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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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2
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Qian S, Yang Q, Cai C, Dong J, Cai S. Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study. Brain Sci 2024; 14:507. [PMID: 38790485 PMCID: PMC11118919 DOI: 10.3390/brainsci14050507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.
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Affiliation(s)
| | | | | | | | - Shuhui Cai
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (S.Q.); (Q.Y.); (C.C.); (J.D.)
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3
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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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4
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Ghin F, Eggert E, Gholamipourbarogh N, Talebi N, Beste C. Response stopping under conflict: The integrative role of representational dynamics associated with the insular cortex. Hum Brain Mapp 2024; 45:e26643. [PMID: 38664992 PMCID: PMC11046082 DOI: 10.1002/hbm.26643] [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: 08/18/2023] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 04/29/2024] Open
Abstract
Coping with distracting inputs during goal-directed behavior is a common challenge, especially when stopping ongoing responses. The neural basis for this remains debated. Our study explores this using a conflict-modulation Stop Signal task, integrating group independent component analysis (group-ICA), multivariate pattern analysis (MVPA), and EEG source localization analysis. Consistent with previous findings, we show that stopping performance is better in congruent (nonconflicting) trials than in incongruent (conflicting) trials. Conflict effects in incongruent trials compromise stopping more due to the need for the reconfiguration of stimulus-response (S-R) mappings. These cognitive dynamics are reflected by four independent neural activity patterns (ICA), each coding representational content (MVPA). It is shown that each component was equally important in predicting behavioral outcomes. The data support an emerging idea that perception-action integration in action-stopping involves multiple independent neural activity patterns. One pattern relates to the precuneus (BA 7) and is involved in attention and early S-R processes. Of note, three other independent neural activity patterns were associated with the insular cortex (BA13) in distinct time windows. These patterns reflect a role in early attentional selection but also show the reiterated processing of representational content relevant for stopping in different S-R mapping contexts. Moreover, the insular cortex's role in automatic versus complex response selection in relation to stopping processes is shown. Overall, the insular cortex is depicted as a brain hub, crucial for response selection and cancellation across both straightforward (automatic) and complex (conditional) S-R mappings, providing a neural basis for general cognitive accounts on action control.
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Affiliation(s)
- Filippo Ghin
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany
| | - Elena Eggert
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany
| | - Negin Gholamipourbarogh
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany
| | - Nasibeh Talebi
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of MedicineTU DresdenDresdenGermany
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5
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Voldsbekk I, Kjelkenes R, Frogner ER, Westlye LT, Alnæs D. Testing the sensitivity of diagnosis-derived patterns in functional brain networks to symptom burden in a Norwegian youth sample. Hum Brain Mapp 2024; 45:e26631. [PMID: 38379514 PMCID: PMC10879903 DOI: 10.1002/hbm.26631] [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: 10/24/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/22/2024] Open
Abstract
Aberrant brain network development represents a putative aetiological component in mental disorders, which typically emerge during childhood and adolescence. Previous studies have identified resting-state functional connectivity (RSFC) patterns reflecting psychopathology, but the generalisability to other samples and politico-cultural contexts has not been established. We investigated whether a previously identified cross-diagnostic case-control and autism spectrum disorder (ASD)-specific pattern of RSFC (discovery sample; aged 5-21 from New York City, USA; n = 1666) could be validated in a Norwegian convenience-based youth sample (validation sample; aged 9-25 from Oslo, Norway; n = 531). As a test of generalisability, we investigated if these diagnosis-derived RSFC patterns were sensitive to levels of symptom burden in both samples, based on an independent measure of symptom burden. Both the cross-diagnostic and ASD-specific RSFC pattern were validated across samples. Connectivity patterns were significantly associated with thematically appropriate symptom dimensions in the discovery sample. In the validation sample, the ASD-specific RSFC pattern showed a weak, inverse relationship with symptoms of conduct problems, hyperactivity and prosociality, while the cross-diagnostic pattern was not significantly linked to symptoms. Diagnosis-derived connectivity patterns in a developmental clinical US sample were validated in a convenience sample of Norwegian youth, however, they were not associated with mental health symptoms.
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Affiliation(s)
- Irene Voldsbekk
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Rikka Kjelkenes
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Erik R. Frogner
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo, Department of Neurology, Oslo University HospitalOsloNorway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University HospitalOsloNorway
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6
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Rai S, Graff K, Tansey R, Bray S. How do tasks impact the reliability of fMRI functional connectivity? Hum Brain Mapp 2024; 45:e26535. [PMID: 38348730 PMCID: PMC10884875 DOI: 10.1002/hbm.26535] [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: 06/21/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 02/24/2024] Open
Abstract
While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.
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Affiliation(s)
- Shefali Rai
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Kirk Graff
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Ryann Tansey
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Signe Bray
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of RadiologyUniversity of CalgaryCalgaryAlbertaCanada
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7
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Townes P, Liu C, Panesar P, Devoe D, Lee SY, Taylor G, Arnold PD, Crosbie J, Schachar R. Do ASD and ADHD Have Distinct Executive Function Deficits? A Systematic Review and Meta-Analysis of Direct Comparison Studies. J Atten Disord 2023; 27:1571-1582. [PMID: 37565325 PMCID: PMC10637091 DOI: 10.1177/10870547231190494] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
OBJECTIVE To evaluate if children and adolescents with a diagnosis of ASD or ADHD have distinct executive function (EF) profiles. METHODS Peer-reviewed articles comparing ASD, ADHD, and typically developing individuals under 19 years of age were identified. The domains evaluated were: working memory, response inhibition, planning, cognitive flexibility, attention, processing speed, and visuospatial abilities. RESULTS Fifty-eight articles met inclusion criteria. Analyses were performed on 45 performance metrics from 24 individual tasks. No differences in EF were found between individuals diagnosed with ASD and ADHD. Individuals diagnosed with ASD and ADHD exhibited worse performance in attention, flexibility, visuospatial abilities, working memory, processing speed, and response inhibition than typically developing individuals. Groups did not differ in planning abilities. CONCLUSION Children and adolescents with ASD and ADHD have similar EF profiles. Further research is needed to determine if comorbidity accounts for the commonality in executive dysfunction between each disorder.
