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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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Wang X, Zhao K, Yao L, Fonzo GA, Satterthwaite TD, Rekik I, Zhang Y. Delineating Transdiagnostic Subtypes in Neurodevelopmental Disorders via Contrastive Graph Machine Learning of Brain Connectivity Patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.29.582790. [PMID: 38496573 PMCID: PMC10942316 DOI: 10.1101/2024.02.29.582790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Neurodevelopmental disorders, such as Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD), are characterized by comorbidity and heterogeneity. Identifying distinct subtypes within these disorders can illuminate the underlying neurobiological and clinical characteristics, paving the way for more tailored treatments. We adopted a novel transdiagnostic approach across ADHD and ASD, using cutting-edge contrastive graph machine learning to determine subtypes based on brain network connectivity as revealed by resting-state functional magnetic resonance imaging. Our approach identified two generalizable subtypes characterized by robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the somatomotor network. These subtypes exhibited pronounced differences in major cognitive and behavioural measures. We further demonstrated the generalizability of these subtypes using data collected from independent study sites. Our data-driven approach provides a novel solution for parsing biological heterogeneity in neurodevelopmental disorders.
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Affiliation(s)
- Xuesong Wang
- Data 61, Commonwealth Scientific and Industrial Research Organisation, New South Wales, Australia
| | - Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Lina Yao
- Data 61, Commonwealth Scientific and Industrial Research Organisation, New South Wales, Australia
- School of Computer Science and Engineering, University of New South Wales, New South Wales, Australia
| | - Gregory A Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | | | - Islem Rekik
- BASIRA Lab, Imperial-X and Department of Computing, Imperial College London, London, UK
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
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Zhang J, Zhang Z, Sun H, Ma Y, Yang J, Chen K, Yu X, Qin T, Zhao T, Zhang J, Chu C, Wang J. Personalized functional network mapping for autism spectrum disorder and attention-deficit/hyperactivity disorder. Transl Psychiatry 2024; 14:92. [PMID: 38346949 PMCID: PMC10861462 DOI: 10.1038/s41398-024-02797-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 01/11/2024] [Accepted: 01/22/2024] [Indexed: 02/15/2024] Open
Abstract
Autism spectrum disorder (ASD) and Attention-deficit/hyperactivity disorder (ADHD) are two typical neurodevelopmental disorders that have a long-term impact on physical and mental health. ASD is usually comorbid with ADHD and thus shares highly overlapping clinical symptoms. Delineating the shared and distinct neurophysiological profiles is important to uncover the neurobiological mechanisms to guide better therapy. In this study, we aimed to establish the behaviors, functional connectome, and network properties differences between ASD, ADHD-Combined, and ADHD-Inattentive using resting-state functional magnetic resonance imaging. We used the non-negative matrix fraction method to define personalized large-scale functional networks for each participant. The individual large-scale functional network connectivity (FNC) and graph-theory-based complex network analyses were executed and identified shared and disorder-specific differences in FNCs and network attributes. In addition, edge-wise functional connectivity analysis revealed abnormal edge co-fluctuation amplitude and number of transitions among different groups. Taken together, our study revealed disorder-specific and -shared regional and edge-wise functional connectivity and network differences for ASD and ADHD using an individual-level functional network mapping approach, which provides new evidence for the brain functional abnormalities in ASD and ADHD and facilitates understanding the neurobiological basis for both disorders.
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Affiliation(s)
- Jiang Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Zhiwei Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Hui Sun
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Yingzi Ma
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming, Yunnan, China
| | - Jia Yang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming, Yunnan, China
| | - Kexuan Chen
- Medical School, Kunming University of Science and Technology, Kunming, China
| | - Xiaohui Yu
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming, Yunnan, China
| | - Tianwei Qin
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Tianyu Zhao
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Jingyue Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China.
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming, Yunnan, China.
