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Yang H, Vu T, Long Q, Calhoun V, Adali T. Identification of Homogeneous Subgroups from Resting-State fMRI Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23063264. [PMID: 36991975 PMCID: PMC10051904 DOI: 10.3390/s23063264] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/04/2023] [Accepted: 03/14/2023] [Indexed: 06/12/2023]
Abstract
The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.
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Affiliation(s)
- Hanlu Yang
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Trung Vu
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Qunfang Long
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
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2
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Identification of subgroups of children in the Australian Autism Biobank using latent class analysis. Child Adolesc Psychiatry Ment Health 2023; 17:27. [PMID: 36805686 PMCID: PMC9940381 DOI: 10.1186/s13034-023-00565-3] [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] [Received: 01/22/2023] [Accepted: 01/26/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND The identification of reproducible subtypes within autistic populations is a priority research area in the context of neurodevelopment, to pave the way for identification of biomarkers and targeted treatment recommendations. Few previous studies have considered medical comorbidity alongside behavioural, cognitive, and psychiatric data in subgrouping analyses. This study sought to determine whether differing behavioural, cognitive, medical, and psychiatric profiles could be used to distinguish subgroups of children on the autism spectrum in the Australian Autism Biobank (AAB). METHODS Latent profile analysis was used to identify subgroups of children on the autism spectrum within the AAB (n = 1151), utilising data on social communication profiles and restricted, repetitive, and stereotyped behaviours (RRBs), in addition to their cognitive, medical, and psychiatric profiles. RESULTS Our study identified four subgroups of children on the autism spectrum with differing profiles of autism traits and associated comorbidities. Two subgroups had more severe clinical and cognitive phenotype, suggesting higher support needs. For the 'Higher Support Needs with Prominent Language and Cognitive Challenges' subgroup, social communication, language and cognitive challenges were prominent, with prominent sensory seeking behaviours. The 'Higher Support Needs with Prominent Medical and Psychiatric and Comorbidity' subgroup had the highest mean scores of challenges relating to social communication and RRBs, with the highest probability of medical and psychiatric comorbidity, and cognitive scores similar to the overall group mean. Individuals within the 'Moderate Support Needs with Emotional Challenges' subgroup, had moderate mean scores of core traits of autism, and the highest probability of depression and/or suicidality. A fourth subgroup contained individuals with fewer challenges across domains (the 'Fewer Support Needs Group'). LIMITATIONS Data utilised to identify subgroups within this study was cross-sectional as longitudinal data was not available. CONCLUSIONS Our findings support the holistic appraisal of support needs for children on the autism spectrum, with assessment of the impact of co-occurring medical and psychiatric conditions in addition to core autism traits, adaptive functioning, and cognitive functioning. Replication of our analysis in other cohorts of children on the autism spectrum is warranted, to assess whether the subgroup structure we identified is applicable in a broader context beyond our specific dataset.
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3
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Slater K, Williams JA, Schofield PN, Russell S, Pendleton SC, Karwath A, Fanning H, Ball S, Hoehndorf R, Gkoutos GV. Klarigi: Characteristic explanations for semantic biomedical data. Comput Biol Med 2023; 153:106425. [PMID: 36638616 DOI: 10.1016/j.compbiomed.2022.106425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 12/04/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Abstract
Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such as biosample gene expression profiles or patient phenotypes, and is useful for a range of tasks including differential diagnosis and causative variant prioritisation. These approaches, however, usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they are not designed to solve the problem of deriving cohesive, characteristic, and discriminatory sets of classes for entity groups. We have developed a new tool, called Klarigi, which introduces multiple scoring heuristics for identification of classes that are both compositional and discriminatory for groups of entities annotated with ontology classes. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets, in addition to significance testing apposite to traditional enrichment approaches. We describe the design and implementation of Klarigi, including its scoring and explanation determination methods, and evaluate its use in application to two test cases with clinical significance, comparing and contrasting methods and results with literature-based and enrichment analysis methods. We demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. We conclude that Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data.
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Affiliation(s)
- Karin Slater
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; MRC Health Data Research UK (HDR UK), Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK.
| | - John A Williams
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Paul N Schofield
- Department of Physiology, Development, and Neuroscience, University of Cambridge, UK
| | - Sophie Russell
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK
| | - Samantha C Pendleton
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK
| | - Andreas Karwath
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; MRC Health Data Research UK (HDR UK), Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Hilary Fanning
- Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Simon Ball
- Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, UK
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; NIHR Experimental Cancer Medicine Centre, UK; NIHR Surgical Reconstruction and Microbiology Research Centre, UK; NIHR Biomedical Research Centre, UK; MRC Health Data Research UK (HDR UK), Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
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4
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Torres A, Montiel-Nava C. Clinical and demographic differences by sex in autistic Venezuelan children: A cross-sectional study. RESEARCH IN DEVELOPMENTAL DISABILITIES 2022; 128:104276. [PMID: 35728436 DOI: 10.1016/j.ridd.2022.104276] [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: 11/09/2020] [Revised: 05/31/2022] [Accepted: 06/05/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Sex differences in symptom severity and adaptive function in children with ASD have been historically inconsistent and studies are predominantly from American- and European-residing populations. Alike, there is limited information on the complex interplay between sex, intelligence, adaptive function, and autism symptom severity; this is crucial to identify given their predictive value for health outcomes in autism AIM: This study aimed to identify sex differences in autism symptom severity and adaptive function in a sample of Venezuelan children. METHOD One-hundred-and-three Venezuelan children ages 3-7 completed a comprehensive assessment for symptom severity, adaptive functioning, and intelligence. RESULTS Sex differences were not present in any autism diagnostic domain or adaptive function.Symptom severity was not a significant predictor for adaptive function, which contrasts with studies sampling American children. CONCLUSION This study corroborates other findings based on non-American children, where symptom severity was not a function of adaptive function. Awareness of the interplay of culture, sex-related standards, and autism symptomatology will result in better identification and diagnosis of autism regardless of sex or cultural background. What this paper adds? This paper aids the current literature on sex difference on both autism symptom severity and adaptive function. It also provides a snapshot of the relationship between symptom severity, adaptive function, and other psychological variables that influence the outcome of children with ASD.
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Affiliation(s)
- Andy Torres
- The University of Texas Rio Grande Valley, Department of Psychological Science, 1201W University Dr, Edinburg, TX 78539, USA.
| | - Cecilia Montiel-Nava
- The University of Texas Rio Grande Valley, Department of Psychological Science, 1201W University Dr, Edinburg, TX 78539, USA.
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5
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Applications of Unsupervised Machine Learning in Autism Spectrum Disorder Research: a Review. REVIEW JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS 2022. [DOI: 10.1007/s40489-021-00299-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
AbstractLarge amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of unsupervised machine learning in ASD research and provide insight into the types of questions being answered with these methods.
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6
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Artificial Intelligence for Autism Spectrum Disorders. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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7
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Slater K, Williams JA, Karwath A, Fanning H, Ball S, Schofield PN, Hoehndorf R, Gkoutos GV. Multi-faceted semantic clustering with text-derived phenotypes. Comput Biol Med 2021; 138:104904. [PMID: 34600327 PMCID: PMC8573608 DOI: 10.1016/j.compbiomed.2021.104904] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 02/03/2023]
Abstract
Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients or drugs. Constructing patient phenotype profiles using formal ontologies enables their analysis via semantic similarity, in turn enabling the use of background knowledge in clustering or classification analyses. However, traditional semantic similarity approaches collapse complex relationships between patient phenotypes into a unitary similarity scores for each pair of patients. Moreover, single scores may be based only on matching terms with the greatest information content (IC), ignoring other dimensions of patient similarity. This process necessarily leads to a loss of information in the resulting representation of patient similarity, and is especially apparent when using very large text-derived and highly multi-morbid phenotype profiles. Moreover, it renders finding a biological explanation for similarity very difficult; the black box problem. In this article, we explore the generation of multiple semantic similarity scores for patients based on different facets of their phenotypic manifestation, which we define through different sub-graphs in the Human Phenotype Ontology. We further present a new methodology for deriving sets of qualitative class descriptions for groups of entities described by ontology terms. Leveraging this strategy to obtain meaningful explanations for our semantic clusters alongside other evaluation techniques, we show that semantic clustering with ontology-derived facets enables the representation, and thus identification of, clinically relevant phenotype relationships not easily recoverable using overall clustering alone. In this way, we demonstrate the potential of faceted semantic clustering for gaining a deeper and more nuanced understanding of text-derived patient phenotypes.
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Affiliation(s)
- Karin Slater
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; MRC Health Data Research UK (HDR UK) Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK.
| | - John A Williams
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Andreas Karwath
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; MRC Health Data Research UK (HDR UK) Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Hilary Fanning
- Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Simon Ball
- Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
| | - Paul N Schofield
- Dept of Physiology, Development, and Neuroscience, University of Cambridge, UK
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Saudi Arabia
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; NIHR Experimental Cancer Medicine Centre, UK; NIHR Surgical Reconstruction and Microbiology Research Centre, UK; NIHR Biomedical Research Centre, UK; MRC Health Data Research UK (HDR UK) Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
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8
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Shu C, Green Snyder L, Shen Y, Chung WK. Imputing cognitive impairment in SPARK, a large autism cohort. Autism Res 2021; 15:156-170. [PMID: 34636158 DOI: 10.1002/aur.2622] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 08/26/2021] [Accepted: 09/24/2021] [Indexed: 11/10/2022]
Abstract
Diverse large cohorts are necessary for dissecting subtypes of autism, and intellectual disability is one of the most robust endophenotypes for analysis. However, current cognitive assessment methods are not feasible at scale. We developed five commonly used machine learning models to predict cognitive impairment (FSIQ<80 and FSIQ<70) and FSIQ scores among 521 children with autism using parent-reported online surveys in SPARK, and evaluated them in an independent set (n = 1346) with a missing data rate up to 70%. We assessed accuracy, sensitivity, and specificity by comparing predicted cognitive levels against clinical IQ data. The elastic-net model has good performance (AUC = 0.876, sensitivity = 0.772, specificity = 0.803) using 129 predictive features to impute cognitive impairment (FSIQ<80). Top-ranked predictive features included parent-reported language and cognitive levels, age at autism diagnosis, and history of services. Prediction of FSIQ<70 and FSIQ scores also showed good performance. We show cognitive levels can be imputed with high accuracy for children with autism, using commonly collected parent-reported data and standardized surveys. The current model offers a method for large-scale autism studies seeking estimates of cognitive ability when standardized psychometric testing is not feasible. LAY SUMMARY: Children with autism who have more severe learning challenges or cognitive impairment have different needs that are important to consider in research studies. When children in our study were missing standardized cognitive testing scores, we were able to use machine learning with other information to correctly "guess" when they have cognitive impairment about 80% of the time. We can use this information in research in the future to develop more appropriate treatments for children with autism and cognitive impairment.