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Yang CM, Shin J, Kim JI, Lim YB, Park SH, Kim BN. Classifying Children with ADHD Based on Prefrontal Functional Near-infrared Spectroscopy Using Machine Learning. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2023; 21:693-700. [PMID: 37859442 PMCID: PMC10591175 DOI: 10.9758/cpn.22.1025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 10/21/2023]
Abstract
Objective : Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adults characterized by cognitive and emotional self-control deficiencies. Previous functional near-infrared spectroscopy (fNIRS) studies found significant group differences between ADHD children and healthy controls during cognitive flexibility tasks in several brain regions. This study aims to apply a machine learning approach to identify medication-naive ADHD patients and healthy control (HC) groups using task-based fNIRS data. Methods : fNIRS signals from 33 ADHD children and 39 HC during the Stroop task were analyzed. In addition, regularized linear discriminant analysis (RLDA) was used to identify ADHD individuals from healthy controls, and classification performance was evaluated. Results : We found that participants can be correctly classified in RLDA leave-one-out cross validation, with a sensitivity of 0.67, specificity of 0.93, and accuracy of 0.82. Conclusion : RLDA using only fNIRS data can effectively discriminate children with ADHD from HC. This study suggests the potential utility of the fNIRS signal as a diagnostic biomarker for ADHD children.
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Affiliation(s)
- Chan-Mo Yang
- Department of Psychiatry, Wonkwang University School of Medicine, Iksan, Korea
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Jaeyoung Shin
- Department of Electronic Engineering, Wonkwang University, Iksan, Korea
| | | | - You Bin Lim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - So Hyun Park
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Bung-Nyun Kim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
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9
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Stancioiu F, Bogdan R, Dumitrescu R. Neuron-Specific Enolase (NSE) as a Biomarker for Autistic Spectrum Disease (ASD). Life (Basel) 2023; 13:1736. [PMID: 37629593 PMCID: PMC10455327 DOI: 10.3390/life13081736] [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: 06/23/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
Autistic spectrum disease (ASD) is an increasingly common diagnosis nowadays with a prevalence of 1-2% in most countries. Its complex causality-a combination of genetic, immune, metabolic, and environmental factors-is translated into pleiomorphic developmental disorders of various severity, which have two main aspects in common: repetitive, restrictive behaviors and difficulties in social interaction varying from awkward habits and verbalization to a complete lack of interest for the outside world. The wide variety of ASD causes also makes it very difficult to find a common denominator-a disease biomarker and medication-and currently, there is no commonly used diagnostic and therapeutic strategy besides clinical evaluation and psychotherapy. In the CORDUS clinical study, we have administered autologous cord blood to ASD kids who had little or no improvement after other treatments and searched for a biomarker which could help predict the degree of improvement in each patient. We have found that the neuron-specific enolase (NSE) was elevated above the normal clinical range (less than 16.3 ng/mL) in the vast majority of ASD kids tested in our study (40 of 41, or 97.5%). This finding opens up a new direction for diagnostic confirmation, dynamic evaluation, and therapeutic intervention for ASD kids.
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Affiliation(s)
| | - Raluca Bogdan
- Medicover Hospital Bucharest, 013982 Bucharest, Romania
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10
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van de Sande DMJ, Merkofer JP, Amirrajab S, Veta M, van Sloun RJG, Versluis MJ, Jansen JFA, van den Brink JS, Breeuwer M. A review of machine learning applications for the proton MR spectroscopy workflow. Magn Reson Med 2023. [PMID: 37402235 DOI: 10.1002/mrm.29793] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/20/2023] [Accepted: 06/20/2023] [Indexed: 07/06/2023]
Abstract
This literature review presents a comprehensive overview of machine learning (ML) applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the MR field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.
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Affiliation(s)
- Dennis M J van de Sande
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Julian P Merkofer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Sina Amirrajab
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Mitko Veta
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ruud J G van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Philips Research, Eindhoven, The Netherlands
| | | | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- MR R&D - Clinical Science, Philips Healthcare, Best, The Netherlands
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11
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Cassidy-Bushrow AE, Sitarik AR, Johnson CC, Johnson-Hooper TM, Kassem Z, Levin AM, Lynch SV, Ownby DR, Phillips JM, Yong GJM, Wegienka G, Straughen JK. Early-life gut microbiota and attention deficit hyperactivity disorder in preadolescents. Pediatr Res 2023; 93:2051-2060. [PMID: 35440767 PMCID: PMC9582043 DOI: 10.1038/s41390-022-02051-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 12/13/2021] [Accepted: 12/31/2021] [Indexed: 11/09/2022]
Abstract
BACKGROUND Gut microbiota maturation coincides with nervous system development. Cross-sectional data suggest gut microbiota of individuals with and without attention deficit hyperactivity disorder (ADHD) differs. We hypothesized that infant gut microbiota composition is associated with later ADHD development in our on-going birth cohort study, WHEALS. METHODS Gut microbiota was profiled using 16S ribosomal RNA and the internal transcribed spacer region 2 (ITS2) sequencing in stool samples from 1 month and 6 months of age. ADHD was defined by parent-reported or medical record doctor diagnosis at age 10. RESULTS A total of 314 children had gut microbiota and ADHD data; 59 (18.8%) had ADHD. After covariate adjustment, bacterial phylogenetic diversity (p = 0.017) and bacterial composition (unweighted UniFrac p = 0.006, R2 = 0.9%) at age 6 months were associated with development of ADHD. At 1 month of age, 18 bacterial and 3 fungal OTUs were associated with ADHD development. At 6 months of age, 51 bacterial OTUs were associated with ADHD; 14 of the order Lactobacillales. Three fungal OTUs at 6 months of age were associated with ADHD development. CONCLUSIONS Infant gut microbiota is associated with ADHD development in pre-adolescents. Further studies replicating these findings and evaluating potential mechanisms of the association are needed. IMPACT Cross-sectional studies suggest that the gut microbiota of individuals with and without ADHD differs. We found evidence that the bacterial gut microbiota of infants at 1 month and 6 months of age is associated with ADHD at age 10 years. We also found novel evidence that the fungal gut microbiota in infancy (ages 1 month and 6 months) is associated with ADHD at age 10 years. This study addresses a gap in the literature in providing longitudinal evidence for an association of the infant gut microbiota with later ADHD development.