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de Lacy N, Ramshaw MJ. Selectively predicting the onset of ADHD, oppositional defiant disorder, and conduct disorder in early adolescence with high accuracy. Front Psychiatry 2023; 14:1280326. [PMID: 38144472 PMCID: PMC10739523 DOI: 10.3389/fpsyt.2023.1280326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 11/13/2023] [Indexed: 12/26/2023] Open
Abstract
Introduction The externalizing disorders of attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD) are common in adolescence and are strong predictors of adult psychopathology. While treatable, substantial diagnostic overlap complicates intervention planning. Understanding which factors predict the onset of each disorder and disambiguating their different predictors is of substantial translational interest. Materials and methods We analyzed 5,777 multimodal candidate predictors from children aged 9-10 years and their parents in the ABCD cohort to predict the future onset of ADHD, ODD, and CD at 2-year follow-up. We used deep learning optimized with an innovative AI algorithm to jointly optimize model training, perform automated feature selection, and construct individual-level predictions of illness onset and all prevailing cases at 11-12 years and examined relative predictive performance when candidate predictors were restricted to only neural metrics. Results Multimodal models achieved ~86-97% accuracy, 0.919-0.996 AUROC, and ~82-97% precision and recall in testing in held-out, unseen data. In neural-only models, predictive performance dropped substantially but nonetheless achieved accuracy and AUROC of ~80%. Parent aggressive and externalizing traits uniquely differentiated the onset of ODD, while structural MRI metrics in the limbic system were specific to CD. Psychosocial measures of sleep disorders, parent mental health and behavioral traits, and school performance proved valuable across all disorders. In neural-only models, structural and functional MRI metrics in subcortical regions and cortical-subcortical connectivity were emphasized. Overall, we identified a strong correlation between accuracy and final predictor importance. Conclusion Deep learning optimized with AI can generate highly accurate individual-level predictions of the onset of early adolescent externalizing disorders using multimodal features. While externalizing disorders are frequently co-morbid in adolescents, certain predictors were specific to the onset of ODD or CD vs. ADHD. To our knowledge, this is the first machine learning study to predict the onset of all three major adolescent externalizing disorders with the same design and participant cohort to enable direct comparisons, analyze >200 multimodal features, and include many types of neuroimaging metrics. Future study to test our observations in external validation data will help further test the generalizability of these findings.
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Affiliation(s)
- Nina de Lacy
- Huntsman Mental Health Institute, Salt Lake City, UT, United States
- Department of Psychiatry, University of Utah, Salt Lake City, UT, United States
| | - Michael J. Ramshaw
- Huntsman Mental Health Institute, Salt Lake City, UT, United States
- Department of Psychiatry, University of Utah, Salt Lake City, UT, United States
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Wei L, Zhang Y, Zhai W, Wang H, Zhang J, Jin H, Feng J, Qin Q, Xu H, Li B, Liu J. Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders. Front Neurosci 2022; 16:987248. [PMID: 36523439 PMCID: PMC9745118 DOI: 10.3389/fnins.2022.987248] [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: 07/06/2022] [Accepted: 11/07/2022] [Indexed: 11/29/2023] Open
Abstract
INTRODUCTION Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear. OBJECTS The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state. MATERIALS AND METHODS The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients' clinical characteristics. RESULTS For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the "triple networks" system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores. CONCLUSION Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease.
