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Liloia D, Zamfira DA, Tanaka M, Manuello J, Crocetta A, Keller R, Cozzolino M, Duca S, Cauda F, Costa T. Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research. Neurosci Biobehav Rev 2024; 164:105791. [PMID: 38960075 DOI: 10.1016/j.neubiorev.2024.105791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 05/22/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Denisa Adina Zamfira
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Masaru Tanaka
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Szeged, Hungary
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Annachiara Crocetta
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Mauro Cozzolino
- Department of Humanities, Philosophical and Educational Sciences, University of Salerno, Fisciano, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
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Mohammad S, Gentreau M, Dubol M, Rukh G, Mwinyi J, Schiöth HB. Association of polygenic scores for autism with volumetric MRI phenotypes in cerebellum and brainstem in adults. Mol Autism 2024; 15:34. [PMID: 39113134 PMCID: PMC11304666 DOI: 10.1186/s13229-024-00611-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.
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Affiliation(s)
- Salahuddin Mohammad
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Mélissa Gentreau
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Manon Dubol
- Department of Women's and Children's Health, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Gull Rukh
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Jessica Mwinyi
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Helgi B Schiöth
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
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Zhao Z, Okada N, Yagishita S, Yahata N, Nitta N, Shibata S, Abe Y, Morita S, Kumagai E, Tanaka KF, Suhara T, Takumi T, Kasai K, Jinde S. Correlations of brain structure with the social behavior of 15q11-13 duplication mice, an animal model of autism. Neurosci Res 2024:S0168-0102(24)00100-7. [PMID: 39097003 DOI: 10.1016/j.neures.2024.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 06/07/2024] [Accepted: 07/31/2024] [Indexed: 08/05/2024]
Abstract
Duplication of chromosome 15q11-13 has been reported to be one of the most frequent cytogenetic copy number variations in autism spectrum disorder (ASD), and a mouse model of paternal 15q11-13 duplication was generated, termed 15q dup mice. While previous studies have replicated some of the behavioral and brain structural phenotypes of ASD separately, the relationship between brain structure and behavior has rarely been examined. In this study, we performed behavioral experiments related to anxiety and social behaviors and magnetic resonance imaging (MRI) using the same set of 15q dup and wild-type mice. 15q dup mice showed increased anxiety and a tendency toward alterations in social behaviors, as reported previously, as well as variability in terms of sociability. MRI analysis revealed that a lower sociability index was correlated with a smaller gray matter volume in the right medial entorhinal cortex. These results may help to understand how variability in behavioral phenotypes of ASD arises even in individuals with the same genetic background and to determine the individual differences in neurodevelopmental trajectory correlated with specific brain structures that underlie these phenotypes.
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Affiliation(s)
- Zhilei Zhao
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8655, Japan; International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8655, Japan; International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Sho Yagishita
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, Faculty of Medicine Bldg, The University of Tokyo, 1 #NC207, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Noriaki Yahata
- National Institutes for Quantum Sciences and Technology, Anagawa 4-9-1, Inage-ku, Chiba 263-8555, Japan
| | - Nobuhiro Nitta
- National Institutes for Quantum Sciences and Technology, Anagawa 4-9-1, Inage-ku, Chiba 263-8555, Japan; Central Institute for Experimental Animals, 3-25-12 Tonomachi, Kawasaki Ward, Kawasaki, Kanagawa 210-0821, Japan
| | - Sayaka Shibata
- National Institutes for Quantum Sciences and Technology, Anagawa 4-9-1, Inage-ku, Chiba 263-8555, Japan
| | - Yoshifumi Abe
- Division of Brain Sciences, Institute for Advanced Medical Research, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan
| | - Susumu Morita
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Eureka Kumagai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Kenji F Tanaka
- Division of Brain Sciences, Institute for Advanced Medical Research, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo 160-8582, Japan
| | - Tetsuya Suhara
- National Institutes for Quantum Sciences and Technology, Anagawa 4-9-1, Inage-ku, Chiba 263-8555, Japan
| | - Toru Takumi
- Department of Physiology and Cell Biology, Kobe University School of Medicine, 7-5-1 Kusunoki-cho, Chuo, Kobe 650-0017, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8655, Japan; International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Seiichiro Jinde
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8655, Japan.
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Tigga NP, Garg S, Goyal N, Raj J, Das B. Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network. Technol Health Care 2024:THC240550. [PMID: 38943414 DOI: 10.3233/thc-240550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2024]
Abstract
BACKGROUND Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities. OBJECTIVE This study aims to utilize EEG data collected from both autistic and typically developing children to investigate the potential of a Graph Convolutional Neural Network (GCNN) in predicting ASD based on neurological abnormalities revealed through EEG signals. METHODS In this study, EEG data were gathered from eight autistic children and eight typically developing children diagnosed using the Childhood Autism Rating Scale at the Central Institute of Psychiatry, Ranchi. EEG recording was done using a HydroCel GSN with 257 channels, and 71 channels with 10-10 international equivalents were utilized. Electrodes were divided into 12 brain regions. A GCNN was introduced for ASD prediction, preceded by autoregressive and spectral feature extraction. RESULTS The anterior-frontal brain region, crucial for cognitive functions like emotion, memory, and social interaction, proved most predictive of ASD, achieving 87.07% accuracy. This underscores the suitability of the GCNN method for EEG-based ASD detection. CONCLUSION The detailed dataset collected enhances understanding of the neurological basis of ASD, benefiting healthcare practitioners involved in ASD diagnosis.
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Affiliation(s)
- Neha Prerna Tigga
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India
| | - Shruti Garg
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India
| | - Nishant Goyal
- Department of Psychiatry, Central Institute of Psychiatry, Kanke, Ranchi, India
| | - Justin Raj
- Department of Psychiatry, Central Institute of Psychiatry, Kanke, Ranchi, India
| | - Basudeb Das
- Department of Psychiatry, Central Institute of Psychiatry, Kanke, Ranchi, India
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Yin Z, Ding X, Zhang X, Wu Z, Wang L, Xu X, Li G. Early autism diagnosis based on path signature and Siamese unsupervised feature compressor. Cereb Cortex 2024; 34:72-83. [PMID: 38696605 DOI: 10.1093/cercor/bhae069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/07/2024] [Accepted: 02/07/2024] [Indexed: 05/04/2024] Open
Abstract
Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.
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Affiliation(s)
- Zhuowen Yin
- School of Electronics and Information Engineering, South China University of Technology, 510641 Guangzhou, Guangdong Province, China
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Xinyao Ding
- School of Electronics and Information Engineering, South China University of Technology, 510641 Guangzhou, Guangdong Province, China
- The Affiliated Shenzhen School of Guangdong Experimental High School, 518100 Shenzhen, Guangdong Province, China
| | - Xin Zhang
- School of Electronics and Information Engineering, South China University of Technology, 510641 Guangzhou, Guangdong Province, China
- Pazhou Lab, 510330 Guangzhou, Guangdong Province, China
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Xiangmin Xu
- Pazhou Lab, 510330 Guangzhou, Guangdong Province, China
- School of Future Technology, South China University of Technology, 510641 Guangzhou, Guangdong Province, China
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
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Santos Musachio EA, da Silva Andrade S, Meichtry LB, Fernandes EJ, de Almeida PP, Janner DE, Dahleh MMM, Guerra GP, Prigol M. Exposure to Bisphenol F and Bisphenol S during development induces autism-like endophenotypes in adult Drosophila melanogaster. Neurotoxicol Teratol 2024; 103:107348. [PMID: 38554851 DOI: 10.1016/j.ntt.2024.107348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/21/2024] [Accepted: 03/27/2024] [Indexed: 04/02/2024]
Abstract
Bisphenol F (BPF) and Bisphenol S (BPS) are being widely used by the industry with the claim of "safer substances", even with the scarcity of toxicological studies. Given the etiological gap of autism spectrum disorder (ASD), the environment may be a causal factor, so we investigated whether exposure to BPF and BPS during the developmental period can induce ASD-like modeling in adult flies. Drosophila melanogaster flies were exposed during development (embryonic and larval period) to concentrations of 0.25, 0.5, and 1 mM of BPF and BPS, separately inserted into the food. When they transformed into pupae were transferred to a standard diet, ensuring that the flies (adult stage) did not have contact with bisphenols. Thus, after hatching, consolidated behavioral tests were carried out for studies with ASD-type models in flies. It was observed that 1 mM BPF and BPS caused hyperactivity (evidenced by open-field test, negative geotaxis, increased aggressiveness and reproduction of repetitive behaviors). The flies belonging to the 1 mM groups of BPF and BPS also showed reduced cognitive capacity, elucidated by the learning behavior through aversive stimulus. Within the population dynamics that flies exposed to 1 mM BPF and 0.5 and 1 mM BPS showed a change in social interaction, remaining more distant from each other. Exposure to 1 mM BPF, 0.5 and 1 mM BPS increased brain size and reduced Shank immunoreactivity of adult flies. These findings complement each other and show that exposure to BPF and BPS during the development period can elucidate a model with endophenotypes similar to ASD in adult flies. Furthermore, when analyzing comparatively, BPS demonstrated a greater potential for damage when compared to BPF. Therefore, in general these data sets contradict the idea that these substances can be used freely.
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Affiliation(s)
- Elize A Santos Musachio
- Laboratory of Pharmacological and Toxicological Evaluations Applied to Bioactive Molecules, Federal University of Pampa, Itaqui, RS, Brazil
| | - Stefani da Silva Andrade
- Laboratory of Pharmacological and Toxicological Evaluations Applied to Bioactive Molecules, Federal University of Pampa, Itaqui, RS, Brazil
| | - Luana Barreto Meichtry
- Laboratory of Pharmacological and Toxicological Evaluations Applied to Bioactive Molecules, Federal University of Pampa, Itaqui, RS, Brazil
| | - Eliana Jardim Fernandes
- Laboratory of Pharmacological and Toxicological Evaluations Applied to Bioactive Molecules, Federal University of Pampa, Itaqui, RS, Brazil
| | - Pamela Piardi de Almeida
- Laboratory of Pharmacological and Toxicological Evaluations Applied to Bioactive Molecules, Federal University of Pampa, Itaqui, RS, Brazil
| | - Dieniffer Espinosa Janner
- Laboratory of Pharmacological and Toxicological Evaluations Applied to Bioactive Molecules, Federal University of Pampa, Itaqui, RS, Brazil
| | - Mustafa Munir Mustafa Dahleh
- Laboratory of Pharmacological and Toxicological Evaluations Applied to Bioactive Molecules, Federal University of Pampa, Itaqui, RS, Brazil
| | - Gustavo Petri Guerra
- Laboratory of Pharmacological and Toxicological Evaluations Applied to Bioactive Molecules, Federal University of Pampa, Itaqui, RS, Brazil; Department of Food Science and Technology, Federal University of Pampa, Itaqui, RS, Brazil
| | - Marina Prigol
- Laboratory of Pharmacological and Toxicological Evaluations Applied to Bioactive Molecules, Federal University of Pampa, Itaqui, RS, Brazil; Department of Nutrition, Federal University of Pampa, Itaqui, RS, Brazil.
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Newman BT, Jacokes Z, Venkadesh S, Webb SJ, Kleinhans NM, McPartland JC, Druzgal TJ, Pelphrey KA, Van Horn JD. Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder. PLoS One 2024; 19:e0301964. [PMID: 38630783 PMCID: PMC11023574 DOI: 10.1371/journal.pone.0301964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 03/26/2024] [Indexed: 04/19/2024] Open
Abstract
The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a new approach to calculating axonal conduction velocity termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel formulation for calculating aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.