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Affiliation(s)
- Chang Shu
- Department of Pediatrics, Columbia University Irving Medical Center, New York, New York, USA.,Department of Systems Biology, Columbia University Irving Medical Center, New York, New York, USA
| | - LeeAnne Green Snyder
- Simons Foundation Autism Research Initiative, Simons Foundation, New York, New York, USA
| | - Yufeng Shen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York, USA.,Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
| | - Wendy K Chung
- Department of Pediatrics, Columbia University Irving Medical Center, New York, New York, USA.,Simons Foundation Autism Research Initiative, Simons Foundation, New York, New York, USA.,Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
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9
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Waddington F, Franke B, Hartman C, Buitelaar JK, Rommelse N, Mota NR. A polygenic risk score analysis of ASD and ADHD across emotion recognition subtypes. Am J Med Genet B Neuropsychiatr Genet 2021; 186:401-411. [PMID: 32815639 PMCID: PMC9290011 DOI: 10.1002/ajmg.b.32818] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 04/06/2020] [Accepted: 07/29/2020] [Indexed: 11/17/2022]
Abstract
This study investigated the genetic components of ADHD and ASD by examining the cross-disorder trait of emotion recognition problems. The genetic burden for ADHD and ASD on previously identified emotion recognition factors (speed and accuracy of visual and auditory emotion recognition) and classes (Class 1: Average visual, impulsive auditory; Class 2: Average-strong visual & auditory; Class 3: Impulsive & imprecise visual, average auditory; Class 4: Weak visual & auditory) was assessed using ASD and ADHD polygenic risk scores (PRS). Our sample contained 552 participants: 74 with ADHD, 85 with ASD, 60 with ASD + ADHD, 177 unaffected siblings of ADHD or ASD probands, and 156 controls. ADHD- and ASD-PRS, calculated from the latest ADHD and ASD GWAS meta-analyses, were analyzed across these emotion recognition factors and classes using linear mixed models. Unexpectedly, the analysis of emotion recognition factors showed higher ASD-PRS to be associated with faster visual emotion recognition. The categorical analysis of emotion recognition classes showed ASD-PRS to be reduced in Class 3 compared to the other classes (p value threshold [pT] = 1, p = .021). A dimensional analysis identified a high ADHD-PRS reduced the probability of being assigned to the Class 1 or Class 3 (pT = .05, p = .028 and p = .044, respectively). Though these nominally significant results did not pass FDR correction, they potentially indicate different indirect causative chains from genetics via emotion recognition to ADHD and ASD, which need to be verified in future research.
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Affiliation(s)
- Francesca Waddington
- Department of Human Genetics, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenNetherlands,Donders Institute for Brain, Cognition and Behaviour, Centre for Medical NeuroscienceRadboud UniversityNijmegenNetherlands
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenNetherlands,Donders Institute for Brain, Cognition and Behaviour, Centre for Medical NeuroscienceRadboud UniversityNijmegenNetherlands,Department of Psychiatry, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenNetherlands
| | - Catharina Hartman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center GroningenUniversity of GroningenGroningenNetherlands
| | - Jan K. Buitelaar
- Donders Institute for Brain, Cognition and Behaviour, Centre for Medical NeuroscienceRadboud UniversityNijmegenNetherlands,Karakter Child and Adolescent Psychiatry University CentreNijmegenNetherlands,Department of Cognitive NeuroscienceRadboud University Medical CenterNijmegenNetherlands
| | - Nanda Rommelse
- Donders Institute for Brain, Cognition and Behaviour, Centre for Medical NeuroscienceRadboud UniversityNijmegenNetherlands,Department of Psychiatry, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenNetherlands,Karakter Child and Adolescent Psychiatry University CentreNijmegenNetherlands
| | - Nina Roth Mota
- Department of Human Genetics, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenNetherlands,Donders Institute for Brain, Cognition and Behaviour, Centre for Medical NeuroscienceRadboud UniversityNijmegenNetherlands
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10
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Seo WS. An update on the cause and treatment of sleep disturbance in children and adolescents with autism spectrum disorder. Yeungnam Univ J Med 2021; 38:275-281. [PMID: 34510867 PMCID: PMC8688794 DOI: 10.12701/yujm.2021.01410] [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: 08/06/2021] [Accepted: 08/25/2021] [Indexed: 11/30/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by abnormalities in social communication/interaction and restrictive, repetitive patterns of behavior. ASD is a relatively common psychiatric disorder, with a prevalence of approximately 1.7% in children. Although many children and adolescents with ASD visit the hospital for medical help for emotional and behavioral problems such as mood instability and self-harming behavior, there are also many visits for sleep disturbances such as insomnia and sleep resistance. Sleep disturbances are likely to increase fatigue and daytime sleepiness, impaired concentration, negatively impact on daytime functioning, and pose challenges in controlling anger and aggressive behavior. Sleep disturbance in children and adolescents with ASD negatively affects the quality of life, nothing to say the quality of life of their families and school members. In this review, sleep disturbances that are common in children and adolescents with ASD and adolescents are presented. The developmental and behavioral impacts of sleep disturbances in ASD were also considered. Finally, non-pharmacological and pharmacological treatments for sleep disturbances in children and adolescents with ASD and adolescents are reviewed.
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Affiliation(s)
- Wan Seok Seo
- Department of Psychiatry, Yeungnam University College of Medicine, Daegu, Korea
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11
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McCracken JT, Anagnostou E, Arango C, Dawson G, Farchione T, Mantua V, McPartland J, Murphy D, Pandina G, Veenstra-VanderWeele J. Drug development for Autism Spectrum Disorder (ASD): Progress, challenges, and future directions. Eur Neuropsychopharmacol 2021; 48:3-31. [PMID: 34158222 PMCID: PMC10062405 DOI: 10.1016/j.euroneuro.2021.05.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 05/13/2021] [Accepted: 05/18/2021] [Indexed: 12/11/2022]
Abstract
In 2017, facing lack of progress and failures encountered in targeted drug development for Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders, the ISCTM with the ECNP created the ASD Working Group charged to identify barriers to progress and recommending research strategies for the field to gain traction. Working Group international academic, regulatory and industry representatives held multiple in-person meetings, teleconferences, and subgroup communications to gather a wide range of perspectives on lessons learned from extant studies, current challenges, and paths for fundamental advances in ASD therapeutics. This overview delineates the barriers identified, and outlines major goals for next generation biomedical intervention development in ASD. Current challenges for ASD research are many: heterogeneity, lack of validated biomarkers, need for improved endpoints, prioritizing molecular targets, comorbidities, and more. The Working Group emphasized cautious but unwavering optimism for therapeutic progress for ASD core features given advances in the basic neuroscience of ASD and related disorders. Leveraging genetic data, intermediate phenotypes, digital phenotyping, big database discovery, refined endpoints, and earlier intervention, the prospects for breakthrough treatments are substantial. Recommendations include new priorities for expanded research funding to overcome challenges in translational clinical ASD therapeutic research.
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Affiliation(s)
- James T McCracken
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, United States.
| | | | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Univesitario Gregorio Maranon, and School of Medicine, Universidad Complutense de Madrid, CIBERSAM, Madrid, Spain
| | - Geraldine Dawson
- Duke University Medical Center, Durham, North Carolina, United States
| | - Tiffany Farchione
- Food and Drug Administration, Silver Spring, Maryland, United States
| | - Valentina Mantua
- Food and Drug Administration, Silver Spring, Maryland, United States
| | | | - Declan Murphy
- Institute of Psychiatry, Psychology and Neuroscience, King's College De Crespigny Park, Denmark Hill, London SE5 8AF, United Kingdom
| | - Gahan Pandina
- Neuroscience Therapeutic Area, Janssen Research & Development, Pennington, New Jersey, United States
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12
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Gardner-Hoag J, Novack M, Parlett-Pelleriti C, Stevens E, Dixon D, Linstead E. Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study. JMIR Med Inform 2021; 9:e27793. [PMID: 34076577 PMCID: PMC8209527 DOI: 10.2196/27793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 04/23/2021] [Accepted: 04/29/2021] [Indexed: 11/13/2022] Open
Abstract
Background Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking. Objective The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups. Methods Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed. Participants were clustered based on 8 observed challenging behaviors using k means, and multiple linear regression was performed to test interactions between skill mastery and treatment hours, cluster assignment, and gender. Results Seven clusters were identified, which demonstrated a single dominant challenging behavior. For some clusters, significant differences in treatment response were found. Specifically, a cluster characterized by low levels of stereotypy was found to have significantly higher levels of skill mastery than clusters characterized by self-injurious behavior and aggression (P<.003). Conclusions These findings have implications on the treatment of individuals with autism spectrum disorder. Self-injurious behavior and aggression were prevalent among participants with the worst treatment response, thus interventions targeting these challenging behaviors may be worth prioritizing. Furthermore, the use of unsupervised machine learning models to identify types of autism spectrum disorder shows promise.
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Affiliation(s)
- Julie Gardner-Hoag
- Schmid College of Science and Technology, Chapman University, Orange, CA, United States
| | - Marlena Novack
- Center for Autism and Related Disorders, Woodland Hills, CA, United States
| | | | - Elizabeth Stevens
- Fowler School of Engineering, Chapman University, Orange, CA, United States
| | - Dennis Dixon
- Center for Autism and Related Disorders, Woodland Hills, CA, United States
| | - Erik Linstead
- Fowler School of Engineering, Chapman University, Orange, CA, United States
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13
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Agelink van Rentergem JA, Deserno MK, Geurts HM. Validation strategies for subtypes in psychiatry: A systematic review of research on autism spectrum disorder. Clin Psychol Rev 2021; 87:102033. [PMID: 33962352 DOI: 10.1016/j.cpr.2021.102033] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 02/14/2021] [Accepted: 04/14/2021] [Indexed: 12/11/2022]
Abstract
Heterogeneity within autism spectrum disorder (ASD) is recognized as a challenge to both biological and psychological research, as well as clinical practice. To reduce unexplained heterogeneity, subtyping techniques are often used to establish more homogeneous subtypes based on metrics of similarity and dissimilarity between people. We review the ASD literature to create a systematic overview of the subtyping procedures and subtype validation techniques that are used in this field. We conducted a systematic review of 156 articles (2001-June 2020) that subtyped participants (range N of studies = 17-20,658), of which some or all had an ASD diagnosis. We found a large diversity in (parametric and non-parametric) methods and (biological, psychological, demographic) variables used to establish subtypes. The majority of studies validated their subtype results using variables that were measured concurrently, but were not included in the subtyping procedure. Other investigations into subtypes' validity were rarer. In order to advance clinical research and the theoretical and clinical usefulness of identified subtypes, we propose a structured approach and present the SUbtyping VAlidation Checklist (SUVAC), a checklist for validating subtyping results.