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Affiliation(s)
- Lei Wei
- Network Center, Air Force Medical University, Xi’an, China
| | - Yao Zhang
- Military Medical Center, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Wensheng Zhai
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Junchao Zhang
- Network Center, Air Force Medical University, Xi’an, China
| | - Haojie Jin
- Network Center, Air Force Medical University, Xi’an, China
| | - Jianfei Feng
- Network Center, Air Force Medical University, Xi’an, China
| | - Qin Qin
- Network Center, Air Force Medical University, Xi’an, China
| | - Hao Xu
- Network Center, Air Force Medical University, Xi’an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Jian Liu
- Network Center, Air Force Medical University, Xi’an, China
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Sabag M, Geva R. Hyper and hypo attention networks activations affect social development in children with autism spectrum disorder. Front Hum Neurosci 2022; 16:902041. [PMID: 36034110 PMCID: PMC9403843 DOI: 10.3389/fnhum.2022.902041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 07/21/2022] [Indexed: 12/01/2022] Open
Abstract
Children with autism spectrum disorder (ASD) experience a range of social and non-social attention deficits. To date, most studies assessed the neurological framework or discrete behavioral traits related to one attention network, leaving a gap in the understanding of the developmental cascade affecting the inter-relations among attention networks in ASD in a pervasive manner. We propose a theoretical framework that integrates the behavioral deficits and neurological manifestations through a cohesive developmental prism of attention networks’ activations while assessing their impact on social deficits in children with ASD. Insights arising from the model suggest hyper-and-hypoactivation of posterior attention networks leads to an altered prefrontal anterior attention network weight in ways that conjointly impact social performance in ASD. This perspective on how attention networks develop and interact in ASD may inform future research directions regarding ASD and attention development.
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Affiliation(s)
- Maya Sabag
- Department of Psychology, Bar-Ilan University, Ramat Gan, Israel
- The Developmental Neuropsychology Lab, The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Ronny Geva
- Department of Psychology, Bar-Ilan University, Ramat Gan, Israel
- The Developmental Neuropsychology Lab, The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
- *Correspondence: Ronny Geva,
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Kato R, Balasubramani PP, Ramanathan D, Mishra J. Utility of Cognitive Neural Features for Predicting Mental Health Behaviors. SENSORS (BASEL, SWITZERLAND) 2022; 22:3116. [PMID: 35590804 PMCID: PMC9100783 DOI: 10.3390/s22093116] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 04/15/2022] [Accepted: 04/16/2022] [Indexed: 06/15/2023]
Abstract
Cognitive dysfunction underlies common mental health behavioral symptoms including depression, anxiety, inattention, and hyperactivity. In this study of 97 healthy adults, we aimed to classify healthy vs. mild-to-moderate self-reported symptoms of each disorder using cognitive neural markers measured with an electroencephalography (EEG). We analyzed source-reconstructed EEG data for event-related spectral perturbations in the theta, alpha, and beta frequency bands in five tasks, a selective attention and response inhibition task, a visuospatial working memory task, a Flanker interference processing task, and an emotion interference task. From the cortical source activation features, we derived augmented features involving co-activations between any two sources. Logistic regression on the augmented feature set, but not the original feature set, predicted the presence of psychiatric symptoms, particularly for anxiety and inattention with >80% sensitivity and specificity. We also computed current flow closeness and betweenness centralities to identify the “hub” source signal predictors. We found that the Flanker interference processing task was the most useful for assessing the connectivity hubs in general, followed by the inhibitory control go-nogo paradigm. Overall, these interpretable machine learning analyses suggest that EEG biomarkers collected on a rapid suite of cognitive assessments may have utility in classifying diverse self-reported mental health symptoms.
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Affiliation(s)
- Ryosuke Kato
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, CA 92037, USA; (R.K.); (D.R.); (J.M.)
| | | | - Dhakshin Ramanathan
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, CA 92037, USA; (R.K.); (D.R.); (J.M.)
- Department of Mental Health, VA San Diego Medical Center, San Diego, CA 92037, USA
| | - Jyoti Mishra
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California, San Diego, CA 92037, USA; (R.K.); (D.R.); (J.M.)