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Affiliation(s)
- Andrea E Cassidy-Bushrow
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA.
- Center for Urban Responses to Environmental Stressors, Wayne State University, Detroit, MI, USA.
| | | | - Christine Cole Johnson
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
- Center for Urban Responses to Environmental Stressors, Wayne State University, Detroit, MI, USA
| | - Tisa M Johnson-Hooper
- Department of Pediatrics, Henry Ford Hospital, Detroit, MI, USA
- Center for Autism and Developmental Disabilities, Henry Ford Hospital, Detroit, MI, USA
| | - Zeinab Kassem
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
| | - Albert M Levin
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
| | - Susan V Lynch
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Dennis R Ownby
- Division of Allergy and Clinical Immunology, Department of Pediatrics, Augusta University, Augusta, GA, USA
| | - Jannel M Phillips
- Center for Autism and Developmental Disabilities, Henry Ford Hospital, Detroit, MI, USA
- Department of Psychiatry and Behavioral Health Services, Division of Neuropsychology, Henry Ford Hospital, Detroit, MI, USA
| | - Germaine J M Yong
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Ganesa Wegienka
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
- Center for Urban Responses to Environmental Stressors, Wayne State University, Detroit, MI, USA
| | - Jennifer K Straughen
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
- Center for Urban Responses to Environmental Stressors, Wayne State University, Detroit, MI, USA
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Nakai N, Sato M, Yamashita O, Sekine Y, Fu X, Nakai J, Zalesky A, Takumi T. Virtual reality-based real-time imaging reveals abnormal cortical dynamics during behavioral transitions in a mouse model of autism. Cell Rep 2023; 42:112258. [PMID: 36990094 DOI: 10.1016/j.celrep.2023.112258] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/16/2023] [Accepted: 02/28/2023] [Indexed: 03/30/2023] Open
Abstract
Functional connectivity (FC) can provide insight into cortical circuit dysfunction in neuropsychiatric disorders. However, dynamic changes in FC related to locomotion with sensory feedback remain to be elucidated. To investigate FC dynamics in locomoting mice, we develop mesoscopic Ca2+ imaging with a virtual reality (VR) environment. We find rapid reorganization of cortical FC in response to changing behavioral states. By using machine learning classification, behavioral states are accurately decoded. We then use our VR-based imaging system to study cortical FC in a mouse model of autism and find that locomotion states are associated with altered FC dynamics. Furthermore, we identify FC patterns involving the motor area as the most distinguishing features of the autism mice from wild-type mice during behavioral transitions, which might correlate with motor clumsiness in individuals with autism. Our VR-based real-time imaging system provides crucial information to understand FC dynamics linked to behavioral abnormality of neuropsychiatric disorders.
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Affiliation(s)
- Nobuhiro Nakai
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Department of Physiology and Cell Biology, Kobe University School of Medicine, Chuo, Kobe 650-0017, Japan
| | - Masaaki Sato
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Department of Neuropharmacology, Hokkaido University Graduate School of Medicine, Kita, Sapporo 060-8638, Japan.
| | - Okito Yamashita
- RIKEN Center for Advanced Intelligence Project, Chuo, Tokyo 103-0027, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, Seika, Kyoto 619-0288, Japan
| | - Yukiko Sekine
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Xiaochen Fu
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Junichi Nakai
- Division of Oral Physiology, Department of Disease Management Dentistry, Tohoku University Graduate School of Dentistry, Aoba, Sendai 980-8575, Japan
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Toru Takumi
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Department of Physiology and Cell Biology, Kobe University School of Medicine, Chuo, Kobe 650-0017, Japan; RIKEN Center for Biosystems Dynamics Research, Chuo, Kobe 650-0047, Japan.
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13
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Nigg JT, Karalunas SL, Mooney MA, Wilmot B, Nikolas MA, Martel MM, Tipsord J, Nousen EK, Schmitt C, Ryabinin P, Musser ED, Nagel BJ, Fair DA. The Oregon ADHD-1000: A new longitudinal data resource enriched for clinical cases and multiple levels of analysis. Dev Cogn Neurosci 2023; 60:101222. [PMID: 36848718 PMCID: PMC9984785 DOI: 10.1016/j.dcn.2023.101222] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/31/2023] [Accepted: 02/20/2023] [Indexed: 02/27/2023] Open
Abstract
The fields of developmental psychopathology, developmental neuroscience, and behavioral genetics are increasingly moving toward a data sharing model to improve reproducibility, robustness, and generalizability of findings. This approach is particularly critical for understanding attention-deficit/hyperactivity disorder (ADHD), which has unique public health importance given its early onset, high prevalence, individual variability, and causal association with co-occurring and later developing problems. A further priority concerns multi-disciplinary/multi-method datasets that can span different units of analysis. Here, we describe a public dataset using a case-control design for ADHD that includes: multi-method, multi-measure, multi-informant, multi-trait data, and multi-clinician evaluation and phenotyping. It spans > 12 years of annual follow-up with a lag longitudinal design allowing age-based analyses spanning age 7-19 + years with a full age range from 7 to 21. Measures span genetic and epigenetic (DNA methylation) array data; EEG, functional and structural MRI neuroimaging; and psychophysiological, psychosocial, clinical and functional outcomes data. The resource also benefits from an autism spectrum disorder add-on cohort and a cross sectional case-control ADHD cohort from a different geographical region for replication and generalizability. Datasets allowing for integration from genes to nervous system to behavior represent the "next generation" of researchable cohorts for ADHD and developmental psychopathology.