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Affiliation(s)
- Joost A Agelink van Rentergem
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Dutch Autism & ADHD Research Center, the Netherlands.
| | - Marie K Deserno
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Dutch Autism & ADHD Research Center, the Netherlands
| | - Hilde M Geurts
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Dutch Autism & ADHD Research Center, the Netherlands; Dr. Leo Kannerhuis, the Netherlands
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14
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Lin PI, Moni MA, Gau SSF, Eapen V. Identifying Subgroups of Patients With Autism by Gene Expression Profiles Using Machine Learning Algorithms. Front Psychiatry 2021; 12:637022. [PMID: 34054599 PMCID: PMC8149626 DOI: 10.3389/fpsyt.2021.637022] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 04/13/2021] [Indexed: 12/22/2022] Open
Abstract
Objectives: The identification of subgroups of autism spectrum disorder (ASD) may partially remedy the problems of clinical heterogeneity to facilitate the improvement of clinical management. The current study aims to use machine learning algorithms to analyze microarray data to identify clusters with relatively homogeneous clinical features. Methods: The whole-genome gene expression microarray data were used to predict communication quotient (SCQ) scores against all probes to select differential expression regions (DERs). Gene set enrichment analysis was performed for DERs with a fold-change >2 to identify hub pathways that play a role in the severity of social communication deficits inherent to ASD. We then used two machine learning methods, random forest classification (RF) and support vector machine (SVM), to identify two clusters using DERs. Finally, we evaluated how accurately the clusters predicted language impairment. Results: A total of 191 DERs were initially identified, and 54 of them with a fold-change >2 were selected for the pathway analysis. Cholesterol biosynthesis and metabolisms pathways appear to act as hubs that connect other trait-associated pathways to influence the severity of social communication deficits inherent to ASD. Both RF and SVM algorithms can yield a classification accuracy level >90% when all 191 DERs were analyzed. The ASD subtypes defined by the presence of language impairment, a strong indicator for prognosis, can be predicted by transcriptomic profiles associated with social communication deficits and cholesterol biosynthesis and metabolism. Conclusion: The results suggest that both RF and SVM are acceptable options for machine learning algorithms to identify AD subgroups characterized by clinical homogeneity related to prognosis.
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Affiliation(s)
- Ping-I Lin
- School of Psychiatry, The University of New South Wales, Sydney, NSW, Australia.,South Western Sydney Local Health District, Liverpool, NSW, Australia
| | - Mohammad Ali Moni
- School of Psychiatry, The University of New South Wales, Sydney, NSW, Australia
| | - Susan Shur-Fen Gau
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Valsamma Eapen
- School of Psychiatry, The University of New South Wales, Sydney, NSW, Australia.,South Western Sydney Local Health District, Liverpool, NSW, Australia
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15
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Artificial Intelligence for Autism Spectrum Disorders. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_249-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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16
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Karmakar A, Bhattacharya M, Adhya J, Chatterjee S, Dogra AK. The trend of association between autism traits in mothers and severity of autism symptomatology in children. ADVANCES IN AUTISM 2020. [DOI: 10.1108/aia-01-2020-0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
Autism spectrum disorders (ASD) are heterogeneous disorders, and heterogeneity lies both at genetic and phenotypic levels. To better understand the etiology and pathway that may contribute to autism symptomatology, it is important to study milder expressions of autism characteristics – autistic traits or milder expressions of autism phenotype, especially in intergenerational context. This study aims to see the trend of association, if any, between child autism symptom and mothers’ autism phenotype as well as mothers’ theory of mind and to see if mothers’ theory of mind was associated with their own autistic traits.
Design/methodology/approach
Data were collected from 96 mothers of children with varying symptom severity of autism (mild, moderate and severe) using Autism Spectrum Quotient and faux pas recognition test. Analysis of variance, trend analysis and t-test were done.
Findings
Results showed a linear trend of relationship between mothers’ autism phenotype and child symptom severity. However, the groups did not have significant differences in theory of mind. Only a few components of theory of mind were found to be associated with autistic traits. These findings question the prevailing idea that theory of mind can be a reliable endophenotype of autism.
Research limitations/implications
There has been a lack of research assessing the possible link between parents’ autism phenotype and symptom severity of ASD children. This study is a preliminary step towards that direction. This study indicates a probability of shared genetic liability between mothers and offspring, which would have important consequences for understanding the mechanisms that lead to autism.
Practical implications
This study offers implications for treatment planning of those with clinical ASD. An awareness of parental factors is critical for any holistic intervention plan when a family seeks treatment for their child. This study suggests that while individualising interventions, clinicians may consider possible presence of high levels of autistic traits and related cognitive features present in the probands’ parents.
Originality/value
There has been lack of research assessing the possible link between parents’ autism phenotype and symptom severity of ASD children. This study, even though preliminary, is a step towards that direction. This study suggests that autism traits might be influenced by common genetic variation and indicates a probability of shared genetic liability between mothers and offspring, which would have important consequences for understanding the mechanisms that lead to autism.
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17
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Nunes A, Trappenberg T, Alda M. The definition and measurement of heterogeneity. Transl Psychiatry 2020; 10:299. [PMID: 32839448 PMCID: PMC7445182 DOI: 10.1038/s41398-020-00986-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 07/21/2020] [Accepted: 08/10/2020] [Indexed: 12/31/2022] Open
Abstract
Heterogeneity is an important concept in psychiatric research and science more broadly. It negatively impacts effect size estimates under case-control paradigms, and it exposes important flaws in our existing categorical nosology. Yet, our field has no precise definition of heterogeneity proper. We tend to quantify heterogeneity by measuring associated correlates such as entropy or variance: practices which are akin to accepting the radius of a sphere as a measure of its volume. Under a definition of heterogeneity as the degree to which a system deviates from perfect conformity, this paper argues that its proper measure roughly corresponds to the size of a system's event/sample space, and has units known as numbers equivalent. We arrive at this conclusion through focused review of more than 100 years of (re)discoveries of indices by ecologists, economists, statistical physicists, and others. In parallel, we review psychiatric approaches for quantifying heterogeneity, including but not limited to studies of symptom heterogeneity, microbiome biodiversity, cluster-counting, and time-series analyses. We argue that using numbers equivalent heterogeneity measures could improve the interpretability and synthesis of psychiatric research on heterogeneity. However, significant limitations must be overcome for these measures-largely developed for economic and ecological research-to be useful in modern translational psychiatric science.
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Affiliation(s)
- Abraham Nunes
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Thomas Trappenberg
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada.
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18
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Kaczkurkin AN, Moore TM, Sotiras A, Xia CH, Shinohara RT, Satterthwaite TD. Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth. Biol Psychiatry 2020; 88:51-62. [PMID: 32087950 PMCID: PMC7305976 DOI: 10.1016/j.biopsych.2019.12.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 11/07/2019] [Accepted: 12/11/2019] [Indexed: 01/31/2023]
Abstract
Psychiatric disorders show high rates of comorbidity and nonspecificity of presenting clinical symptoms, while demonstrating substantial heterogeneity within diagnostic categories. Notably, many of these psychiatric disorders first manifest in youth. We review progress and next steps in efforts to parse heterogeneity in psychiatric symptoms in youths by identifying abnormalities within neural circuits. To address this fundamental challenge in psychiatry, a number of methods have been proposed. We provide an overview of these methods, broadly organized into dimensional versus categorical approaches and single-view versus multiview approaches. Dimensional approaches including factor analysis and canonical correlation analysis aim to capture dimensional associations between psychopathology and brain measures across a continuous spectrum from health to disease. In contrast, categorical approaches, such as clustering and community detection, aim to identify subtypes of individuals within a class of symptoms or brain features. We highlight several studies that apply these methods to samples of youths and discuss issues to consider when using these approaches. Finally, we end by highlighting avenues for future research.
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Affiliation(s)
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Cedric Huchuan Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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19
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Big data approaches to develop a comprehensive and accurate tool aimed at improving autism spectrum disorder diagnosis and subtype stratification. LIBRARY HI TECH 2020. [DOI: 10.1108/lht-08-2019-0175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeAutism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches.Design/methodology/approachMultiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data.FindingsBy utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool.Originality/valueThis study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.
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20
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Asif M, Martiniano HFMC, Marques AR, Santos JX, Vilela J, Rasga C, Oliveira G, Couto FM, Vicente AM. Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning. Transl Psychiatry 2020; 10:43. [PMID: 32066720 PMCID: PMC7026098 DOI: 10.1038/s41398-020-0721-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 12/27/2019] [Accepted: 01/08/2020] [Indexed: 12/05/2022] Open
Abstract
The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype makes molecular diagnosis and patient prognosis challenging tasks. To establish more precise genotype-phenotype correlations in ASD, we developed a novel machine-learning integrative approach, which seeks to delineate associations between patients' clinical profiles and disrupted biological processes, inferred from their copy number variants (CNVs) that span brain genes. Clustering analysis of the relevant clinical measures from 2446 ASD cases in the Autism Genome Project identified two distinct phenotypic subgroups. Patients in these clusters differed significantly in ADOS-defined severity, adaptive behavior profiles, intellectual ability, and verbal status, the latter contributing the most for cluster stability and cohesion. Functional enrichment analysis of brain genes disrupted by CNVs in these ASD cases identified 15 statistically significant biological processes, including cell adhesion, neural development, cognition, and polyubiquitination, in line with previous ASD findings. A Naive Bayes classifier, generated to predict the ASD phenotypic clusters from disrupted biological processes, achieved predictions with a high precision (0.82) but low recall (0.39), for a subset of patients with higher biological Information Content scores. This study shows that milder and more severe clinical presentations can have distinct underlying biological mechanisms. It further highlights how machine-learning approaches can reduce clinical heterogeneity by using multidimensional clinical measures, and establishes genotype-phenotype correlations in ASD. However, predictions are strongly dependent on patient's information content. Findings are therefore a first step toward the translation of genetic information into clinically useful applications, and emphasize the need for larger datasets with very complete clinical and biological information.