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Chen Y, Lei D, Cao H, Niu R, Chen F, Chen L, Zhou J, Hu X, Huang X, Guo L, Sweeney JA, Gong Q. Altered single-subject gray matter structural networks in drug-naïve attention deficit hyperactivity disorder children. Hum Brain Mapp 2022; 43:1256-1264. [PMID: 34797010 PMCID: PMC8837581 DOI: 10.1002/hbm.25718] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/09/2021] [Accepted: 11/04/2021] [Indexed: 02/05/2023] Open
Abstract
Altered topological organization of brain structural covariance networks has been observed in attention deficit hyperactivity disorder (ADHD). However, results have been inconsistent, potentially related to confounding medication effects. In addition, since structural networks are traditionally constructed at the group level, variabilities in individual structural features remain to be well characterized. Structural brain imaging with MRI was performed on 84 drug-naïve children with ADHD and 83 age-matched healthy controls. Single-subject gray matter (GM) networks were obtained based on areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared using nonparametric permutation testing. Compared with healthy subjects, GM networks in ADHD patients demonstrated significantly altered topological characteristics, including higher global and local efficiency and clustering coefficient, and shorter path length. In addition, ADHD patients exhibited abnormal centrality in corticostriatal circuitry including the superior frontal gyrus, orbitofrontal gyrus, medial superior frontal gyrus, precentral gyrus, middle temporal gyrus, and pallidum (all p < .05, false discovery rate [FDR] corrected). Altered global and nodal topological efficiencies were associated with the severity of hyperactivity symptoms and the performance on the Stroop and Wisconsin Card Sorting Test tests (all p < .05, FDR corrected). ADHD combined and inattention subtypes were differentiated by nodal attributes of amygdala (p < .05, FDR corrected). Alterations in GM network topologies were observed in drug-naïve ADHD patients, in particular in frontostriatal loops and amygdala. These alterations may contribute to impaired cognitive functioning and impulsive behavior in ADHD.
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Affiliation(s)
- Ying Chen
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
- Department of PsychiatryWest China Hospital of Sichuan UniversityChengduChina
| | - Du Lei
- Department of Psychiatry and Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Hengyi Cao
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
- Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNew YorkUSA
- Division of Psychiatry ResearchZucker Hillside HospitalGlen OaksNew YorkUSA
- Department of PsychiatryUniversity of CincinnatiCincinnatiOhioUSA
| | - Running Niu
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Fuqin Chen
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Lizhou Chen
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Jinbo Zhou
- Department of PsychiatryWest China Hospital of Sichuan UniversityChengduChina
| | - Xinyu Hu
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
| | - Lanting Guo
- Department of PsychiatryWest China Hospital of Sichuan UniversityChengduChina
| | - John A. Sweeney
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
- Department of Psychiatry and Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of RadiologyWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceHuaxi Xiamen Hospital of Sichuan UniversityXiamenFujianChina
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Vaidya CJ, Klein C. Comorbidity of Attention-Deficit Hyperactivity Disorder and Autism Spectrum Disorders: Current Status and Promising Directions. Curr Top Behav Neurosci 2022; 57:159-177. [PMID: 35397063 DOI: 10.1007/7854_2022_334] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
High rates of co-occurring Attention-Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD) suggest common causal pathways, which await elucidation. What is well-established, however, is the negative impact of comorbid ADHD and ASD on outcomes for everyday living, particularly in social interaction and communication and on broader psychopathology. Neurocognitive approaches suggest correlates of comorbidity are rooted in functional connectivity networks associated with executive control. There is support for familial origins, with molecular-genetic studies suggesting a causal role of pleiotropic genes. Further investigation is needed to elucidate fully how genetic risk for ADHD and ASD affects neurodevelopment and to identify structural and functional neural correlates and their behavioral sequelae. Identification of intermediate phenotypes is necessary to advance understanding, which requires studies that include the full spectrum of ASD and ADHD symptom severity, use longitudinal designs and multivariate methods to probe broad constructs, such as executive and social function, and consider other sources of heterogeneity, such as age, sex, and other psychopathology. Randomized efficacy trials targeting comorbid symptomatology are needed to mitigate negative developmental outcomes.
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Affiliation(s)
- Chandan J Vaidya
- Department of Psychology, Georgetown University, Washington, DC, USA.
