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Drehmer I, Santos-Terra J, Gottfried C, Deckmann I. mTOR signaling pathway as a pathophysiologic mechanism in preclinical models of autism spectrum disorder. Neuroscience 2024; 563:33-42. [PMID: 39481829 DOI: 10.1016/j.neuroscience.2024.10.050] [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: 09/05/2024] [Revised: 10/23/2024] [Accepted: 10/28/2024] [Indexed: 11/03/2024]
Abstract
Autism spectrum disorder (ASD) is a highly prevalent multifactorial disorder characterized by social deficits and stereotypies. Despite extensive research efforts, the etiology of ASD remains poorly understood. However, studies using preclinical models have identified the mechanistic target of rapamycin kinase (mTOR) signaling pathway as a key player in ASD-related features. This review examines genetic and environmental models of ASD, focusing on their association with the mTOR pathway. We organize findings on alterations within this pathway, providing insights about the potential mechanisms involved in the onset and maintenance of ASD symptoms. Our analysis highlights the central role of mTOR hyperactivation in disrupting autophagic processes, neural organization, and neurotransmitter pathways, which collectively contribute to ASD phenotypes. The review also discusses the therapeutic potential of mTOR pathway inhibitors, such as rapamycin, in mitigating ASD characteristics. These insights underscore the importance of the mTOR pathway as a target for future research and therapeutic intervention in ASD. This review innovates by bringing the convergence of disrupted mTOR signaling in preclinical models and clinical data associated with ASD.
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Affiliation(s)
- Isabela Drehmer
- Translational Research Group on Autism Spectrum Disorder - GETTEA, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; National Institute of Science and Technology in Neuroimmunomodulation - INCT-NIM, Brazil; Autism Wellbeing and Research Development - AWARD - Initiative BR-UK-CA, Brazil; Psychiatry Molecular Laboratory, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil; Department of Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Júlio Santos-Terra
- Translational Research Group on Autism Spectrum Disorder - GETTEA, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; National Institute of Science and Technology in Neuroimmunomodulation - INCT-NIM, Brazil; Autism Wellbeing and Research Development - AWARD - Initiative BR-UK-CA, Brazil; Psychiatry Molecular Laboratory, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Department of Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Carmem Gottfried
- Translational Research Group on Autism Spectrum Disorder - GETTEA, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; National Institute of Science and Technology in Neuroimmunomodulation - INCT-NIM, Brazil; Autism Wellbeing and Research Development - AWARD - Initiative BR-UK-CA, Brazil; Psychiatry Molecular Laboratory, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Department of Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Iohanna Deckmann
- Translational Research Group on Autism Spectrum Disorder - GETTEA, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; National Institute of Science and Technology in Neuroimmunomodulation - INCT-NIM, Brazil; Autism Wellbeing and Research Development - AWARD - Initiative BR-UK-CA, Brazil; Psychiatry Molecular Laboratory, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Department of Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
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2
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Persichetti AS, Shao J, Gotts SJ, Martin A. A functional parcellation of the whole brain in high-functioning individuals with autism spectrum disorder reveals atypical patterns of network organization. Mol Psychiatry 2024:10.1038/s41380-024-02764-6. [PMID: 39349967 DOI: 10.1038/s41380-024-02764-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 09/19/2024] [Accepted: 09/23/2024] [Indexed: 10/09/2024]
Abstract
Researchers studying autism spectrum disorder (ASD) lack a comprehensive map of the functional network topography in the ASD brain. We used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in a group of seventy high-functioning individuals with ASD and a group of seventy typically developing (TD) individuals. The rs-fMRI data were collected using an imaging sequence optimized to achieve high temporal signal-to-noise ratio (tSNR) across the whole-brain. We identified functional networks using a parcellation routine that intrinsically incorporates internal consistency and repeatability of the networks by keeping only network distinctions that agree across halves of the data over multiple random iterations in each group. The groups were tightly matched on tSNR, in-scanner motion, age, and IQ. We compared the maps from each group and found that functional networks in the ASD group are atypical in three seemingly related ways: (1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, (2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and (3) subcortical structures and the hippocampus are atypically integrated with the neocortex. These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.
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Affiliation(s)
- Andrew S Persichetti
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Jiayu Shao
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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Chiappini E, Massaccesi C, Korb S, Steyrl D, Willeit M, Silani G. Neural Hyperresponsivity During the Anticipation of Tangible Social and Nonsocial Rewards in Autism Spectrum Disorder: A Concurrent Neuroimaging and Facial Electromyography Study. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:948-957. [PMID: 38642898 DOI: 10.1016/j.bpsc.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Atypical anticipation of social reward has been shown to lie at the core of the social challenges faced by individuals with autism spectrum disorder (ASD). However, previous research has yielded inconsistent results and has often overlooked crucial characteristics of stimuli. Here, we investigated ASD reward processing using social and nonsocial tangible stimuli, carefully matched on several key dimensions. METHODS We examined the anticipation and consumption of social (interpersonal touch) and nonsocial (flavored milk) rewards in 25 high-functioning individuals with ASD and 25 neurotypical adult individuals. In addition to subjective ratings of wanting and liking, we measured physical energetic expenditure to obtain the rewards, brain activity with neuroimaging, and facial reactions through electromyography on a trial-by-trial basis. RESULTS Participants with ASD did not exhibit reduced motivation for social or nonsocial rewards; their subjective ratings, motivated efforts, and facial reactions were comparable to those of neurotypical participants. However, anticipation of higher-value rewards increased neural activation in lateral parietal cortices, sensorimotor regions, and the orbitofrontal cortex. Moreover, participants with ASD exhibited hyperconnectivity between frontal medial regions and occipital regions and the thalamus. CONCLUSIONS Individuals with ASD who experienced rewards with tangible characteristics, whether social or nonsocial, displayed typical subjective and objective motivational and hedonic responses. Notably, the observed hyperactivations in sensory and attentional nodes during anticipation suggest atypical sensory overprocessing of forthcoming rewards rather than decreased reward value. While these atypicalities may not have manifested in observable behavior here, they could impact real-life social interactions that require nuanced predictions, potentially leading to the misperception of reduced interest in rewarding social stimuli in ASD.
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Affiliation(s)
- Emilio Chiappini
- Department of Clinical and Health Psychology, University of Vienna, Vienna, Austria; Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany.
| | - Claudia Massaccesi
- Department of Clinical and Health Psychology, University of Vienna, Vienna, Austria; Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria
| | - Sebastian Korb
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria; Centre for Brain Science, Department of Psychology, University of Essex, Colchester, United Kingdom
| | - David Steyrl
- Department of Cognition, Emotion, and Methods in Psychology, University of Vienna, Vienna, Austria
| | - Matthäus Willeit
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Vienna, Austria
| | - Giorgia Silani
- Department of Clinical and Health Psychology, University of Vienna, Vienna, Austria.
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Li M, Izumoto M, Wang Y, Kato Y, Iwatani Y, Hirata I, Mizuno Y, Tachibana M, Mohri I, Kagitani-Shimono K. Altered white matter connectivity of ventral language networks in autism spectrum disorder: An automated fiber quantification analysis with multi-site datasets. Neuroimage 2024; 297:120731. [PMID: 39002786 DOI: 10.1016/j.neuroimage.2024.120731] [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: 02/27/2024] [Revised: 06/28/2024] [Accepted: 07/11/2024] [Indexed: 07/15/2024] Open
Abstract
Comprehension and pragmatic deficits are prevalent in autism spectrum disorder (ASD) and are potentially linked to altered connectivity in the ventral language networks. However, previous magnetic resonance imaging studies have not sufficiently explored the microstructural abnormalities in the ventral fiber tracts underlying comprehension dysfunction in ASD. Additionally, the precise locations of white matter (WM) changes in the long tracts of patients with ASD remain poorly understood. In the current study, we applied the automated fiber-tract quantification (AFQ) method to investigate the fine-grained WM properties of the ventral language pathway and their relationships with comprehension and symptom manifestation in ASD. The analysis included diffusion/T1 weighted imaging data of 83 individuals with ASD and 83 age-matched typically developing (TD) controls. Case-control comparisons were performed on the diffusion metrics of the ventral tracts at both the global and point-wise levels. We also explored correlations between diffusion metrics, comprehension performance, and ASD traits, and conducted subgroup analyses based on age range to examine developmental moderating effects. Individuals with ASD exhibited remarkable hypoconnectivity in the ventral tracts, particularly in the temporal portions of the left inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF). These WM abnormalities were associated with poor comprehension and more severe ASD symptoms. Furthermore, WM alterations in the ventral tract and their correlation with comprehension dysfunction were more prominent in younger children with ASD than in adolescents. These findings indicate that WM disruptions in the temporal portions of the left ILF/IFOF are most notable in ASD, potentially constituting the core neurological underpinnings of comprehension and communication deficits in autism. Moreover, impaired WM connectivity and comprehension ability in patients with ASD appear to improve with age.
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Affiliation(s)
- Min Li
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Maya Izumoto
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Yide Wang
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Yoko Kato
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Yoshiko Iwatani
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Ikuko Hirata
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Yoshifumi Mizuno
- Research Center for Child Mental Development, University of Fukui, Fukui, Japan
| | - Masaya Tachibana
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Ikuko Mohri
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Kuriko Kagitani-Shimono
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan.
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Chandra NK, Sitek KR, Chandrasekaran B, Sarkar A. Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:10.1162/imag_a_00258. [PMID: 39421593 PMCID: PMC11485223 DOI: 10.1162/imag_a_00258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultrahigh-field functional magnetic resonance imaging (fMRI) acquisition have enabled novel noninvasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high-temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.
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Affiliation(s)
- Noirrit Kiran Chandra
- The University of Texas at Dallas, Department of Mathematical Sciences, Richardson, TX 76010, USA
| | - Kevin R. Sitek
- Northwestern University, Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Evanston, IL 60208, USA
| | - Bharath Chandrasekaran
- Northwestern University, Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Evanston, IL 60208, USA
| | - Abhra Sarkar
- The University of Texas at Austin, Department of Statistics and Data Sciences, Austin, TX 78712, USA
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Morella I, Brambilla R, Herault Y. Editorial: Cellular and molecular mechanisms in social and repetitive behaviours: a focus on cortico-striatal circuitry. Front Cell Neurosci 2024; 18:1470882. [PMID: 39175505 PMCID: PMC11338908 DOI: 10.3389/fncel.2024.1470882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Accepted: 07/30/2024] [Indexed: 08/24/2024] Open
Affiliation(s)
- Ilaria Morella
- Dipartimento di Biologia e Biotecnologie “Lazzaro Spallanzani”, Università di Pavia, Pavia, Italy
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom
| | - Riccardo Brambilla
- Dipartimento di Biologia e Biotecnologie “Lazzaro Spallanzani”, Università di Pavia, Pavia, Italy
| | - Yann Herault
- INSERM U964 Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Illkirch-Graffenstaden, Alsace, France
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7
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Yang Y, Wang Q, Wang C, Buxbaum J, Ionita-Laza I. KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies. Am J Hum Genet 2024; 111:1448-1461. [PMID: 38821058 PMCID: PMC11267528 DOI: 10.1016/j.ajhg.2024.05.003] [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: 09/10/2023] [Revised: 05/02/2024] [Accepted: 05/06/2024] [Indexed: 06/02/2024] Open
Abstract
Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders.