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Affiliation(s)
- Muhammad Asif
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, LASIGE, Universidade de Lisboa, Lisboa, Portugal
| | - Hugo F. M. C. Martiniano
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - Ana Rita Marques
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - João Xavier Santos
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - Joana Vilela
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - Celia Rasga
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - Guiomar Oliveira
- grid.28911.330000000106861985Unidade de Neurodesenvolvimento e Autismo (UNDA), Serviço do Centro de Desenvolvimento da Criança, Centro de Investigação e Formação Clínica, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal ,grid.8051.c0000 0000 9511 4342Faculty of Medicine, Institute for Biomedical Imaging and Life Sciences, Universidade de Coimbra, Coimbra, Portugal ,grid.8051.c0000 0000 9511 4342Faculty of Medicine, University Clinic of Pediatrics, University of Coimbra, Coimbra, Portugal
| | - Francisco M. Couto
- grid.9983.b0000 0001 2181 4263Faculdade de Ciências, LASIGE, Universidade de Lisboa, Lisboa, Portugal
| | - Astrid M. Vicente
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
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21
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Zheng S, Hume KA, Able H, Bishop SL, Boyd BA. Exploring Developmental and Behavioral Heterogeneity among Preschoolers with ASD: A Cluster Analysis on Principal Components. Autism Res 2020; 13:796-809. [DOI: 10.1002/aur.2263] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 11/17/2019] [Accepted: 12/23/2019] [Indexed: 12/11/2022]
Affiliation(s)
- Shuting Zheng
- STAR Center for ASD and NDDs, Department of PsychiatryUniversity of California San Francisco San Francisco California
| | - Kara A. Hume
- Frank Porter Graham Child Development Institute, University of North Carolina at Chapel Hill Chapel Hill North Carolina
| | - Harriet Able
- School of Education, University of North Carolina at Chapel Hill Chapel Hill North Carolina
| | - Somer L. Bishop
- STAR Center for ASD and NDDs, Department of PsychiatryUniversity of California San Francisco San Francisco California
| | - Brian A. Boyd
- Department of Applied Behavioral ScienceJuniper Gardens Children's Project, University of Kansas Kansas City Kansas
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22
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Brunklaus A, Leu C, Gramm M, Pérez-Palma E, Iqbal S, Lal D. Time to move beyond genetics towards biomedical data-driven translational genomic research in severe paediatric epilepsies. Eur J Paediatr Neurol 2020; 24:35-39. [PMID: 31924506 DOI: 10.1016/j.ejpn.2019.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 12/06/2019] [Indexed: 11/18/2022]
Abstract
By accumulating ever greater amounts of genomic data, scientists have identified >100 genes associated with Mendelian forms of epilepsy and neurodevelopmental disorders with seizures. For most of the identified genes a wide range of genetic variants have been identified and affected patients are clinically heterogeneous. It is not clear to which degree the clinical heterogeneity can be attributed to the disease causing variant alone. We need to improve our current understanding of biophysical effects of variants on protein function and the role of polygenic background in modifying the clinical representation. In addition, longitudinal clinical data need to be recorded using standardized methods and shared across research centers to build large virtual cohorts for each single gene disorder. Without large, comprehensive, longitudinal datasets, studying the interplay of environmental factors and genetic factors will be challenging. As a community, we must work together to set the foundation for biorepositories and the collection and sharing of 'big data' in order to allow genetic-phenotypic characterization of the epilepsies and to fully utilize the potential for drug discovery, and patient-specific tailored management.
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Affiliation(s)
- Andreas Brunklaus
- The Paediatric Neurosciences Research Group, Royal Hospital for Children, Glasgow, G51 4TF, UK; School of Medicine, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Costin Leu
- Genomic Medicine Institute, Lerner Research Institute Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Marie Gramm
- Cologne Center for Genomics (CCG), University of Cologne, 50931, Cologne, Germany
| | - Eduardo Pérez-Palma
- Genomic Medicine Institute, Lerner Research Institute Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Sumaiya Iqbal
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Dennis Lal
- Genomic Medicine Institute, Lerner Research Institute Cleveland Clinic, Cleveland, OH, 44195, USA; Cologne Center for Genomics (CCG), University of Cologne, 50931, Cologne, Germany; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
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23
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Pirooznia M, Niranjan T, Chen YC, Tunc I, Goes FS, Avramopoulos D, Potash JB, Huganir RL, Zandi PP, Wang T. Affected Sib-Pair Analyses Identify Signaling Networks Associated With Social Behavioral Deficits in Autism. Front Genet 2019; 10:1186. [PMID: 31827489 PMCID: PMC6892440 DOI: 10.3389/fgene.2019.01186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 10/25/2019] [Indexed: 11/29/2022] Open
Abstract
Autism spectrum disorders (ASDs) are characterized by deficits in three core behavioral domains: reciprocal social interactions, communication, and restricted interests and/or repetitive behaviors. Several hundreds of risk genes for autism have been identified, however, it remains a challenge to associate these genes with specific core behavioral deficits. In multiplex autism families, affected sibs often show significant differences in severity of individual core phenotypes. We hypothesize that a higher mutation burden contributes to a larger difference in the severity of specific core phenotypes between affected sibs. We tested this hypothesis on social behavioral deficits in autism. We sequenced synaptome genes (n = 1,886) in affected male sib-pairs (n = 274) in families from the Autism Genetics Research Exchange (AGRE) and identified rare (MAF ≤ 1%) and predicted functional variants. We selected affected sib-pairs with a large (≥10; n = 92 pairs) or a small (≤4; n = 108 pairs) difference in total cumulative Autism Diagnostic Interview-Revised (ADI-R) social scores (SOCT_CS). We compared burdens of unshared variants present only in sibs with severe social deficits and found a higher burden in SOCT_CS≥10 compared to SOCT_CS ≤ 4 (SOCT_CS≥10: 705.1 ± 16.2; SOCT_CS ≤ 4, 668.3 ± 9.0; p = 0.025). Unshared SOCT_CS≥10 genes only in sibs with severe social deficits are significantly enriched in the SFARI gene set. Network analyses of these genes using InWeb_IM, molecular signatures database (MSigDB), and GeNetMeta identified enrichment for phosphoinositide 3-kinase (PI3K)-AKT-mammalian target of rapamycin (mTOR) (Enrichment Score [eScore] p value = 3.36E−07; n = 8 genes) and Nerve growth factor (NGF) (eScore p value = 8.94E−07; n = 9 genes) networks. These studies support a key role for these signaling networks in social behavioral deficits and present a novel approach to associate risk genes and signaling networks with core behavioral domains in autism.
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Affiliation(s)
- Mehdi Pirooznia
- Bioinformatics and Computational Biology Core Facility, National Heart Lung and Blood Institute, NIH, Bethesda, MD, United States.,Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Tejasvi Niranjan
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Yun-Ching Chen
- Bioinformatics and Computational Biology Core Facility, National Heart Lung and Blood Institute, NIH, Bethesda, MD, United States
| | - Ilker Tunc
- Bioinformatics and Computational Biology Core Facility, National Heart Lung and Blood Institute, NIH, Bethesda, MD, United States
| | - Fernando S Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Dimitrios Avramopoulos
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - James B Potash
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Richard L Huganir
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Peter P Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Department of Mental Health and Epidemiology, Johns Hopkins University School of Public Health, Baltimore, MD, United States
| | - Tao Wang
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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24
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Berends D, Dissanayake C, Lawson LP. Differences in Cognition and Behaviour in Multiplex and Simplex Autism: Does Prior Experience Raising a Child with Autism Matter? J Autism Dev Disord 2019; 49:3401-3411. [PMID: 31102196 DOI: 10.1007/s10803-019-04052-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Previous research has found multiplex (MPX) children have an advantage in cognition compared to simplex (SPX) children. However, MPX parent's previous experience with older diagnosed siblings has not been considered. We used a large database sample to investigate the MPX advantage and contribution of birth order. Children from the Autism Genetic Resource Exchange (AGRE) were stratified into first- (MPX1, n = 152) and second-affected MPX (MPX2, n = 143), SPX (n = 111), and only-child SPX (SPXOC, n = 23) groups. Both MPX groups had higher cognitive scores compared to SPX groups, with no differences between MPX1 and MPX2 groups. No differences were found for autism symptoms or adaptive behaviour. These results suggest parent experience due to birth order is an unlikely contributor to the MPX cognitive advantage.
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Affiliation(s)
- Daniel Berends
- Olga Tennison Autism Research Centre, School of Psychology and Public Health, College of Science, Health and Engineering, La Trobe University, Plenty Road & Kingsbury Drive, Melbourne, VIC, 3086, Australia
| | - Cheryl Dissanayake
- Olga Tennison Autism Research Centre, School of Psychology and Public Health, College of Science, Health and Engineering, La Trobe University, Plenty Road & Kingsbury Drive, Melbourne, VIC, 3086, Australia
| | - Lauren P Lawson
- Olga Tennison Autism Research Centre, School of Psychology and Public Health, College of Science, Health and Engineering, La Trobe University, Plenty Road & Kingsbury Drive, Melbourne, VIC, 3086, Australia.
- Cooperative Research Centre for Living with Autism (Autism CRC), Brisbane, Australia.
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25
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Wolfers T, Floris DL, Dinga R, van Rooij D, Isakoglou C, Kia SM, Zabihi M, Llera A, Chowdanayaka R, Kumar VJ, Peng H, Laidi C, Batalle D, Dimitrova R, Charman T, Loth E, Lai MC, Jones E, Baumeister S, Moessnang C, Banaschewski T, Ecker C, Dumas G, O’Muircheartaigh J, Murphy D, Buitelaar JK, Marquand AF, Beckmann CF. From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder. Neurosci Biobehav Rev 2019; 104:240-254. [DOI: 10.1016/j.neubiorev.2019.07.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 07/10/2019] [Accepted: 07/15/2019] [Indexed: 11/17/2022]
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Huang X, Gong Q, Sweeney JA, Biswal BB. Progress in psychoradiology, the clinical application of psychiatric neuroimaging. Br J Radiol 2019; 92:20181000. [PMID: 31170803 PMCID: PMC6732936 DOI: 10.1259/bjr.20181000] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 05/09/2019] [Accepted: 05/21/2019] [Indexed: 02/05/2023] Open
Abstract
Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.
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Affiliation(s)
| | | | - John A. Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, USA
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Stevens E, Dixon DR, Novack MN, Granpeesheh D, Smith T, Linstead E. Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning. Int J Med Inform 2019; 129:29-36. [DOI: 10.1016/j.ijmedinf.2019.05.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2018] [Revised: 02/25/2019] [Accepted: 05/09/2019] [Indexed: 12/31/2022]
Affiliation(s)
- Elizabeth Stevens
- Chapman University, Schmid College of Science and Technology, Orange, CA, United States
| | - Dennis R Dixon
- Center for Autism and Related Disorders, Woodland Hills, CA, United States
| | - Marlena N Novack
- Center for Autism and Related Disorders, Woodland Hills, CA, United States
| | - Doreen Granpeesheh
- Center for Autism and Related Disorders, Woodland Hills, CA, United States
| | - Tristram Smith
- University of Rochester Medical Center, Rochester, NY, United States
| | - Erik Linstead
- Chapman University, Schmid College of Science and Technology, Orange, CA, United States.