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Affiliation(s)
- Joel T Nigg
- Department of Psychiatry & Behavioral Neuroscience, Oregon Health & Science University, USA.
| | | | - Michael A Mooney
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, USA
| | - Beth Wilmot
- Oregon Clinical and Translational Research Institute, Oregon Health & Science University, USA
| | - Molly A Nikolas
- Department of Psychological and Brain Sciences, University of Iowa, USA
| | | | - Jessica Tipsord
- Department of Psychiatry & Behavioral Neuroscience, Oregon Health & Science University, USA
| | - Elizabeth K Nousen
- Department of Psychiatry & Behavioral Neuroscience, Oregon Health & Science University, USA
| | - Colleen Schmitt
- Department of Psychiatry & Behavioral Neuroscience, Oregon Health & Science University, USA
| | - Peter Ryabinin
- Knight Cancer Institute, Oregon Health & Science University, USA
| | - Erica D Musser
- Department of Psychology, Florida International University, USA
| | - Bonnie J Nagel
- Department of Psychiatry & Behavioral Neuroscience, Oregon Health & Science University, USA
| | - Damien A Fair
- Department of Pediatrics, Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, University of Minnesota, USA.
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14
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Deng H, Huang Z, Li Z, Cao L, He Y, Sun N, Zeng Y, Wu J. Systematic bibliometric and visualized analysis of research hotspots and trends in attention-deficit hyperactivity disorder neuroimaging. Front Neurosci 2023; 17:1098526. [PMID: 37056309 PMCID: PMC10086162 DOI: 10.3389/fnins.2023.1098526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 02/17/2023] [Indexed: 03/30/2023] Open
Abstract
IntroductionThis study focused on the research hotspots and development trends of the neuroimaging of attention deficit hyperactivity disorder (ADHD) in the past thirty years.MethodsThe Web of Science database was searched for articles about ADHD neuroimaging from January 1992 to September 2022. CiteSpace was used to analyze the co-occurrence of keywords in literature, partnerships between authors, institutions, and countries, the sudden occurrence of keywords, clustering of keywords over time, and analysis of references, cited authors, and cited journals.Results2,621 articles were included. More and more articles have been published every year in the last years. These articles mainly come from 435 institutions and 65 countries/regions led by the United States. King's College London had the highest number of publications. The study identified 634 authors, among which Buitelaar, J. K. published the largest number of articles and Castellanos, F. X. was co-cited most often. The most productive and cited journal was Biological psychiatry. In recent years, burst keywords were resting-state fMRI, machine learning, functional connectivity, and networks. And a timeline chart of the cluster of keywords showed that “children” had the longest time span.ConclusionsIncreased attention has been paid to ADHD neuroimaging. This work might assist researchers to identify new insight on potential collaborators and cooperative institutions, hot topics, and research directions.
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Affiliation(s)
- Haiyin Deng
- Department of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Zhenming Huang
- Department of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Zhaoying Li
- Department of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Lei Cao
- Department of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Youze He
- Department of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Ning Sun
- Rehabilitation Medicine Center and Institute of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Zeng
- Department of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Jingsong Wu
- Department of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- *Correspondence: Jingsong Wu
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15
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Functional connectivity based brain signatures of behavioral regulation in children with ADHD, DCD, and ADHD-DCD. Dev Psychopathol 2023; 35:85-94. [PMID: 34937602 DOI: 10.1017/s0954579421001449] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Behavioral regulation problems have been associated with daily-life and mental health challenges in children with neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD) and developmental coordination disorder (DCD). Here, we investigated transdiagnostic brain signatures associated with behavioral regulation. Resting-state fMRI data were collected from 115 children (31 typically developing (TD), 35 ADHD, 21 DCD, 28 ADHD-DCD) aged 7-17 years. Behavioral regulation was measured using the Behavior Rating Inventory of Executive Function and was found to differ between children with ADHD (i.e., children with ADHD and ADHD-DCD) and without ADHD (i.e., TD children and children with DCD). Functional connectivity (FC) maps were computed for 10 regions of interest and FC maps were tested for correlations with behavioral regulation scores. Across the entire sample, greater behavioral regulation problems were associated with stronger negative FC within prefrontal pathways and visual reward pathways, as well as with weaker positive FC in frontostriatal reward pathways. These findings significantly increase our knowledge on FC in children with and without ADHD and highlight the potential of FC as brain-based signatures of behavioral regulation across children with differing neurodevelopmental conditions.