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Affiliation(s)
- Yi Yang
- Department of Biostatistics, City University of Hong Kong, Hong Kong SAR, China; School of Data Science, City University of Hong Kong, Hong Kong SAR, China.
| | - Qi Wang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Chen Wang
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - Joseph Buxbaum
- Departments of Psychiatry, Neuroscience, and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Columbia University, New York, NY 10032, USA; Department of Statistics, Lund University, Lund, Sweden
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Yang Y, Song P, Wang Y. Assessing the impact of repetitive transcranial magnetic stimulation on effective connectivity in autism spectrum disorder: An initial exploration using TMS-EEG analysis. Heliyon 2024; 10:e31746. [PMID: 38828287 PMCID: PMC11140796 DOI: 10.1016/j.heliyon.2024.e31746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/05/2024] Open
Abstract
Initial indications propose that repetitive transcranial magnetic stimulation (rTMS) could mitigate clinical manifestations in patients with autism spectrum disorder (ASD). Nevertheless, the precise mechanisms responsible for these therapeutic and behavioral outcomes remain elusive. We examined alterations in effective connectivity induced by rTMS using concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in children with ASD. TMS-EEG data were acquired from 12 children diagnosed with ASD both before and following rTMS treatment. The rTMS intervention regimen included delivering 5-s trains at a frequency of 15 Hz, with 10-min intervals between trains, targeting the left parietal lobe. This was conducted on each consecutive weekday over 3 weeks, totaling 15 sessions. The dynamic EEG network analysis revealed that following the rTMS intervention, long-range feedback connections within the brains of ASD patients were strengthened (e.g., frontal to parietal regions, frontal to occipital regions, and frontal to posterior temporal regions), and short-range connections were weakened (e.g., between the bilateral occipital regions, and between the occipital and posterior temporal regions). In alignment with alterations in network connectivity, there was a corresponding amelioration in fundamental ASD symptoms, as assessed through clinical scales post-treatment. According to our findings, people with ASD may have increased long-range frontal-posterior feedback connection on application of rTMS to the parietal lobe.
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Affiliation(s)
- Yingxue Yang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Beijing Key Laboratory of Neuromodulation, Beijing, 100053, China
| | - Penghui Song
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Beijing Key Laboratory of Neuromodulation, Beijing, 100053, China
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Beijing Key Laboratory of Neuromodulation, Beijing, 100053, China
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9
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Leone R, Zuglian C, Brambilla R, Morella I. Understanding copy number variations through their genes: a molecular view on 16p11.2 deletion and duplication syndromes. Front Pharmacol 2024; 15:1407865. [PMID: 38948459 PMCID: PMC11211608 DOI: 10.3389/fphar.2024.1407865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/16/2024] [Indexed: 07/02/2024] Open
Abstract
Neurodevelopmental disorders (NDDs) include a broad spectrum of pathological conditions that affect >4% of children worldwide, share common features and present a variegated genetic origin. They include clinically defined diseases, such as autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder (ADHD), motor disorders such as Tics and Tourette's syndromes, but also much more heterogeneous conditions like intellectual disability (ID) and epilepsy. Schizophrenia (SCZ) has also recently been proposed to belong to NDDs. Relatively common causes of NDDs are copy number variations (CNVs), characterised by the gain or the loss of a portion of a chromosome. In this review, we focus on deletions and duplications at the 16p11.2 chromosomal region, associated with NDDs, ID, ASD but also epilepsy and SCZ. Some of the core phenotypes presented by human carriers could be recapitulated in animal and cellular models, which also highlighted prominent neurophysiological and signalling alterations underpinning 16p11.2 CNVs-associated phenotypes. In this review, we also provide an overview of the genes within the 16p11.2 locus, including those with partially known or unknown function as well as non-coding RNAs. A particularly interesting interplay was observed between MVP and MAPK3 in modulating some of the pathological phenotypes associated with the 16p11.2 deletion. Elucidating their role in intracellular signalling and their functional links will be a key step to devise novel therapeutic strategies for 16p11.2 CNVs-related syndromes.
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Affiliation(s)
- Roberta Leone
- Università di Pavia, Dipartimento di Biologia e Biotecnologie “Lazzaro Spallanzani”, Pavia, Italy
| | - Cecilia Zuglian
- Università di Pavia, Dipartimento di Biologia e Biotecnologie “Lazzaro Spallanzani”, Pavia, Italy
| | - Riccardo Brambilla
- Università di Pavia, Dipartimento di Biologia e Biotecnologie “Lazzaro Spallanzani”, Pavia, Italy
- Cardiff University, School of Biosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, United Kingdom
| | - Ilaria Morella
- Cardiff University, School of Biosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, United Kingdom
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10
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Shan X, Wang P, Yin Q, Li Y, Wang X, Feng Y, Xiao J, Li L, Huang X, Chen H, Duan X. Atypical dynamic neural configuration in autism spectrum disorder and its relationship to gene expression profiles. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02476-w. [PMID: 38861168 DOI: 10.1007/s00787-024-02476-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/18/2024] [Indexed: 06/12/2024]
Abstract
Although it is well recognized that autism spectrum disorder (ASD) is associated with atypical dynamic functional connectivity patterns, the dynamic changes in brain intrinsic activity over each time point and the potential molecular mechanisms associated with atypical dynamic temporal characteristics in ASD remain unclear. Here, we employed the Hidden Markov Model (HMM) to explore the atypical neural configuration at every scanning time point in ASD, based on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange. Subsequently, partial least squares regression and pathway enrichment analysis were employed to explore the potential molecular mechanism associated with atypical neural dynamics in ASD. 8 HMM states were inferred from rs-fMRI data. Compared to typically developing, individuals on the autism spectrum showed atypical state-specific temporal characteristics, including number of states and occurrences, mean life time and transition probability between states. Moreover, these atypical temporal characteristics could predict communication difficulties of ASD, and states assoicated with negative activation in default mode network and frontoparietal network, and positive activation in somatomotor network, ventral attention network, and limbic network, had higher predictive contribution. Furthermore, a total of 321 genes was revealed to be significantly associated with atypical dynamic brain states of ASD, and these genes are mainly enriched in neurodevelopmental pathways. Our study provides new insights into characterizing the atypical neural dynamics from a moment-to-moment perspective, and indicates a linkage between atypical neural configuration and gene expression in ASD.
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Affiliation(s)
- Xiaolong Shan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Peng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Qing Yin
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Youyi Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Xiaotian Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Yu Feng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Lei Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Xinyue Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
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11
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Zhou R, Sun C, Sun M, Ruan Y, Li W, Gao X. Altered intra- and inter-network connectivity in autism spectrum disorder. Aging (Albany NY) 2024; 16:10004-10015. [PMID: 38862259 PMCID: PMC11210244 DOI: 10.18632/aging.205913] [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: 12/01/2023] [Accepted: 05/03/2024] [Indexed: 06/13/2024]
Abstract
OBJECTIVE A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored. METHODS We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector machine was used to discriminate ASD from HC. RESULTS Through different sparsities, ASD exhibited significantly decreased intra-network connectivity within default mode network and dorsal attention network, increased connectivity between limbic network and subcortical network, and decreased connectivity between default mode network and limbic network. Using the classifier trained on altered intra- and inter-network connectivity, multivariate pattern analyses classified the ASD from HC with 71.74% accuracy, 70.21% specificity and 75.56% sensitivity in 10% sparsity of functional connectivity. CONCLUSIONS ASD showed characteristic reorganization of the brain networks and this provided new insight into the underlying process of the functional connectome dysfunction in ASD.
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Affiliation(s)
- Rui Zhou
- School of Zhang Jian, Nantong University, Nantong, China
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
| | - Chenhao Sun
- Department of Radiology, Rugao Jian’an Hospital, Nantong, China
| | - Mingxiang Sun
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Yudi Ruan
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
| | - Weikai Li
- College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
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12
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Chen Y, Yan J, Jiang M, Zhang T, Zhao Z, Zhao W, Zheng J, Yao D, Zhang R, Kendrick KM, Jiang X. Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7275-7286. [PMID: 35286265 DOI: 10.1109/tnnls.2022.3154755] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. However, previous studies are largely node-centralized and ignore edge features for graph classification tasks, resulting in moderate performance of graph classification accuracy. Moreover, the generalizability of GNN model is still far from satisfactory in brain disorder [e.g., autism spectrum disorder (ASD)] identification due to considerable individual differences in symptoms among patients as well as data heterogeneity among different sites. In order to address the above limitations, this study proposes a novel adversarial learning-based node-edge graph attention network (AL-NEGAT) for ASD identification based on multimodal MRI data. First, both node and edge features are modeled based on structural and functional MRI data to leverage complementary brain information and preserved in the constructed weighted adjacent matrix for individuals through the attention mechanism in the proposed NEGAT. Second, two AL methods are employed to improve the generalizability of NEGAT. Finally, a gradient-based saliency map strategy is utilized for model interpretation to identify important brain regions and connections contributing to the classification. Experimental results based on the public Autism Brain Imaging Data Exchange I (ABIDE I) data demonstrate that the proposed framework achieves a classification accuracy of 74.7% between ASD and typical developing (TD) groups based on 1007 subjects across 17 different sites and outperforms the state-of-the-art methods, indicating satisfying classification ability and generalizability of the proposed AL-NEGAT model. Our work provides a powerful tool for brain disorder identification.
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13
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Degré-Pelletier J, Danis É, Thérien VD, Bernhardt B, Barbeau EB, Soulières I. Differential neural correlates underlying visuospatial versus semantic reasoning in autistic children. Cereb Cortex 2024; 34:19-29. [PMID: 38696600 PMCID: PMC11065103 DOI: 10.1093/cercor/bhae093] [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: 10/31/2023] [Revised: 01/25/2024] [Accepted: 02/20/2024] [Indexed: 05/04/2024] Open
Abstract
While fronto-posterior underconnectivity has often been reported in autism, it was shown that different contexts may modulate between-group differences in functional connectivity. Here, we assessed how different task paradigms modulate functional connectivity differences in a young autistic sample relative to typically developing children. Twenty-three autistic and 23 typically developing children aged 6 to 15 years underwent functional magnetic resonance imaging (fMRI) scanning while completing a reasoning task with visuospatial versus semantic content. We observed distinct connectivity patterns in autistic versus typical children as a function of task type (visuospatial vs. semantic) and problem complexity (visual matching vs. reasoning), despite similar performance. For semantic reasoning problems, there was no significant between-group differences in connectivity. However, during visuospatial reasoning problems, we observed occipital-occipital, occipital-temporal, and occipital-frontal over-connectivity in autistic children relative to typical children. Also, increasing the complexity of visuospatial problems resulted in increased functional connectivity between occipital, posterior (temporal), and anterior (frontal) brain regions in autistic participants, more so than in typical children. Our results add to several studies now demonstrating that the connectivity alterations in autistic relative to neurotypical individuals are much more complex than previously thought and depend on both task type and task complexity and their respective underlying cognitive processes.
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Affiliation(s)
- Janie Degré-Pelletier
- Laboratory on Intelligence and Development in Autism, Department of Psychology, Université du Québec à Montréal, C.P. 8888 Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada
- Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, 7070, Boulevard Perras, Montréal, Quebec H1E 1A4, Canada
| | - Éliane Danis
- Laboratory on Intelligence and Development in Autism, Department of Psychology, Université du Québec à Montréal, C.P. 8888 Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada
- Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, 7070, Boulevard Perras, Montréal, Quebec H1E 1A4, Canada
| | - Véronique D Thérien
- Laboratory on Intelligence and Development in Autism, Department of Psychology, Université du Québec à Montréal, C.P. 8888 Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada
- Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, 7070, Boulevard Perras, Montréal, Quebec H1E 1A4, Canada
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801, University street, Montreal, Quebec H3A 2B4, Canada
| | - Elise B Barbeau
- Laboratory on Intelligence and Development in Autism, Department of Psychology, Université du Québec à Montréal, C.P. 8888 Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada
| | - Isabelle Soulières
- Laboratory on Intelligence and Development in Autism, Department of Psychology, Université du Québec à Montréal, C.P. 8888 Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada
- Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, 7070, Boulevard Perras, Montréal, Quebec H1E 1A4, Canada
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14
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Shan X, Uddin LQ, Ma R, Xu P, Xiao J, Li L, Huang X, Feng Y, He C, Chen H, Duan X. Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder. Biol Psychiatry 2024; 95:870-880. [PMID: 37741308 DOI: 10.1016/j.biopsych.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/25/2023] [Accepted: 09/15/2023] [Indexed: 09/25/2023]
Abstract
BACKGROUND Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder. METHODS We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns. RESULTS Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD. CONCLUSIONS Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.