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Rubenstein E, Wiggins LD, Schieve LA, Bradley C, DiGuiseppi C, Moody E, Pandey J, Pretzel RE, Howard AG, Olshan AF, Pence BW, Daniels J. Associations between parental broader autism phenotype and child autism spectrum disorder phenotype in the Study to Explore Early Development. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2019; 23:436-448. [PMID: 29376397 PMCID: PMC6027594 DOI: 10.1177/1362361317753563] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The autism spectrum disorder phenotype varies by social and communication ability and co-occurring developmental, behavioral, and medical conditions. Etiology is also diverse, with myriad potential genetic origins and environmental risk factors. Examining the influence of parental broader autism phenotype-a set of sub-clinical characteristics of autism spectrum disorder-on child autism spectrum disorder phenotypes may help reduce heterogeneity in potential genetic predisposition for autism spectrum disorder. We assessed the associations between parental broader autism phenotype and child phenotype among children of age 30-68 months enrolled in the Study to Explore Early Development (N = 707). Child autism spectrum disorder phenotype was defined by a replication of latent classes derived from multiple developmental and behavioral measures: Mild Language Delay with Cognitive Rigidity, Mild Language and Motor Delay with Dysregulation (e.g. anxiety/depression), General Developmental Delay, and Significant Developmental Delay with Repetitive Motor Behaviors. Scores on the Social Responsiveness Scale-Adult measured parent broader autism phenotype. Broader autism phenotype in at least one parent was associated with a child having increased odds of being classified as mild language and motor delay with dysregulation compared to significant developmental delay with repetitive motor behaviors (odds ratio: 2.44; 95% confidence interval: 1.16, 5.09). Children of parents with broader autism phenotype were more likely to have a phenotype qualitatively similar to broader autism phenotype presentation; this may have implications for etiologic research.
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Affiliation(s)
| | | | | | | | | | - Eric Moody
- University of Colorado-Anschutz Medical Campus, USA
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Syriopoulou- Delli CK, Papaefstathiou E. Review of cluster analysis of phenotypic data in Autism Spectrum Disorders: distinct subtypes or a severity gradient model? INTERNATIONAL JOURNAL OF DEVELOPMENTAL DISABILITIES 2019; 66:13-21. [PMID: 34141364 PMCID: PMC8115451 DOI: 10.1080/20473869.2018.1542561] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2018] [Revised: 09/09/2018] [Accepted: 10/26/2018] [Indexed: 06/12/2023]
Abstract
Background: Individuals with autism spectrum disorder (ASD) form a heterogeneous group, posing a challenge for clinical definition. Additional problems regarding the diverse clinical presentation arise from changes in diagnostic criteria according to the latest Diagnostic and Statistical Manual of Mental Disorders (DSM-5), with exclusion of individuals who met earlier criteria or inclusion of more than previously. Objectives: To investigate studies that have attempted to reduce the heterogeneity of ASD based on cluster analysis of phenotypic data and to clarify whether ASD should be interpreted as 'a unitary spectrum,' with a severity gradient, or defined by distinct subtypes. This will allow better understanding of the disorder with implications for its treatment and prognosis. Methods: A literature search was made through PubMed, Researchgate and Google Scholar for studies of ASD populations. In addition, reference lists from identified studies were reviewed. Results: Only 10 studies were found that dealt with the heterogeneity of ASD and its different subtypes, based on the review prerequisites. Most of the studies appear to support the existence of subtypes within ASD, but it remains unclear whether these are considered as different specific subtypes with characteristic profiles of symptoms or as a part of a severity gradient across symptom domains. Conclusions: Drawing definitive conclusions from the published studies about the nature of ASD is difficult, due to the fundamental methodological differences among the studies and their inconsistent findings. This review shed light on a number of discrepancies regarding the current classification of ASD. However, future research will be necessary to provide a more definite answer on the question of a definition based on separate diagnostic subtypes or on a severity gradient by including larger samples that are followed longitudinal and by applying better diagnostic system and choosing the appropriate variables.
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Affiliation(s)
| | - Elpis Papaefstathiou
- Department of Educational and Social Policy, University of Macedonia, Thessaloniki, Greece
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Rubenstein E, Schieve L, Wiggins L, Rice C, Van Naarden Braun K, Christensen D, Durkin M, Daniels J, Lee LC. Trends in documented co-occurring conditions in children with autism spectrum disorder, 2002-2010. RESEARCH IN DEVELOPMENTAL DISABILITIES 2018; 83:168-178. [PMID: 30227350 PMCID: PMC6741291 DOI: 10.1016/j.ridd.2018.08.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 08/28/2018] [Indexed: 06/01/2023]
Abstract
BACKGROUND Autism spectrumdisorder (ASD) commonly presents with co-occurring medical conditions (CoCs). Little is known about patterns in CoCs in a time of rising ASD prevalence. AIMS To describe trends in number and type of documented CoCs in 8-year-old children with ASD. METHODS We used Autism and Developmental Disabilities Monitoring Network (ADDM) data, a multi-source active surveillance system monitoring ASD prevalence among 8-year-old children across the US. Data from surveillance years 2002, 2006, 2008, and 2010 were used to describe trends in count, categories, and individual CoCs. RESULTS Mean number of CoCs increased from 0.94 CoCs in 2002 to 1.06 CoCs in 2010 (p < 0.001). The percentage of children with ASD with any CoC increased from 44.5% to 56.4% (p < 0.001). CoCs with the greatest increases were in general developmental disability (10.4% to 14.5%), language disorder (18.9% to 23.6%), and motor developmental disability (10.5% to 15.6%). Sex modified the relationship between developmental (P = 0.02) and psychiatric (P < 0.001) CoCs and surveillance year. Race/ethnicity modified the relationship between neurological conditions (P = 0.04) and surveillance year. CONCLUSIONS The increase in the percentage of children with ASD and CoCs may suggest the ASD phenotype has changed over time or clinicians are more likely to diagnose CoCs.
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Affiliation(s)
- Eric Rubenstein
- Department of Epidemiology, University of North Carolina at Chapel Hill, 116A South Merrit Mill, Chapel Hill, NC 27516, United States.
| | - Laura Schieve
- National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, 1600 Clifton Rd, MS E-86, Atlanta, GA 30333, United States
| | - Lisa Wiggins
- National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, 1600 Clifton Rd, MS E-86, Atlanta, GA 30333, United States
| | - Catherine Rice
- Department of Psychiatry, Emory University School of Medicine, 1551 Shoup Court, Atlanta, GA 30322, United States
| | - Kim Van Naarden Braun
- National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, 1600 Clifton Rd, MS E-86, Atlanta, GA 30333, United States
| | - Deborah Christensen
- National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, 1600 Clifton Rd, MS E-86, Atlanta, GA 30333, United States
| | - Maureen Durkin
- Department of Population Health Sciences, University of Wisconsin, 610 Walnut Street, Madison, WI 53726, United States
| | - Julie Daniels
- Department of Epidemiology, University of North Carolina at Chapel Hill, 116A South Merrit Mill, Chapel Hill, NC 27516, United States
| | - Li-Ching Lee
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, United States
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31
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Waddington F, Hartman C, de Bruijn Y, Lappenschaar M, Oerlemans A, Buitelaar J, Franke B, Rommelse N. An emotion recognition subtyping approach to studying the heterogeneity and comorbidity of autism spectrum disorders and attention-deficit/hyperactivity disorder. J Neurodev Disord 2018; 10:31. [PMID: 30442088 PMCID: PMC6238263 DOI: 10.1186/s11689-018-9249-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 10/31/2018] [Indexed: 11/10/2022] Open
Abstract
Background Emotion recognition dysfunction has been reported in both autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). This suggests that emotion recognition is a cross-disorder trait that may be utilised to understand the heterogeneous psychopathology of ASD and ADHD. We aimed to identify emotion recognition subtypes and to examine their relation with quantitative and diagnostic measures of ASD and ADHD to gain further insight into disorder comorbidity and heterogeneity. Methods Factor mixture modelling was used on speed and accuracy measures of auditory and visual emotion recognition tasks. These were administered to children and adolescents with ASD (N = 89), comorbid ASD + ADHD (N = 64), their unaffected siblings (N = 122), ADHD (N = 111), their unaffected siblings (N = 69), and controls (N = 220). Identified classes were compared on diagnostic and quantitative symptom measures. Results A four-class solution was revealed, with the following emotion recognition abilities: (1) average visual, impulsive auditory; (2) average-strong visual and auditory; (3) impulsive/imprecise visual, average auditory; (4) weak visual and auditory. The weakest performing class (4) contained the highest percentage of patients (66.07%) and the lowest percentage controls (10.09%), scoring the highest on ASD/ADHD measures. The best performing class (2) demonstrated the opposite: 48.98% patients, 15.26% controls with relatively low scores on ASD/ADHD measures. Conclusions Subgroups of youths can be identified that differ both in quantitative and qualitative aspects of emotion recognition abilities. Weak emotion recognition abilities across sensory domains are linked to an increased risk for ASD as well as ADHD, although emotion recognition impairments alone are neither necessary nor sufficient parts of these disorders. Electronic supplementary material The online version of this article (10.1186/s11689-018-9249-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Francesca Waddington
- Department of Human Genetics, Radboud University Medical Center Nijmegen, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands. .,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
| | - Catharina Hartman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Yvette de Bruijn
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.,Karakter Child and Adolescent Psychiatry University Centre, Reinier Postlaan 12, 6525 GC, Nijmegen, The Netherlands
| | - Martijn Lappenschaar
- Department of Geriatrics, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Anoek Oerlemans
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jan Buitelaar
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.,Karakter Child and Adolescent Psychiatry University Centre, Reinier Postlaan 12, 6525 GC, Nijmegen, The Netherlands.,Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center Nijmegen, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.,Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Nanda Rommelse
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands. .,Karakter Child and Adolescent Psychiatry University Centre, Reinier Postlaan 12, 6525 GC, Nijmegen, The Netherlands. .,Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands.
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32
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Braam W, Ehrhart F, Maas APHM, Smits MG, Curfs L. Low maternal melatonin level increases autism spectrum disorder risk in children. RESEARCH IN DEVELOPMENTAL DISABILITIES 2018; 82:79-89. [PMID: 29501372 DOI: 10.1016/j.ridd.2018.02.017] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 02/05/2018] [Accepted: 02/17/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND It is assumed that autism spectrum disorder (ASD) is caused by a combination of de novo inherited variation and common variation as well as environmental factors. It often co-occurs with intellectual disability (ID). Almost eight hundred potential causative genetic variations have been found in ASD patients. However, not one of them is responsible for more than 1% of ASD cases. Low melatonin levels are a frequent finding in ASD patients. Melatonin levels are negatively correlated with severity of autistic impairments, it is important for normal neurodevelopment and is highly effective in protecting DNA from oxidative damage. Melatonin deficiency could be a major factor, and well a common heritable variation, that increases the susceptibility to environmental risk factors for ASD. ASD is already present at birth. As the fetus does not produce melatonin, low maternal melatonin levels may be involved. METHODS We measured 6-sulfatoxymelatonin in urine of 60 mothers of a child with ASD and controls. RESULTS 6-sulfatoxymelatonin levels were significantly lower in mothers with an ASD child than in controls (p = 0.012). CONCLUSIONS Low parental melatonin levels could be one of the contributors to ASD and possibly ID etiology. Our findings need to be duplicated on a larger scale. If our hypothesis is correct, this could lead to policies to detect future parents who are at risk and to treatment strategies to ASD and intellectual disability risk.