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Affiliation(s)
- Benjamin T. Newman
- Department of Psychology, University of Virginia, Charlottesville, VA, United States of America
- UVA School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Zachary Jacokes
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA, United States of America
| | - Siva Venkadesh
- Department of Psychology, University of Virginia, Charlottesville, VA, United States of America
| | - Sara J. Webb
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle WA, United States of America
- Seattle Children’s Research Institute, Seattle WA, United States of America
| | - Natalia M. Kleinhans
- Department of Radiology, Integrated Brain Imaging Center, University of Washington, Seattle, WA, United States of America
| | - James C. McPartland
- Yale Child Study Center, New Haven, CT, United States of America
- Yale Center for Brain and Mind Health, New Haven, CT, United States of America
| | - T. Jason Druzgal
- UVA School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Kevin A. Pelphrey
- UVA School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, United States of America
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA, United States of America
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Koc E, Kalkan H, Bilgen S. Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images. AUTISM RESEARCH AND TREATMENT 2023; 2023:4136087. [PMID: 38152612 PMCID: PMC10752691 DOI: 10.1155/2023/4136087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 10/19/2023] [Accepted: 11/22/2023] [Indexed: 12/29/2023]
Abstract
This study aims to increase the accuracy of autism spectrum disorder (ASD) diagnosis based on cognitive and behavioral phenotypes through multiple neuroimaging modalities. We apply machine learning (ML) algorithms to classify ASD patients and healthy control (HC) participants using structural magnetic resonance imaging (s-MRI) together with resting state functional MRI (rs-f-MRI and f-MRI) data from the large multisite data repository ABIDE (autism brain imaging data exchange) and identify important brain connectivity features. The 2D f-MRI images were converted into 3D s-MRI images, and datasets were preprocessed using the Montreal Neurological Institute (MNI) atlas. The data were then denoised to remove any confounding factors. We show, by using three fusion strategies such as early fusion, late fusion, and cross fusion, that, in this implementation, hybrid convolutional recurrent neural networks achieve better performance in comparison to either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The proposed model classifies subjects as autistic or not according to how functional and anatomical connectivity metrics provide an overall diagnosis based on the autism diagnostic observation schedule (ADOS) standard. Our hybrid network achieved an accuracy of 96% by fusing s-MRI and f-MRI together, which outperforms the methods used in previous studies.
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Affiliation(s)
- Emel Koc
- Istanbul Okan University, Istanbul, Türkiye
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9
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Ong LT, Fan SWD. Morphological and Functional Changes of Cerebral Cortex in Autism Spectrum Disorder. INNOVATIONS IN CLINICAL NEUROSCIENCE 2023; 20:40-47. [PMID: 38193097 PMCID: PMC10773605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by early-onset impairments in socialization, communication, repetitive behaviors, and restricted interests. ASD exhibits considerable heterogeneity, with clinical presentations varying across individuals and age groups. The pathophysiology of ASD is hypothesized to be due to abnormal brain development influenced by a combination of genetic and environmental factors. One of the most consistent morphological parameters for assessing the abnormal brain structures in patients with ASD is cortical thickness. Studies have shown changes in the cortical thickness within the frontal, temporal, parietal, and occipital lobes of individuals with ASD. These changes in cortical thickness often correspond to specific clinical features observed in individuals with ASD. Furthermore, the aberrant brain anatomical features and cortical thickness alterations may lead to abnormal brain connectivity and synaptic structure. Additionally, ASD is associated with cortical hyperplasia in early childhood, followed by a cortical plateau and subsequent decline in later stages of development. However, research in this area has yielded contradictory findings regarding the cortical thickness across various brain regions in ASD.
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Affiliation(s)
- Leong Tung Ong
- Both authors are with Faculty of Medicine, University of Malaya in Kuala Lumpur, Malaysia
| | - Si Wei David Fan
- Both authors are with Faculty of Medicine, University of Malaya in Kuala Lumpur, Malaysia
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10
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Li G, Chen MH, Li G, Wu D, Lian C, Sun Q, Rushmore RJ, Wang L. Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism. J Autism Dev Disord 2023; 53:2475-2489. [PMID: 35389185 PMCID: PMC9537344 DOI: 10.1007/s10803-022-05535-w] [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] [Accepted: 03/15/2022] [Indexed: 10/18/2022]
Abstract
Previous studies have demonstrated abnormal brain overgrowth in children with autism spectrum disorder (ASD), but the development of specific brain regions, such as the amygdala and hippocampal subfields in infants, is incompletely documented. To address this issue, we performed the first MRI study of amygdala and hippocampal subfields in infants from 6 to 24 months of age using a longitudinal dataset. A novel deep learning approach, Dilated-Dense U-Net, was proposed to address the challenge of low tissue contrast and small structural size of these subfields. We performed a volume-based analysis on the segmentation results. Our results show that infants who were later diagnosed with ASD had larger left and right volumes of amygdala and hippocampal subfields than typically developing controls.
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Affiliation(s)
- Guannan Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
- Department of Radiology and Biomedical Research Imaging Center, Bioinformatics Building, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Meng-Hsiang Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, Bioinformatics Building, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Di Wu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Chunfeng Lian
- Department of Radiology and Biomedical Research Imaging Center, Bioinformatics Building, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Quansen Sun
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - R Jarrett Rushmore
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA
- Center for Morphometric Analysis, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, MA, 02129, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, Bioinformatics Building, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA.
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11
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Cohen AL, Kroeck MR, Wall J, McManus P, Ovchinnikova A, Sahin M, Krueger DA, Bebin EM, Northrup H, Wu JY, Warfield SK, Peters JM, Fox MD. Tubers Affecting the Fusiform Face Area Are Associated with Autism Diagnosis. Ann Neurol 2023; 93:577-590. [PMID: 36394118 PMCID: PMC9974824 DOI: 10.1002/ana.26551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 11/11/2022] [Accepted: 11/13/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVE Tuberous sclerosis complex (TSC) is associated with focal brain "tubers" and a high incidence of autism spectrum disorder (ASD). The location of brain tubers associated with autism may provide insight into the neuroanatomical substrate of ASD symptoms. METHODS We delineated tuber locations for 115 TSC participants with ASD (n = 31) and without ASD (n = 84) from the Tuberous Sclerosis Complex Autism Center of Excellence Research Network. We tested for associations between ASD diagnosis and tuber burden within the whole brain, specific lobes, and at 8 regions of interest derived from the ASD neuroimaging literature, including the anterior cingulate, orbitofrontal and posterior parietal cortices, inferior frontal and fusiform gyri, superior temporal sulcus, amygdala, and supplemental motor area. Next, we performed an unbiased data-driven voxelwise lesion symptom mapping (VLSM) analysis. Finally, we calculated the risk of ASD associated with positive findings from the above analyses. RESULTS There were no significant ASD-related differences in tuber burden across the whole brain, within specific lobes, or within a priori regions derived from the ASD literature. However, using VLSM analysis, we found that tubers involving the right fusiform face area (FFA) were associated with a 3.7-fold increased risk of developing ASD. INTERPRETATION Although TSC is a rare cause of ASD, there is a strong association between tuber involvement of the right FFA and ASD diagnosis. This highlights a potentially causative mechanism for developing autism in TSC that may guide research into ASD symptoms more generally. ANN NEUROL 2023;93:577-590.
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Affiliation(s)
- Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mallory R Kroeck
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Juliana Wall
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter McManus
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Arina Ovchinnikova
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Darcy A Krueger
- Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - E Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School at University of Texas Health Science Center at Houston and Children's Memorial Hermann Hospital, Houston, TX, USA
| | - Joyce Y Wu
- Division of Neurology & Epilepsy, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jurriaan M Peters
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
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12
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Singh AP, Jain VS, Yu JPJ. Diffusion radiomics for subtyping and clustering in autism spectrum disorder: A preclinical study. Magn Reson Imaging 2023; 96:116-125. [PMID: 36496097 PMCID: PMC9815912 DOI: 10.1016/j.mri.2022.12.003] [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: 09/16/2022] [Revised: 10/24/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
Autism spectrum disorder (ASD) is a highly prevalent, heterogenous neurodevelopmental disorder. Neuroimaging methods such as functional, structural, and diffusion MRI have been used to identify candidate imaging biomarkers for ASD, but current findings remain non-specific and likely arise from the heterogeneity present in ASD. To account for this, efforts to subtype ASD have emerged as a potential strategy for both the study of ASD and advancement of tailored behavioral therapies and therapeutics. Towards these ends, to improve upon current neuroimaging methods, we propose combining biologically sensitive neurite orientation dispersion and density index (NODDI) diffusion MR imaging with radiomics image processing to create a new methodological approach that, we hypothesize, can sensitively and specifically capture neurobiology. We demonstrate this method can sensitively distinguish differences between four genetically distinct rat models of ASD (Fmr1, Pten, Nrxn1, Disc1). Further, we demonstrate diffusion radiomic analyses hold promise for subtyping in ASD as we show unsupervised clustering of NODDI radiomic data generates clusters specific to the underlying genetic differences between the animal models. Taken together, our findings suggest the unique application of radiomic analysis on NODDI diffusion MRI may have the capacity to sensitively and specifically disambiguate the neurobiological heterogeneity present in the ASD population.
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Affiliation(s)
- Ajay P Singh
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.; Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA; Graduate Program in Cellular and Molecular Biology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Vansh S Jain
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - John-Paul J Yu
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.; Graduate Program in Cellular and Molecular Biology, University of Wisconsin-Madison, Madison, WI 53706, USA; Neuroscience Training Program, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.
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13
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Cheng W, Sun Z, Cai K, Wu J, Dong X, Liu Z, Shi Y, Yang S, Zhang W, Chen A. Relationship between Overweight/Obesity and Social Communication in Autism Spectrum Disorder Children: Mediating Effect of Gray Matter Volume. Brain Sci 2023; 13:brainsci13020180. [PMID: 36831723 PMCID: PMC9954689 DOI: 10.3390/brainsci13020180] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/10/2023] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
With advances in medical diagnostic technology, the healthy development of children with autism spectrum disorder (ASD) is receiving more and more attention. In this article, the mediating effect of brain gray matter volume (GMV) between overweight/obesity and social communication (SC) was investigated through the analysis of the relationship between overweight/obesity and SC in autism spectrum disorder children. In total, 101 children with ASD aged 3-12 years were recruited from three special educational centers (Yangzhou, China). Overweight/obesity in children with ASD was indicated by their body mass index (BMI); the Social Responsiveness Scale, Second Edition (SRS-2) was used to assess their social interaction ability, and structural Magnetic Resonance Imaging (sMRI) was used to measure GMV. A mediation model was constructed using the Process plug-in to analyze the mediating effect of GMV between overweight/obesity and SC in children with ASD. The results revealed that: overweight/obesity positively correlated with SRS-2 total points (p = 0.01); gray matter volume in the left dorsolateral superior frontal gyrus (Frontal_Sup_L GMV) negatively correlated with SRS-2 total points (p = 0.001); and overweight/obesity negatively correlated with Frontal_Sup_L GMV (p = 0.001). The Frontal_Sup_L GMV played a partial mediating role in the relationship between overweight/obesity and SC, accounting for 36.6% of total effect values. These findings indicate the significant positive correlation between overweight/obesity and SC; GMV in the left dorsolateral superior frontal gyrus plays a mediating role in the relationship between overweight/obesity and SC. The study may provide new evidence toward comprehensively revealing the overweight/obesity and SC relationship.