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Affiliation(s)
- Xiaolong Shan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Rui Ma
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Pengfei Xu
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinming Xiao
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinyue Huang
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu Feng
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Changchun He
- College of Blockchain Industry, Chengdu University of Information Technology, Chengdu, China
| | - Huafu Chen
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xujun Duan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
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15
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Zhai S, Otsuka S, Xu J, Clarke VRJ, Tkatch T, Wokosin D, Xie Z, Tanimura A, Agarwal HK, Ellis-Davies GCR, Contractor A, Surmeier DJ. Ca 2+ -dependent phosphodiesterase 1 regulates the plasticity of striatal spiny projection neuron glutamatergic synapses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.590962. [PMID: 38712260 PMCID: PMC11071484 DOI: 10.1101/2024.04.24.590962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Long-term synaptic plasticity at glutamatergic synapses on striatal spiny projection neurons (SPNs) is central to learning goal-directed behaviors and habits. Although considerable attention has been paid to the mechanisms underlying synaptic strengthening and new learning, little scrutiny has been given to those involved in the attenuation of synaptic strength that attends suppression of a previously learned association. Our studies revealed a novel, non-Hebbian, long-term, postsynaptic depression of glutamatergic SPN synapses induced by interneuronal nitric oxide (NO) signaling (NO-LTD) that was preferentially engaged at quiescent synapses. This form of plasticity was gated by local Ca 2+ influx through CaV1.3 Ca 2+ channels and stimulation of phosphodiesterase 1 (PDE1), which degraded cyclic guanosine monophosphate (cGMP) and blunted NO signaling. Consistent with this model, mice harboring a gain-of-function mutation in the gene coding for the pore-forming subunit of CaV1.3 channels had elevated depolarization-induced dendritic Ca 2+ entry and impaired NO-LTD. Extracellular uncaging of glutamate and intracellular uncaging of cGMP suggested that this Ca 2+ -dependent regulation of PDE1 activity allowed for local regulation of dendritic NO signaling. This inference was supported by simulation of SPN dendritic integration, which revealed that dendritic spikes engaged PDE1 in a branch-specific manner. In a mouse model of Parkinson's disease (PD), NO-LTD was absent not because of a postsynaptic deficit in NO signaling machinery, but rather due to impaired interneuronal NO release. Re-balancing intrastriatal neuromodulatory signaling in the PD model restored NO release and NO-LTD. Taken together, these studies provide novel insights into the mechanisms governing NO-LTD in SPN and its role in psychomotor disorders, like PD.
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16
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Ortug A, Guo Y, Feldman HA, Ou Y, Warren JLA, Dieuveuil H, Baumer NT, Faja SK, Takahashi E. Autism-associated brain differences can be observed in utero using MRI. Cereb Cortex 2024; 34:bhae117. [PMID: 38602735 PMCID: PMC11008691 DOI: 10.1093/cercor/bhae117] [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: 01/18/2023] [Revised: 03/01/2024] [Accepted: 03/02/2024] [Indexed: 04/12/2024] Open
Abstract
Developmental changes that occur before birth are thought to be associated with the development of autism spectrum disorders. Identifying anatomical predictors of early brain development may contribute to our understanding of the neurobiology of autism spectrum disorders and allow for earlier and more effective identification and treatment of autism spectrum disorders. In this study, we used retrospective clinical brain magnetic resonance imaging data from fetuses who were diagnosed with autism spectrum disorders later in life (prospective autism spectrum disorders) in order to identify the earliest magnetic resonance imaging-based regional volumetric biomarkers. Our results showed that magnetic resonance imaging-based autism spectrum disorder biomarkers can be found as early as in the fetal period and suggested that the increased volume of the insular cortex may be the most promising magnetic resonance imaging-based fetal biomarker for the future emergence of autism spectrum disorders, along with some additional, potentially useful changes in regional volumes and hemispheric asymmetries.
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Affiliation(s)
- Alpen Ortug
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Yurui Guo
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Henry A Feldman
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Yangming Ou
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Jose Luis Alatorre Warren
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Harrison Dieuveuil
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Nicole T Baumer
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Susan K Faja
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Division of Developmental Medicine, Laboratories of Cognitive Neuroscience, Boston Children's Hospital, Harvard Medical School, Brookline, MA 02115, United States
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
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17
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Yoon N, Kim S, Oh MR, Kim M, Lee JM, Kim BN. Intrinsic network abnormalities in children with autism spectrum disorder: an independent component analysis. Brain Imaging Behav 2024; 18:430-443. [PMID: 38324235 DOI: 10.1007/s11682-024-00858-x] [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: 01/16/2024] [Indexed: 02/08/2024]
Abstract
Aberrant intrinsic brain networks are consistently observed in individuals with autism spectrum disorder. However, studies examining the strength of functional connectivity across brain regions have yielded conflicting results. Therefore, this study aimed to investigate the functional connectivity of the resting brain in children with low-functioning autism, including during the early developmental stages. We explored the functional connectivity of 43 children with autism spectrum disorder and 54 children with typical development aged 2 to 12 years using resting-state functional magnetic resonance imaging data. We used independent component analysis to classify the brain regions into six intrinsic networks and analyzed the functional connectivity within each network. Moreover, we analyzed the relationship between functional connectivity and clinical scores. In children with autism, the under-connectivities were observed within several brain networks, including the cognitive control, default mode, visual, and somatomotor networks. In contrast, we found over-connectivities between the subcortical, visual, and somatomotor networks in children with autism compared with children with typical development. Moderate effect sizes were observed in entire networks (Cohen's d = 0.43-0.77). These network alterations were significantly correlated with clinical scores such as the communication sub-score (r = - 0.442, p = 0.045) and the calibrated severity score (r = - 0.435, p = 0.049) of the Autism Diagnostic Observation Schedule. These opposing results observed based on the brain areas suggest that aberrant neurodevelopment proceeds in various ways depending on the functional brain regions in individuals with autism spectrum disorder.
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Affiliation(s)
- Narae Yoon
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 101 Daehakno, Jongno-gu, Seoul, Korea
| | - Sohui Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Mee Rim Oh
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Minji Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Sanhak-kisulkwan Bldg., #319, 222 Wangsipri-ro, Sungdong-gu, Seoul, 133-791, Republic of Korea.
| | - Bung-Nyun Kim
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 101 Daehakno, Jongno-gu, Seoul, Korea.
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18
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Yang C, Wang XK, Ma SZ, Lee NY, Zhang QR, Dong WQ, Zang YF, Yuan LX. Abnormal functional connectivity of the reward network is associated with social communication impairments in autism spectrum disorder: A large-scale multi-site resting-state fMRI study. J Affect Disord 2024; 347:608-618. [PMID: 38070748 DOI: 10.1016/j.jad.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/28/2023] [Accepted: 12/02/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND The social motivation hypothesis proposes that the social deficits of autism spectrum disorder (ASD) are related to reward system dysfunction. However, functional connectivity (FC) patterns of the reward network in ASD have not been systematically explored yet. METHODS The reward network was defined as eight regions of interest (ROIs) per hemisphere, including the nucleus accumbens (NAc), caudate, putamen, anterior cingulate cortex (ACC), ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), amygdala, and insula. We computed both the ROI-wise resting-state FC and seed-based whole-brain FC in 298 ASD participants and 348 typically developing (TD) controls from the Autism Brain Imaging Data Exchange I dataset. Two-sample t-tests were applied to obtain the aberrant FCs. Then, the association between aberrant FCs and clinical symptoms was assessed with Pearson's correlation or Spearman's correlation. In addition, Neurosynth Image Decoder was used to generate word clouds verifying the cognitive functions of the aberrant pathways. Furthermore, a three-way multivariate analysis of variance (MANOVA) was conducted to examine the effects of gender, subtype and age on the atypical FCs. RESULTS For the within network analysis, the left ACC showed weaker FCs with both the right amygdala and left NAc in ASD compared with TD, which were negatively correlated with the Autism Diagnostic Observation Schedule (ADOS) total scores and Social Responsiveness Scale (SRS) total scores respectively. For the whole-brain analysis, weaker FC (i.e., FC between the left vmPFC and left calcarine gyrus, and between the right vmPFC and left precuneus) accompanied by stronger FC (i.e., FC between the left caudate and right insula) were exhibited in ASD relative to TD, which were positively associated with the SRS motivation scores. Additionally, we detected the main effect of age on FC between the left vmPFC and left calcarine gyrus, of subtype on FC between the right vmPFC and left precuneus, of age and age-by-gender interaction on FC between the left caudate and right insula. CONCLUSIONS Our findings highlight the crucial role of abnormal FC patterns of the reward network in the core social deficits of ASD, which have the potential to reveal new biomarkers for ASD.
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Affiliation(s)
- Chen Yang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Xing-Ke Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Sheng-Zhi Ma
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Nathan Yee Lee
- Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Qiu-Rong Zhang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Wen-Qiang Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China; TMS Center, Hangzhou Normal University Affiliated Deqing Hospital, Huzhou, China
| | - Li-Xia Yuan
- School of Physics, Zhejiang University, Hangzhou, China; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing, China.
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Wilkes BJ, Archer DB, Farmer AL, Bass C, Korah H, Vaillancourt DE, Lewis MH. Cortico-basal ganglia white matter microstructure is linked to restricted repetitive behavior in autism spectrum disorder. Mol Autism 2024; 15:6. [PMID: 38254158 PMCID: PMC10804694 DOI: 10.1186/s13229-023-00581-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 12/23/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Restricted repetitive behavior (RRB) is one of two behavioral domains required for the diagnosis of autism spectrum disorder (ASD). Neuroimaging is widely used to study brain alterations associated with ASD and the domain of social and communication deficits, but there has been less work regarding brain alterations linked to RRB. METHODS We utilized neuroimaging data from the National Institute of Mental Health Data Archive to assess basal ganglia and cerebellum structure in a cohort of children and adolescents with ASD compared to typically developing (TD) controls. We evaluated regional gray matter volumes from T1-weighted anatomical scans and assessed diffusion-weighted scans to quantify white matter microstructure with free-water imaging. We also investigated the interaction of biological sex and ASD diagnosis on these measures, and their correlation with clinical scales of RRB. RESULTS Individuals with ASD had significantly lower free-water corrected fractional anisotropy (FAT) and higher free-water (FW) in cortico-basal ganglia white matter tracts. These microstructural differences did not interact with biological sex. Moreover, both FAT and FW in basal ganglia white matter tracts significantly correlated with measures of RRB. In contrast, we found no significant difference in basal ganglia or cerebellar gray matter volumes. LIMITATIONS The basal ganglia and cerebellar regions in this study were selected due to their hypothesized relevance to RRB. Differences between ASD and TD individuals that may occur outside the basal ganglia and cerebellum, and their potential relationship to RRB, were not evaluated. CONCLUSIONS These new findings demonstrate that cortico-basal ganglia white matter microstructure is altered in ASD and linked to RRB. FW in cortico-basal ganglia and intra-basal ganglia white matter was more sensitive to group differences in ASD, whereas cortico-basal ganglia FAT was more closely linked to RRB. In contrast, basal ganglia and cerebellar volumes did not differ in ASD. There was no interaction between ASD diagnosis and sex-related differences in brain structure. Future diffusion imaging investigations in ASD may benefit from free-water estimation and correction in order to better understand how white matter is affected in ASD, and how such measures are linked to RRB.
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Affiliation(s)
- Bradley J Wilkes
- Department of Applied Physiology and Kinesiology, University of Florida, P.O. Box 118205, Gainesville, FL, 32611, USA.