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Fittipaldi S, Armony JL, García AM, Migeot J, Cadaveira M, Ibáñez A, Baez S. Emotional descriptions increase accidental harm punishment and its cortico-limbic signatures during moral judgment in autism. Sci Rep 2023; 13:1745. [PMID: 36720905 PMCID: PMC9889714 DOI: 10.1038/s41598-023-27709-x] [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: 09/21/2022] [Accepted: 01/06/2023] [Indexed: 02/01/2023] Open
Abstract
Individuals with autism spectrum disorder (ASD) present difficulties in integrating mental state information in complex moral tasks. Yet, ASD research has not examined whether this process is influenced by emotions, let alone while capturing its neural bases. We investigated how language-induced emotions modulate intent-based moral judgment in ASD. In a fMRI task, 30 adults with ASD and 27 neurotypical controls read vignettes whose protagonists commit harm either accidentally or intentionally, and then decided how much punishment the protagonist deserved. Emotional content was manipulated across scenarios through the use of graphic language (designed to trigger arousing negative responses) vs. plain (just-the-facts, emotionless) language. Off-line functional connectivity correlates of task performance were also analyzed. In ASD, emotional (graphic) descriptions amplified punishment ratings of accidental harms, associated with increased activity in fronto-temporo-limbic, precentral, and postcentral/supramarginal regions (critical for emotional and empathic processes), and reduced connectivity among the orbitofrontal cortex and the angular gyrus (involved in mentalizing). Language manipulation did not influence intentional harm processing in ASD. In conclusion, in arousing and ambiguous social situations that lack intentionality clues (i.e. graphic accidental harm scenarios), individuals with ASD would misuse their emotional responses as the main source of information to guide their moral decisions. Conversely, in face of explicit harmful intentions, they would be able to compensate their socioemotional alterations and assign punishment through non-emotional pathways. Despite limitations, such as the small sample size and low ecological validity of the task, results of the present study proved reliable and have relevant theoretical and translational implications.
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Affiliation(s)
- Sol Fittipaldi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, USA
- Global Brain Health Institute (GBHI), Trinity College Dublin (TCD), Dublin, Ireland
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Jorge L Armony
- Douglas Mental Health University Institute and Dept. of Psychiatry, McGill University, Montreal, Canada
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, USA
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - Joaquín Migeot
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Center for Social and Cognitive Neuroscience, School of Psychology (CSCN), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | | | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, USA
- Global Brain Health Institute (GBHI), Trinity College Dublin (TCD), Dublin, Ireland
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
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17
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Liu A, Lu Y, Gong C, Sun J, Wang B, Jiang Z. Bibliometric Analysis of Research Themes and Trends of the Co-Occurrence of Autism and ADHD. Neuropsychiatr Dis Treat 2023; 19:985-1002. [PMID: 37138730 PMCID: PMC10149780 DOI: 10.2147/ndt.s404801] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 04/05/2023] [Indexed: 05/05/2023] Open
Abstract
Background In recent years, there has been a growing body of research suggesting that ASD and ADHD are two disorders that often co-exist. Despite the rapid development of research, little is known about their etiology, diagnostic markers, and interventions, which has led us to review and summarise the development of the field in the hope that this will provide an opportunity to look for future directions. Methods A bibliometric approach was used to analyse papers in the field of ASD co-morbidities in ADHD on Web of Science from 1991-2022, using CiteSpace and VOSview to map the country/institution, journal, author, co-citation, and keyword networks in the field and to visualise the results. Results A total of 3284 papers were included, showing an increasing trend in terms of posting trends. Research on co-morbidities of ASD has proven to be mainly focused on universities. The USA (1662) published the most relevant literature in this area, followed by the UK (651) and Sweden (388). Lichtenstein P is the most published author (84), and research into the pathogenesis of ASD co-occurring ADHD and related clinical diagnostics is currently at the forefront of the field. Conclusion This analysis identifies the most influential institutions and countries, cited journals, and authors in the field of ASD co-morbid ADHD research. The future direction of ASD co-occurring ADHD should be based on improving case identification, discovering the etiological and diagnostic markers of ASD and ADHD, and finding more effective clinical interventions.
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Affiliation(s)
- Annan Liu
- College of Rehabilitation Medicine, Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China
- Neurolab for Child Rehabilitation, Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China
| | - Yiwen Lu
- The First Affiliated Hospital, Guangxi Medicine University, Nanning, Guangxi Province, People’s Republic of China
| | - Chao Gong
- College of Rehabilitation Medicine, Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China
- Neurolab for Child Rehabilitation, Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China
| | - Jiaxing Sun
- College of Rehabilitation Medicine, Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China
- Neurolab for Child Rehabilitation, Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China
| | - Bobo Wang
- College of Rehabilitation Medicine, Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China
- Neurolab for Child Rehabilitation, Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China
| | - Zhimei Jiang
- Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China
- Correspondence: Zhimei Jiang, Jiamusi University, Jiamusi, Heilongjiang Province, People’s Republic of China, Email
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18
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Lavigne-Cerván R, Sánchez-Muñoz de León M, Juárez-Ruiz de Mier R, Romero-González M, Gamboa-Ternero S, Rodríguez-Infante G, Romero-Pérez JF. Proposal for an Integrative Cognitive-Emotional Conception of ADHD. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15421. [PMID: 36430140 PMCID: PMC9691192 DOI: 10.3390/ijerph192215421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/16/2022] [Accepted: 11/19/2022] [Indexed: 06/16/2023]
Abstract
Although numerous efforts have been made to deepen our understanding of the etiology of Attention Deficit Hyperactivity Disorder (ADHD), no explanation of its origins, nor of its consequences, has yet found a consensus within the scientific community. This study performs a theoretical review of various research studies and provides a reflection on the role of emotions in the origin of the disorder, at the neuroanatomical and functional level. To this end, theoretical models (single and multiple origin) and applied studies are reviewed in order to broaden the perspective on the relevance of the executive system in ADHD; it is suggested that this construct is not only composed and activated by cognitive processes and functions, but also includes elements of an emotional and motivational nature. Consequently, it is shown that ADHD is involved in social development and in a person's ability to adapt to the environment.