- Children's Research Institute, Children's National Health System, Washington, DC, USA.
| | - Christoph Klein
- Clinic for Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Child and Adolescent Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
- Psychiatry Department, National and Kapodistrian University of Athens, Medical School, University General Hospital "ATTIKON", Athens, Greece
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Bathelt J, Geurts HM, Borsboom D. More than the sum of its parts: Merging network psychometrics and
network neuroscience with application in autism. Netw Neurosci 2021; 6:445-466. [PMID: 35733421 PMCID: PMC9207995 DOI: 10.1162/netn_a_00222] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/08/2021] [Indexed: 11/05/2022] Open
Abstract
Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach in neuroimaging research. In this paper, we introduce a methodology for combining network psychometrics and network neuroscience. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10–21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates. The article introduces a method to combine common practices in network psychometrics and network neuroimaging. Namely, we use the unique variance in behavioural measures as regressors to identify unique neural correlates. This enables the description of brain-level and behavioural-level data into a joint network while keeping the dimensionality of the results manageable and interpretable. We illustrate this approach by showing the network of autistic traits and their correlates in resting-state functional connectivity.
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Affiliation(s)
- Joe Bathelt
- Department of Psychology, Royal Holloway, University of London, Egham, Surrey TW20 0EX, United Kingdom
- Department of Psychology, University of Amsterdam
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11
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Benkarim O, Paquola C, Park BY, Hong SJ, Royer J, Vos de Wael R, Lariviere S, Valk S, Bzdok D, Mottron L, C Bernhardt B. Connectivity alterations in autism reflect functional idiosyncrasy. Commun Biol 2021; 4:1078. [PMID: 34526654 PMCID: PMC8443598 DOI: 10.1038/s42003-021-02572-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 08/17/2021] [Indexed: 02/08/2023] Open
Abstract
Autism spectrum disorder (ASD) is commonly understood as an alteration of brain networks, yet case-control analyses against typically-developing controls (TD) have yielded inconsistent results. Here, we devised a novel approach to profile the inter-individual variability in functional network organization and tested whether such idiosyncrasy contributes to connectivity alterations in ASD. Studying a multi-centric dataset with 157 ASD and 172 TD, we obtained robust evidence for increased idiosyncrasy in ASD relative to TD in default mode, somatomotor and attention networks, but also reduced idiosyncrasy in lateral temporal cortices. Idiosyncrasy increased with age and significantly correlated with symptom severity in ASD. Furthermore, while patterns of functional idiosyncrasy were not correlated with ASD-related cortical thickness alterations, they co-localized with the expression patterns of ASD risk genes. Notably, we could demonstrate that patterns of atypical idiosyncrasy in ASD closely overlapped with connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between inter-individual heterogeneity in autism and functional signatures. Our findings provide novel biomarkers to study atypical brain development and may consolidate prior research findings on the variable nature of connectome level anomalies in autism.
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Affiliation(s)
- Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sara Lariviere
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sofie Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- INM-7, FZ Jülich, Jülich, Germany
| | - Danilo Bzdok
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Laurent Mottron
- Centre de recherche du CIUSSSNIM et Département de Psychiatrie, Université de Montréal, Montreal, QC, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
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12
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An executive functioning perspective in neurofibromatosis type 1: from ADHD and autism spectrum disorder to research domains. Childs Nerv Syst 2020; 36:2321-2332. [PMID: 32617712 DOI: 10.1007/s00381-020-04745-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 06/11/2020] [Indexed: 01/31/2023]
Abstract
PURPOSE Neurofibromatosis type 1 (NF1) is a rare monogenic disorder associated with executive function (EF) deficits and heightened risk for attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The goal of this paper is to understand how EFs provide a common foundation to understand vulnerabilities for ADHD and ASD within NF1. METHODS A literature review and synthesis was conducted. RESULTS EF difficulties in working memory, inhibitory control, cognitive flexibility, and planning are evident in NF1, ADHD, and ASD. However, relatively little is known about the heterogeneity of EFs and ADHD and ASD outcomes in NF1. Assessment of ADHD and ASD in NF1 is based on behavioral symptoms without understanding neurobiological contributions. Recent efforts are promoting the use of dimensional and multidisciplinary methods to better understand normal and abnormal behavior, including integrating information from genetics to self-report measures. CONCLUSION NF1 is a monogenic disease with well-developed molecular and phenotypic research as well as complementary animal models. NF1 presents an excellent opportunity to advance our understanding of the neurobiological impact of known pathogenic variation in normal and abnormal neural pathways implicated in human psychopathology. EFs are core features of NF1, ADHD, and ASD, and these neurodevelopmental outcomes are highly prevalent in NF1. We propose a multilevel approach for understanding EFs in patients with NF1.This is essential to advance targeted interventions for NF1 patients and to advance the exciting field of research in this condition.