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt School of Medicine, Nashville, TN, USA
- Department of Neurology, Vanderbilt Genetics Institute, Vanderbilt School of Medicine, Nashville, TN, USA
| | - Anna L Farmer
- Department of Psychology, University of Florida, Gainesville, FL, USA
| | - Carly Bass
- Department of Psychiatry, University of Florida, Gainesville, FL, USA
| | - Hannah Korah
- Department of Pharmacology, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - David E Vaillancourt
- Department of Applied Physiology and Kinesiology, University of Florida, P.O. Box 118205, Gainesville, FL, 32611, USA
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
- Department of Neurology, Fixel Center for Neurological Diseases, Program in Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, USA
| | - Mark H Lewis
- Department of Psychology, University of Florida, Gainesville, FL, USA
- Department of Psychiatry, University of Florida, Gainesville, FL, USA
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20
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Evans MM, Kim J, Abel T, Nickl-Jockschat T, Stevens HE. Developmental Disruptions of the Dorsal Striatum in Autism Spectrum Disorder. Biol Psychiatry 2024; 95:102-111. [PMID: 37652130 PMCID: PMC10841118 DOI: 10.1016/j.biopsych.2023.08.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 08/10/2023] [Accepted: 08/24/2023] [Indexed: 09/02/2023]
Abstract
Autism spectrum disorder (ASD) is an increasingly prevalent neurodevelopmental condition characterized by social and communication deficits as well as patterns of restricted, repetitive behavior. Abnormal brain development has long been postulated to underlie ASD, but longitudinal studies aimed at understanding the developmental course of the disorder have been limited. More recently, abnormal development of the striatum in ASD has become an area of interest in research, partially due to overlap of striatal functions and deficit areas in ASD, as well as the critical role of the striatum in early development, when ASD is first detected. Focusing on the dorsal striatum and the associated symptom domain of restricted, repetitive behavior, we review the current literature on dorsal striatal abnormalities in ASD, including studies on functional connectivity, morphometry, and cellular and molecular substrates. We highlight that observed striatal abnormalities in ASD are often dynamic across development, displaying disrupted developmental trajectories. Important findings include an abnormal trajectory of increasing corticostriatal functional connectivity with age and increased striatal growth during childhood in ASD. We end by discussing striatal findings from animal models of ASD. In sum, the studies reviewed here demonstrate a key role for developmental disruptions of the dorsal striatum in the pathogenesis of ASD. Directing attention toward these findings will improve our understanding of ASD and of how associated deficits may be better addressed.
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Affiliation(s)
- Maya M Evans
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa
| | - Jaekyoon Kim
- Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa
| | - Ted Abel
- Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa
| | - Thomas Nickl-Jockschat
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa; Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa
| | - Hanna E Stevens
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa.
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21
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Shang J, Shen E, Yu Y, Jin A, Wang X, Xiang D. Relationship between abnormal intrinsic functional connectivity of subcortices and autism symptoms in high-functioning adults with autism spectrum disorder. Psychiatry Res Neuroimaging 2024; 337:111762. [PMID: 38043369 DOI: 10.1016/j.pscychresns.2023.111762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 10/02/2023] [Accepted: 11/03/2023] [Indexed: 12/05/2023]
Abstract
PURPOSE This study explores subcortices and their intrinsic functional connectivity (iFC) in autism spectrum disorder (ASD) adults and investigates their relationship with clinical severity. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 74 ASD patients, and 63 gender and age-matched typically developing (TD) adults. Independent component analysis (ICA) was conducted to evaluate subcortical patterns of basal ganglia (BG) and thalamus. These two brain areas were treated as regions of interest to further calculate whole-brain FC. In addition, we employed multivariate machine learning to identify subcortices-based FC brain patterns and clinical scores to classify ASD adults from those TD subjects. RESULTS In ASD individuals, autism diagnostic observation schedule (ADOS) was negatively correlated with the BG network. Similarly, social responsiveness scale (SRS) was negatively correlated with the thalamus network. The BG-based iFC analysis revealed adults with ASD versus TD had lower FC, and its FC with the right medial temporal lobe (MTL), was positively correlated with SRS and ADOS separately. ASD could be predicted with a balanced accuracy of around 60.0 % using brain patterns and 84.7 % using clinical variables. CONCLUSION Our results revealed the abnormal subcortical iFC may be related to autism symptoms.
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Affiliation(s)
- Jing Shang
- Department of Psychiatry, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Erwei Shen
- School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
| | - Yang Yu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Aiying Jin
- Department of Nursing, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Xuemei Wang
- Department of Psychiatry, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China.
| | - Dehui Xiang
- School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China.
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22
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Persichetti AS, Shao J, Gotts SJ, Martin A. A functional parcellation of the whole brain in individuals with autism spectrum disorder reveals atypical patterns of network organization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571854. [PMID: 38168156 PMCID: PMC10760210 DOI: 10.1101/2023.12.15.571854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
BACKGROUND Researchers studying autism spectrum disorder (ASD) lack a comprehensive map of the functional network topography in the ASD brain. We used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in a group of seventy individuals with ASD and a group of seventy typically developing (TD) individuals. METHODS The rs-fMRI data were collected using an imaging sequence optimized to achieve high temporal signal-to-noise ratio (tSNR) across the whole-brain. We identified functional networks using a parcellation routine that intrinsically incorporates stability and replicability of the networks by keeping only network distinctions that agree across halves of the data over multiple random iterations in each group. The groups were tightly matched on tSNR, in-scanner motion, age, and IQ. RESULTS We compared the maps from each group and found that functional networks in the ASD group are atypical in three seemingly related ways: 1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, 2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and 3) subcortical structures and the hippocampus are atypically integrated with the neocortex. CONCLUSIONS These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.
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Affiliation(s)
- Andrew S Persichetti
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Jiayu Shao
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892
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23
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Zhu J, Jiao Y, Chen R, Wang XH, Han Y. Aberrant dynamic and static functional connectivity of the striatum across specific low-frequency bands in patients with autism spectrum disorder. Psychiatry Res Neuroimaging 2023; 336:111749. [PMID: 37977097 DOI: 10.1016/j.pscychresns.2023.111749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 10/06/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Dysfunctions of the striatum have been repeatedly observed in autism spectrum disorder (ASD). However, previous studies have explored the static functional connectivity (sFC) of the striatum in a single frequency band, ignoring the dynamics and frequency specificity of brain FC. Therefore, we investigated the dynamic FC (dFC) and sFC of the striatum in the slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) frequency bands. METHODS Data of 47 ASD patients and 47 typically developing (TD) controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. A seed-based approach was used to compute the dFC and sFC. Then, a two-sample t-test was performed. For regions showing abnormal sFC and dFC, we performed clinical correlation analysis and constructed support vector machine (SVM) models. RESULTS The middle frontal gyrus (MFG), precuneus, and medial superior frontal gyrus (mPFC) showed both dynamic and static alterations. The reduced striatal dFC in the right MFG was associated with autism symptoms. The dynamic‒static FC model had a great performance in ASD classification, with 95.83 % accuracy. CONCLUSIONS The striatal dFC and sFC were altered in ASD, which were frequency specific. Examining brain activity using dynamic and static FC provides a comprehensive view of brain activity.
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Affiliation(s)
- Junsa Zhu
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China
| | - Yun Jiao
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China; Network Information Center, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China.
| | - Ran Chen
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China
| | - Xun-Heng Wang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Yunyan Han
- Public Health School of Dalian Medical University, Dalian 116000, China
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24
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Hikishima K, Tsurugizawa T, Kasahara K, Takagi R, Yoshinaka K, Nitta N. Brain-wide mapping of resting-state networks in mice using high-frame rate functional ultrasound. Neuroimage 2023; 279:120297. [PMID: 37500027 DOI: 10.1016/j.neuroimage.2023.120297] [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: 04/21/2023] [Revised: 06/21/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023] Open
Abstract
Functional ultrasound (fUS) imaging is a method for visualizing deep brain activity based on cerebral blood volume changes coupled with neural activity, while functional MRI (fMRI) relies on the blood-oxygenation-level-dependent signal coupled with neural activity. Low-frequency fluctuations (LFF) of fMRI signals during resting-state can be measured by resting-state fMRI (rsfMRI), which allows functional imaging of the whole brain, and the distributions of resting-state network (RSN) can then be estimated from these fluctuations using independent component analysis (ICA). This procedure provides an important method for studying cognitive and psychophysiological diseases affecting specific brain networks. The distributions of RSNs in the brain-wide area has been reported primarily by rsfMRI. RSNs using rsfMRI are generally computed from the time-course of fMRI signals for more than 5 min. However, a recent dynamic functional connectivity study revealed that RSNs are still not perfectly stable even after 10 min. Importantly, fUS has a higher temporal resolution and stronger correlation with neural activity compared with fMRI. Therefore, we hypothesized that fUS applied during the resting-state for a shorter than 5 min would provide similar RSNs compared to fMRI. High temporal resolution rsfUS data were acquired at 10 Hz in awake mice. The quality of the default mode network (DMN), a well-known RSN, was evaluated using signal-noise separation (SNS) applied to different measurement durations of rsfUS. The results showed that the SNS did not change when the measurement duration was increased to more than 210 s. Next, we measured short-duration rsfUS multi-slice measurements in the brain-wide area. The results showed that rsfUS with the short duration succeeded in detecting RSNs distributed in the brain-wide area consistent with RSNs detected by 11.7-T MRI under awake conditions (medial prefrontal cortex and cingulate cortex in the anterior DMN, retrosplenial cortex and visual cortex in the posterior DMN, somatosensory and motor cortexes in the lateral cortical network, thalamus, dorsal hippocampus, and medial cerebellum), confirming the reliability of the RSNs detected by rsfUS. However, bilateral RSNs located in the secondary somatosensory cortex, ventral hippocampus, auditory cortex, and lateral cerebellum extracted from rsfUS were different from the unilateral RSNs extracted from rsfMRI. These findings indicate the potential of rsfUS as a method for analyzing functional brain networks and should encourage future research to elucidate functional brain networks and their relationships with disease model mice.
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Affiliation(s)
- Keigo Hikishima
- Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan; Okinawa Institute of Science and Technology Graduate University (OIST), Okinawa, Japan.
| | - Tomokazu Tsurugizawa
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan
| | - Kazumi Kasahara
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan
| | - Ryo Takagi
- Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan
| | - Kiyoshi Yoshinaka
- Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan
| | - Naotaka Nitta
- Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan
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25
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Wang M, Zheng H, Zhou W, Yang B, Wang L, Chen S, Dong GH. Disrupted dynamic network reconfiguration of the executive and reward networks in internet gaming disorder. Psychol Med 2023; 53:5478-5487. [PMID: 36004801 DOI: 10.1017/s0033291722002665] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Studies have shown that people with internet gaming disorder (IGD) exhibit impaired executive control of gaming cravings; however, the neural mechanisms underlying this process remain unknown. In addition, these conclusions were based on the hypothesis that brain networks are temporally static, neglecting dynamic changes in cognitive processes. METHODS Resting-state fMRI data were collected from 402 subjects [162 subjects with IGD and 240 recreational game users (RGUs)]. The community structure (recruitment and integration) of the executive control network (ECN) and the basal ganglia network (BGN), which represents the reward network, of patients with IGD and RGUs were compared. Mediation effects among the different networks were analyzed. RESULTS Compared to RGUs, subjects with IGD had a lower recruitment coefficient within the right ECN. Further analysis showed that only male subjects had a lower recruitment coefficient. Mediation analysis showed that the integration coefficient of the right ECN mediated the relationship between the recruitment coefficients of both the right ECN and the BGN in RGUs. CONCLUSIONS Male subjects with IGD had a lower recruitment coefficient than RGUs, which impairing their impulse control. The mediation results suggest that top-down executive control of the ECN is absent in subjects with IGD. Together, these findings could explain why subjects with IGD exhibit impaired executive control of gaming cravings; these results have important therapeutic implications for developing effective interventions for IGD.