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Affiliation(s)
- Rocío Lavigne-Cerván
- Department of Developmental and Educational Psychology, University of Malaga, 29071 Malaga, Spain
| | | | | | - Marta Romero-González
- Department of Developmental and Educational Psychology, University of Malaga, 29071 Malaga, Spain
| | - Sara Gamboa-Ternero
- Department of Developmental and Educational Psychology, University of Alicante, 03690 Alicante, Spain
| | - Gemma Rodríguez-Infante
- Department of Developmental and Educational Psychology, University of Malaga, 29071 Malaga, Spain
| | - Juan F. Romero-Pérez
- Department of Developmental and Educational Psychology, University of Malaga, 29071 Malaga, Spain
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Horien C, Floris DL, Greene AS, Noble S, Rolison M, Tejavibulya L, O'Connor D, McPartland JC, Scheinost D, Chawarska K, Lake EMR, Constable RT. Functional Connectome-Based Predictive Modeling in Autism. Biol Psychiatry 2022; 92:626-642. [PMID: 35690495 PMCID: PMC10948028 DOI: 10.1016/j.biopsych.2022.04.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 04/14/2022] [Accepted: 04/17/2022] [Indexed: 01/08/2023]
Abstract
Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut.
| | - Dorothea L Floris
- Methods of Plasticity Research, Department of Psychology, University of Zürich, Zurich, Switzerland; Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Max Rolison
- Yale Child Study Center, New Haven, Connecticut
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut
| | - David O'Connor
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - James C McPartland
- Department of Psychology, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Katarzyna Chawarska
- Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut.
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20
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Michelini G, Norman LJ, Shaw P, Loo SK. Treatment biomarkers for ADHD: Taking stock and moving forward. Transl Psychiatry 2022; 12:444. [PMID: 36224169 PMCID: PMC9556670 DOI: 10.1038/s41398-022-02207-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 11/09/2022] Open
Abstract
The development of treatment biomarkers for psychiatric disorders has been challenging, particularly for heterogeneous neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD). Promising findings are also rarely translated into clinical practice, especially with regard to treatment decisions and development of novel treatments. Despite this slow progress, the available neuroimaging, electrophysiological (EEG) and genetic literature provides a solid foundation for biomarker discovery. This article gives an updated review of promising treatment biomarkers for ADHD which may enhance personalized medicine and novel treatment development. The available literature points to promising pre-treatment profiles predicting efficacy of various pharmacological and non-pharmacological treatments for ADHD. These candidate predictive biomarkers, particularly those based on low-cost and non-invasive EEG assessments, show promise for the future stratification of patients to specific treatments. Studies with repeated biomarker assessments further show that different treatments produce distinct changes in brain profiles, which track treatment-related clinical improvements. These candidate monitoring/response biomarkers may aid future monitoring of treatment effects and point to mechanistic targets for novel treatments, such as neurotherapies. Nevertheless, existing research does not support any immediate clinical applications of treatment biomarkers for ADHD. Key barriers are the paucity of replications and external validations, the use of small and homogeneous samples of predominantly White children, and practical limitations, including the cost and technical requirements of biomarker assessments and their unknown feasibility and acceptability for people with ADHD. We conclude with a discussion of future directions and methodological changes to promote clinical translation and enhance personalized treatment decisions for diverse groups of individuals with ADHD.
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Affiliation(s)
- Giorgia Michelini
- grid.4868.20000 0001 2171 1133Department of Biological and Experimental Psychology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA USA
| | - Luke J. Norman
- grid.416868.50000 0004 0464 0574Office of the Clinical Director, NIMH, Bethesda, MD USA
| | - Philip Shaw
- grid.416868.50000 0004 0464 0574Office of the Clinical Director, NIMH, Bethesda, MD USA ,grid.280128.10000 0001 2233 9230Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, MD USA
| | - Sandra K. Loo
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA USA
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21
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Bahathiq RA, Banjar H, Bamaga AK, Jarraya SK. Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging. Front Neuroinform 2022; 16:949926. [PMID: 36246393 PMCID: PMC9554556 DOI: 10.3389/fninf.2022.949926] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.
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Affiliation(s)
- Reem Ahmed Bahathiq
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haneen Banjar
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed K. Bamaga
- Neuromuscular Medicine Unit, Department of Pediatric, Faculty of Medicine and King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Salma Kammoun Jarraya
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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22
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Hawco C, Dickie EW, Herman G, Turner JA, Argyelan M, Malhotra AK, Buchanan RW, Voineskos AN. A longitudinal multi-scanner multimodal human neuroimaging dataset. Sci Data 2022; 9:332. [PMID: 35701471 PMCID: PMC9198098 DOI: 10.1038/s41597-022-01386-3] [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: 04/13/2021] [Accepted: 05/12/2022] [Indexed: 11/09/2022] Open
Abstract
Human neuroimaging has led to an overwhelming amount of research into brain function in healthy and clinical populations. However, a better appreciation of the limitations of small sample studies has led to an increased number of multi-site, multi-scanner protocols to understand human brain function. As part of a multi-site project examining social cognition in schizophrenia, a group of "travelling human phantoms" had structural T1, diffusion, and resting-state functional MRIs obtained annually at each of three sites. Scan protocols were carefully harmonized across sites prior to the study. Due to scanner upgrades at each site (all sites acquired PRISMA MRIs during the study) and one participant being replaced, the end result was 30 MRI scans across 4 people, 6 MRIs, and 4 years. This dataset includes multiple neuroimaging modalities and repeated scans across six MRIs. It can be used to evaluate differences across scanners, consistency of pipeline outputs, or test multi-scanner harmonization approaches.