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Affiliation(s)
- Wiebe Braam
- 's Heeren Loo, Department Advisium, Wekerom, The Netherlands; Governor Kremers Centre, Maastricht University Medical Centre, The Netherlands.
| | - Friederike Ehrhart
- Governor Kremers Centre, Maastricht University Medical Centre, The Netherlands; Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, The Netherlands
| | - Anneke P H M Maas
- Governor Kremers Centre, Maastricht University Medical Centre, The Netherlands; Department of Special Education, Radboud University, Nijmegen, The Netherlands
| | - Marcel G Smits
- Governor Kremers Centre, Maastricht University Medical Centre, The Netherlands; Multidisciplinary expert centre for sleep-wake disturbances and chronobiology, Gelderse Vallei Hospital, Ede, The Netherlands
| | - Leopold Curfs
- Governor Kremers Centre, Maastricht University Medical Centre, The Netherlands
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Matta J, Zhao J, Ercal G, Obafemi-Ajayi T. Applications of node-based resilience graph theoretic framework to clustering autism spectrum disorders phenotypes. APPLIED NETWORK SCIENCE 2018; 3:38. [PMID: 30839816 PMCID: PMC6214326 DOI: 10.1007/s41109-018-0093-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 08/08/2018] [Indexed: 06/09/2023]
Abstract
With the growing ubiquity of data in network form, clustering in the context of a network, represented as a graph, has become increasingly important. Clustering is a very useful data exploratory machine learning tool that allows us to make better sense of heterogeneous data by grouping data with similar attributes based on some criteria. This paper investigates the application of a novel graph theoretic clustering method, Node-Based Resilience clustering (NBR-Clust), to address the heterogeneity of Autism Spectrum Disorder (ASD) and identify meaningful subgroups. The hypothesis is that analysis of these subgroups would reveal relevant biomarkers that would provide a better understanding of ASD phenotypic heterogeneity useful for further ASD studies. We address appropriate graph constructions suited for representing the ASD phenotype data. The sample population is drawn from a very large rigorous dataset: Simons Simplex Collection (SSC). Analysis of the results performed using graph quality measures, internal cluster validation measures, and clinical analysis outcome demonstrate the potential usefulness of resilience measure clustering for biomedical datasets. We also conduct feature extraction analysis to characterize relevant biomarkers that delineate the resulting subgroups. The optimal results obtained favored predominantly a 5-cluster configuration.
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Affiliation(s)
- John Matta
- Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL USA
| | - Junya Zhao
- Department of Computer Science, Missouri State University, Springfield, MO USA
| | - Gunes Ercal
- Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL USA
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Grisanzio KA, Goldstein-Piekarski AN, Wang MY, Rashed Ahmed AP, Samara Z, Williams LM. Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders. JAMA Psychiatry 2018; 75:201-209. [PMID: 29197929 PMCID: PMC5838569 DOI: 10.1001/jamapsychiatry.2017.3951] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices. OBJECTIVE To propose and demonstrate 1 approach for identifying subtypes within a transdiagnostic sample. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study analyzed data from the Brain Research and Integrative Neuroscience Network Foundation Database that had been collected at the University of Sydney and University of Adelaide between 2006 and 2010 and replicated at Stanford University between 2013 and 2017. The study included 420 individuals with a primary diagnosis of major depressive disorder (n = 100), panic disorder (n = 53), posttraumatic stress disorder (n = 47), or no disorder (healthy control participants) (n = 220). Data were analyzed between October 2016 and October 2017. MAIN OUTCOMES AND MEASURES We followed a data-driven approach to achieve the primary study outcome of identifying transdiagnostic subtypes. First, machine learning with a hierarchical clustering algorithm was implemented to classify participants based on self-reported negative mood, anxiety, and stress symptoms. Second, the robustness and generalizability of the subtypes were tested in an independent sample. Third, we assessed whether symptom subtypes were expressed at behavioral and physiological levels of functioning. Fourth, we evaluated the clinically meaningful differences in functional capacity of the subtypes. Findings were interpreted relative to a complementary diagnostic frame of reference. RESULTS Four hundred twenty participants with a mean (SD) age of 39.8 (14.1) years were included in the final analysis; 256 (61.0%) were female. We identified 6 distinct subtypes characterized by tension (n=81; 19%), anxious arousal (n=55; 13%), general anxiety (n=38; 9%), anhedonia (n=29; 7%), melancholia (n=37; 9%), and normative mood (n=180; 43%), and these subtypes were replicated in an independent sample. Subtypes were expressed through differences in cognitive control (F5,383 = 5.13, P < .001, ηp2 = 0.063), working memory (F5,401 = 3.29, P = .006, ηp2 = 0.039), electroencephalography-recorded β power in a resting paradigm (F5,357 = 3.84, P = .002, ηp2 = 0.051), electroencephalography-recorded β power in an emotional paradigm (F5,365 = 3.56, P = .004, ηp2 = 0.047), social functional capacity (F5,414 = 21.33, P < .001, ηp2 = 0.205), and emotional resilience (F5,376 = 15.10, P < .001, ηp2 = 0.171). CONCLUSIONS AND RELEVANCE These findings offer a data-driven framework for identifying robust subtypes that signify specific, coherent, meaningful associations between symptoms, behavior, brain function, and observable real-world function, and that cut across DSM-IV-defined diagnoses of major depressive disorder, panic disorder, and posttraumatic stress disorder.
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Affiliation(s)
- Katherine A. Grisanzio
- Department of Psychiatry and Behavioral Sciences,
Stanford University, Stanford, California,Sierra-Pacific Mental Illness Research, Education, and
Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Andrea N. Goldstein-Piekarski
- Department of Psychiatry and Behavioral Sciences,
Stanford University, Stanford, California,Sierra-Pacific Mental Illness Research, Education, and
Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Michelle Yuyun Wang
- Brain Resource International Database, Brain Resource
Ltd, Woolloomooloo, Sydney, Australia
| | | | - Zoe Samara
- Department of Psychiatry and Behavioral Sciences,
Stanford University, Stanford, California,Sierra-Pacific Mental Illness Research, Education, and
Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Leanne M. Williams
- Department of Psychiatry and Behavioral Sciences,
Stanford University, Stanford, California,Sierra-Pacific Mental Illness Research, Education, and
Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
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Abstract
To reduce phenotypic heterogeneity of Autism spectrum disorders (ASD) and add to the current diagnostic discussion this study aimed at identifying clinically meaningful ASD subgroups. Cluster analyses were used to describe empirically derived groups based on the Autism Diagnostic Interview-revised (ADI-R) in a large sample of n = 463 individuals with ASD aged 3-21. Three clusters were observed. Most severely affected individuals regarding all core symptoms were allocated to cluster 2. Cluster 3 comprised moderate symptom severity of social communication impairments (SCI) and less stereotyped repetitive behavior (RRB). Minor SCI and relatively more RRB characterized cluster 1. This study offers support for both, a symptom profile, and a gradient model of ASD within the spectrum due to the sample included.
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36
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Souders MC, Zavodny S, Eriksen W, Sinko R, Connell J, Kerns C, Schaaf R, Pinto-Martin J. Sleep in Children with Autism Spectrum Disorder. Curr Psychiatry Rep 2017; 19:34. [PMID: 28502070 PMCID: PMC5846201 DOI: 10.1007/s11920-017-0782-x] [Citation(s) in RCA: 130] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The purposes of this paper are to provide an overview of the state of the science of sleep in children with autism spectrum disorder (ASD), present hypotheses for the high prevalence of insomnia in children with ASD, and present a practice pathway for promoting optimal sleep. Approximately two thirds of children with ASD have chronic insomnia, and to date, the strongest evidence on promoting sleep is for sleep education, environmental changes, behavioral interventions, and exogenous melatonin. The Sleep Committee of the Autism Treatment Network (ATN) developed a practice pathway, based on expert consensus, to capture best practices for screening, identification, and treatment for sleep problems in ASD in 2012. An exemplar case is presented to integrate key constructs of the practice pathway and address arousal and sensory dysregulation in a child with ASD and anxiety disorder. This paper concludes with next steps for dissemination of the practice pathway and future directions for research of sleep problems in ASD.
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Affiliation(s)
- Margaret C Souders
- University of Pennsylvania School of Nursing, 418 Curie Blvd, Philadelphia, PA, 19104, USA.
| | - Stefanie Zavodny
- University of Pennsylvania School of Nursing, 418 Curie Blvd, Philadelphia, PA, 19104, USA
| | - Whitney Eriksen
- University of Pennsylvania School of Nursing, 418 Curie Blvd, Philadelphia, PA, 19104, USA
| | - Rebecca Sinko
- Thomas Jefferson University, 130 S. 9th St, Philadelphia, PA, 19107, USA
| | - James Connell
- AJ Drexel Autism Institute, 3020 Market St #560, Philadelphia, PA, 19104, USA
| | - Connor Kerns
- AJ Drexel Autism Institute, 3020 Market St #560, Philadelphia, PA, 19104, USA
| | - Roseann Schaaf
- Thomas Jefferson University, 130 S. 9th St, Philadelphia, PA, 19107, USA
| | - Jennifer Pinto-Martin
- University of Pennsylvania School of Nursing, 418 Curie Blvd, Philadelphia, PA, 19104, USA
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Al-Jawahiri R, Milne E. Resources available for autism research in the big data era: a systematic review. PeerJ 2017; 5:e2880. [PMID: 28097074 PMCID: PMC5237363 DOI: 10.7717/peerj.2880] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Accepted: 12/07/2016] [Indexed: 12/31/2022] Open
Abstract
Recently, there has been a move encouraged by many stakeholders towards generating big, open data in many areas of research. One area where big, open data is particularly valuable is in research relating to complex heterogeneous disorders such as Autism Spectrum Disorder (ASD). The inconsistencies of findings and the great heterogeneity of ASD necessitate the use of big and open data to tackle important challenges such as understanding and defining the heterogeneity and potential subtypes of ASD. To this end, a number of initiatives have been established that aim to develop big and/or open data resources for autism research. In order to provide a useful data reference for autism researchers, a systematic search for ASD data resources was conducted using the Scopus database, the Google search engine, and the pages on 'recommended repositories' by key journals, and the findings were translated into a comprehensive list focused on ASD data. The aim of this review is to systematically search for all available ASD data resources providing the following data types: phenotypic, neuroimaging, human brain connectivity matrices, human brain statistical maps, biospecimens, and ASD participant recruitment. A total of 33 resources were found containing different types of data from varying numbers of participants. Description of the data available from each data resource, and links to each resource is provided. Moreover, key implications are addressed and underrepresented areas of data are identified.
