining interindividual variability. Further, it could disclose successful features of interaction associated with better response trajectories or to different ASD behavioral phenotypes that could require specific dyadic modalities.
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Affiliation(s)
- Giulio Bertamini
- Laboratory of Observation, Diagnosis and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, TN, Italy; (G.B.); (A.B.); (S.P.); (E.P.)
- Data Science for Health (DSH), Bruno Kessler Foundation (FBK), 38123 Povo, TN, Italy
| | - Arianna Bentenuto
- Laboratory of Observation, Diagnosis and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, TN, Italy; (G.B.); (A.B.); (S.P.); (E.P.)
| | - Silvia Perzolli
- Laboratory of Observation, Diagnosis and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, TN, Italy; (G.B.); (A.B.); (S.P.); (E.P.)
| | - Eleonora Paolizzi
- Laboratory of Observation, Diagnosis and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, TN, Italy; (G.B.); (A.B.); (S.P.); (E.P.)
| | - Cesare Furlanello
- Hk3 Lab, 38068 Rovereto, TN, Italy;
- Orobix Life, 24121 Bergamo, BG, Italy
| | - Paola Venuti
- Laboratory of Observation, Diagnosis and Education (ODFLab), Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, TN, Italy; (G.B.); (A.B.); (S.P.); (E.P.)
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Loss CM, Teodoro L, Rodrigues GD, Moreira LR, Peres FF, Zuardi AW, Crippa JA, Hallak JEC, Abílio VC. Is Cannabidiol During Neurodevelopment a Promising Therapy for Schizophrenia and Autism Spectrum Disorders? Front Pharmacol 2021; 11:635763. [PMID: 33613289 PMCID: PMC7890086 DOI: 10.3389/fphar.2020.635763] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 12/24/2020] [Indexed: 01/22/2023] Open
Abstract
Schizophrenia and autism spectrum disorders (ASD) are psychiatric neurodevelopmental disorders that cause high levels of functional disabilities. Also, the currently available therapies for these disorders are limited. Therefore, the search for treatments that could be beneficial for the altered course of the neurodevelopment associated with these disorders is paramount. Preclinical and clinical evidence points to cannabidiol (CBD) as a promising strategy. In this review, we discuss clinical and preclinical studies on schizophrenia and ASD investigating the behavioral, molecular, and functional effects of chronic treatment with CBD (and with cannabidivarin for ASD) during neurodevelopment. In summary, the results point to CBD's beneficial potential for the progression of these disorders supporting further investigations to strengthen its use.
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Affiliation(s)
- Cássio Morais Loss
- Molecular and Behavioral Neuroscience Laboratory, Departamento de Farmacologia, Universidade Federal de São Paulo, São Paulo, Brazil.,National Institute for Translational Medicine (INCT-TM), National Council for Scientific and Technological Development (CNPq/CAPES/FAPESP), Ribeirão Preto, Brazil
| | - Lucas Teodoro
- Molecular and Behavioral Neuroscience Laboratory, Departamento de Farmacologia, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Gabriela Doná Rodrigues
- Molecular and Behavioral Neuroscience Laboratory, Departamento de Farmacologia, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Lucas Roberto Moreira
- Molecular and Behavioral Neuroscience Laboratory, Departamento de Farmacologia, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Fernanda Fiel Peres
- Molecular and Behavioral Neuroscience Laboratory, Departamento de Farmacologia, Universidade Federal de São Paulo, São Paulo, Brazil.,National Institute for Translational Medicine (INCT-TM), National Council for Scientific and Technological Development (CNPq/CAPES/FAPESP), Ribeirão Preto, Brazil
| | - Antonio Waldo Zuardi
- National Institute for Translational Medicine (INCT-TM), National Council for Scientific and Technological Development (CNPq/CAPES/FAPESP), Ribeirão Preto, Brazil.,Department of Neuroscience and Behavior, Ribeirão Preto Medical School, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - José Alexandre Crippa
- National Institute for Translational Medicine (INCT-TM), National Council for Scientific and Technological Development (CNPq/CAPES/FAPESP), Ribeirão Preto, Brazil.,Department of Neuroscience and Behavior, Ribeirão Preto Medical School, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - Jaime Eduardo Cecilio Hallak
- National Institute for Translational Medicine (INCT-TM), National Council for Scientific and Technological Development (CNPq/CAPES/FAPESP), Ribeirão Preto, Brazil.,Department of Neuroscience and Behavior, Ribeirão Preto Medical School, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - Vanessa Costhek Abílio
- Molecular and Behavioral Neuroscience Laboratory, Departamento de Farmacologia, Universidade Federal de São Paulo, São Paulo, Brazil.,National Institute for Translational Medicine (INCT-TM), National Council for Scientific and Technological Development (CNPq/CAPES/FAPESP), Ribeirão Preto, Brazil
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34
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Zhao Z, Zhu Z, Zhang X, Tang H, Xing J, Hu X, Lu J, Peng Q, Qu X. Atypical Head Movement during Face-to-Face Interaction in Children with Autism Spectrum Disorder. Autism Res 2021; 14:1197-1208. [PMID: 33529500 DOI: 10.1002/aur.2478] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/30/2020] [Accepted: 01/07/2021] [Indexed: 11/11/2022]