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Affiliation(s)
- Min Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Hui Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, PR China
| | - Weiran Zhou
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Bo Yang
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Shuaiyu Chen
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Guang-Heng Dong
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
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26
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Longo F, Aryal S, Anastasiades PG, Maltese M, Baimel C, Albanese F, Tabor J, Zhu JD, Oliveira MM, Gastaldo D, Bagni C, Santini E, Tritsch NX, Carter AG, Klann E. Cell-type-specific disruption of cortico-striatal circuitry drives repetitive patterns of behavior in fragile X syndrome model mice. Cell Rep 2023; 42:112901. [PMID: 37505982 PMCID: PMC10552611 DOI: 10.1016/j.celrep.2023.112901] [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: 12/05/2022] [Revised: 05/18/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Individuals with fragile X syndrome (FXS) are frequently diagnosed with autism spectrum disorder (ASD), including increased risk for restricted and repetitive behaviors (RRBs). Consistent with observations in humans, FXS model mice display distinct RRBs and hyperactivity that are consistent with dysfunctional cortico-striatal circuits, an area relatively unexplored in FXS. Using a multidisciplinary approach, we dissect the contribution of two populations of striatal medium spiny neurons (SPNs) in the expression of RRBs in FXS model mice. Here, we report that dysregulated protein synthesis at cortico-striatal synapses is a molecular culprit of the synaptic and ASD-associated motor phenotypes displayed by FXS model mice. Cell-type-specific translational profiling of the FXS mouse striatum reveals differentially translated mRNAs, providing critical information concerning potential therapeutic targets. Our findings uncover a cell-type-specific impact of the loss of fragile X messenger ribonucleoprotein (FMRP) on translation and the sequence of neuronal events in the striatum that drive RRBs in FXS.
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Affiliation(s)
- Francesco Longo
- Center for Neural Science, New York University, New York, NY 10003, USA; Institute for Neuroscience and Physiology, University of Gothenburg, 40530 Gothenburg, Sweden; Sackler Institute of Graduate Biomedical Sciences, NYU School of Medicine, New York, NY 10016, USA
| | - Sameer Aryal
- Center for Neural Science, New York University, New York, NY 10003, USA; NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | | | - Marta Maltese
- Fresco Institute for Parkinson's and Movement Disorders, New York University Langone Health, New York, NY 10016, USA; Department of Fundamental Neurosciences, University of Lausanne, 1005 Lausanne, Switzerland
| | - Corey Baimel
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Federica Albanese
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Joanna Tabor
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Jeffrey D Zhu
- Center for Neural Science, New York University, New York, NY 10003, USA
| | | | - Denise Gastaldo
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata," 1005 Rome, Italy
| | - Claudia Bagni
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata," 1005 Rome, Italy
| | - Emanuela Santini
- Center for Neural Science, New York University, New York, NY 10003, USA; Department of Neuroscience, Biomedicum, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Nicolas X Tritsch
- NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Fresco Institute for Parkinson's and Movement Disorders, New York University Langone Health, New York, NY 10016, USA
| | - Adam G Carter
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Eric Klann
- Center for Neural Science, New York University, New York, NY 10003, USA; NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA.
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27
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Kilpatrick S, Irwin C, Singh KK. Human pluripotent stem cell (hPSC) and organoid models of autism: opportunities and limitations. Transl Psychiatry 2023; 13:217. [PMID: 37344450 PMCID: PMC10284884 DOI: 10.1038/s41398-023-02510-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 05/09/2023] [Accepted: 06/05/2023] [Indexed: 06/23/2023] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder caused by genetic or environmental perturbations during early development. Diagnoses are dependent on the identification of behavioral abnormalities that likely emerge well after the disorder is established, leaving critical developmental windows uncharacterized. This is further complicated by the incredible clinical and genetic heterogeneity of the disorder that is not captured in most mammalian models. In recent years, advancements in stem cell technology have created the opportunity to model ASD in a human context through the use of pluripotent stem cells (hPSCs), which can be used to generate 2D cellular models as well as 3D unguided- and region-specific neural organoids. These models produce profoundly intricate systems, capable of modeling the developing brain spatiotemporally to reproduce key developmental milestones throughout early development. When complemented with multi-omics, genome editing, and electrophysiology analysis, they can be used as a powerful tool to profile the neurobiological mechanisms underlying this complex disorder. In this review, we will explore the recent advancements in hPSC-based modeling, discuss present and future applications of the model to ASD research, and finally consider the limitations and future directions within the field to make this system more robust and broadly applicable.
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Affiliation(s)
- Savannah Kilpatrick
- Donald K. Johnson Eye Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Biochemistry and Biomedical Science, McMaster University, Hamilton, ON, Canada
| | - Courtney Irwin
- Donald K. Johnson Eye Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Karun K Singh
- Donald K. Johnson Eye Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
- Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada.
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28
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Xie H, Moraczewski D, McNaughton KA, Warnell KR, Alkire D, Merchant JS, Kirby LA, Yarger HA, Redcay E. Social reward network connectivity differs between autistic and neurotypical youth during social interaction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543807. [PMID: 37333161 PMCID: PMC10274709 DOI: 10.1101/2023.06.05.543807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
A core feature of autism is difficulties with social interaction. Atypical social motivation is proposed to underlie these difficulties. However, prior work testing this hypothesis has shown mixed support and has been limited in its ability to understand real-world social-interactive processes in autism. We attempted to address these limitations by scanning neurotypical and autistic youth (n = 86) during a text-based reciprocal social interaction that mimics a "live" chat and elicits social reward processes. We focused on task-evoked functional connectivity (FC) of regions responsible for motivational-reward and mentalizing processes within the broader social reward circuitry. We found that task-evoked FC between these regions was significantly modulated by social interaction and receipt of social-interactive reward. Compared to neurotypical peers, autistic youth showed significantly greater task-evoked connectivity of core regions in the mentalizing network (e.g., posterior superior temporal sulcus) and the amygdala, a key node in the reward network. Furthermore, across groups, the connectivity strength between these mentalizing and reward regions was negatively correlated with self-reported social motivation and social reward during the scanner task. Our results highlight an important role of FC within the broader social reward circuitry for social-interactive reward. Specifically, greater context-dependent FC (i.e., differences between social engagement and non-social engagement) may indicate an increased "neural effort" during social reward and relate to differences in social motivation within autistic and neurotypical populations.
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Affiliation(s)
- Hua Xie
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
- Center for Neuroscience Research, Children’s National Hospital, Washington, D.C., USA
- The George Washington University School of Medicine, Washington, D.C., USA
| | - Dustin Moraczewski
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Kathryn A. McNaughton
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | | | - Diana Alkire
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Junaid S. Merchant
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Laura A. Kirby
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Heather A. Yarger
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Elizabeth Redcay
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
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Morris CW, Watkins DS, Shah NR, Pennington T, Hens B, Qi G, Doud EH, Mosley AL, Atwood BK, Baucum AJ. Spinophilin Limits Metabotropic Glutamate Receptor 5 Scaffolding to the Postsynaptic Density and Cell Type Specifically Mediates Excessive Grooming. Biol Psychiatry 2023; 93:976-988. [PMID: 36822932 PMCID: PMC10191892 DOI: 10.1016/j.biopsych.2022.12.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 12/02/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Grooming dysfunction is a hallmark of the obsessive-compulsive spectrum disorder trichotillomania. Numerous preclinical studies have utilized SAPAP3-deficient mice for understanding the neurobiology of repetitive grooming, suggesting that excessive grooming is caused by increased metabotropic glutamate receptor 5 (mGluR5) activity in striatal direct- and indirect-pathway medium spiny neurons (MSNs). However, the MSN subtype-specific signaling mechanisms that mediate mGluR5-dependent adaptations underlying excessive grooming are not fully understood. Here, we investigated the MSN subtype-specific roles of the striatal signaling hub protein spinophilin in mediating repetitive motor dysfunction associated with mGluR5 function. METHODS Quantitative proteomics and immunoblotting were utilized to identify how spinophilin impacts mGluR5 phosphorylation and protein interaction changes. Plasticity and repetitive motor dysfunction associated with mGluR5 action were measured using our novel conditional spinophilin mouse model in which spinophilin was knocked out from striatal direct-pathway MSNs and/or indirect-pathway MSNs. RESULTS Loss of spinophilin only in indirect-pathway MSNs decreased performance of a novel motor repertoire, but loss of spinophilin in either MSN subtype abrogated striatal plasticity associated with mGluR5 function and prevented excessive grooming caused by SAPAP3 knockout mice or treatment with the mGluR5-specific positive allosteric modulator VU0360172 without impacting locomotion-relevant behavior. Biochemically, we determined that the spinophilin-mGluR5 interaction correlates with grooming behavior and that loss of spinophilin shifts mGluR5 interactions from lipid raft-associated proteins toward postsynaptic density proteins implicated in psychiatric disorders. CONCLUSIONS These results identify spinophilin as a novel striatal signaling hub molecule in MSNs that cell subtype specifically mediates behavioral, functional, and molecular adaptations associated with repetitive motor dysfunction in psychiatric disorders.
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Affiliation(s)
- Cameron W Morris
- Medical Neurosciences Graduate Program, Indiana University School of Medicine, Indianapolis, Indiana
| | - Darryl S Watkins
- Medical Neurosciences Graduate Program, Indiana University School of Medicine, Indianapolis, Indiana
| | - Nikhil R Shah
- Medical Neurosciences Graduate Program, Indiana University School of Medicine, Indianapolis, Indiana; Medical Scientists Training Program, Indiana University School of Medicine, Indianapolis, Indiana
| | - Taylor Pennington
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana
| | - Basant Hens
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Guihong Qi
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana; Center for Proteome Analysis, Indiana University School of Medicine, Indianapolis, Indiana
| | - Emma H Doud
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana; Center for Proteome Analysis, Indiana University School of Medicine, Indianapolis, Indiana; Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, Indiana
| | - Amber L Mosley
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana; Center for Proteome Analysis, Indiana University School of Medicine, Indianapolis, Indiana; Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, Indiana; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Brady K Atwood
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana; Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Anthony J Baucum
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana; Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, Indiana; Department of Biology, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana.
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30
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Wang S, Li X. A revisit of the amygdala theory of autism: Twenty years after. Neuropsychologia 2023; 183:108519. [PMID: 36803966 PMCID: PMC10824605 DOI: 10.1016/j.neuropsychologia.2023.108519] [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: 07/09/2022] [Revised: 01/23/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.
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Affiliation(s)
- Shuo Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA; Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
| | - Xin Li
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
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31
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Buch AM, Vértes PE, Seidlitz J, Kim SH, Grosenick L, Liston C. Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder. Nat Neurosci 2023; 26:650-663. [PMID: 36894656 PMCID: PMC11446249 DOI: 10.1038/s41593-023-01259-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 01/17/2023] [Indexed: 03/11/2023]
Abstract
The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.
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Affiliation(s)
- Amanda M Buch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - So Hyun Kim
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Autism and the Developing Brain, Weill Cornell Medicine, White Plains, NY, USA
- School of Psychology, Korea University, Seoul, South Korea
| | - Logan Grosenick
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
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32
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Barik K, Watanabe K, Bhattacharya J, Saha G. Functional connectivity based machine learning approach for autism detection in young children using MEG signals. J Neural Eng 2023; 20. [PMID: 36812588 DOI: 10.1088/1741-2552/acbe1f] [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: 09/03/2022] [Accepted: 02/22/2023] [Indexed: 02/24/2023]
Abstract
Objective.Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.Approach.We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique.Main results.To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50-100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1-4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity.Significance.Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.