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Affiliation(s)
- Reem Al-Jawahiri
- Department of Psychology, University of Sheffield , Sheffield , United Kingdom
| | - Elizabeth Milne
- Department of Psychology, University of Sheffield , Sheffield , United Kingdom
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Marquand AF, Wolfers T, Mennes M, Buitelaar J, Beckmann CF. Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:433-447. [PMID: 27642641 PMCID: PMC5013873 DOI: 10.1016/j.bpsc.2016.04.002] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 04/06/2016] [Accepted: 04/06/2016] [Indexed: 01/03/2023]
Abstract
Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels, including symptoms, disease course, and biological underpinnings. These form a substantial barrier to understanding disease mechanisms and developing effective, personalized treatments. In response, many studies have aimed to stratify psychiatric disorders, aiming to find more consistent subgroups on the basis of many types of data. Such approaches have received renewed interest after recent research initiatives, such as the National Institute of Mental Health Research Domain Criteria and the European Roadmap for Mental Health Research, both of which emphasize finding stratifications that are based on biological systems and that cut across current classifications. We first introduce the basic concepts for stratifying psychiatric disorders and then provide a methodologically oriented and critical review of the existing literature. This shows that the predominant clustering approach that aims to subdivide clinical populations into more coherent subgroups has made a useful contribution but is heavily dependent on the type of data used; it has produced many different ways to subgroup the disorders we review, but for most disorders it has not converged on a consistent set of subgroups. We highlight problems with current approaches that are not widely recognized and discuss the importance of validation to ensure that the derived subgroups index clinically relevant variation. Finally, we review emerging techniques-such as those that estimate normative models for mappings between biology and behavior-that provide new ways to parse the heterogeneity underlying psychiatric disorders and evaluate all methods to meeting the objectives of such as the National Institute of Mental Health Research Domain Criteria and Roadmap for Mental Health Research.
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Affiliation(s)
- Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Department of Neuroimaging (AFM), Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Maarten Mennes
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Jan Buitelaar
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Karakter Child and Adolescent Psychiatric University Centre, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (CFB), University of Oxford, London, United Kingdom
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Abstract
Abstract
ASD research is at an important crossroads. The ASD diagnosis is important for assigning a child to early behavioral intervention and explaining a child’s condition. But ASD research has not provided a diagnosis-specific medical treatment, or a consistent early predictor, or a unified life course. If the ASD diagnosis also lacks biological and construct validity, a shift away from studying ASD-defined samples would be warranted. Consequently, this paper reviews recent findings for the neurobiological validity of ASD, the construct validity of ASD diagnostic criteria, and the construct validity of ASD spectrum features. The findings reviewed indicate that the ASD diagnosis lacks biological and construct validity. The paper concludes with proposals for research going forward.
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Zhao Y, Castellanos FX. Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders--promises and limitations. J Child Psychol Psychiatry 2016; 57:421-39. [PMID: 26732133 PMCID: PMC4760897 DOI: 10.1111/jcpp.12503] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/17/2015] [Indexed: 12/22/2022]
Abstract
BACKGROUND Psychiatric science remains descriptive, with a categorical nosology intended to enhance interobserver reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles. FINDINGS A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain-behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality, and heterogeneity of neuropsychiatric data collected from multiple sources ('broad' data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits, and behaviors ('deep' data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders. CONCLUSIONS We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis.
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Affiliation(s)
- Yihong Zhao
- Department of Child and Adolescent Psychiatry, NYU Child Study Center at NYU Langone Medical Center, New York, NY 10016, USA
| | - F. Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Child Study Center at NYU Langone Medical Center, New York, NY 10016, USA,Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
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41
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Tao Y, Gao H, Ackerman B, Guo W, Saffen D, Shugart YY. Evidence for contribution of common genetic variants within chromosome 8p21.2-8p21.1 to restricted and repetitive behaviors in autism spectrum disorders. BMC Genomics 2016; 17:163. [PMID: 26931105 PMCID: PMC4774106 DOI: 10.1186/s12864-016-2475-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2015] [Accepted: 02/15/2016] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Restricted and Repetitive Behaviors (RRB), one of the core symptom categories for Autism Spectrum Disorders (ASD), comprises heterogeneous groups of behaviors. Previous research indicates that there are two or more factors (subcategories) within the RRB domain. In an effort to identify common variants associated with RRB, we have carried out a genome-wide association study (GWAS) using the Autism Genetic Resource Exchange (AGRE) dataset (n = 1,335, all ASD probands of European ancestry) for each identified RRB subcategory, while allowing for comparisons of associated single nucleotide polymorphisms (SNPs) with associated SNPs in the same set of probands analyzed using all the RRB subcategories as phenotypes in a multivariate linear mixed model. The top ranked SNPs were then explored in an independent dataset. RESULTS Using principal component analysis of item scores obtained from Autism Diagnostic Interview-Revised (ADI-R), two distinct subcategories within Restricted and Repetitive Behaviors were identified: Repetitive Sensory Motor (RSM) and Insistence on Sameness (IS). Quantitative RSM and IS scores were subsequently used as phenotypes in a GWAS using the AGRE ASD cohort. Although no associated SNPs with genome-wide significance (P < 5.0E-08) were detected when RSM or IS were analyzed independently, three SNPs approached genome-wide significance when RSM and IS were considered together using multivariate association analysis. These included the top IS-associated SNP, rs62503729 (P-value = 6.48E-08), which is located within chromosome 8p21.2-8p21.1, a locus previously linked to schizophrenia. Notably, all of the most significantly associated SNPs are located in close proximity to STMN4 and PTK2B, genes previously shown to function in neuron development. In addition, several of the top-ranked SNPs showed correlations with STMN4 mRNA expression in adult CEU (Caucasian and European descent) human prefrontal cortex. However, the association signals within chromosome 8p21.2-8p21.1 failed to replicate in an independent sample of 2,588 ASD probands; the insufficient sample size and between-study heterogeneity are possible explanations for the non-replication. CONCLUSIONS Our analysis indicates that RRB in ASD can be represented by two distinct subcategories: RSM and IS. Subsequent univariate and multivariate genome-wide association studies of these RRB subcategories enabled the detection of associated SNPs at 8p21.2-8p21.1. Although these results did not replicate in an independent ASD dataset, genomic features of this region and pathway analysis suggest that common variants in 8p21.2-8p21.1 may contribute to RRB, particularly IS. Together, these observations warrant future studies to elucidate the possible contributions of common variants in 8p21.2-8p21.1 to the etiology of RSM and IS in ASD.
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Affiliation(s)
- Yu Tao
- Department of Cellular and Genetic Medicine, School of Basic Medical Sciences, Fudan University, 130Dong'an Road, Shanghai, 200032, China.
| | - Hui Gao
- Department of Cellular and Genetic Medicine, School of Basic Medical Sciences, Fudan University, 130Dong'an Road, Shanghai, 200032, China.
| | - Benjamin Ackerman
- JohnsHopkins University, Baltimore, MD, USA. .,Unit on Statistical Genomics, Intramural Research Program, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA.
| | - Wei Guo
- Unit on Statistical Genomics, Intramural Research Program, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA.
| | - David Saffen
- Department of Cellular and Genetic Medicine, School of Basic Medical Sciences, Fudan University, 130Dong'an Road, Shanghai, 200032, China.
| | - Yin Yao Shugart
- Unit on Statistical Genomics, Intramural Research Program, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA.
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Baum SH, Stevenson RA, Wallace MT. Behavioral, perceptual, and neural alterations in sensory and multisensory function in autism spectrum disorder. Prog Neurobiol 2015; 134:140-60. [PMID: 26455789 PMCID: PMC4730891 DOI: 10.1016/j.pneurobio.2015.09.007] [Citation(s) in RCA: 222] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 08/21/2015] [Accepted: 09/05/2015] [Indexed: 01/24/2023]
Abstract
Although sensory processing challenges have been noted since the first clinical descriptions of autism, it has taken until the release of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in 2013 for sensory problems to be included as part of the core symptoms of autism spectrum disorder (ASD) in the diagnostic profile. Because sensory information forms the building blocks for higher-order social and cognitive functions, we argue that sensory processing is not only an additional piece of the puzzle, but rather a critical cornerstone for characterizing and understanding ASD. In this review we discuss what is currently known about sensory processing in ASD, how sensory function fits within contemporary models of ASD, and what is understood about the differences in the underlying neural processing of sensory and social communication observed between individuals with and without ASD. In addition to highlighting the sensory features associated with ASD, we also emphasize the importance of multisensory processing in building perceptual and cognitive representations, and how deficits in multisensory integration may also be a core characteristic of ASD.
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Affiliation(s)
- Sarah H Baum
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Ryan A Stevenson
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Mark T Wallace
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA; Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, USA; Department of Psychology, Vanderbilt University, Nashville, TN, USA; Department of Psychiatry, Vanderbilt University, Nashville, TN, USA.
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43
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Giarelli E, Reiff M. Mothers' appreciation of chromosomal microarray analysis for autism spectrum disorder. J SPEC PEDIATR NURS 2015; 20:244-58. [PMID: 26112659 DOI: 10.1111/jspn.12121] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2015] [Revised: 05/06/2015] [Accepted: 05/11/2015] [Indexed: 01/08/2023]
Abstract
PURPOSE The aim of this study was to examine mothers' experiences with chromosomal microarray analysis (CMA) for a child with autism spectrum disorder (ASD). DESIGN AND METHODS This is a descriptive qualitative study using thematic content analysis of in-depth interview with 48 mothers of children who had genetic testing for ASD. RESULTS The principal theme, "something is missing," included missing knowledge about genetics, information on use of the results, explanations of the relevance to the diagnosis, and relevance to life-long care. Two subordinate themes were (a) disappreciation of the helpfulness of scientific information to explain the diagnosis, and (b) returning to personal experience for interpretation. PRACTICE IMPLICATIONS The test "appreciated" in value when results could be linked to the phenotype.