Abstract
The present study implemented an objective head pose tracking technique-OpenFace 2.0 to quantify the three dimensional head movement. Children with autism spectrum disorder (ASD) and typical development (TD) were engaged in a structured conversation with an interlocutress while wearing an eye tracker. We computed the head movement stereotypy with multiscale entropy analysis. In addition, the head rotation range (RR) and the amount of rotation per minute (ARPM) were calculated to quantify the extent of head movement. Results demonstrated that the ASD group had significantly higher level of movement stereotypy, RR and ARPM in all the three directions of head movement. Further analyses revealed that the extent of head movement could be significantly explained by movement stereotypy, but not by the amount of visual fixation to the interlocutress. These results demonstrated the atypical head movement dynamics in children with ASD during live interaction. It is proposed that head movement might potentially provide novel objective biomarkers of ASD. LAY SUMMARY: Our study used an objective tool to quantify head movement in children with autism. Results showed that children with autism had more stereotyped and greater head movement. We suggest that head movement tracking technique be widely used in autism research.
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Affiliation(s)
- Zhong Zhao
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Zhipeng Zhu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xiaobin Zhang
- Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Haiming Tang
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Jiayi Xing
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Xinyao Hu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
| | - Jianping Lu
- Department of Child Psychiatry of Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Qiongling Peng
- Developmental Behavioral Pediatric Department, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Xingda Qu
- Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China
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35
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Hiremath CS, Sagar KJV, Yamini BK, Girimaji AS, Kumar R, Sravanti SL, Padmanabha H, Vykunta Raju KN, Kishore MT, Jacob P, Saini J, Bharath RD, Seshadri SP, Kumar M. Emerging behavioral and neuroimaging biomarkers for early and accurate characterization of autism spectrum disorders: a systematic review. Transl Psychiatry 2021; 11:42. [PMID: 33441539 PMCID: PMC7806884 DOI: 10.1038/s41398-020-01178-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 11/19/2020] [Accepted: 12/01/2020] [Indexed: 01/29/2023] Open
Abstract
The possibility of early treatment and a better outcome is the direct product of early identification and characterization of any pathological condition. Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in social communication, restricted, and repetitive patterns of behavior. In recent times, various tools and methods have been developed for the early identification and characterization of ASD features as early as 6 months of age. Thorough and exhaustive research has been done to identify biomarkers in ASD using noninvasive neuroimaging and various molecular methods. By employing advanced assessment tools such as MRI and behavioral assessment methods for accurate characterization of the ASD features and may facilitate pre-emptive interventional and targeted therapy programs. However, the application of advanced quantitative MRI methods is still confined to investigational/laboratory settings, and the clinical implication of these imaging methods in personalized medicine is still in infancy. Longitudinal research studies in neurodevelopmental disorders are the need of the hour for accurate characterization of brain-behavioral changes that could be monitored over a period of time. These findings would be more reliable and consistent with translating into the clinics. This review article aims to focus on the recent advancement of early biomarkers for the characterization of ASD features at a younger age using behavioral and quantitative MRI methods.
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Affiliation(s)
- Chandrakanta S Hiremath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Kommu John Vijay Sagar
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - B K Yamini
- Department of Speech Pathology and Audiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Akhila S Girimaji
- Department of Speech Pathology and Audiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Raghavendra Kumar
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Sanivarapu Lakshmi Sravanti
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Hansashree Padmanabha
- Department of Neurology, National Institute of Mental Health and Neuroscience, Bengaluru, India
| | - K N Vykunta Raju
- Department of Pediatric Neurology, Indira Gandhi Institute of Child Health, Bengaluru, India
| | - M Thomas Kishore
- Department of Clinical Psychology, National Institute of Mental Health and Neuroscience, Bengaluru, India
| | - Preeti Jacob
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Rose D Bharath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Shekhar P Seshadri
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Manoj Kumar
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India.