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Affiliation(s)
- Wei Cheng
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Zhiyuan Sun
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Kelong Cai
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Jingjing Wu
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Xiaoxiao Dong
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Zhimei Liu
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Yifan Shi
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Sixin Yang
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Weike Zhang
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Aiguo Chen
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
- Correspondence: ; Tel.: +86-139-5272-5968
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14
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Ye F, Du L, Liu B, Gao X, Yang A, Liu D, Chen Y, Lv K, Xu P, Chen Y, Liu J, Zhang L, Li S, Shmuel A, Zhang Q, Ma G. Application of pseudocontinuous arterial spin labeling perfusion imaging in children with autism spectrum disorders. Front Neurosci 2022; 16:1045585. [PMID: 36425476 PMCID: PMC9680558 DOI: 10.3389/fnins.2022.1045585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/17/2022] [Indexed: 11/11/2022] Open
Abstract
Introduction Pseudocontinuous Arterial Spin Labeling (pCASL) perfusion imaging allows non-invasive quantification of regional cerebral blood flow (CBF) as part of a multimodal magnetic resonance imaging (MRI) protocol. This study aimed to compare regional CBF in autism spectrum disorders (ASD) individuals with their age-matched typically developing (TD) children using pCASL perfusion imaging. Materials and methods This cross-sectional study enrolled 17 individuals with ASD and 13 TD children. All participants underwent pCASL examination on a 3.0 T MRI scanner. Children in two groups were assessed for clinical characteristics and developmental profiles using Autism Behavior Checklist (ABC) and Gesell development diagnosis scale (GDDS), respectively. We compared CBF in different cerebral regions of ASD and TD children. We also assessed the association between CBF and clinical characteristics/developmental profile. Results Compared with TD children, individuals with ASD demonstrated a reduction in CBF in the left frontal lobe, the bilateral parietal lobes, and the bilateral temporal lobes. Within the ASD group, CBF was significantly higher in the right parietal lobe than in the left side. Correlation analysis of behavior characteristics and CBF in different regions showed a positive correlation between body and object domain scores on the ABC and CBF of the bilateral occipital lobes, and separately, between language domain scores and CBF of the left frontal lobe. The score of the social and self-help domain was negatively correlated with the CBF of the left frontal lobe, the left parietal lobe, and the left temporal lobe. Conclusion Cerebral blood flow was found to be negatively correlated with scores in the social and self-help domain, and positively correlated with those in the body and object domain, indicating that CBF values are a potential MRI-based biomarker of disease severity in ASD patients. The findings may provide novel insight into the pathophysiological mechanisms of ASD.
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Affiliation(s)
- Fang Ye
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Department of Radiology, Peking University, Cancer Hospital and Institute, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xinying Gao
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Die Liu
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Pengfei Xu
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Yuanmei Chen
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
| | - Jing Liu
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Lipeng Zhang
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shijun Li
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Qi Zhang
- Department of Pediatrics, China-Japan Friendship Hospital, Beijing, China
- Qi Zhang,
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
- *Correspondence: Guolin Ma,
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15
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Bahathiq RA, Banjar H, Bamaga AK, Jarraya SK. Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging. Front Neuroinform 2022; 16:949926. [PMID: 36246393 PMCID: PMC9554556 DOI: 10.3389/fninf.2022.949926] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.
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Affiliation(s)
- Reem Ahmed Bahathiq
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haneen Banjar
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed K. Bamaga
- Neuromuscular Medicine Unit, Department of Pediatric, Faculty of Medicine and King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Salma Kammoun Jarraya
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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16
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Ambrosino S, Elbendary H, Lequin M, Rijkelijkhuizen D, Banaschewski T, Baron-Cohen S, Bast N, Baumeister S, Buitelaar J, Charman T, Crawley D, Dell'Acqua F, Hayward H, Holt R, Moessnang C, Persico AM, Sacco R, San José Cáceres A, Tillmann J, Loth E, Ecker C, Oranje B, Murphy D, Durston S. In-depth characterization of neuroradiological findings in a large sample of individuals with autism spectrum disorder and controls. Neuroimage Clin 2022; 35:103118. [PMID: 35868222 PMCID: PMC9421485 DOI: 10.1016/j.nicl.2022.103118] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 06/14/2022] [Accepted: 07/12/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a group of neurodevelopmental conditions associated with quantitative differences in cortical and subcortical brain morphometry. Qualitative assessment of brain morphology provides complementary information on the possible underlying neurobiology. Studies of neuroradiological findings in ASD have rendered mixed results, and await robust replication in a sizable and independent sample. METHODS We systematically and comprehensively assessed neuroradiological findings in a large cohort of participants with ASD and age-matched controls (total N = 620, 348 ASD and 272 controls), including 70 participants with intellectual disability (47 ASD, 23 controls). We developed a comprehensive scoring system, augmented by standardized biometric measures. RESULTS There was a higher incidence of neuroradiological findings in individuals with ASD (89.4 %) compared to controls (83.8 %, p = .042). Certain findings were also more common in ASD, in particular opercular abnormalities (OR 1.9, 95 % CI 1.3-3.6) and mega cisterna magna (OR 2.4, 95 % CI 1.4-4.0) reached significance when using FDR, whereas increases in macrocephaly (OR 2.0, 95 % CI 1.2-3.2), cranial deformities (OR 2.4, 95 % CI: 1.0-5.8), calvarian / dural thickening (OR 1.5, 95 % CI 1.0-2.3), ventriculomegaly (OR 3.4, 95 % CI 1.3-9.2), and hypoplasia of the corpus callosum (OR 2.7, 95 % CI 1.1-6.3) did not survive this correction. Furthermore, neuroradiological findings were more likely to occur in isolation in controls, whereas they clustered more frequently in ASD. The incidence of neuroradiological findings was higher in individuals with mild intellectual disability (95.7 %), irrespective of ASD diagnosis. CONCLUSION There was a subtly higher prevalence of neuroradiological findings in ASD, which did not appear to be specific to the condition. Individual findings or clusters of findings may point towards the neurodevelopmental mechanisms involved in individual cases. As such, clinical MRI assessments may be useful to guide further etiopathological (genetic) investigations, and are potentially valuable to fundamental ASD research.
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Affiliation(s)
- Sara Ambrosino
- University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Hasnaa Elbendary
- Clinical Genetics Department, Human Genetics and Genome Research Division of the National Research Center, Cairo, Egypt
| | - Maarten Lequin
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Dominique Rijkelijkhuizen
- University Medical Center Utrecht, Utrecht, the Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Nico Bast
- Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Tony Charman
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Daisy Crawley
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, United Kingdom
| | - Flavio Dell'Acqua
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, United Kingdom
| | - Hannah Hayward
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, United Kingdom
| | - Rosemary Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Carolin Moessnang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Antonio M Persico
- Child and Adolescent Neuropsychiatry Program at Modena University Hospital, & Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; Child Neuropsychiatry / Neurodevelopmental Disorders Unit, University "Campus Bio-Medico", Rome, Italy
| | - Roberto Sacco
- Child Neuropsychiatry / Neurodevelopmental Disorders Unit, University "Campus Bio-Medico", Rome, Italy
| | - Antonia San José Cáceres
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, United Kingdom; Instituto de Investigación Sanitaria Gregorio Marañón (IISGM), Madrid and CIBERSAM (Centro Investigación Biomédica en Red Salud Mental), Spain
| | - Julian Tillmann
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Eva Loth
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, United Kingdom
| | - Christine Ecker
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, United Kingdom; Department of Child and Adolescent Psychiatry, University Hospital, Goethe University, Frankfurt am Main, Germany
| | - Bob Oranje
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
| | - Declan Murphy
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, United Kingdom
| | - Sarah Durston
- University Medical Center Utrecht, Utrecht, the Netherlands
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17
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The Neurology and Psychopathology of Pica. Curr Neurol Neurosci Rep 2022; 22:531-536. [PMID: 35674869 DOI: 10.1007/s11910-022-01218-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW Pica is defined by the American Psychiatric Association's Diagnostic and Statistical Manual, 5th edition (DSM 5) as the ongoing ingestion of materials with no nutritive or food value. More specifically such ingestions must be unremitting for at least 1 month and occur at a developmentally inconsistent age for such behavior. This article reviews the association of pica with pregnancy, micronutrient deficiencies, psychiatric disorders, dementia, and developmental disorders with emphasis on autism spectrum disorders (ASD). RECENT FINDINGS Some variants of non-nutritive consumption are prevalent behavioral norms in non-western cultures, so not all picas should be considered pathological. However, the strong association of pica with iron deficiency anemia (IDA) lends credence to the hypothesis that dopamine transmission may be disrupted in this disorder. Picas associated with ASD are resistant to medications but can be treated with applied behavioral analysis therapy (ABA). Etiological hypotheses for pica are explored with a focus on neurobiological, neuroimaging, and psychiatric correlations. Pharmacological management and behavior modification strategies are also discussed. The possibility that pica is a form of addiction analogous to food cravings is introduced and suggested as an area for further research pursuits.
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18
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Tsurugizawa T. Translational Magnetic Resonance Imaging in Autism Spectrum Disorder From the Mouse Model to Human. Front Neurosci 2022; 16:872036. [PMID: 35585926 PMCID: PMC9108701 DOI: 10.3389/fnins.2022.872036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/30/2022] [Indexed: 11/26/2022] Open
Abstract
Autism spectrum disorder (ASD) is a heterogeneous syndrome characterized by behavioral features such as impaired social communication, repetitive behavior patterns, and a lack of interest in novel objects. A multimodal neuroimaging using magnetic resonance imaging (MRI) in patients with ASD shows highly heterogeneous abnormalities in function and structure in the brain associated with specific behavioral features. To elucidate the mechanism of ASD, several ASD mouse models have been generated, by focusing on some of the ASD risk genes. A specific behavioral feature of an ASD mouse model is caused by an altered gene expression or a modification of a gene product. Using these mouse models, a high field preclinical MRI enables us to non-invasively investigate the neuronal mechanism of the altered brain function associated with the behavior and ASD risk genes. Thus, MRI is a promising translational approach to bridge the gap between mice and humans. This review presents the evidence for multimodal MRI, including functional MRI (fMRI), diffusion tensor imaging (DTI), and volumetric analysis, in ASD mouse models and in patients with ASD and discusses the future directions for the translational study of ASD.
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Affiliation(s)
- Tomokazu Tsurugizawa
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
- Faculty of Engineering, University of Tsukuba, Tsukuba, Japan
- *Correspondence: Tomokazu Tsurugizawa,
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19
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Stogiannos N, Carlier S, Harvey-Lloyd JM, Brammer A, Nugent B, Cleaver K, McNulty JP, dos Reis CS, Malamateniou C. A systematic review of person-centred adjustments to facilitate magnetic resonance imaging for autistic patients without the use of sedation or anaesthesia. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2022; 26:782-797. [PMID: 34961364 PMCID: PMC9008560 DOI: 10.1177/13623613211065542] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
LAY ABSTRACT Autistic patients often undergo magnetic resonance imaging examinations. Within this environment, it is usual to feel anxious and overwhelmed by noises, lights or other people. The narrow scanners, the loud noises and the long examination time can easily cause panic attacks. This review aims to identify any adaptations for autistic individuals to have a magnetic resonance imaging scan without sedation or anaesthesia. Out of 4442 articles screened, 53 more relevant were evaluated and 21 were finally included in this study. Customising communication, different techniques to improve the environment, using technology for familiarisation and distraction have been used in previous studies. The results of this study can be used to make suggestions on how to improve magnetic resonance imaging practice and the autistic patient experience. They can also be used to create training for the healthcare professionals using the magnetic resonance imaging scanners.
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Affiliation(s)
| | - Sarah Carlier
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
- University of Lausanne, Switzerland
| | | | | | - Barbara Nugent
- City, University of London, UK
- MRI Safety Matters® Organisation, UK
- NHS National Education for Scotland, UK
| | | | | | - Cláudia Sá dos Reis
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
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20
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Ali MT, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Ghazal M, Yousaf J, Abu Khalifeh H, Casanova M, Barnes G, El-Baz A. The Role of Structure MRI in Diagnosing Autism. Diagnostics (Basel) 2022; 12:165. [PMID: 35054330 PMCID: PMC8774643 DOI: 10.3390/diagnostics12010165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 12/30/2022] Open
Abstract
This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.