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Affiliation(s)
- Ellen Giarelli
- College of Nursing and Health Professions, Doctoral Nursing Program, Drexel University
| | - Marian Reiff
- Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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44
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De Rubeis S, Buxbaum JD. Genetics and genomics of autism spectrum disorder: embracing complexity. Hum Mol Genet 2015; 24:R24-31. [PMID: 26188008 DOI: 10.1093/hmg/ddv273] [Citation(s) in RCA: 127] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Accepted: 07/09/2015] [Indexed: 01/19/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder (NDD) characterized by impairments in social communication and social interaction and the presence of repetitive behaviors and/or restricted interests. ASD has profound etiological and clinical heterogeneity, which has impeded the identification of risk factors and pathophysiological processes underlying the disorder. A constellation of (i) types of genetic variation, (ii) modes of inheritance and (iii) specific genomic loci and genes have all recently been implicated in ASD risk, and these findings are currently being extended with functional analyses in model organisms and genotype-phenotype correlation studies. The overlap of risk loci between ASD and other NDDs raises intriguing questions around the mechanisms of risk. In this review, we will touch upon these aspects of ASD and how they might be addressed.
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Affiliation(s)
- Silvia De Rubeis
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
| | - Joseph D Buxbaum
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA Department of Neuroscience, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA and The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA
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45
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Aldinger KA, Lane CJ, Veenstra-VanderWeele J, Levitt P. Patterns of Risk for Multiple Co-Occurring Medical Conditions Replicate Across Distinct Cohorts of Children with Autism Spectrum Disorder. Autism Res 2015; 8:771-81. [PMID: 26011086 PMCID: PMC4736680 DOI: 10.1002/aur.1492] [Citation(s) in RCA: 79] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Accepted: 03/23/2015] [Indexed: 12/20/2022]
Abstract
Children with autism spectrum disorder (ASD) may present with multiple medical conditions in addition to ASD symptoms. This study investigated whether there are predictive patterns of medical conditions that co-occur with ASD, which could inform medical evaluation and treatment in ASD, as well as potentially identify etiologically meaningful subgroups. Medical history data were queried in the multiplex family Autism Genetic Resource Exchange (AGRE). Fourteen medical conditions were analyzed. Replication in the Simons Simplex Collection (SSC) was attempted using available medical condition data on gastrointestinal disturbances (GID), sleep problems, allergy and epilepsy. In the AGRE cohort, no discrete clusters emerged among 14 medical conditions. GID and seizures were enriched in unaffected family members, and together with sleep problems, were represented in both AGRE and SSC. Further analysis of these medical conditions identified predictive co-occurring patterns in both samples. For a child with ASD, the presence of GID predicts sleep problems and vice versa, with an approximately 2-fold odds ratio in each direction. These risk patterns were replicated in the SSC sample, and in addition, there was increased risk for seizures and sleep problems to co-occur with GID. In these cohorts, seizure alone was not predictive of the other conditions co-occurring, but behavioral impairments were more severe as the number of co-occurring medical symptoms increased. These findings indicate that interdisciplinary clinical care for children with ASD will benefit from evaluation for specific patterns of medical conditions in the affected child and their family members.
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Affiliation(s)
- Kimberly A Aldinger
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington.,Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Christianne J Lane
- Columbia University and the New York State Psychiatric Institute, New York, New York
| | - Jeremy Veenstra-VanderWeele
- Program in Developmental Neurogenetics, Institute for the Developing Mind, Children's Hospital Los Angeles, Los Angeles, California
| | - Pat Levitt
- Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, California
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46
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Chaste P, Klei L, Sanders SJ, Hus V, Murtha MT, Lowe JK, Willsey AJ, Moreno-De-Luca D, Yu TW, Fombonne E, Geschwind D, Grice DE, Ledbetter DH, Mane SM, Martin DM, Morrow EM, Walsh CA, Sutcliffe JS, Martin CL, Beaudet AL, Lord C, State MW, Cook EH, Devlin B. A genome-wide association study of autism using the Simons Simplex Collection: Does reducing phenotypic heterogeneity in autism increase genetic homogeneity? Biol Psychiatry 2015; 77:775-84. [PMID: 25534755 PMCID: PMC4379124 DOI: 10.1016/j.biopsych.2014.09.017] [Citation(s) in RCA: 115] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2014] [Revised: 09/02/2014] [Accepted: 09/03/2014] [Indexed: 12/13/2022]
Abstract
BACKGROUND Phenotypic heterogeneity in autism has long been conjectured to be a major hindrance to the discovery of genetic risk factors, leading to numerous attempts to stratify children based on phenotype to increase power of discovery studies. This approach, however, is based on the hypothesis that phenotypic heterogeneity closely maps to genetic variation, which has not been tested. Our study examines the impact of subphenotyping of a well-characterized autism spectrum disorder (ASD) sample on genetic homogeneity and the ability to discover common genetic variants conferring liability to ASD. METHODS Genome-wide genotypic data of 2576 families from the Simons Simplex Collection were analyzed in the overall sample and phenotypic subgroups defined on the basis of diagnosis, IQ, and symptom profiles. We conducted a family-based association study, as well as estimating heritability and evaluating allele scores for each phenotypic subgroup. RESULTS Association analyses revealed no genome-wide significant association signal. Subphenotyping did not increase power substantially. Moreover, allele scores built from the most associated single nucleotide polymorphisms, based on the odds ratio in the full sample, predicted case status in subsets of the sample equally well and heritability estimates were very similar for all subgroups. CONCLUSIONS In genome-wide association analysis of the Simons Simplex Collection sample, reducing phenotypic heterogeneity had at most a modest impact on genetic homogeneity. Our results are based on a relatively small sample, one with greater homogeneity than the entire population; if they apply more broadly, they imply that analysis of subphenotypes is not a productive path forward for discovering genetic risk variants in ASD.
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Affiliation(s)
- Pauline Chaste
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; FondaMental Foundation, Créteil; Centre Hospitalier Sainte Anne, Paris, France.
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh School of
Medicine, Pittsburgh, Pennsylvania, USA
| | - Stephan J. Sanders
- Department of Genetics, Yale University School of Medicine, New
Haven, Connecticut, USA,Department of Psychiatry, University of California at San Francisco,
California, USA
| | - Vanessa Hus
- Department of Psychology, University of Michigan, Ann Arbor, MI,
USA
| | - Michael T. Murtha
- Program on Neurogenetics, Yale University School of Medicine, New
Haven, Connecticut, USA
| | - Jennifer K. Lowe
- Neurogenetics Program, Department of Neurology and Center for Autism
Research and Treatment, Semel Institute, David Geffen School of Medicine, University
of California Los Angeles, Los Angeles, California, USA
| | - A. Jeremy Willsey
- Department of Genetics, Yale University School of Medicine, New
Haven, Connecticut, USA,Department of Psychiatry, University of California at San Francisco,
California, USA
| | - Daniel Moreno-De-Luca
- Program on Neurogenetics, Yale University School of Medicine, New
Haven, Connecticut, USA,Department of Psychiatry, Yale University School of Medicine, New
Haven, Connecticut, USA
| | - Timothy W. Yu
- Division of Genetics, Children's Hospital Boston, Harvard
Medical School, Boston, Massachusetts, USA
| | - Eric Fombonne
- Department of Psychiatry and Institute for Development and
disability, Oregon Health & Science University, Portland, Oregon, USA
| | - Daniel Geschwind
- Neurogenetics Program, Department of Neurology and Center for
Autism Research and Treatment, Semel Institute, David Geffen School of Medicine,
University of California Los Angeles, Los Angeles, California, USA
| | - Dorothy E. Grice
- Department of Psychiatry, Mount Sinai School of Medicine, New York,
New York, USA
| | - David H. Ledbetter
- Autism and Developmental Medicine Institute, Geisinger Health
System, Danville, Pennsylvania, USA
| | | | - Donna M. Martin
- Departments of Pediatrics and Human Genetics, University of
Michigan Medical Center, Ann Arbor, Michigan, USA
| | - Eric M. Morrow
- Department of Molecular Biology, Cell Biology and Biochemistry,
Brown University, Providence, Rhode Island, USA,Department of Psychiatry and Human Behavior, Brown University,
Providence, Rhode Island, USA
| | - Christopher A. Walsh
- Howard Hughes Medical Institute and Division of Genetics,
Children's Hospital Boston, and Neurology and Pediatrics, Harvard Medical
School Center for Life Sciences, Boston, Massachusetts, USA
| | - James S. Sutcliffe
- Departments of Molecular Physiology & Biophysics and
Psychiatry, Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN,
USA
| | - Christa Lese Martin
- Autism and Developmental Medicine Institute, Geisinger Health
System, Danville, Pennsylvania, USA
| | - Arthur L. Beaudet
- Department of Human and Molecular Genetics, Baylor College of
Medicine, Houston, Texas, USA
| | - Catherine Lord
- Center for Autism and the Developing Brain, Weill Cornell Medical
College, White Plains, New York, USA
| | - Matthew W. State
- Department of Genetics, Yale University School of Medicine, New
Haven, Connecticut, USA,Department of Psychiatry, University of California at San Francisco,
California, USA
| | - Edwin H. Cook
- Institute for Juvenile Research, Department of Psychiatry,
University of Illinois at Chicago, Chicago, Illinois, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of
Medicine, Pittsburgh, Pennsylvania, USA
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48
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Tunc B, Ghanbari Y, Smith AR, Pandey J, Browne A, Schultz RT, Verma R. PUNCH: Population Characterization of Heterogeneity. Neuroimage 2014; 98:50-60. [PMID: 24799135 DOI: 10.1016/j.neuroimage.2014.04.068] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Revised: 04/06/2014] [Accepted: 04/26/2014] [Indexed: 01/21/2023] Open
Abstract
Neuropsychiatric disorders are notoriously heterogeneous in their presentation, which precludes straightforward and objective description of the differences between affected and typical populations that therefore makes finding reliable biomarkers a challenge. This difficulty underlines the need for reliable methods to capture sample characteristics of heterogeneity using a single continuous measure, incorporating the multitude of scores used to describe different aspects of functioning. This study addresses this challenge by proposing a general method of identifying and quantifying the heterogeneity of any clinical population using a severity measure called the PUNCH (Population Characterization of Heterogeneity). PUNCH is a decision level fusion technique to incorporate decisions of various phenotypic scores, while providing interpretable weights for scores. We provide applications of our framework to simulated datasets and to a large sample of youth with Autism Spectrum Disorder (ASD). Next we stratify PUNCH scores in our ASD sample and show how severity moderates findings of group differences in diffusion weighted brain imaging data; more severely affected subgroups of ASD show expanded differences compared to age and gender matched healthy controls. Results demonstrate the ability of our measure in quantifying the underlying heterogeneity of the clinical samples, and suggest its utility in providing researchers with reliable severity assessments incorporating population heterogeneity.
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Affiliation(s)
- Birkan Tunc
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yasser Ghanbari
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alex R Smith
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Juhi Pandey
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Aaron Browne
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Robert T Schultz
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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