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36
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Rahman MM, Usman OL, Muniyandi RC, Sahran S, Mohamed S, Razak RA. A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder. Brain Sci 2020; 10:brainsci10120949. [PMID: 33297436 PMCID: PMC7762227 DOI: 10.3390/brainsci10120949] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/02/2020] [Accepted: 12/05/2020] [Indexed: 12/27/2022] Open
Abstract
Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning's speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.
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Affiliation(s)
- Md. Mokhlesur Rahman
- Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia; (M.M.R.); (O.L.U.)
| | - Opeyemi Lateef Usman
- Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia; (M.M.R.); (O.L.U.)
| | - Ravie Chandren Muniyandi
- Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia; (M.M.R.); (O.L.U.)
- Correspondence: ; Tel.: +60-123249577
| | - Shahnorbanun Sahran
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia;
| | - Suziyani Mohamed
- Centre of Community Education and Wellbeing, Faculty of Education, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia;
| | - Rogayah A Razak
- Speech Science Programme, Center for Rehabilitation and Special Needs Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Kuala Lumpur 50300, Malaysia;
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37
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Philippsen A, Nagai Y. Deficits in Prediction Ability Trigger Asymmetries in Behavior and Internal Representation. Front Psychiatry 2020; 11:564415. [PMID: 33329104 PMCID: PMC7716881 DOI: 10.3389/fpsyt.2020.564415] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 10/20/2020] [Indexed: 12/22/2022] Open
Abstract
Predictive coding is an emerging theoretical framework for explaining human perception and behavior. The proposed underlying mechanism is that signals encoding sensory information are integrated with signals representing the brain's prior prediction. Imbalance or aberrant precision of the two signals has been suggested as a potential cause for developmental disorders. Computational models may help to understand how such aberrant tendencies in prediction affect development and behavior. In this study, we used a computational approach to test the hypothesis that parametric modifications of prediction ability generate a spectrum of network representations that might reflect the spectrum from typical development to potential disorders. Specifically, we trained recurrent neural networks to draw simple figure trajectories, and found that altering reliance on sensory and prior signals during learning affected the networks' performance and the emergent internal representation. Specifically, both overly strong or weak reliance on predictions impaired network representations, but drawing performance did not always reflect this impairment. Thus, aberrant predictive coding causes asymmetries in behavioral output and internal representations. We discuss the findings in the context of autism spectrum disorder, where we hypothesize that too weak or too strong a reliance on predictions may be the cause of the large diversity of symptoms associated with this disorder.
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Affiliation(s)
- Anja Philippsen
- International Research Center for Neurointelligence (IRCN), The University of Tokyo, Tokyo, Japan
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38
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Wang K, Li N, Xu M, Huang M, Huang F. Glyoxalase 1 Inhibitor Alleviates Autism-like Phenotype in a Prenatal Valproic Acid-Induced Mouse Model. ACS Chem Neurosci 2020; 11:3786-3792. [PMID: 33166134 DOI: 10.1021/acschemneuro.0c00482] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Autism spectrum disorder (ASD) is a severe neurological and developmental disorder that impairs a person's ability to socialize and communicate and affects behavior. The number of patients diagnosed with ASD has risen rapidly. However, the pathophysiology of ASD is poorly understood, and drugs for ASD treatment are strikingly limited. This study aims to evaluate the roles of glyoxalase 1 (GLO1)-methylglyoxal (MG)-γ-aminobutyric acid (GABA) signaling in ASD using a valproic acid (VPA)-induced animal model of autism. The GLO1 levels were analyzed by RT-qPCR and Western blot assay, and MG levels were measured with a Methylglyoxal Assay Kit. The open-field and sniff duration tests were used to assess the interest and anxiety of VPA mice. The three-chamber, marble-burying, and tail-flick tests were applied to determine the sociability, repetitive behavior, and nociceptive threshold of VPA mice. Our results demonstrated that increased GLO1 and decreased MG were observed in VPA mice. Administration of S-p-bromobenzylglutathione cyclopentyl diester (BrBzGCp2), a GLO1 inhibitor, was beneficial for alleviating anxiety, reducing repetitive behavior, and improving the impaired sociability and nociceptive threshold of VPA mice. BrBzGCp2 treatment may be developed as a promising therapeutic strategy for patients with ASD.