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Affiliation(s)
- Kasturi Barik
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Katsumi Watanabe
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan
| | - Joydeep Bhattacharya
- Department of Psychology, Goldsmiths, University of London, London, United Kingdom
| | - Goutam Saha
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
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Rotaru DC, Wallaard I, de Vries M, van der Bie J, Elgersma Y. UBE3A expression during early postnatal brain development is required for proper dorsomedial striatal maturation. JCI Insight 2023; 8:e166073. [PMID: 36810252 PMCID: PMC9977510 DOI: 10.1172/jci.insight.166073] [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: 10/07/2022] [Accepted: 01/05/2023] [Indexed: 02/23/2023] Open
Abstract
Angelman syndrome (AS) is a severe neurodevelopmental disorder (NDD) caused by loss of functional ubiquitin protein ligase E3A (UBE3A). Previous studies showed that UBE3A plays an important role in the first postnatal weeks of mouse brain development, but its precise role is unknown. Since impaired striatal maturation has been implicated in several mouse models for NDDs, we studied the importance of UBE3A in striatal maturation. We used inducible Ube3a mouse models to investigate the maturation of medium spiny neurons (MSNs) from dorsomedial striatum. MSNs of mutant mice matured properly till postnatal day 15 (P15) but remained hyperexcitable with fewer excitatory synaptic events at later ages, indicative of stalled striatal maturation in Ube3a mice. Reinstatement of UBE3A expression at P21 fully restored MSN excitability but only partially restored synaptic transmission and the operant conditioning behavioral phenotype. Gene reinstatement at P70 failed to rescue both electrophysiological and behavioral phenotypes. In contrast, deletion of Ube3a after normal brain development did not result in these electrophysiological and behavioral phenotypes. This study emphasizes the role of UBE3A in striatal maturation and the importance of early postnatal reinstatement of UBE3A expression to obtain a full rescue of behavioral phenotypes associated with striatal function in AS.
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Affiliation(s)
- Diana C. Rotaru
- Department of Clinical Genetics and
- ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus MC, Rotterdam, Netherlands
| | - Ilse Wallaard
- Department of Clinical Genetics and
- ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus MC, Rotterdam, Netherlands
| | - Maud de Vries
- Department of Clinical Genetics and
- ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus MC, Rotterdam, Netherlands
| | - Julia van der Bie
- Department of Clinical Genetics and
- ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus MC, Rotterdam, Netherlands
| | - Ype Elgersma
- Department of Clinical Genetics and
- ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus MC, Rotterdam, Netherlands
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Hong SJ, Mottron L, Park BY, Benkarim O, Valk SL, Paquola C, Larivière S, Vos de Wael R, Degré-Pelletier J, Soulieres I, Ramphal B, Margolis A, Milham M, Di Martino A, Bernhardt BC. A convergent structure-function substrate of cognitive imbalances in autism. Cereb Cortex 2023; 33:1566-1580. [PMID: 35552620 PMCID: PMC9977381 DOI: 10.1093/cercor/bhac156] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/28/2022] [Accepted: 03/29/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a common neurodevelopmental diagnosis showing substantial phenotypic heterogeneity. A leading example can be found in verbal and nonverbal cognitive skills, which vary from elevated to impaired compared with neurotypical individuals. Moreover, deficits in verbal profiles often coexist with normal or superior performance in the nonverbal domain. METHODS To study brain substrates underlying cognitive imbalance in ASD, we capitalized categorical and dimensional IQ profiling as well as multimodal neuroimaging. RESULTS IQ analyses revealed a marked verbal to nonverbal IQ imbalance in ASD across 2 datasets (Dataset-1: 155 ASD, 151 controls; Dataset-2: 270 ASD, 490 controls). Neuroimaging analysis in Dataset-1 revealed a structure-function substrate of cognitive imbalance, characterized by atypical cortical thickening and altered functional integration of language networks alongside sensory and higher cognitive areas. CONCLUSION Although verbal and nonverbal intelligence have been considered as specifiers unrelated to autism diagnosis, our results indicate that intelligence disparities are accentuated in ASD and reflected by a consistent structure-function substrate affecting multiple brain networks. Our findings motivate the incorporation of cognitive imbalances in future autism research, which may help to parse the phenotypic heterogeneity and inform intervention-oriented subtyping in ASD.
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Affiliation(s)
- Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea
- Center for the Developing Brain, Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Laurent Mottron
- Centre de Recherche du CIUSSSNIM and Department of Psychiatry and Addictology, Université de Montréal, 7070 boulevard Perras, Montréal, Quebec H1E 1A4, Canada
| | - Bo-yong Park
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea
- Department of Data Science, Inha Univerisity, Incheon 22212, South Korea
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Sofie L Valk
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
- Otto Hahn group Cognitive neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraβe 1A. Leipzig D-04103, Germany
- Institute of Neuroscience and Medicine, Research Centre Wilhelm-Johnen-Strasse, Jülich 52425, Germany
- Institute of Systems Neuroscience, Heinrich Heine University, Moorenstr. 5, Düsseldorf 40225, Germany
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
- Institute of Neuroscience and Medicine, Research Centre Wilhelm-Johnen-Strasse, Jülich 52425, Germany
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Janie Degré-Pelletier
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
- Department of Psychology, Université du Québec à Montréal, 100 rue Sherbrooke Ouest, Montréal, Québec H2X 3P2, Canada
| | - Isabelle Soulieres
- Department of Psychology, Université du Québec à Montréal, 100 rue Sherbrooke Ouest, Montréal, Québec H2X 3P2, Canada
| | - Bruce Ramphal
- Department of Psychiatry, The New York State Psychiatric Institute and the College of Physicians Surgeons, Columbia University, 1051 Riverside Drive, New York, NY 10032, United States
| | - Amy Margolis
- Department of Psychiatry, The New York State Psychiatric Institute and the College of Physicians Surgeons, Columbia University, 1051 Riverside Drive, New York, NY 10032, United States
| | - Michael Milham
- Center for the Developing Brain, Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY 10962, United States
| | - Adriana Di Martino
- Autism Center, Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
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McNaughton KA, Kirby LA, Warnell KR, Alkire D, Merchant JS, Moraczewski D, Yarger HA, Thurm A, Redcay E. Social-interactive reward elicits similar neural response in autism and typical development and predicts future social experiences. Dev Cogn Neurosci 2023; 59:101197. [PMID: 36640623 PMCID: PMC9852551 DOI: 10.1016/j.dcn.2023.101197] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 10/07/2022] [Accepted: 01/05/2023] [Indexed: 01/08/2023] Open
Abstract
Challenges in initiating and responding to social-interactive exchanges are a key diagnostic feature of autism spectrum disorder, yet investigations into the underlying neural mechanisms of social interaction have been hampered by reliance on non-interactive approaches. Using an innovative social-interactive neuroscience approach, we investigated differences between youth with autism and youth with typical development in neural response to a chat-based social-interactive reward, as well as factors such as age and self-reported social enjoyment that may account for heterogeneity in that response. We found minimal group differences in neural and behavioral response to social-interactive reward, and variation within both groups was related to self-reported social enjoyment during the task. Furthermore, neural sensitivity to social-interactive reward predicted future enjoyment of a face-to-face social interaction with a novel peer. These findings have important implications for understanding the nature of social reward and peer interactions in typical development as well as for future research informing social interactions in individuals on the autism spectrum.
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Affiliation(s)
- Kathryn A McNaughton
- Neuroscience and Cognitive Science Program, University of Maryland College Park, USA; Department of Psychology, University of Maryland College Park, USA.
| | | | | | - Diana Alkire
- Division of Extramural Research, National Institute on Drug Abuse, USA
| | - Junaid S Merchant
- Neuroscience and Cognitive Science Program, University of Maryland College Park, USA; Department of Psychology, University of Maryland College Park, USA
| | | | - Heather A Yarger
- Neuroscience and Cognitive Science Program, University of Maryland College Park, USA; Department of Psychology, University of Maryland College Park, USA
| | - Audrey Thurm
- Office of the Clinical Director, National Institute of Mental Health, USA
| | - Elizabeth Redcay
- Neuroscience and Cognitive Science Program, University of Maryland College Park, USA; Department of Psychology, University of Maryland College Park, USA
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Kisaretova P, Tsybko A, Bondar N, Reshetnikov V. Molecular Abnormalities in BTBR Mice and Their Relevance to Schizophrenia and Autism Spectrum Disorders: An Overview of Transcriptomic and Proteomic Studies. Biomedicines 2023; 11:289. [PMID: 36830826 PMCID: PMC9953015 DOI: 10.3390/biomedicines11020289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023] Open
Abstract
Animal models of psychopathologies are of exceptional interest for neurobiologists because these models allow us to clarify molecular mechanisms underlying the pathologies. One such model is the inbred BTBR strain of mice, which is characterized by behavioral, neuroanatomical, and physiological hallmarks of schizophrenia (SCZ) and autism spectrum disorders (ASDs). Despite the active use of BTBR mice as a model object, the understanding of the molecular features of this strain that cause the observed behavioral phenotype remains insufficient. Here, we analyzed recently published data from independent transcriptomic and proteomic studies on hippocampal and corticostriatal samples from BTBR mice to search for the most consistent aberrations in gene or protein expression. Next, we compared reproducible molecular signatures of BTBR mice with data on postmortem samples from ASD and SCZ patients. Taken together, these data helped us to elucidate brain-region-specific molecular abnormalities in BTBR mice as well as their relevance to the anomalies seen in ASDs or SCZ in humans.
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Affiliation(s)
- Polina Kisaretova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Prospekt Akad. Lavrentyeva 10, Novosibirsk 630090, Russia
- Department of Natural Sciences, Novosibirsk State University, Pirogova Street 2, Novosibirsk 630090, Russia
| | - Anton Tsybko
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Prospekt Akad. Lavrentyeva 10, Novosibirsk 630090, Russia
| | - Natalia Bondar
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Prospekt Akad. Lavrentyeva 10, Novosibirsk 630090, Russia
| | - Vasiliy Reshetnikov
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Prospekt Akad. Lavrentyeva 10, Novosibirsk 630090, Russia
- Department of Biotechnology, Sirius University of Science and Technology, 1 Olympic Avenue, Sochi 354340, Russia
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Functional connectivity directionality between large-scale resting-state networks across typical and non-typical trajectories in children and adolescence. PLoS One 2022; 17:e0276221. [PMID: 36454744 PMCID: PMC9714732 DOI: 10.1371/journal.pone.0276221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 10/04/2022] [Indexed: 12/02/2022] Open
Abstract
Mental disorders often emerge during adolescence and have been associated with age-related differences in connection strengths of brain networks (static functional connectivity), manifesting in non-typical trajectories of brain development. However, little is known about the direction of information flow (directed functional connectivity) in this period of functional brain progression. We employed dynamic graphical models (DGM) to estimate directed functional connectivity from resting state functional magnetic resonance imaging data on 1143 participants, aged 6 to 17 years from the healthy brain network (HBN) sample. We tested for effects of age, sex, cognitive abilities and psychopathology on estimates of direction flow. Across participants, we show a pattern of reciprocal information flow between visual-medial and visual-lateral connections, in line with findings in adults. Investigating directed connectivity patterns between networks, we observed a positive association for age and direction flow from the cerebellar to the auditory network, and for the auditory to the sensorimotor network. Further, higher cognitive abilities were linked to lower information flow from the visual occipital to the default mode network. Additionally, examining the degree networks overall send and receive information to each other, we identified age-related effects implicating the right frontoparietal and sensorimotor network. However, we did not find any associations with psychopathology. Our results suggest that the directed functional connectivity of large-scale resting-state brain networks is sensitive to age and cognition during adolescence, warranting further studies that may explore directed relationships at rest and trajectories in more fine-grained network parcellations and in different populations.