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Affiliation(s)
- Mohamed T. Ali
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ahmed Shalaby
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Jawad Yousaf
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Hadil Abu Khalifeh
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Manuel Casanova
- Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, SC 29425, USA;
| | - Gregory Barnes
- Department of Neurology, Norton Children’s Autism Center, University of Louisville, Louisville, KY 40208, USA;
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
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21
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Zhao X, Zhu S, Cao Y, Cheng P, Lin Y, Sun Z, Jiang W, Du Y. Abnormalities of Gray Matter Volume and Its Correlation with Clinical Symptoms in Adolescents with High-Functioning Autism Spectrum Disorder. Neuropsychiatr Dis Treat 2022; 18:717-730. [PMID: 35401002 PMCID: PMC8983641 DOI: 10.2147/ndt.s349247] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 03/04/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Previous studies have indicated abnormal gray matter volume (GMV) in individuals with autism spectrum disorder (ASD); however, there is little consistency across the findings within these studies, partly due to small sample size and great heterogeneity among participants between studies. Additionally, few studies have explored the correlation between clinical symptoms and GMV abnormalities in individuals with ASD. Here, the current study examined GMV alterations in whole brain and their correlations with clinical symptoms in a relatively large and homogeneous sample of participants with ASD matched typically developing (TD) controls. METHODS Forty-eight adolescents with high-functioning ASD and 29 group-matched TD controls underwent structural magnetic resonance images. Voxel-based morphometry was applied to investigate regional GMV alterations. The participants with ASD were examined for the severity of clinical symptoms with Autism Behavior Checklist (ABC). The relationship between GMV abnormalities and clinical symptoms was explored in ASD group using voxel-wise correlation analysis within brain regions that showed significant GMV alterations in individuals with ASD compared with TD controls. RESULTS We found increased GMV in multiple brain regions, including the inferior frontal gyrus, medial frontal gyrus, superior frontal gyrus, superior temporal gyrus, occipital pole, anterior cingulate, cerebellum anterior lobe, cerebellum posterior lobe, and midbrain, as well as decreased GMV in cerebellum posterior lobe in individuals with ASD. The correlation analysis showed the GMV in the left fusiform was negatively associated with the scores of sensory factor, and the GMV in the right cerebellum anterior lobe was positively associated with the scores of social self-help factor. CONCLUSION Our results indicated that widespread GMV abnormalities of brain regions occurred in individuals with ASD, suggesting a potential neural basis for the pathogenesis and symptomatology of ASD.
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Affiliation(s)
- Xiaoxin Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Shuyi Zhu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Yang Cao
- Suzhou Guangji Hospital, Suzhou, People's Republic of China
| | - Peipei Cheng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Yuxiong Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Zhixin Sun
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Wenqing Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Yasong Du
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
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22
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Rafiee F, Rezvani Habibabadi R, Motaghi M, Yousem DM, Yousem IJ. Brain MRI in Autism Spectrum Disorder: Narrative Review and Recent Advances. J Magn Reson Imaging 2021; 55:1613-1624. [PMID: 34626442 DOI: 10.1002/jmri.27949] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/27/2021] [Accepted: 09/27/2021] [Indexed: 01/31/2023] Open
Abstract
Autism spectrum disorder (ASD) is neuropsychiatric continuum of disorders characterized by persistent deficits in social communication and restricted repetitive patterns of behavior which impede optimal functioning. Early detection and intervention in ASD children can mitigate the deficits in social interaction and result in a better outcome. Various non-invasive imaging methods and molecular techniques have been developed for the early identification of ASD characteristics. There is no general consensus on specific neuroimaging features of autism; however, quantitative magnetic resonance techniques have provided valuable structural and functional information in understanding the neuropathophysiology of ASD and how the autistic brain changes during childhood, adolescence, and adulthood. In this review of decades of ASD neuroimaging research, we identify the structural, functional, and molecular imaging clues that most accurately point to the diagnosis of ASD vs. typically developing children. These studies highlight the 1) exaggerated synaptic pruning, 2) anomalous gyrification, 3) interhemispheric under- and overconnectivity, and 4) excitatory glutamate and inhibitory GABA imbalance theories of ASD. The application of these various theories to the analysis of a patient with ASD is mitigated often by superimposed comorbid neuropsychological disorders, evolving brain maturation processes, and pharmacologic and behavioral interventions that may affect the structure and function of the brain. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Faranak Rafiee
- Department of Radiology, Fara Parto Medical Imaging and Interventional Radiology Center, Shiraz, Iran
| | - Roya Rezvani Habibabadi
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, Maryland, USA
| | - Mina Motaghi
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA
| | - David M Yousem
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, Maryland, USA
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23
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Leming MJ, Baron-Cohen S, Suckling J. Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI. Mol Autism 2021; 12:34. [PMID: 33971956 PMCID: PMC8112019 DOI: 10.1186/s13229-021-00439-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 04/16/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Autism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied. METHODS We introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs. LIMITATIONS While this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism. RESULTS Our models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity. CONCLUSION This study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism.
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Affiliation(s)
- Matthew J Leming
- Department of Psychiatry, University of Cambridge, Robinson Way, Cambridge, Cambridgeshire, CB2 0SZ, UK.
- Center for Systems Biology, Massachusetts General Hospital, 149 13th Street, Boston, MA, 02129, USA.
| | - Simon Baron-Cohen
- Department of Psychiatry, University of Cambridge, Robinson Way, Cambridge, Cambridgeshire, CB2 0SZ, UK
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Robinson Way, Cambridge, Cambridgeshire, CB2 0SZ, UK
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24
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Gao J, Chen M, Li Y, Gao Y, Li Y, Cai S, Wang J. Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks. Front Neurosci 2021; 14:629630. [PMID: 33584183 PMCID: PMC7877487 DOI: 10.3389/fnins.2020.629630] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 12/29/2020] [Indexed: 12/18/2022] Open
Abstract
Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients' families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.
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Affiliation(s)
- Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Mingren Chen
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Li
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yachun Gao
- School of Physics, University of Electronic Science and Technology of China, Chengdu, China
| | - Yanling Li
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China
| | - Shimin Cai
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiaojian Wang
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
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25
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Guan J, Wang Y, Lin Y, Yin Q, Zhuang Y, Ji G. Cell Type-Specific Predictive Models Perform Prioritization of Genes and Gene Sets Associated With Autism. Front Genet 2021; 11:628539. [PMID: 33519924 PMCID: PMC7844401 DOI: 10.3389/fgene.2020.628539] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 12/16/2020] [Indexed: 12/13/2022] Open
Abstract
Bulk transcriptomic analyses of autism spectrum disorder (ASD) have revealed dysregulated pathways, while the brain cell type-specific molecular pathology of ASD still needs to be studied. Machine learning-based studies can be conducted for ASD, prioritizing high-confidence gene candidates and promoting the design of effective interventions. Using human brain nucleus gene expression of ASD and controls, we construct cell type-specific predictive models for ASD based on individual genes and gene sets, respectively, to screen cell type-specific ASD-associated genes and gene sets. These two kinds of predictive models can predict the diagnosis of a nucleus with known cell type. Then, we construct a multi-label predictive model for predicting the cell type and diagnosis of a nucleus at the same time. Our findings suggest that layer 2/3 and layer 4 excitatory neurons, layer 5/6 cortico-cortical projection neurons, parvalbumin interneurons, and protoplasmic astrocytes are preferentially affected in ASD. The functions of genes with predictive power for ASD are different and the top important genes are distinct across different cells, highlighting the cell-type heterogeneity of ASD. The constructed predictive models can promote the diagnosis of ASD, and the prioritized cell type-specific ASD-associated genes and gene sets may be used as potential biomarkers of ASD.
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Affiliation(s)
- Jinting Guan
- Department of Automation, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yang Wang
- Department of Automation, Xiamen University, Xiamen, China
| | - Yiping Lin
- Department of Automation, Xiamen University, Xiamen, China
| | - Qingyang Yin
- Department of Automation, Xiamen University, Xiamen, China
| | - Yibo Zhuang
- Xiamen YLZ Yihui Technology Co., Ltd., Xiamen, China
| | - Guoli Ji
- Department of Automation, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, China
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26
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A diagnostic unified classification model for classifying multi-sized and multi-modal brain graphs using graph alignment. J Neurosci Methods 2020; 348:109014. [PMID: 33309587 DOI: 10.1016/j.jneumeth.2020.109014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 11/11/2020] [Accepted: 11/26/2020] [Indexed: 11/21/2022]
Abstract
BACKGROUND Presence of multimodal brain graphs derived from different neuroimaging modalities is inarguably one of the most critical challenges in building unified classification models that can be trained and tested on any brain graph regardless of its size and the modality it was derived from. EXISTING METHODS One solution is to learn a model for each modality independently, which is cumbersome and becomes more time-consuming as the number of modalities increases. Another traditional solution is to build a model inputting multimodal brain graphs for the target prediction task; however, this is only applicable to datasets where all samples have joint neuro-modalities. NEW METHOD In this paper, we propose to build a unified brain graph classification model trained on unpaired multimodal brain graphs, which can classify any brain graph of any size. This is enabled by incorporating a graph alignment step where all multi-modal graphs of different sizes and heterogeneous distributions are mapped to a common template graph. Next, we design a graph alignment strategy to the target fixed-size template and further apply linear discriminant analysis (LDA) to the aligned graphs as a supervised dimensionality reduction technique for the target classification task. RESULTS We tested our method on unpaired autistic and healthy brain connectomes derived from functional and morphological MRI datasets (two modalities). CONCLUSION Our results showed that our unified model method not only has great promise in solving such a challenging problem but achieves comparable performance to models trained on each modality independently.
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27
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Prem S, Millonig JH, DiCicco-Bloom E. Dysregulation of Neurite Outgrowth and Cell Migration in Autism and Other Neurodevelopmental Disorders. ADVANCES IN NEUROBIOLOGY 2020; 25:109-153. [PMID: 32578146 DOI: 10.1007/978-3-030-45493-7_5] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Despite decades of study, elucidation of the underlying etiology of complex developmental disorders such as autism spectrum disorder (ASD), schizophrenia (SCZ), intellectual disability (ID), and bipolar disorder (BPD) has been hampered by the inability to study human neurons, the heterogeneity of these disorders, and the relevance of animal model systems. Moreover, a majority of these developmental disorders have multifactorial or idiopathic (unknown) causes making them difficult to model using traditional methods of genetic alteration. Examination of the brains of individuals with ASD and other developmental disorders in both post-mortem and MRI studies shows defects that are suggestive of dysregulation of embryonic and early postnatal development. For ASD, more recent genetic studies have also suggested that risk genes largely converge upon the developing human cerebral cortex between weeks 8 and 24 in utero. Yet, an overwhelming majority of studies in autism rodent models have focused on postnatal development or adult synaptic transmission defects in autism related circuits. Thus, studies looking at early developmental processes such as proliferation, cell migration, and early differentiation, which are essential to build the brain, are largely lacking. Yet, interestingly, a few studies that did assess early neurodevelopment found that alterations in brain structure and function associated with neurodevelopmental disorders (NDDs) begin as early as the initial formation and patterning of the neural tube. By the early to mid-2000s, the derivation of human embryonic stem cells (hESCs) and later induced pluripotent stem cells (iPSCs) allowed us to study living human neural cells in culture for the first time. Specifically, iPSCs gave us the unprecedented ability to study cells derived from individuals with idiopathic disorders. Studies indicate that iPSC-derived neural cells, whether precursors or "matured" neurons, largely resemble cortical cells of embryonic humans from weeks 8 to 24. Thus, these cells are an excellent model to study early human neurodevelopment, particularly in the context of genetically complex diseases. Indeed, since 2011, numerous studies have assessed developmental phenotypes in neurons derived from individuals with both genetic and idiopathic forms of ASD and other NDDs. However, while iPSC-derived neurons are fetal in nature, they are post-mitotic and thus cannot be used to study developmental processes that occur before terminal differentiation. Moreover, it is important to note that during the 8-24-week window of human neurodevelopment, neural precursor cells are actively undergoing proliferation, migration, and early differentiation to form the basic cytoarchitecture of the brain. Thus, by studying NPCs specifically, we could gain insight into how early neurodevelopmental processes contribute to the pathogenesis of NDDs. Indeed, a few studies have explored NPC phenotypes in NDDs and have uncovered dysregulations in cell proliferation. Yet, few studies have explored migration and early differentiation phenotypes of NPCs in NDDs. In this chapter, we will discuss cell migration and neurite outgrowth and the role of these processes in neurodevelopment and NDDs. We will begin by reviewing the processes that are important in early neurodevelopment and early cortical development. We will then delve into the roles of neurite outgrowth and cell migration in the formation of the brain and how errors in these processes affect brain development. We also provide review of a few key molecules that are involved in the regulation of neurite outgrowth and migration while discussing how dysregulations in these molecules can lead to abnormalities in brain structure and function thereby highlighting their contribution to pathogenesis of NDDs. Then we will discuss whether neurite outgrowth, migration, and the molecules that regulate these processes are associated with ASD. Lastly, we will review the utility of iPSCs in modeling NDDs and discuss future goals for the study of NDDs using this technology.