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Affiliation(s)
- Kui Wang
- Psychiatric Ward, Qingdao Mental Health Center, Qingdao University, No 299 Nanjing Road, Qingdao, 266034 Shandong, China
| | - Na Li
- Psychiatric Ward, Qingdao Mental Health Center, Qingdao University, No 299 Nanjing Road, Qingdao, 266034 Shandong, China
| | - Min Xu
- Psychiatric Ward, Qingdao Mental Health Center, Qingdao University, No 299 Nanjing Road, Qingdao, 266034 Shandong, China
| | - Meng Huang
- Department of Laboratory Medicine, Lao-shan Disease Area, the Affiliated Hospital of Qingdao University, Qingdao, 266000 Shandong, China
| | - Fei Huang
- Psychiatric Ward, Qingdao Mental Health Center, Qingdao University, No 299 Nanjing Road, Qingdao, 266034 Shandong, China
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39
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Abstract
Prospective longitudinal studies of idiopathic autism spectrum disorder (ASD) have provided insights into early symptoms and predictors of ASD during infancy, well before ASD can be diagnosed at age 2-3 years. However, research on the emergence of ASD in disorders with a known genetic etiology, contextualized in a developmental framework, is currently lacking. Using a biobehavioral multimethod approach, we (a) determined the rate of ASD in N = 51 preschoolers with fragile X syndrome (FXS) using a clinical best estimate (CBE) procedure with differential diagnoses of comorbid psychiatric disorders and (b) investigated trajectories of ASD symptoms and physiological arousal across infancy as predictors of ASD in preschoolers with FXS. ASD was not diagnosed if intellectual ability or psychiatric disorders better accounted for the symptoms. Our results determined that 60.7% of preschoolers with FXS met the Diagnostic and Statistical Manual of Mental Disorders (fifth edition) (DSM-5) criteria for ASD using the CBE procedure. In addition, 92% of these preschoolers presented with developmental delay and 45.4% also met criteria for psychiatric disorders, either anxiety, ADHD, or both. ASD diagnoses in preschoolers with FXS were predicted by elevated scores on traditional ASD screeners in addition to elevated autonomic arousal and avoidant eye contact from infancy.
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40
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Nogay HS, Adeli H. Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging. Rev Neurosci 2020; 31:/j/revneuro.ahead-of-print/revneuro-2020-0043/revneuro-2020-0043.xml. [PMID: 32866134 DOI: 10.1515/revneuro-2020-0043] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/25/2020] [Indexed: 02/24/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long diagnostic period encountered in the early years of life. If diagnosed early, the negative effects of this disease can be reduced by starting special education early. Machine learning (ML), an increasingly ubiquitous technology, can be applied for the early diagnosis of ASD. The aim of this study is to examine and provide a comprehensive state-of-the-art review of ML research for the diagnosis of ASD based on (a) structural magnetic resonance image (MRI), (b) functional MRI and (c) hybrid imaging techniques over the past decade. The accuracy of the studies with a large number of participants is in general lower than those with fewer participants leading to the conclusion that further large-scale studies are needed. An examination of the age of the participants shows that the accuracy of the automated diagnosis of ASD is higher at a younger age range. ML technology is expected to contribute significantly to the early and rapid diagnosis of ASD in the coming years and become available to clinicians in the near future. This review is aimed to facilitate that.
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Affiliation(s)
- Hidir Selcuk Nogay
- Department of Electrical and Energy, Kayseri University, Kayseri, Turkey
- The Ohio State University, Mathematical Bioscience Institute, Columbus, OH, USA
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, The Ohio State University, Columbus, US
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41
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Vargason T, Grivas G, Hollowood-Jones KL, Hahn J. Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements. Semin Pediatr Neurol 2020; 34:100803. [PMID: 32446437 PMCID: PMC7248126 DOI: 10.1016/j.spen.2020.100803] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.
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Affiliation(s)
- Troy Vargason
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Genevieve Grivas
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Kathryn L Hollowood-Jones
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Juergen Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY; Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY.