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13
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Bu X, Liang K, Lin Q, Gao Y, Qian A, Chen H, Chen W, Wang M, Yang C, Huang X. Exploring white matter functional networks in children with attention-deficit/hyperactivity disorder. Brain Commun 2020; 2:fcaa113. [PMID: 33215081 PMCID: PMC7660033 DOI: 10.1093/braincomms/fcaa113] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 06/22/2020] [Accepted: 06/26/2020] [Indexed: 02/05/2023] Open
Abstract
Attention-deficit/hyperactivity disorder has been identified to involve the impairment of large-scale functional networks within grey matter, and recent studies have suggested that white matter, which also encodes neural activity, can manifest intrinsic functional organization similar to that of grey matter. However, the alterations in white matter functional networks in attention-deficit/hyperactivity disorder remain unknown. We recruited a total of 99 children, including 66 drug-naive patients and 33 typically developing controls aged from 6 to 14, to characterize the alterations in functional networks within white matter in drug-naive children with attention-deficit/hyperactivity disorder. Using clustering analysis, resting-state functional MRI data in the white matter were parsed into different networks. Intrinsic activity within each network and connectivity between networks and the associations between network activity strength and clinical symptoms were assessed. We identified eight distinct white matter functional networks: the default mode network, the somatomotor network, the dorsal attention network, the ventral attention network, the visual network, the deep frontoparietal network, the deep frontal network and the inferior corticospinal-posterior cerebellum network. The default mode, somatomotor, dorsal attention and ventral attention networks showed lower spontaneous neural activity in patients. In particular, the default mode network and the somatomotor network largely showed higher connectivity with other networks, which correlated with more severe hyperactive behaviour, while the dorsal and ventral attention networks mainly had lower connectivity with other networks, which correlated with poor attention performance. In conclusion, there are two distinct patterns of white matter functional networks in children with attention-deficit/hyperactivity disorder, with one being the hyperactivity-related hot networks including default mode network and somatomotor network and the other being inattention-related cold networks including dorsal attention and ventral attention network. These results extended upon our understanding of brain functional networks in attention-deficit/hyperactivity disorder from the perspective of white matter dysfunction.
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Affiliation(s)
- Xuan Bu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Kaili Liang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Qingxia Lin
- Department of Psychiatry, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325003, China
| | - Yingxue Gao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Andan Qian
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325003, China
| | - Hong Chen
- Department of Psychiatry, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325003, China
| | - Wanying Chen
- Department of Psychiatry, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325003, China
| | - Meihao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325003, China
| | - Chuang Yang
- Department of Psychiatry, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325003, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
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14
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Carroll L, Braeutigam S, Dawes JM, Krsnik Z, Kostovic I, Coutinho E, Dewing JM, Horton CA, Gomez-Nicola D, Menassa DA. Autism Spectrum Disorders: Multiple Routes to, and Multiple Consequences of, Abnormal Synaptic Function and Connectivity. Neuroscientist 2020; 27:10-29. [PMID: 32441222 PMCID: PMC7804368 DOI: 10.1177/1073858420921378] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Autism spectrum disorders (ASDs) are a heterogeneous group of
neurodevelopmental disorders of genetic and environmental etiologies.