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Affiliation(s)
- Smrithi Prem
- Graduate Program in Neuroscience, Rutgers University, Piscataway, NJ, USA
| | - James H Millonig
- Department of Neuroscience and Cell Biology, Center for Advanced Biotechnology and Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Emanuel DiCicco-Bloom
- Department of Neuroscience and Cell Biology/Pediatrics, Rutgers Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA.
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28
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Hashem S, Nisar S, Bhat AA, Yadav SK, Azeem MW, Bagga P, Fakhro K, Reddy R, Frenneaux MP, Haris M. Genetics of structural and functional brain changes in autism spectrum disorder. Transl Psychiatry 2020; 10:229. [PMID: 32661244 PMCID: PMC7359361 DOI: 10.1038/s41398-020-00921-3] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 06/05/2020] [Accepted: 06/09/2020] [Indexed: 12/21/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurological and developmental disorder characterized by social impairment and restricted interactive and communicative behaviors. It may occur as an isolated disorder or in the context of other neurological, psychiatric, developmental, and genetic disorders. Due to rapid developments in genomics and imaging technologies, imaging genetics studies of ASD have evolved in the last few years. Increased risk for ASD diagnosis is found to be related to many specific single-nucleotide polymorphisms, and the study of genetic mechanisms and noninvasive imaging has opened various approaches that can help diagnose ASD at the nascent level. Identifying risk genes related to structural and functional changes in the brain of ASD patients provide a better understanding of the disease's neuropsychiatry and can help identify targets for therapeutic intervention that could be useful for the clinical management of ASD patients.
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Affiliation(s)
- Sheema Hashem
- Functional and Molecular Imaging Laboratory, Sidra Medicine, Doha, Qatar
| | - Sabah Nisar
- Functional and Molecular Imaging Laboratory, Sidra Medicine, Doha, Qatar
| | - Ajaz A Bhat
- Functional and Molecular Imaging Laboratory, Sidra Medicine, Doha, Qatar
| | | | | | - Puneet Bagga
- Center for Magnetic Resonance and Optical Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Khalid Fakhro
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
- Department of Genetic Medicine, Weill Cornell Medical College, Doha, Qatar
| | - Ravinder Reddy
- Center for Magnetic Resonance and Optical Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Mohammad Haris
- Functional and Molecular Imaging Laboratory, Sidra Medicine, Doha, Qatar.
- Laboratory Animal Research Center, Qatar University, Doha, Qatar.
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29
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Chen T, Chen Y, Yuan M, Gerstein M, Li T, Liang H, Froehlich T, Lu L. The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study. JMIR Med Inform 2020; 8:e15767. [PMID: 32041690 PMCID: PMC7244998 DOI: 10.2196/15767] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 12/01/2019] [Accepted: 02/09/2020] [Indexed: 01/28/2023] Open
Abstract
Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. Objective This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. Methods We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. Results On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. Conclusions Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.
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Affiliation(s)
- Tao Chen
- School of Information Management, Wuhan University, Wuhan, China.,School of Information Technology, Shangqiu Normal University, Shangqiu, China
| | - Ye Chen
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Mengxue Yuan
- School of Information Management, Wuhan University, Wuhan, China
| | - Mark Gerstein
- Program in Neurodevelopment and Regeneration, Yale University, New Haven, CT, United States.,Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, United States.,Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States.,Department of Computer Science, Yale University, New Haven, CT, United States
| | - Tingyu Li
- Children Nutrition Research Center, Chongqing, China.,Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Chongqing, China
| | - Huiying Liang
- Guangzhou Women and Children's Medical Center, Guangzhou, China.,Guangzhou Medical University, Guangzhou, China
| | - Tanya Froehlich
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.,Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Long Lu
- School of Information Management, Wuhan University, Wuhan, China.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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Samia P, Kanana M, King J, Donald KA, Newton CR, Denckla C. Childhood autism spectrum disorder: insights from a tertiary hospital cohort in Kenya. AFRICAN JOURNAL OF HEALTH SCIENCES 2020; 33:12-21. [PMID: 33343175 PMCID: PMC7746135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
INTRODUCTION Autism spectrum disorder (ASD) is characterized by impairments in behavior, social communication, and interaction. There is little data on ASD from sub-Saharan Africa (SSA) describing clinical characteristics in large cohorts of patients. Preliminary studies report a high male sex ratio, excess of nonverbal cases, possible infectious etiologies, and comorbidities e.g. epilepsy. OBJECTIVE To describe the clinical characteristics of children diagnosed with ASD in an African context. METHODS A retrospective medical chart review identified 116 children diagnosed with ASD according to DSM-5 criteria at a pediatric neurology clinic in Nairobi, Kenya. RESULTS The male to female ratio was 4.3:1. The median age at presentation was 3 years with speech delay as the most common reason for presentation. Expressive language delay was observed in 90% of the population. Sixty percent who obtained imaging had normal MRI brain findings. Only 44% and 34% of children had access to speech therapy and occupational therapy respectively. Epilepsy and ADHD were the most prevalent comorbidities. CONCLUSION An early median age at presentation and preponderance of male gender is observed. Access to speech therapy and other interventions was low. A prospective study would help determine outcomes for similar children following appropriate interventions.
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Affiliation(s)
- Pauline Samia
- Department of Paediatrics and Child Health, Aga Khan University, Nairobi, Kenya
| | - Maureen Kanana
- Department of Paediatrics and Child Health, Aga Khan University, Nairobi, Kenya
| | - Julie King
- School of Public Health, Department of Epidemiology and Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Kirsten A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, South Africa
| | | | - Christy Denckla
- School of Public Health, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
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Correlation between diffusion tensor imaging measures and the reading and cognitive performance of Arabic readers: dyslexic children perspective. Neuroradiology 2020; 62:525-531. [PMID: 31955236 DOI: 10.1007/s00234-020-02368-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 01/10/2020] [Indexed: 10/25/2022]
Abstract
PURPOSE To investigate the correlation between the diffusion tensor imaging (DTI) measures and the reading, spelling, writing, rapid naming, memory, and motor abilities in Arabic dyslexic children. This could verify the influence of possible white matter alterations on the abilities of those children. METHODS Twenty native Arabic-speaking children with dyslexia (15 males and 5 females; 8.2 years ± 1) underwent DTI of the brain on 1.5 T scanner. Diffusion-weighted images were acquired in 32 noncollinear direction. Tractography of the arcuate fasciculus (AF) was performed. Region of interest (ROI)-based approach was also used. Regions encompass superior longitudinal fasciculus (SLF), anterior and superior corona radiata (CR), and posterior limb of internal capsule (PLIC) were analyzed. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were measured. The aptitudes of those children were evaluated by the dyslexia assessment test. These abilities were statistically correlated with the FA and ADC of the AF and other ROIs. RESULTS The reduction of FA of right AF was related to worse overall reading and related abilities performance. The ADC of right SLF was negatively correlated with memory abilities. The ADC of right PLIC was positively correlated with writing performance. Other relations were also found. CONCLUSION White matter microstructural DTI measurements in the right AF, right PLIC, SLF, and left anterior and superior CR are correlated to reading, spelling, writing, memory, and rapid naming abilities of the participants. The DTI measures could be promising regarding their use as a biomarker for follow-up in developmental dyslexia.
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Paquette N, Gajawelli N, Lepore N. Structural neuroimaging. HANDBOOK OF CLINICAL NEUROLOGY 2020; 174:251-264. [PMID: 32977882 DOI: 10.1016/b978-0-444-64148-9.00018-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Characterizing the neuroanatomical correlates of brain development is essential in understanding brain-behavior relationships and neurodevelopmental disorders. Advances in brain MRI acquisition protocols and image processing techniques have made it possible to detect and track with great precision anatomical brain development and pediatric neurologic disorders. In this chapter, we provide a brief overview of the modern neuroimaging techniques for pediatric brain development and review key normal brain development studies. Characteristic disorders affecting neurodevelopment in childhood, such as prematurity, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), epilepsy, and brain cancer, and key neuroanatomical findings are described and then reviewed. Large datasets of typically developing children and children with various neurodevelopmental conditions are now being acquired to help provide the biomarkers of such impairments. While there are still several challenges in imaging brain structures specific to the pediatric populations, such as subject cooperation and tissues contrast variability, considerable imaging research is now being devoted to solving these problems and improving pediatric data analysis.
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Affiliation(s)
- Natacha Paquette
- CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States
| | - Niharika Gajawelli
- CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology, Children's Hospital of Los Angeles and University of Southern California, Los Angeles, CA, United States.
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Al-Dewik N, Al-Jurf R, Styles M, Tahtamouni S, Alsharshani D, Alsharshani M, Ahmad AI, Khattab A, Al Rifai H, Walid Qoronfleh M. Overview and Introduction to Autism Spectrum Disorder (ASD). ADVANCES IN NEUROBIOLOGY 2020; 24:3-42. [PMID: 32006355 DOI: 10.1007/978-3-030-30402-7_1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder generally manifesting in the first few years of life and tending to persist into adolescence and adulthood. It is characterized by deficits in communication and social interaction and restricted, repetitive patterns of behavior, interests, and activities. It is a disorder with multifactorial etiology. In this chapter, we will focus on the most important and common epidemiological studies, pathogenesis, screening, and diagnostic tools along with an explication of genetic testing in ASD.