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Abstract
Recent advances in genomics have revealed a wide spectrum of genetic variants associated with neurodevelopmental disorders at an unprecedented scale. An increasing number of studies have consistently identified mutations-both inherited and de novo-impacting the function of specific brain circuits. This suggests that, during brain development, alterations in distinct neural circuits, cell types, or broad regulatory pathways ultimately shaping synapses might be a dysfunctional process underlying these disorders. Here, we review findings from human studies and animal model research to provide a comprehensive description of synaptic and circuit mechanisms implicated in neurodevelopmental disorders. We discuss how specific synaptic connections might be commonly disrupted in different disorders and the alterations in cognition and behaviors emerging from imbalances in neuronal circuits. Moreover, we review new approaches that have been shown to restore or mitigate dysfunctional processes during specific critical windows of brain development. Considering the heterogeneity of neurodevelopmental disorders, we also highlight the recent progress in developing improved clinical biomarkers and strategies that will help to identify novel therapeutic compounds and opportunities for early intervention.
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Affiliation(s)
- David Exposito-Alonso
- Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom;
- Current affiliation: Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA;
| | - Beatriz Rico
- Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom;
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39
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Maisterrena A, Matas E, Mirfendereski H, Balbous A, Marchand S, Jaber M. The State of the Dopaminergic and Glutamatergic Systems in the Valproic Acid Mouse Model of Autism Spectrum Disorder. Biomolecules 2022; 12:1691. [PMID: 36421705 PMCID: PMC9688008 DOI: 10.3390/biom12111691] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 08/23/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is a progressive neurodevelopmental disorder mainly characterized by deficits in social communication and stereotyped behaviors and interests. Here, we aimed to investigate the state of several key players in the dopamine and glutamate neurotransmission systems in the valproic acid (VPA) animal model that was administered to E12.5 pregnant females as a single dose (450 mg/kg). We report no alterations in the number of mesencephalic dopamine neurons or in protein levels of tyrosine hydroxylase in either the striatum or the nucleus accumbens. In females prenatally exposed to VPA, levels of dopamine were slightly decreased while the ratio of DOPAC/dopamine was increased in the dorsal striatum, suggesting increased turn-over of dopamine tone. In turn, levels of D1 and D2 dopamine receptor mRNAs were increased in the nucleus accumbens of VPA mice suggesting upregulation of the corresponding receptors. We also report decreased protein levels of striatal parvalbumin and increased levels of p-mTOR in the cerebellum and the motor cortex of VPA mice. mRNA levels of mGluR1, mGluR4, and mGluR5 and the glutamate receptor subunits NR1, NR2A, and NR2B were not altered by VPA, nor were protein levels of NR1, NR2A, and NR2B and those of BDNF and TrkB. These findings are of interest as clinical trials aiming at the dopamine and glutamate systems are being considered.
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Affiliation(s)
- Alexandre Maisterrena
- Laboratoire de Neurosciences Expérimentales et Cliniques, Inserm, Université de Poitiers, 86000 Poitiers, France
| | - Emmanuel Matas
- Laboratoire de Neurosciences Expérimentales et Cliniques, Inserm, Université de Poitiers, 86000 Poitiers, France
| | - Helene Mirfendereski
- Pharmacologie des Agents Anti-Infectieux et Antibiorésistance, Inserm, Université de Poitiers, 86000 Poitiers, France
- CHU de Poitiers, 86000 Poitiers, France
| | - Anais Balbous
- Laboratoire de Neurosciences Expérimentales et Cliniques, Inserm, Université de Poitiers, 86000 Poitiers, France
- CHU de Poitiers, 86000 Poitiers, France
| | - Sandrine Marchand
- Pharmacologie des Agents Anti-Infectieux et Antibiorésistance, Inserm, Université de Poitiers, 86000 Poitiers, France
- CHU de Poitiers, 86000 Poitiers, France
| | - Mohamed Jaber
- Laboratoire de Neurosciences Expérimentales et Cliniques, Inserm, Université de Poitiers, 86000 Poitiers, France
- CHU de Poitiers, 86000 Poitiers, France
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40
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Alamdari SB, Sadeghi Damavandi M, Zarei M, Khosrowabadi R. Cognitive theories of autism based on the interactions between brain functional networks. Front Hum Neurosci 2022; 16:828985. [PMID: 36310850 PMCID: PMC9614840 DOI: 10.3389/fnhum.2022.828985] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 08/15/2022] [Indexed: 12/03/2022] Open
Abstract
Cognitive functions are directly related to interactions between the brain's functional networks. This functional organization changes in the autism spectrum disorder (ASD). However, the heterogeneous nature of autism brings inconsistency in the findings, and specific pattern of changes based on the cognitive theories of ASD still requires to be well-understood. In this study, we hypothesized that the theory of mind (ToM), and the weak central coherence theory must follow an alteration pattern in the network level of functional interactions. The main aim is to understand this pattern by evaluating interactions between all the brain functional networks. Moreover, the association between the significantly altered interactions and cognitive dysfunctions in autism is also investigated. We used resting-state fMRI data of 106 subjects (5-14 years, 46 ASD: five female, 60 HC: 18 female) to define the brain functional networks. Functional networks were calculated by applying four parcellation masks and their interactions were estimated using Pearson's correlation between pairs of them. Subsequently, for each mask, a graph was formed based on the connectome of interactions. Then, the local and global parameters of the graph were calculated. Finally, statistical analysis was performed using a two-sample t-test to highlight the significant differences between autistic and healthy control groups. Our corrected results show significant changes in the interaction of default mode, sensorimotor, visuospatial, visual, and language networks with other functional networks that can support the main cognitive theories of autism. We hope this finding sheds light on a better understanding of the neural underpinning of autism.
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Affiliation(s)
| | | | - Mojtaba Zarei
- University of Southern Denmark, Neurology Unit, Odense, Denmark
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
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Nebel MB, Lidstone DE, Wang L, Benkeser D, Mostofsky SH, Risk BB. Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder? Neuroimage 2022; 257:119296. [PMID: 35561944 PMCID: PMC9233079 DOI: 10.1016/j.neuroimage.2022.119296] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 05/03/2022] [Accepted: 05/09/2022] [Indexed: 12/13/2022] Open
Abstract
The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.
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Affiliation(s)
- Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
| | - Daniel E Lidstone
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Liwei Wang
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - David Benkeser
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Department of Psychiatry and Behavioral Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Benjamin B Risk
- Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA, United States
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Ursino M, Serra M, Tarasi L, Ricci G, Magosso E, Romei V. Bottom-up vs. top-down connectivity imbalance in individuals with high-autistic traits: An electroencephalographic study. Front Syst Neurosci 2022; 16:932128. [PMID: 36032324 PMCID: PMC9412751 DOI: 10.3389/fnsys.2022.932128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/27/2022] [Indexed: 11/25/2022] Open
Abstract
Brain connectivity is often altered in autism spectrum disorder (ASD). However, there is little consensus on the nature of these alterations, with studies pointing to either increased or decreased connectivity strength across the broad autism spectrum. An important confound in the interpretation of these contradictory results is the lack of information about the directionality of the tested connections. Here, we aimed at disambiguating these confounds by measuring differences in directed connectivity using EEG resting-state recordings in individuals with low and high autistic traits. Brain connectivity was estimated using temporal Granger Causality applied to cortical signals reconstructed from EEG. Between-group differences were summarized using centrality indices taken from graph theory (in degree, out degree, authority, and hubness). Results demonstrate that individuals with higher autistic traits exhibited a significant increase in authority and in degree in frontal regions involved in high-level mechanisms (emotional regulation, decision-making, and social cognition), suggesting that anterior areas mostly receive information from more posterior areas. Moreover, the same individuals exhibited a significant increase in the hubness and out degree over occipital regions (especially the left and right pericalcarine regions, where the primary visual cortex is located), suggesting that these areas mostly send information to more anterior regions. Hubness and authority appeared to be more sensitive indices than the in degree and out degree. The observed brain connectivity differences suggest that, in individual with higher autistic traits, bottom-up signaling overcomes top-down channeled flow. This imbalance may contribute to some behavioral alterations observed in ASD.
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Affiliation(s)
- Mauro Ursino
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
- *Correspondence: Mauro Ursino,
| | - Michele Serra
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
| | - Luca Tarasi
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Cesena, Italy
| | - Giulia Ricci
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
| | - Elisa Magosso
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
| | - Vincenzo Romei
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, Rome, Italy
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Transcriptomic analysis in the striatum reveals the involvement of Nurr1 in the social behavior of prenatally valproic acid-exposed male mice. Transl Psychiatry 2022; 12:324. [PMID: 35945212 PMCID: PMC9363495 DOI: 10.1038/s41398-022-02056-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/23/2022] [Accepted: 07/01/2022] [Indexed: 11/30/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that exhibits neurobehavioral deficits characterized by abnormalities in social interactions, deficits in communication as well as restricted interests, and repetitive behaviors. The basal ganglia is one of the brain regions implicated as dysfunctional in ASD. In particular, the defects in corticostriatal function have been reported to be involved in the pathogenesis of ASD. Surface deformation of the striatum in the brains of patients with ASD and their correlation with behavioral symptoms was reported in magnetic resonance imaging (MRI) studies. We demonstrated that prenatal valproic acid (VPA) exposure induced synaptic and molecular changes and decreased neuronal activity in the striatum. Using RNA sequencing (RNA-Seq), we analyzed transcriptome alterations in striatal tissues from 10-week-old prenatally VPA-exposed BALB/c male mice. Among the upregulated genes, Nurr1 was significantly upregulated in striatal tissues from prenatally VPA-exposed mice. Viral knockdown of Nurr1 by shRNA significantly rescued the reduction in dendritic spine density and the number of mature dendritic spines in the striatum and markedly improved social deficits in prenatally VPA-exposed mice. In addition, treatment with amodiaquine, which is a known ligand for Nurr1, mimicked the social deficits and synaptic abnormalities in saline-exposed mice as observed in prenatally VPA-exposed mice. Furthermore, PatDp+/- mice, a commonly used ASD genetic mouse model, also showed increased levels of Nurr1 in the striatum. Taken together, these results suggest that the increase in Nurr1 expression in the striatum is a mechanism related to the changes in synaptic deficits and behavioral phenotypes of the VPA-induced ASD mouse model.
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44
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The sense of agency for brain disorders: A comprehensive review and proposed framework. Neurosci Biobehav Rev 2022; 139:104759. [PMID: 35780975 DOI: 10.1016/j.neubiorev.2022.104759] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/24/2022] [Accepted: 06/26/2022] [Indexed: 11/21/2022]
Abstract
Sense of Agency (SoA) refers to the feeling of control over voluntary actions and the outcomes of those actions. Several brain disorders are characterized by an abnormal SoA. To date, there is no robust treatment for aberrant agency across disorders; this is, in large part, due to gaps in our understanding of the cognitive mechanisms and neural correlates of the SoA. This apparent gap stems from a lack of synthesis in established findings. As such, the current review reconciles previously established findings into a novel neurocognitive framework for future investigations of the SoA in brain disorders, which we term the Agency in Brain Disorders Framework (ABDF). In doing so, we highlight key top-down and bottom-up cues that contribute to agency prospectively (i.e., prior to action execution) and retrospectively (i.e., after action execution). We then examine brain disorders, including schizophrenia, autism spectrum disorders (ASD), obsessive-compulsive disorders (OCD), and cortico-basal syndrome (CBS), within the ABDF, to demonstrate its potential utility in investigating neurocognitive mechanisms underlying phenotypically variable presentations of the SoA in brain disorders.