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Affiliation(s)
- Colin Hawco
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada. .,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
| | - Erin W Dickie
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Gabrielle Herman
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychology & Neuroscience Institute, Georgia State University, Atlanta, GA, United States
| | - Miklos Argyelan
- The Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Anil K Malhotra
- The Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Aristotle N Voineskos
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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23
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Lei J, Deng Y, Ma S. Downregulation of TGIF2 is possibly correlated with neuronal apoptosis and autism-like symptoms in mice. Brain Behav 2022; 12:e2610. [PMID: 35592894 PMCID: PMC9226810 DOI: 10.1002/brb3.2610] [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: 11/29/2021] [Revised: 03/14/2022] [Accepted: 04/24/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND TGFB-induced factor homeobox 2 (TGIF2) has been reported to exert essential functions in brain development. This study aimed to elucidate the correlation of TGIF2 with autism, a neurodevelopmental condition which presents with severe communication problems. METHODS An autism-related gene expression dataset GSE36315 was used to analyze aberrantly expressed genes in autistic brain tissues. Maternal mice were treated with valproate (VPA), and their offspring were selected as model mice with autism. The functions of TGIF2 in autism-like symptoms in mice were examined by behavioral tests and histological examination of their hippocampal tissues. Mouse hippocampal neurons were extracted for in vitro studies. A gene set enrichment analysis was performed to analyze the signaling pathways involved, and the upstream factors influencing TGIF2 expression were explored in the ENCODE database and validated by ChIP-qPCR assays. RESULTS TGIF2 was poorly expressed in autistic patients in the GSE36315 dataset as well as in the temporal cortex tissues of autistic mice. Adenovirus-mediated overexpression of TGIF2 suppressed autism-like symptoms and neuronal apoptosis in autistic mice. TGIF2 activated the Wnt/β-catenin signaling pathway. TGIF2 could be regulated by monomethylation of histone H3 Lys4 (H3K4me1). The histone demethylase LSD1 was highly expressed in the tissues of autistic mice and bound to TGIF2 promoter, which was possibly responsible for TGIF2 downregulation. CONCLUSION This research suggests that the downregulation of TGIF2, possibly regulated by LSD1/H3K4me1, is correlated with neuronal apoptosis and development of autism in mice through the inactivation of the Wnt/β-catenin pathway.
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Affiliation(s)
- Jing Lei
- Department of the Ninth Pediatrics, Hunan Provincial People's Hospital (the First-Affiliated Hospital of Hunan Normal University), Changsha, P. R. China
| | - Yijue Deng
- Department of Graduate School, Hunan University of Chinese Medicine, Changsha, P. R. China
| | - Songdong Ma
- Hunan Provincial Key Laboratory of Pediatric Respirology, Hunan Provincial People's Hospital (the First-Affiliated Hospital of Hunan Normal University), Changsha, P. R. China
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24
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Wu YY, Hu YS, Wang J, Zang YF, Zhang Y. Toward Precise Localization of Abnormal Brain Activity: 1D CNN on Single Voxel fMRI Time-Series. Front Comput Neurosci 2022; 16:822237. [PMID: 35573265 PMCID: PMC9094401 DOI: 10.3389/fncom.2022.822237] [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: 11/25/2021] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is one of the best techniques for precise localization of abnormal brain activity non-invasively. Machine-learning approaches have been widely used in neuroimaging studies; however, few studies have investigated the single-voxel modeling of fMRI data under cognitive tasks. We proposed a hybrid one-dimensional (1D) convolutional neural network (1D-CNN) based on the temporal dynamics of single-voxel fMRI time-series and successfully differentiated two continuous task states, namely, self-initiated (SI) and visually guided (VG) motor tasks. First, 25 activation peaks were identified from the contrast maps of SI and VG tasks in a blocked design. Then, the fMRI time-series of each peak voxel was transformed into a temporal-frequency domain by using continuous wavelet transform across a broader frequency range (0.003–0.313 Hz, with a step of 0.01 Hz). The transformed time-series was inputted into a 1D-CNN model for the binary classification of SI and VG continuous tasks. Compared with the univariate analysis, e.g., amplitude of low-frequency fluctuation (ALFF) at each frequency band, including, wavelet-ALFF, the 1D-CNN model highly outperformed wavelet-ALFF, with more efficient decoding models [46% of 800 models showing area under the curve (AUC) > 0.61] and higher decoding accuracies (94% of the efficient models), especially on the high-frequency bands (>0.1 Hz). Moreover, our results also demonstrated the advantages of wavelet decompositions over the original fMRI series by showing higher decoding performance on all peak voxels. Overall, this study suggests a great potential of single-voxel analysis using 1D-CNN and wavelet transformation of fMRI series with continuous, naturalistic, steady-state task design or resting-state design. It opens new avenues to precise localization of abnormal brain activity and fMRI-guided precision brain stimulation therapy.