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Affiliation(s)
- Nader Al-Dewik
- Clinical and Metabolic Genetics Section, Pediatrics Department, Hamad General Hospital (HGH), Women's Wellness and Research Center (WWRC) and Interim Translational Research Institute (iTRI), Hamad Medical Corporation (HMC), Doha, Qatar. .,College of Health and Life Sciences, Hamad Bin Khalifa University (HBKU), Doha, Qatar. .,Faculty of Health and Social Care Sciences, Kingston University, St. George's University of London, London, UK.
| | - Rana Al-Jurf
- Department of Biomedical Science, College of Health Science, Qatar University, Doha, Qatar
| | - Meghan Styles
- Health Profession Awareness Program, Health Facilities Development, Hamad Medical Corporation (HMC), Doha, Qatar
| | - Sona Tahtamouni
- Child Development Center, Hamad Medical Corporation, Doha, Qatar
| | - Dalal Alsharshani
- College of Health and Life Sciences, Hamad Bin Khalifa University (HBKU), Doha, Qatar
| | - Mohammed Alsharshani
- Diagnostic Genetics Division (DGD), Department of Laboratory Medicine and Pathology (DLMP), Hamad Medical Corporation (HMC), Doha, Qatar
| | - Amal I Ahmad
- Qatar Rehabilitation Institute (QRI), Hamad Medical Corporation (HMC), Doha, Qatar
| | - Azhar Khattab
- Qatar Rehabilitation Institute (QRI), Hamad Medical Corporation (HMC), Doha, Qatar
| | - Hilal Al Rifai
- Department of Pediatrics and Neonatology, Newborn Screening Unit, Hamad Medical Corporation, Doha, Qatar
| | - M Walid Qoronfleh
- Research and Policy Department, World Innovation Summit for Health (WISH), Qatar Foundation, Doha, Qatar
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Hyman SL, Levy SE, Myers SM. Identification, Evaluation, and Management of Children With Autism Spectrum Disorder. Pediatrics 2020; 145:peds.2019-3447. [PMID: 31843864 DOI: 10.1542/peds.2019-3447] [Citation(s) in RCA: 494] [Impact Index Per Article: 123.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder with reported prevalence in the United States of 1 in 59 children (approximately 1.7%). Core deficits are identified in 2 domains: social communication/interaction and restrictive, repetitive patterns of behavior. Children and youth with ASD have service needs in behavioral, educational, health, leisure, family support, and other areas. Standardized screening for ASD at 18 and 24 months of age with ongoing developmental surveillance continues to be recommended in primary care (although it may be performed in other settings), because ASD is common, can be diagnosed as young as 18 months of age, and has evidenced-based interventions that may improve function. More accurate and culturally sensitive screening approaches are needed. Primary care providers should be familiar with the diagnostic criteria for ASD, appropriate etiologic evaluation, and co-occurring medical and behavioral conditions (such as disorders of sleep and feeding, gastrointestinal tract symptoms, obesity, seizures, attention-deficit/hyperactivity disorder, anxiety, and wandering) that affect the child's function and quality of life. There is an increasing evidence base to support behavioral and other interventions to address specific skills and symptoms. Shared decision making calls for collaboration with families in evaluation and choice of interventions. This single clinical report updates the 2007 American Academy of Pediatrics clinical reports on the evaluation and treatment of ASD in one publication with an online table of contents and section view available through the American Academy of Pediatrics Gateway to help the reader identify topic areas within the report.
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Affiliation(s)
- Susan L Hyman
- Golisano Children's Hospital, University of Rochester, Rochester, New York;
| | - Susan E Levy
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; and
| | - Scott M Myers
- Geisinger Autism & Developmental Medicine Institute, Danville, Pennsylvania
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Li G, Chen MH, Li G, Wu D, Lian C, Sun Q, Shen D, Wang L. A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism. GRAPH LEARNING IN MEDICAL IMAGING : FIRST INTERNATIONAL WORKSHOP, GLMI 2019, HELD IN CONJUNCTION WITH MICCAI 2019, SHENZHEN, CHINA, OCTOBER 17, 2019, PROCEEDINGS 2019; 11849:164-171. [PMID: 32104792 PMCID: PMC7043018 DOI: 10.1007/978-3-030-35817-4_20] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Currently, there are still no early biomarkers to detect infants with risk of autism spectrum disorder (ASD), which is mainly diagnosed based on behavioral observations at three or four years of age. Since intervention efforts may miss a critical developmental window after 2 years old, it is clinically significant to identify imaging-based biomarkers at an early stage for better intervention, before behavioral diagnostic signs of ASD typically arising. Previous studies on older children and young adults with ASD demonstrate altered developmental trajectories of the amygdala and hippocampus. However, our knowledge on their developmental trajectories in early postnatal stages remains very limited. In this paper, for the first time, we propose a volume-based analysis of the amygdala and hippocampal subfields of the infant subjects with risk of ASD at 6, 12, and 24 months of age. To address the challenge of low tissue contrast and small structural size of infant amygdala and hippocampal subfields, we propose a novel deep-learning approach, dilated-dense U-Net, to digitally segment the amygdala and hippocampal subfields in a longitudinal dataset, the National Database for Autism Research (NDAR). A volume-based analysis is then performed based on the segmentation results. Our study shows that the overgrowth of amygdala and cornu ammonis sectors (CA) 1-3 May start from 6 months of age, which may be related to the emergence of autistic spectrum disorder.
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Affiliation(s)
- Guannan Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Meng-Hsiang Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Di Wu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Chunfeng Lian
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Quansen Sun
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Górriz JM, Ramírez J, Segovia F, Martínez FJ, Lai MC, Lombardo MV, Baron-Cohen S, Suckling J. A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms. Int J Neural Syst 2019; 29:1850058. [DOI: 10.1142/s0129065718500582] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above [Formula: see text] on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism ([Formula: see text], [Formula: see text]/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.
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Affiliation(s)
- Juan M. Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain
| | - F. Segovia
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain
| | - Francisco J. Martínez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain
| | - Meng-Chuan Lai
- Centre for Addiction and Mental Health and The Hospital for Sick Children, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M6J 1H4, Canada
| | - Michael V. Lombardo
- Department of Psychology, University of Cyprus, 2109 Aglantzia, Nicosia, Cyprus
| | - Simon Baron-Cohen
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
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Braden BB, Riecken C. Thinning Faster? Age-Related Cortical Thickness Differences in Adults with Autism Spectrum Disorder. RESEARCH IN AUTISM SPECTRUM DISORDERS 2019; 64:31-38. [PMID: 32565887 PMCID: PMC7304569 DOI: 10.1016/j.rasd.2019.03.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
BACKGROUND Over the course of the last 30 years, autism spectrum disorder (ASD) diagnoses have increased, thus identifying a large group of aging individuals with ASD. Currently, little is known regarding how aging will affect these individual's neuroanatomy, compared to the neurotypical (NT) population. Because of the anatomical overlap of ASD-related cortical pathology and age-related cortical thinning, both following an anterior-to-posterior severity gradient, we hypothesize adults with ASD will show larger age-related cortical thinning than NT adults. METHODS We analyzed cortical measurements using available data from the multi-site Autism Brain Imaging Data Exchange I (ABIDE I; n=282) and our own cohort of middle-age to older adults with and without ASD (n=47) mostly available in ABIDE II (n=35). We compared correlations between cortical measures and age in right-handed adults with ASD (n=157) and similar NT adults (n = 172), controlling for IQ and site. Participants were 18 to 64 years of age (mean=29.8 years; median=26 years). RESULTS We found significant differences between diagnosis groups in the relationship between age and cortical thickness for areas of left frontal lobe (pars opercularis), temporal lobe (inferior gyrus, middle gyrus, banks of the superior temporal sulcus, and entorhinal cortex), parietal lobe (inferior gyrus), and lateral occipital lobe. For all areas, adults with ASD showed a greater negative correlation between age and cortical thickness than NT adults. CONCLUSION As hypothesized, adults with ASD demonstrated exacerbated age-related cortical thinning, compared to NT adults. These differences were the largest and most extensive in the left temporal lobe. Future longitudinal work is warranted to investigate whether differences in brain age trajectories will translate to unique behavioral needs in older adults with ASD.
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Affiliation(s)
- B. Blair Braden
- Department of Speech and Hearing Science, Arizona State University, 976 S Forest Mall, Tempe, AZ 85281
| | - Cory Riecken
- Department of Speech and Hearing Science, Arizona State University, 976 S Forest Mall, Tempe, AZ 85281
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Pichitpunpong C, Thongkorn S, Kanlayaprasit S, Yuwattana W, Plaingam W, Sangsuthum S, Aizat WM, Baharum SN, Tencomnao T, Hu VW, Sarachana T. Phenotypic subgrouping and multi-omics analyses reveal reduced diazepam-binding inhibitor (DBI) protein levels in autism spectrum disorder with severe language impairment. PLoS One 2019; 14:e0214198. [PMID: 30921354 PMCID: PMC6438570 DOI: 10.1371/journal.pone.0214198] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 03/08/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The mechanisms underlying autism spectrum disorder (ASD) remain unclear, and clinical biomarkers are not yet available for ASD. Differences in dysregulated proteins in ASD have shown little reproducibility, which is partly due to ASD heterogeneity. Recent studies have demonstrated that subgrouping ASD cases based on clinical phenotypes is useful for identifying candidate genes that are dysregulated in ASD subgroups. However, this strategy has not been employed in proteome profiling analyses to identify ASD biomarker proteins for specific subgroups. METHODS We therefore conducted a cluster analysis of the Autism Diagnostic Interview-Revised (ADI-R) scores from 85 individuals with ASD to predict subgroups and subsequently identified dysregulated genes by reanalyzing the transcriptome profiles of individuals with ASD and unaffected individuals. Proteome profiling of lymphoblastoid cell lines from these individuals was performed via 2D-gel electrophoresis, and then mass spectrometry. Disrupted proteins were identified and compared to the dysregulated transcripts and reported dysregulated proteins from previous proteome studies. Biological functions were predicted using the Ingenuity Pathway Analysis (IPA) program. Selected proteins were also analyzed by Western blotting. RESULTS The cluster analysis of ADI-R data revealed four ASD subgroups, including ASD with severe language impairment, and transcriptome profiling identified dysregulated genes in each subgroup. Screening via proteome analysis revealed 82 altered proteins in the ASD subgroup with severe language impairment. Eighteen of these proteins were further identified by nano-LC-MS/MS. Among these proteins, fourteen were predicted by IPA to be associated with neurological functions and inflammation. Among these proteins, diazepam-binding inhibitor (DBI) protein was confirmed by Western blot analysis to be expressed at significantly decreased levels in the ASD subgroup with severe language impairment, and the DBI expression levels were correlated with the scores of several ADI-R items. CONCLUSIONS By subgrouping individuals with ASD based on clinical phenotypes, and then performing an integrated transcriptome-proteome analysis, we identified DBI as a novel candidate protein for ASD with severe language impairment. The mechanisms of this protein and its potential use as an ASD biomarker warrant further study.
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Affiliation(s)
- Chatravee Pichitpunpong
- M.Sc. Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Surangrat Thongkorn
- PhD Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Songphon Kanlayaprasit
- PhD Program in Clinical Biochemistry and Molecular Medicine, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Wasana Yuwattana
- B.Sc. Program in Medical Technology, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Waluga Plaingam
- College of Oriental Medicine, Rangsit University, Pathum Thani, Thailand
| | - Siriporn Sangsuthum
- Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Wan Mohd Aizat
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Syarul Nataqain Baharum
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Tewin Tencomnao
- Age-related Inflammation and Degeneration Research Unit, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Valerie Wailin Hu
- Department of Biochemistry and Molecular Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, United States of America
| | - Tewarit Sarachana
- Age-related Inflammation and Degeneration Research Unit, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
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Payabvash S, Palacios EM, Owen JP, Wang MB, Tavassoli T, Gerdes M, Brandes-Aitken A, Cuneo D, Marco EJ, Mukherjee P. White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models. Brain Connect 2019; 9:209-220. [PMID: 30661372 PMCID: PMC6444925 DOI: 10.1089/brain.2018.0658] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Prior neuroimaging studies have reported white matter network underconnectivity as a potential mechanism for autism spectrum disorder (ASD). In this study, we examined the structural connectome of children with ASD using edge density imaging (EDI), and then applied machine-learning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8-12 years were included: 14 with ASD and 33 typically developing children. The edge density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging. Tract-based spatial statistics was used for voxel-wise comparison and coregistration of ED maps in addition to conventional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Tract-based average DTI/connectome metrics were calculated and used as input for different machine-learning models: naïve Bayes, random forest, support vector machines (SVMs), and neural networks. For these models, cross-validation was performed with stratified random sampling ( × 1,000 permutations). The average accuracy among validation samples was calculated. In voxel-wise analysis, the body and splenium of corpus callosum, bilateral superior and posterior corona radiata, and left superior longitudinal fasciculus showed significantly lower ED in children with ASD; whereas, we could not find significant difference in FA, MD, and RD maps between the two study groups. Overall, machine-learning models using tract-based ED metrics had better performance in identification of children with ASD compared with those using FA, MD, and RD. The EDI-based random forest models had greater average accuracy (75.3%), specificity (97.0%), and positive predictive value (81.5%), whereas EDI-based polynomial SVM had greater sensitivity (51.4%) and negative predictive values (77.7%). In conclusion, we found reduced density of connectome edges in the posterior white matter tracts of children with ASD, and demonstrated the feasibility of connectome-based machine-learning algorithms in identification of children with ASD.