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45
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Fujimoto A, Elorette C, Fredericks JM, Fujimoto SH, Fleysher L, Rudebeck PH, Russ BE. Resting-State fMRI-Based Screening of Deschloroclozapine in Rhesus Macaques Predicts Dosage-Dependent Behavioral Effects. J Neurosci 2022; 42:5705-5716. [PMID: 35701162 PMCID: PMC9302458 DOI: 10.1523/jneurosci.0325-22.2022] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/15/2022] [Accepted: 04/29/2022] [Indexed: 01/22/2023] Open
Abstract
Chemogenetic techniques, such as designer receptors exclusively activated by designer drugs (DREADDs), enable transient, reversible, and minimally invasive manipulation of neural activity in vivo Their development in nonhuman primates is essential for uncovering neural circuits contributing to cognitive functions and their translation to humans. One key issue that has delayed the development of chemogenetic techniques in primates is the lack of an accessible drug-screening method. Here, we use resting-state fMRI, a noninvasive neuroimaging tool, to assess the impact of deschloroclozapine (DCZ) on brainwide resting-state functional connectivity in 7 rhesus macaques (6 males and 1 female) without DREADDs. We found that systemic administration of 0.1 mg/kg DCZ did not alter the resting-state functional connectivity. Conversely, 0.3 mg/kg of DCZ was associated with a prominent increase in functional connectivity that was mainly confined to the connections of frontal regions. Additional behavioral tests confirmed a negligible impact of 0.1 mg/kg DCZ on socio-emotional behaviors as well as on reaction time in a probabilistic learning task; 0.3 mg/kg DCZ did, however, slow responses in the probabilistic learning task, suggesting attentional or motivational deficits associated with hyperconnectivity in fronto-temporo-parietal networks. Our study highlights both the excellent selectivity of DCZ as a DREADD actuator, and the side effects of its excess dosage. The results demonstrate the translational value of resting-state fMRI as a drug-screening tool to accelerate the development of chemogenetics in primates.SIGNIFICANCE STATEMENT Chemogenetics, such as designer receptors exclusively activated by designer drugs (DREADDs), can afford control over neural activity with unprecedented spatiotemporal resolution. Accelerating the translation of chemogenetic neuromodulation from rodents to primates requires an approach to screen novel DREADD actuators in vivo Here, we assessed brainwide activity in response to a DREADD actuator deschloroclozapine (DCZ) using resting-state fMRI in macaque monkeys. We demonstrated that low-dose DCZ (0.1 mg/kg) did not change whole-brain functional connectivity or affective behaviors, while a higher dose (0.3 mg/kg) altered frontal functional connectivity and slowed response in a learning task. Our study highlights the excellent selectivity of DCZ at proper dosing, and demonstrates the utility of resting-state fMRI to screen novel chemogenetic actuators in primates.
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Affiliation(s)
- Atsushi Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Catherine Elorette
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - J Megan Fredericks
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Satoka H Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Lazar Fleysher
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Peter H Rudebeck
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Brian E Russ
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York 10962
- Department of Psychiatry, New York University at Langone, New York, New York 10016
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Mapelli L, Soda T, D’Angelo E, Prestori F. The Cerebellar Involvement in Autism Spectrum Disorders: From the Social Brain to Mouse Models. Int J Mol Sci 2022; 23:ijms23073894. [PMID: 35409253 PMCID: PMC8998980 DOI: 10.3390/ijms23073894] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/28/2022] [Accepted: 03/29/2022] [Indexed: 02/04/2023] Open
Abstract
Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders that include a variety of forms and clinical phenotypes. This heterogeneity complicates the clinical and experimental approaches to ASD etiology and pathophysiology. To date, a unifying theory of these diseases is still missing. Nevertheless, the intense work of researchers and clinicians in the last decades has identified some ASD hallmarks and the primary brain areas involved. Not surprisingly, the areas that are part of the so-called “social brain”, and those strictly connected to them, were found to be crucial, such as the prefrontal cortex, amygdala, hippocampus, limbic system, and dopaminergic pathways. With the recent acknowledgment of the cerebellar contribution to cognitive functions and the social brain, its involvement in ASD has become unmistakable, though its extent is still to be elucidated. In most cases, significant advances were made possible by recent technological developments in structural/functional assessment of the human brain and by using mouse models of ASD. Mouse models are an invaluable tool to get insights into the molecular and cellular counterparts of the disease, acting on the specific genetic background generating ASD-like phenotype. Given the multifaceted nature of ASD and related studies, it is often difficult to navigate the literature and limit the huge content to specific questions. This review fulfills the need for an organized, clear, and state-of-the-art perspective on cerebellar involvement in ASD, from its connections to the social brain areas (which are the primary sites of ASD impairments) to the use of monogenic mouse models.
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Affiliation(s)
- Lisa Mapelli
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (T.S.); (E.D.)
- Correspondence: (L.M.); (F.P.)
| | - Teresa Soda
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (T.S.); (E.D.)
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (T.S.); (E.D.)
- Brain Connectivity Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (T.S.); (E.D.)
- Correspondence: (L.M.); (F.P.)
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Mei T, Ma ZH, Guo YQ, Lu B, Cao QJ, Chen X, Yang L, Wang H, Tang XZ, Ji ZZ, Liu JR, Xu LZ, Wang LQ, Yang YL, Li X, Yan CG, Liu J. Frequency-specific age-related changes in the amplitude of spontaneous fluctuations in autism. Transl Pediatr 2022; 11:349-358. [PMID: 35378963 PMCID: PMC8976680 DOI: 10.21037/tp-21-412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 12/30/2021] [Indexed: 11/06/2022] Open
Abstract
Background Autism spectrum disorder is characterized by atypical developmental changes during brain maturation, but regional brain functional changes that occur with age and across different frequency bands are unknown. Therefore, the current study aimed to explore potential age and frequency band-related changes in the regional brain activities in autism. Methods A total of 65 participants who met the DSM-IV criteria for autistic disorder and 55 typically developed (TD) participants (both age 6-30 years) were recruited in the current study. The two groups were matched in age (t=-1.314, P=0.191) and gender (χ2=2.760, P=0.097). The amplitude of low-frequency fluctuations (ALFF) was employed to explore the effect of development on spontaneous brain activity in individuals with autism and in TD participants across slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), and slow-3 (0.073-0.1 Hz) frequency bands. The diagnosis-by-age interaction effect in the whole brain voxels in autism and TD groups was investigated. Results Autism individuals showed significantly higher ALFF in the dorsal striatum in childhood (Caudate cluster: t=3.626, P=0.001; Putamen cluster: t=2.839, P=0.007) and remarkably lower ALFF in the dorsal striatum in adulthood (Caudate cluster: t=-2.198, P=0.038; Putamen cluster: t=-2.314, P=0.030) relative to TD, while no significant differences were observed in adolescence (all P>0.05). In addition, abnormal ALFF amplitudes were specific to the slow-4 (0.027-0.073 Hz) frequency band in the clusters above. Conclusions The current study indicated abnormal development patterns in the spontaneous activity of the dorsal striatum in autism and highlighted the potential role of the slow-4 frequency band in the pathology of autism. Also, the potential brain mechanism of autism was revealed, suggesting that autism-related variations should be investigated in a specific frequency.
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Affiliation(s)
- Ting Mei
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Zeng-Hui Ma
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Yan-Qing Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Qing-Jiu Cao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Liu Yang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Hui Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Xin-Zhou Tang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Zhao-Zheng Ji
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Jing-Ran Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Ling-Zi Xu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Li-Qi Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Yu-Lu Yang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Xue Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- International Big-Data Research Center for Depression (IBRCD), Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Jing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
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Chen G, Chen P, Gong J, Jia Y, Zhong S, Chen F, Wang J, Luo Z, Qi Z, Huang L, Wang Y. Shared and specific patterns of dynamic functional connectivity variability of striato-cortical circuitry in unmedicated bipolar and major depressive disorders. Psychol Med 2022; 52:747-756. [PMID: 32648539 DOI: 10.1017/s0033291720002378] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Accumulating studies have found structural and functional abnormalities of the striatum in bipolar disorder (BD) and major depressive disorder (MDD). However, changes in intrinsic brain functional connectivity dynamics of striato-cortical circuitry have not been investigated in BD and MDD. This study aimed to investigate the shared and specific patterns of dynamic functional connectivity (dFC) variability of striato-cortical circuitry in BD and MDD. METHODS Brain resting-state functional magnetic resonance imaging data were acquired from 128 patients with unmedicated BD II (current episode depressed), 140 patients with unmedicated MDD, and 132 healthy controls (HCs). Six pairs of striatum seed regions were selected: the ventral striatum inferior (VSi) and the ventral striatum superior (VSs), the dorsal-caudal putamen (DCP), the dorsal-rostral putamen (DRP), and the dorsal caudate and the ventral-rostral putamen (VRP). The sliding-window analysis was used to evaluate dFC for each seed. RESULTS Both BD II and MDD exhibited increased dFC variability between the left DRP and the left supplementary motor area, and between the right VRP and the right inferior parietal lobule. The BD II had specific increased dFC variability between the right DCP and the left precentral gyrus compared with MDD and HCs. The MDD had increased dFC variability between the left VSi and the left medial prefrontal cortex compared with BD II and HCs. CONCLUSIONS The patients with BD and MDD shared common dFC alteration in the dorsal striatal-sensorimotor and ventral striatal-cognitive circuitries. The patients with MDD had specific dFC alteration in the ventral striatal-affective circuitry.
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Affiliation(s)
- Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Pan Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - JiaYing Gong
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
- Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Feng Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Jurong Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Zhenye Luo
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Zhangzhang Qi
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
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Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning. Sci Rep 2022; 12:3057. [PMID: 35197468 PMCID: PMC8866395 DOI: 10.1038/s41598-022-06459-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/25/2022] [Indexed: 12/31/2022] Open
Abstract
Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the development of machine learning (ML) models that can integrate neuroimaging features from functional and structural MRI (fMRI and sMRI) to help reveal central nervous system alterations characteristic of ASD. We optimized and compared the performance of 12 of the most popular and powerful ML models. Each was separately trained using 15 different combinations of fMRI and sMRI features and optimized with an unbiased model search. Deep learning models predicted ASD with the highest diagnostic accuracy and generalized well to other MRI datasets. Our model achieves state-of-the-art 80% area under the ROC curve (AUROC) in diagnosis on test data from the IMPAC dataset; and 86% and 79% AUROC on the external ABIDE I and ABIDE II datasets (with further improvement to 93% and 90% after supervised domain adaptation). The highest performing models identified reproducible putative biomarkers for accurate ASD diagnosis in accord with known ASD markers as well as novel cerebellar biomarkers. Such reproducibility lends credence to their tremendous potential for defining and using a set of truly generalizable ASD biomarkers that will advance scientific understanding of neuronal changes in ASD.
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DiCarlo GE, Wallace MT. Modeling dopamine dysfunction in autism spectrum disorder: From invertebrates to vertebrates. Neurosci Biobehav Rev 2022; 133:104494. [PMID: 34906613 PMCID: PMC8792250 DOI: 10.1016/j.neubiorev.2021.12.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 11/29/2021] [Accepted: 12/09/2021] [Indexed: 02/03/2023]
Abstract
Autism Spectrum Disorder (ASD) is a highly heterogeneous neurodevelopmental disorder characterized by deficits in social communication and by patterns of restricted interests and/or repetitive behaviors. The Simons Foundation Autism Research Initiative's Human Gene and CNV Modules now list over 1000 genes implicated in ASD and over 2000 copy number variant loci reported in individuals with ASD. Given this ever-growing list of genetic changes associated with ASD, it has become evident that there is likely not a single genetic cause of this disorder nor a single neurobiological basis of this disorder. Instead, it is likely that many different neurobiological perturbations (which may represent subtypes of ASD) can result in the set of behavioral symptoms that we called ASD. One such of possible subtype of ASD may be associated with dopamine dysfunction. Precise regulation of synaptic dopamine (DA) is required for reward processing and behavioral learning, behaviors which are disrupted in ASD. Here we review evidence for DA dysfunction in ASD and in animal models of ASD. Further, we propose that these studies provide a scaffold for scientists and clinicians to consider subcategorizing the ASD diagnosis based on the genetic changes, neurobiological difference, and behavioral features identified in individuals with ASD.
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Affiliation(s)
- Gabriella E DiCarlo
- Massachusetts General Hospital, Department of Medicine, Boston, MA, United States
| | - Mark T Wallace
- Vanderbilt University Brain Institute, Nashville, TN, United States; Department of Psychology, Vanderbilt University, Nashville, TN, United States; Department of Hearing & Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Pharmacology, Vanderbilt University, Nashville, TN, United States; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.
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