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42
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Girault JB, Piven J. The Neurodevelopment of Autism from Infancy Through Toddlerhood. Neuroimaging Clin N Am 2019; 30:97-114. [PMID: 31759576 DOI: 10.1016/j.nic.2019.09.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Autism spectrum disorder (ASD) emerges during early childhood and is marked by a relatively narrow window in which infants transition from exhibiting normative behavioral profiles to displaying the defining features of the ASD phenotype in toddlerhood. Prospective brain imaging studies in infants at high familial risk for autism have revealed important insights into the neurobiology and developmental unfolding of ASD. In this article, we review neuroimaging studies of brain development in ASD from birth through toddlerhood, relate these findings to candidate neurobiological mechanisms, and discuss implications for future research and translation to clinical practice.
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Affiliation(s)
- Jessica B Girault
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill School of Medicine, 101 Renee Lynne Court, Chapel Hill, NC 27599, USA.
| | - Joseph Piven
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill School of Medicine, 101 Renee Lynne Court, Chapel Hill, NC 27599, USA
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43
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Wang M, Liu X, Hou Y, Zhang H, Kang J, Wang F, Zhao Y, Chen J, Liu X, Wang Y, Wu S. Decrease of GSK-3β Activity in the Anterior Cingulate Cortex of Shank3b -/- Mice Contributes to Synaptic and Social Deficiency. Front Cell Neurosci 2019; 13:447. [PMID: 31749684 PMCID: PMC6843030 DOI: 10.3389/fncel.2019.00447] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 09/18/2019] [Indexed: 12/22/2022] Open
Abstract
Social deficiency is one of the core syndromes of autism spectrum disorders (ASD), for which the underlying developmental mechanism still remains elusive. Anterior cingulate cortex (ACC) plays a key role in integrating social information and regulating social behavior. Recent studies have indicated that synaptic dysfunction in ACC is essential for ASD social defects. In the present study, we investigated the development of synapses and the roles of glycogen synthase kinase 3β (GSK-3β), which mediates multiple synaptic signaling pathways in ACC by using Shank3b−/− mice (a widely used ASD mouse model). Our data revealed that Shank3b mutation abolished the social induced c-Fos expression in ACC. From 4 weeks post-birth, neurons in Shank3b−/− ACC exhibited an obvious decrease in spine density and stubby spines. The length and thickness of post-synaptic density (PSD), the expression of vesicular glutamate transporter 2 (vGlut2) and glutamate receptor 2 (GluR2), and the frequency of miniature excitatory post-synaptic currents (mEPSCs) were significantly reduced in Shank3b−/−ACC. Interestingly, the levels of phosphorylated GSK-3β (Ser9), which inhibits the activity of GSK-3β, decreased along the same time course as the levels of GluR2 increased in ACC during development. Shank3b mutation leads to a dramatic increase of pGSK-3β (Ser9), and decrease of pPSD95 (a substrate of GSK-3β) and GluR2. Local delivery of AAV expressing constitutively active GSK-3β restored the expression of GluR2, increased the spine density and the number of mature spines. More importantly, active GSK-3β significantly promoted the social activity of Shank3b−/− mice. These data, in together, indicate that decrease of GSK-3β activity in ACC may contribute to the synaptic and social defects of Shank3b−/− mice. Enhancing GSK-3β activity may be utilized to treat ASD in the future.
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Affiliation(s)
- Mengmeng Wang
- Department of Neurobiology, Institute of Neurosciences, School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Xinyan Liu
- Department of Neurobiology, Institute of Neurosciences, School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Yilin Hou
- Department of Neurobiology, Institute of Neurosciences, School of Basic Medicine, Fourth Military Medical University, Xi'an, China.,Department of Military Psychology, Fourth Military Medical University, Xi'an, China
| | - Haifeng Zhang
- Department of Neurobiology, Institute of Neurosciences, School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Junjun Kang
- Department of Neurobiology, Institute of Neurosciences, School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Fei Wang
- Department of Neurobiology, Institute of Neurosciences, School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Youyi Zhao
- Department of Neurobiology, Institute of Neurosciences, School of Basic Medicine, Fourth Military Medical University, Xi'an, China.,State Key Laboratory of Military Stomatology and National Clinical Research Center for Oral Diseases and Shaanxi Engineering Research, Department of Anethesiology, Center for Dental Materials and Advanced Manufacture, School of Stomatology, Fourth Military Medical University, Xi'an, China
| | - Jing Chen
- Department of Anatomy, School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Xufeng Liu
- Department of Military Psychology, Fourth Military Medical University, Xi'an, China
| | - Yazhou Wang
- Department of Neurobiology, Institute of Neurosciences, School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Shengxi Wu
- Department of Neurobiology, Institute of Neurosciences, School of Basic Medicine, Fourth Military Medical University, Xi'an, China
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