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Affiliation(s)
- Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
- Department of Radiology, University of Washington, Seattle, Washington
| | - Eva M. Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Julia P. Owen
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
- University of Pittsburg School of Medicine, Pittsburgh, Pennsylvania
| | - Maxwell B. Wang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
- Department of Neurology, University of California, San Francisco, San Francisco, California
| | - Teresa Tavassoli
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
| | - Molly Gerdes
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
| | - Anne Brandes-Aitken
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
| | - Daniel Cuneo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Elysa J. Marco
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
- Department of Pediatrics, University of California, San Francisco, San Francisco, California
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California
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40
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Schoen M, Asoglu H, Bauer HF, Müller HP, Abaei A, Sauer AK, Zhang R, Song TJ, Bockmann J, Kassubek J, Rasche V, Grabrucker AM, Boeckers TM. Shank3 Transgenic and Prenatal Zinc-Deficient Autism Mouse Models Show Convergent and Individual Alterations of Brain Structures in MRI. Front Neural Circuits 2019; 13:6. [PMID: 30853900 PMCID: PMC6395436 DOI: 10.3389/fncir.2019.00006] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 01/16/2019] [Indexed: 12/20/2022] Open
Abstract
Research efforts over the past decades have unraveled both genetic and environmental factors, which contribute to the development of autism spectrum disorders (ASD). It is, to date, largely unknown how different underlying causes result in a common phenotype. However, the individual course of development and the different comorbidities might reflect the heterogeneous genetic and non-genetic contributions. Therefore, it is reasonable to identify commonalities and differences in models of these disorders at the different hierarchical levels of brain function, including genetics/environment, cellular/synaptic functions, brain regions, connectivity, and behavior. To that end, we investigated Shank3 transgenic mouse lines and compared them with a prenatal zinc-deficient (PZD) mouse model of ASD at the level of brain structural alterations in an 11,7 T small animal magnetic resonance imaging (MRI). Animals were measured at 4 and 9 weeks of age. We identified a decreased total brain volume (TBV) and hippocampal size of Shank3−/− mice but a convergent increase of basal ganglia (striatum and globus pallidus) in most mouse lines. Moreover, Shank3 transgenic mice had smaller thalami, whereas PZD mice had this region enlarged. Intriguingly, Shank3 heterozygous knockout mice mostly showed minor abnormalities to full knockouts, which might reflect the importance of proper Shank3 dosage in neuronal cells. Most reported volume changes seemed to be more pronounced at younger age. Our results indicate both convergent and divergent brain region abnormalities in genetic and non-genetic models of ASD. These alterations of brain structures might be mirrored in the reported behavior of both models, which have not been assessed in this study.
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Affiliation(s)
- Michael Schoen
- Institute for Anatomy and Cell Biology, Ulm University, Ulm, Germany
| | - Harun Asoglu
- Institute for Anatomy and Cell Biology, Ulm University, Ulm, Germany
| | - Helen F Bauer
- Institute for Anatomy and Cell Biology, Ulm University, Ulm, Germany
| | | | - Alireza Abaei
- Core Facility Small Animal MRI, Ulm University, Ulm, Germany
| | - Ann Katrin Sauer
- Department of Biological Sciences, University of Limerick, Limerick, Ireland
| | - Rong Zhang
- Neuroscience Research Institute, Peking University, Beijing, China.,Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing, China.,Key Laboratory for Neuroscience, Ministry of Education/National Health and Family Planning Commission, Peking University, Beijing, China
| | - Tian-Jia Song
- Neuroscience Research Institute, Peking University, Beijing, China.,Department of Neurobiology, School of Basic Medical Sciences, Peking University, Beijing, China.,Key Laboratory for Neuroscience, Ministry of Education/National Health and Family Planning Commission, Peking University, Beijing, China
| | - Juergen Bockmann
- Institute for Anatomy and Cell Biology, Ulm University, Ulm, Germany
| | - Jan Kassubek
- Neurology Department, Ulm University, Ulm, Germany
| | - Volker Rasche
- Core Facility Small Animal MRI, Ulm University, Ulm, Germany
| | - Andreas M Grabrucker
- Department of Biological Sciences, University of Limerick, Limerick, Ireland.,Bernal Institute, University of Limerick, Limerick, Ireland.,Health Research Institute (HRI), University of Limerick, Limerick, Ireland
| | - Tobias M Boeckers
- Institute for Anatomy and Cell Biology, Ulm University, Ulm, Germany
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41
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Duffy FH, Als H. Autism, spectrum or clusters? An EEG coherence study. BMC Neurol 2019; 19:27. [PMID: 30764794 PMCID: PMC6375153 DOI: 10.1186/s12883-019-1254-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2018] [Accepted: 02/07/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Autism prevalence continues to grow, yet a universally agreed upon etiology is lacking despite manifold evidence of abnormalities especially in terms of genetics and epigenetics. The authors postulate that the broad definition of an omnibus 'spectrum disorder' may inhibit delineation of meaningful clinical correlations. This paper presents evidence that an objectively defined, EEG based brain measure may be helpful in illuminating the autism spectrum versus subgroups (clusters) question. METHODS Forty objectively defined EEG coherence factors created in prior studies demonstrated reliable separation of neuro-typical controls from subjects with autism, and reliable separation of subjects with Asperger's syndrome from all other subjects within the autism spectrum and from neurotypical controls. In the current study, these forty previously defined EEG coherence factors were used prospectively within a large (N = 430) population of subjects with autism in order to determine quantitatively the potential existence of separate clusters within this population. RESULTS By use of a recently published software package, NbClust, the current investigation determined that the 40 EEG coherence factors reliably identified two distinct clusters within the larger population of subjects with autism. These two clusters demonstrated highly significant differences. Of interest, many more subjects with Asperger's syndrome fell into one rather than the other cluster. CONCLUSIONS EEG coherence factors provide evidence of two highly significant separate clusters within the subject population with autism. The establishment of a unitary "Autism Spectrum Disorder" does a disservice to patients and clinicians, hinders much needed scientific exploration, and likely leads to less than optimal educational and/or interventional efforts.
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Affiliation(s)
- Frank H Duffy
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, USA.
| | - Heidelise Als
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, 300 Longwood Avenue, Enders 107, Boston, MA, 02115, USA
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42
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The yield of structural magnetic resonance imaging in autism spectrum disorders. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2018; 163:374-378. [PMID: 30546152 DOI: 10.5507/bp.2018.074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 11/23/2018] [Indexed: 12/27/2022] Open
Abstract
AIMS The aim of our study was to assess the yield of routine brain magnetic resonance imaging (MRI) performed at our hospital as part of the diagnostic procedures focused on autism. METHODS Our retrospective study involved children who had attended a diagnostic examination focused on autism and underwent brain MRIs between 1998-2015. The International Classification of Diseases, 10th edition was used to make clinical diagnoses. In 489 children (404 boys, 85 girls; mean age 8.0±4.2 years), a diagnosis of a pervasive developmental disorder was confirmed. Forty-five children, where the autism diagnosis was ruled out (but other psychiatric diagnoses found), served as a control group (36 boys, 9 girls; mean age 7.0±2.4 years). We can assume that in such a control group, brain abnormalities might occur at a higher frequency than in truly healthy children which would have the effect of reducing the difference between the groups. RESULTS MRI pathologies were more common in the autistic (45.4 %) compared to the control group (31.8%) but the difference was significant only at the trend level (P=0.085). Hypoplasia of the corpus callosum (CC) was significantly more common in the autistic vs. the control group (13.7 vs. 0%; P=0.009). In contrast, nonmyelinated areas of white matter were significantly more common in controls (31.8 vs.17.3%; P=0.018). Differences in other parameters were not significant. CONCLUSION The occurrence of CC hypoplasia on routine MRI scans could represent a "red flag" for suspicion of autism.
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43
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Li G, Liu M, Sun Q, Shen D, Wang L. Early Diagnosis of Autism Disease by Multi-channel CNNs. MACHINE LEARNING IN MEDICAL IMAGING. MLMI (WORKSHOP) 2018; 11046:303-309. [PMID: 30450494 PMCID: PMC6235442 DOI: 10.1007/978-3-030-00919-9_35] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Currently there are still no early biomarkers to detect infants with risk of autism spectrum disorder (ASD), which is mainly diagnosed based on behavior observations at three or four years old. Since intervention efforts may miss a critical developmental window after 2 years old, it is significant to identify imaging-based biomarkers for early diagnosis of ASD. Although some methods using magnetic resonance imaging (MRI) for brain disease prediction have been proposed in the last decade, few of them were developed for predicting ASD in early age. Inspired by deep multi-instance learning, in this paper, we propose a patch-level data-expanding strategy for multi-channel convolutional neural networks to automatically identify infants with risk of ASD in early age. Experiments were conducted on the National Database for Autism Research (NDAR), with results showing that our proposed method can significantly improve the performance of early diagnosis of ASD.
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Affiliation(s)
- Guannan Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Quansen Sun
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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44
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Cai J, Hu X, Guo K, Yang P, Situ M, Huang Y. Increased Left Inferior Temporal Gyrus Was Found in Both Low Function Autism and High Function Autism. Front Psychiatry 2018; 9:542. [PMID: 30425664 PMCID: PMC6218606 DOI: 10.3389/fpsyt.2018.00542] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 10/11/2018] [Indexed: 02/05/2023] Open
Abstract
Previous neuroimaging studies of autism spectrum disorder (ASD) have focused on subjects with IQ > 70 or ASD without considering IQ levels. It remains unclear whether differences in brain anatomy in this population are associated with variations in clinical phenotype. In this study, 19 children with low functioning autism (LFA) and 19 children with high functioning autism (HFA) were compared with 27 healthy controls (HC). We found increased gray matter volume (GMV) in the left inferior temporal gyrus in subjects with both HFA and LFA and increased GMV of left middle temporal gyrus BA21 was found only in the LFA group. A significant negative correlation was found between the left inferior temporal gyrus (LITG) and the score of repetitive behavior in the HFA group.