Some ASD cases are syndromic: associated with clinically defined
patterns of somatic abnormalities and a neurobehavioral phenotype
(e.g., Fragile X syndrome). Many cases, however, are idiopathic or
non-syndromic. Such disorders present themselves during the early
postnatal period when language, speech, and personality start to
develop. ASDs manifest by deficits in social communication and
interaction, restricted and repetitive patterns of behavior across
multiple contexts, sensory abnormalities across multiple modalities
and comorbidities, such as epilepsy among many others. ASDs are
disorders of connectivity, as synaptic dysfunction is common to both
syndromic and idiopathic forms. While multiple theories have been
proposed, particularly in idiopathic ASDs, none address why certain
brain areas (e.g., frontotemporal) appear more vulnerable than others
or identify factors that may affect phenotypic specificity. In this
hypothesis article, we identify possible routes leading to, and the
consequences of, altered connectivity and review the evidence of
central and peripheral synaptic dysfunction in ASDs. We postulate that
phenotypic specificity could arise from aberrant experience-dependent
plasticity mechanisms in frontal brain areas and peripheral sensory
networks and propose why the vulnerability of these areas could be
part of a model to unify preexisting pathophysiological theories.
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Affiliation(s)
- Liam Carroll
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Sven Braeutigam
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
| | - John M Dawes
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Zeljka Krsnik
- Croatian Institute for Brain Research, Centre of Research Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Ivica Kostovic
- Croatian Institute for Brain Research, Centre of Research Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Ester Coutinho
- Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
| | - Jennifer M Dewing
- Faculty of Medicine, University of Southampton, Southampton, Hampshire, UK
| | - Christopher A Horton
- Sir William Dunn School of Pathology, University of Oxford, Oxford, Oxfordshire, UK
| | - Diego Gomez-Nicola
- Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
| | - David A Menassa
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK.,Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
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15
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Dajani DR, Burrows CA, Nebel MB, Mostofsky SH, Gates KM, Uddin LQ. Parsing Heterogeneity in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder with Individual Connectome Mapping. Brain Connect 2019; 9:673-691. [PMID: 31631690 PMCID: PMC6862970 DOI: 10.1089/brain.2019.0669] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Traditional diagnostic systems for neurodevelopmental disorders define diagnostic categories that are heterogeneous in behavior and underlying neurobiological alterations. The goal of this study was to parse heterogeneity in a core executive function (EF), cognitive flexibility, in children with a range of abilities (N = 132; children with autism spectrum disorder, attention-deficit/hyperactivity disorder [ADHD], and typically developing children) using directed functional connectivity profiles derived from resting-state functional magnetic resonance imaging data. Brain regions activated in response to a cognitive flexibility task in adults were used to guide region-of-interest selection to estimate individual connectivity profiles in this study. We expected to find subgroups of children who differed in their network connectivity metrics and symptom measures. Unexpectedly, we did not find a stable or valid subgrouping solution, which suggests that categorical models of the neural substrates of cognitive flexibility in children may be invalid. Exploratory analyses revealed dimensional associations between network connectivity metrics and ADHD symptomatology and EF ability across the entire sample. Results shed light on the validity of conceptualizing the neural substrates of cognitive flexibility categorically in children. Ultimately, this work may provide a foundation for the development of a revised nosology focused on neurobiological substrates as an alternative to traditional symptom-based classification systems.
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Affiliation(s)
- Dina R. Dajani
- Department of Psychology, University of Miami, Coral Gables, Florida
| | - Catherine A. Burrows
- Institute on Community Integration, University of Minnesota, Minneapolis, Minnesota
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Stewart H. Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kathleen M. Gates
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, North Carolina
| | - Lucina Q. Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida
- Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida
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