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Affiliation(s)
- Jia Cai
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiao Hu
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Kuifang Guo
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Pingyuan Yang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Mingjing Situ
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yi Huang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.,Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
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45
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Vianna P, Gomes JDA, Boquett JA, Fraga LR, Schuch JB, Vianna FSL, Schuler-Faccini L. Zika Virus as a Possible Risk Factor for Autism Spectrum Disorder: Neuroimmunological Aspects. Neuroimmunomodulation 2018; 25:320-327. [PMID: 30630174 DOI: 10.1159/000495660] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 11/16/2018] [Indexed: 11/19/2022] Open
Abstract
The recent outbreak of the Zika virus (ZIKV) and the discovery that perinatal Zika exposure can lead to the Congenital Zika Syndrome has promoted a call for prevention measures. Due to the increased number of babies born with microcephaly, structural brain abnormalities, and neurological alterations in regions affected by ZIKV, investigations were carried out in order to better understand this process. The maternal immune system directly influences the fetal central nervous system, and complications during pregnancy have been associated with neurodevelopmental disorders. Autism spectrum disorder (ASD), a neurodevelopmental disorder commonly manifested in the first years of life, is a disease with multifactorial etiology and is manifested typically by social and communication impairments, as well as stereotyped behaviors. Brain abnormalities, including both anatomically and functionally, can be observed in this disorder, suggesting delays in neuronal maturation and altered brain connectivity. It is known that some viral congenital infections, such as rubella, and cytomegalovirus can interfere with brain development, being associated with brain calcification, microcephaly, and ASD. Here, we reviewed a range of studies evaluating the aspects concerning brain development, immunological status during pregnancy, and neuroimmunomodulation in congenital viral infections, and we discuss if the fetal brain infection caused by ZIKV could predispose to ASD. Finally, we suggest a mechanism encompassing neurological and immunological pathways that could play a role in the development of ASD in infants after ZIKV infection in pregnancy.
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Affiliation(s)
- Priscila Vianna
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- National Institute of Population Medical Genetics (INAGEMP), Porto Alegre, Brazil
| | - Julia do Amaral Gomes
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- National Institute of Population Medical Genetics (INAGEMP), Porto Alegre, Brazil
- Genomic Medicine Laboratory, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Juliano André Boquett
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- National Institute of Population Medical Genetics (INAGEMP), Porto Alegre, Brazil
| | - Lucas Rosa Fraga
- National Institute of Population Medical Genetics (INAGEMP), Porto Alegre, Brazil
- Brazilian Teratogen Information Service (SIAT), Medical Genetics Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
- Department of Morphological Sciences, Institute of Health Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Jaqueline Bohrer Schuch
- Graduate Program in Biomedical Gerontology, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Fernanda Sales Luiz Vianna
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- National Institute of Population Medical Genetics (INAGEMP), Porto Alegre, Brazil
- Brazilian Teratogen Information Service (SIAT), Medical Genetics Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
- Genomic Medicine Laboratory, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Lavínia Schuler-Faccini
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil,
- National Institute of Population Medical Genetics (INAGEMP), Porto Alegre, Brazil,
- Brazilian Teratogen Information Service (SIAT), Medical Genetics Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil,
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46
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Feczko E, Balba NM, Miranda-Dominguez O, Cordova M, Karalunas SL, Irwin L, Demeter DV, Hill AP, Langhorst BH, Grieser Painter J, Van Santen J, Fombonne EJ, Nigg JT, Fair DA. Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm. Neuroimage 2017; 172:674-688. [PMID: 29274502 DOI: 10.1016/j.neuroimage.2017.12.044] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 12/11/2017] [Accepted: 12/14/2017] [Indexed: 12/21/2022] Open
Abstract
DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple cognitive mechanisms. This study attempts to identify and characterize cognitive subtypes within the ASD population using our Functional Random Forest (FRF) machine learning classification model. This model trained a traditional random forest model on measures from seven tasks that reflect multiple levels of information processing. 47 ASD diagnosed and 58 typically developing (TD) children between the ages of 9 and 13 participated in this study. Our RF model was 72.7% accurate, with 80.7% specificity and 63.1% sensitivity. Using the random forest model, the FRF then measures the proximity of each subject to every other subject, generating a distance matrix between participants. This matrix is then used in a community detection algorithm to identify subgroups within the ASD and TD groups, and revealed 3 ASD and 4 TD putative subgroups with unique behavioral profiles. We then examined differences in functional brain systems between diagnostic groups and putative subgroups using resting-state functional connectivity magnetic resonance imaging (rsfcMRI). Chi-square tests revealed a significantly greater number of between group differences (p < .05) within the cingulo-opercular, visual, and default systems as well as differences in inter-system connections in the somato-motor, dorsal attention, and subcortical systems. Many of these differences were primarily driven by specific subgroups suggesting that our method could potentially parse the variation in brain mechanisms affected by ASD.
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Affiliation(s)
- E Feczko
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland OR, 97239, USA.
| | - N M Balba
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - O Miranda-Dominguez
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - M Cordova
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - S L Karalunas
- Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - L Irwin
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - D V Demeter
- The University of Texas at Austin, Department of Psychology, Austin, TX 78713, USA
| | - A P Hill
- Department of Pediatrics, Oregon Health & Science University, Portland, OR 97239, USA; Center for Spoken Language Understanding, Institute on Development & Disability, Oregon Health & Science University, Portland, OR 97239, USA
| | - B H Langhorst
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - J Grieser Painter
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
| | - J Van Santen
- Department of Pediatrics, Oregon Health & Science University, Portland, OR 97239, USA; Center for Spoken Language Understanding, Institute on Development & Disability, Oregon Health & Science University, Portland, OR 97239, USA
| | - E J Fombonne
- Department of Pediatrics, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA
| | - J T Nigg
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA; Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - D A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, USA; Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, USA
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Liu T, Liu X, Yi L, Zhu C, Markey PS, Pelowski M. Assessing autism at its social and developmental roots: A review of Autism Spectrum Disorder studies using functional near-infrared spectroscopy. Neuroimage 2017; 185:955-967. [PMID: 28966083 DOI: 10.1016/j.neuroimage.2017.09.044] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 08/16/2017] [Accepted: 09/20/2017] [Indexed: 12/15/2022] Open
Abstract
We review a relatively new method for studying the developing brain in children and infants with Autism Spectrum Disorder (ASD). Despite advances in behavioral screening and brain imaging, due to paradigms that do not easily allow for testing of awake, very young, and socially-engaged children-i.e., the social and the baby brain-the biological underpinnings of this disorder remain a mystery. We introduce an approach based on functional near-infrared spectroscopy (fNIRS), which offers a noninvasive imaging technique for studying functional activations by measuring changes in the brain's hemodynamic properties. This further enables measurement of brain activation in upright, interactive settings, while maintaining general equivalence to fMRI findings. We review the existing studies that have used fNIRS for ASD, discussing their promise, limitations, and their technical aspects, gearing this study to the researcher who may be new to this technique and highlighting potential targets for future research.
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Affiliation(s)
- Tao Liu
- School of Management, Zhejiang University, Hangzhou, China.
| | - Xingchen Liu
- College of Education and Psychology, Hainan Normal University, Haikou, China
| | - Li Yi
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China; Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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48
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Farah MJ. The Neuroscience of Socioeconomic Status: Correlates, Causes, and Consequences. Neuron 2017; 96:56-71. [DOI: 10.1016/j.neuron.2017.08.034] [Citation(s) in RCA: 303] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 07/31/2017] [Accepted: 08/17/2017] [Indexed: 12/12/2022]
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49
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Zauche LH, Darcy Mahoney AE, Higgins MK. Predictors of Co-occurring Neurodevelopmental Disabilities in Children With Autism Spectrum Disorders. J Pediatr Nurs 2017; 35:113-119. [PMID: 28728761 DOI: 10.1016/j.pedn.2017.04.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 03/30/2017] [Accepted: 04/05/2017] [Indexed: 11/25/2022]
Abstract
PURPOSE Co-occurring neurodevelopmental disabilities (including cognitive and language delays and attention deficit hyperactivity disorder) affect over half of children with ASD and may affect later behavioral, language, and cognitive outcomes beyond the ASD diagnosis. However, no studies have examined predictors of co-occurring neurodevelopmental disabilities in children with ASD. This study investigated whether maternal sociodemographic, perinatal and neonatal factors are associated with co-occurring disabilities. DESIGN AND METHODS This study involved a retrospective analysis of medical records for children diagnosed with ASD between 2009 and 2010 at an Autism Center in the southeast United States. Logistic regression was used to identify predictors of co-occurring neurodevelopmental disabilities. RESULTS Of the 385 children in the sample, 61% had a co-occurring neurodevelopmental disability. Children whose mothers had less education (OR: 0.905), had never been married (OR: 1.803), or had bleeding during pregnancy (OR: 2.233) were more likely to have a co-occurring neurodevelopmental disability. Both preterm birth and African American race were associated with bleeding during pregnancy. CONCLUSIONS Several maternal and perinatal risk factors for ASD were found to put children at risk for further diagnoses of co-occurring neurodevelopmental disabilities. While prematurity, a well-established risk factor for ASD, as well as maternal ethnicity was not found to increase the risk of a co-occurring disability, this study suggests that bleeding during pregnancy may moderate these relationships. PRACTICE IMPLICATIONS Understanding maternal, perinatal, and neonatal risk factors may inform healthcare provider screening for ASD and co-occurring neurodevelopmental disabilities by helping providers recognize infants who present with multiple risk factors.
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Affiliation(s)
- Lauren Head Zauche
- Emory University Nell Hodgson Woodruff School of Nursing, 1520 Clifton Rd NE, Atlanta, GA 30322, United States.
| | - Ashley E Darcy Mahoney
- George Washington University, Autism and Neurodevelopmental Disorders Institute, 2121 Eye Street NW, Washington, D.C. 20052, United States
| | - Melinda K Higgins
- Emory University Nell Hodgson Woodruff School of Nursing, 1520 Clifton Rd NE, Atlanta, GA 30322, United States
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50
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Nickel K, Tebartz van Elst L, Perlov E, Endres D, Müller GT, Riedel A, Fangmeier T, Maier S. Altered white matter integrity in adults with autism spectrum disorder and an IQ >100: a diffusion tensor imaging study. Acta Psychiatr Scand 2017; 135:573-583. [PMID: 28407202 DOI: 10.1111/acps.12731] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/13/2017] [Indexed: 12/16/2022]
Abstract
OBJECTIVE White matter (WM) alterations have been reported in children and adults with autism spectrum disorder (ASD). In particular, impaired connectivity of limbic structures may be related to social deficits. Heterogeneous findings could be explained in terms of differences in sample characteristics and methodology. In this context, non-syndromic forms might differ substantially in WM structure from secondary ASD forms. METHOD In an attempt to recruit a homogeneous study sample, we included adults with high-functioning ASD and an IQ > 100 to decrease the influence of syndromic forms being often associated with cognitive deficits. Diffusion tensor imaging (DTI) was performed in 30 participants with ASD and 30 pairwise-matched controls. Fractional anisotropy (FA) and mean diffusivity (MD) as surrogate imaging markers for WM integrity were calculated. RESULTS We found a significant FA decrease in the ASD group in the genu and body of the corpus callosum (CC). Increased MD was detected in the subgenual anterior cingulate cortex (sACC). CONCLUSION The finding of decreased WM integrity in the genu of the CC is in line with earlier studies reporting a decreased number of interhemispheric fibers in the frontal lobe of ASD. Alterations in the sACC might be associated with 'Theory of mind' deficits.
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Affiliation(s)
- K Nickel
- Section for Experimental Neuropsychiatry, Department for Psychiatry & Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany
| | - L Tebartz van Elst
- Section for Experimental Neuropsychiatry, Department for Psychiatry & Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany
| | - E Perlov
- Section for Experimental Neuropsychiatry, Department for Psychiatry & Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany.,Luzerner Psychiatrie, Hospital St. Urban, St. Urban, Switzerland
| | - D Endres
- Section for Experimental Neuropsychiatry, Department for Psychiatry & Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany
| | - G T Müller
- Section for Experimental Neuropsychiatry, Department for Psychiatry & Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany
| | - A Riedel
- Section for Experimental Neuropsychiatry, Department for Psychiatry & Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany
| | - T Fangmeier
- Section for Experimental Neuropsychiatry, Department for Psychiatry & Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany
| | - S Maier
- Section for Experimental Neuropsychiatry, Department for Psychiatry & Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany
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