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Affiliation(s)
- Yun-Ying Wu
- Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
| | - Yun-Song Hu
- Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
| | - Jue Wang
- Institute of Sports Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
- Transcranial Magnetic Stimulation Center, Deqing Hospital of Hangzhou Normal University, Huzhou, China
- *Correspondence: Yu-Feng Zang
| | - Yu Zhang
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
- Yu Zhang
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25
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Nakua H, Hawco C, Forde NJ, Jacobs GR, Joseph M, Voineskos AN, Wheeler AL, Lai MC, Szatmari P, Kelley E, Liu X, Georgiades S, Nicolson R, Schachar R, Crosbie J, Anagnostou E, Lerch JP, Arnold PD, Ameis SH. Cortico-amygdalar connectivity and externalizing/internalizing behavior in children with neurodevelopmental disorders. Brain Struct Funct 2022; 227:1963-1979. [PMID: 35469103 PMCID: PMC9232404 DOI: 10.1007/s00429-022-02483-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 03/15/2022] [Indexed: 12/31/2022]
Abstract
Background Externalizing and internalizing behaviors contribute to clinical impairment in children with neurodevelopmental disorders (NDDs). Although associations between externalizing or internalizing behaviors and cortico-amygdalar connectivity have been found in clinical and non-clinical pediatric samples, no previous study has examined whether similar shared associations are present across children with different NDDs. Methods Multi-modal neuroimaging and behavioral data from the Province of Ontario Neurodevelopmental Disorders (POND) Network were used. POND participants aged 6–18 years with a primary diagnosis of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or obsessive–compulsive disorder (OCD), as well as typically developing children (TDC) with T1-weighted, resting-state fMRI or diffusion weighted imaging (DWI) and parent-report Child Behavioral Checklist (CBCL) data available, were analyzed (total n = 346). Associations between externalizing or internalizing behavior and cortico-amygdalar structural and functional connectivity indices were examined using linear regressions, controlling for age, gender, and image-modality specific covariates. Behavior-by-diagnosis interaction effects were also examined. Results No significant linear associations (or diagnosis-by-behavior interaction effects) were found between CBCL-measured externalizing or internalizing behaviors and any of the connectivity indices examined. Post-hoc bootstrapping analyses indicated stability and reliability of these null results. Conclusions The current study provides evidence towards an absence of a shared linear relationship between internalizing or externalizing behaviors and cortico-amygdalar connectivity properties across a transdiagnostic sample of children with different primary NDD diagnoses and TDC. Different methodological approaches, including incorporation of multi-dimensional behavioral data (e.g., task-based fMRI) or clustering approaches may be needed to clarify complex brain-behavior relationships relevant to externalizing/internalizing behaviors in heterogeneous clinical NDD populations. Supplementary Information The online version contains supplementary material available at 10.1007/s00429-022-02483-0.
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Affiliation(s)
- Hajer Nakua
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Natalie J Forde
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Grace R Jacobs
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Michael Joseph
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Anne L Wheeler
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Peter Szatmari
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Elizabeth Kelley
- Department of Psychology, Department of Psychiatry, Queens University, Kingston, ON, Canada
| | - Xudong Liu
- Department of Psychology, Department of Psychiatry, Queens University, Kingston, ON, Canada
| | | | - Rob Nicolson
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
| | - Russell Schachar
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jennifer Crosbie
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Evdokia Anagnostou
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
- Department of Pediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON, Canada
- Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Paul D Arnold
- The Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Departments of Psychiatry and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada.
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada.
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26
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Vedmurthy P, Pinto ALR, Lin DDM, Comi AM, Ou Y. Study protocol: retrospectively mining multisite clinical data to presymptomatically predict seizure onset for individual patients with Sturge-Weber. BMJ Open 2022; 12:e053103. [PMID: 35121603 PMCID: PMC8819809 DOI: 10.1136/bmjopen-2021-053103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Secondary analysis of hospital-hosted clinical data can save time and cost compared with prospective clinical trials for neuroimaging biomarker development. We present such a study for Sturge-Weber syndrome (SWS), a rare neurovascular disorder that affects 1 in 20 000-50 000 newborns. Children with SWS are at risk for developing neurocognitive deficit by school age. A critical period for early intervention is before 2 years of age, but early diagnostic and prognostic biomarkers are lacking. We aim to retrospectively mine clinical data for SWS at two national centres to develop presymptomatic biomarkers. METHODS AND ANALYSIS We will retrospectively collect clinical, MRI and neurocognitive outcome data for patients with SWS who underwent brain MRI before 2 years of age at two national SWS care centres. Expert review of clinical records and MRI quality control will be used to refine the cohort. The merged multisite data will be used to develop algorithms for abnormality detection, lesion-symptom mapping to identify neural substrate and machine learning to predict individual outcomes (presence or absence of seizures) by 2 years of age. Presymptomatic treatment in 0-2 years and before seizure onset may delay or prevent the onset of seizures by 2 years of age, and thereby improve neurocognitive outcomes. The proposed work, if successful, will be one of the largest and most comprehensive multisite databases for the presymptomatic phase of this rare disease. ETHICS AND DISSEMINATION This study involves human participants and was approved by Boston Children's Hospital Institutional Review Board: IRB-P00014482 and IRB-P00025916 Johns Hopkins School of Medicine Institutional Review Board: NA_00043846. Participants gave informed consent to participate in the study before taking part. The Institutional Review Boards at Kennedy Krieger Institute and Boston Children's Hospital approval have been obtained at each site to retrospectively study this data. Results will be disseminated by presentations, publication and sharing of algorithms generated.
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Affiliation(s)
- Pooja Vedmurthy
- Department of Neurology and Developmental Medicine, Hugo Moser Research Institute, Baltimore, Maryland, USA
- Department of Neurology and Pediatrics, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Anna L R Pinto
- Department of Neurology, Division of Epilepsy, Harvard Medical School, Boston, Massachusetts, USA
| | - Doris D M Lin
- Neuroradiology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Anne M Comi
- Department of Neurology and Developmental Medicine, Hugo Moser Research Institute, Baltimore, Maryland, USA
- Department of Neurology and Pediatrics, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology and Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Boston Children's Hospital; Harvard Medical School, Boston, MA, USA
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27
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Kurth F, Levitt JG, Gaser C, Alger J, Loo SK, Narr KL, O'Neill J, Luders E. Preliminary evidence for a lower brain age in children with attention-deficit/hyperactivity disorder. Front Psychiatry 2022; 13:1019546. [PMID: 36532197 PMCID: PMC9755736 DOI: 10.3389/fpsyt.2022.1019546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
Attention-deficit hyperactivity disorder (ADHD) is a debilitating disorder with apparent roots in abnormal brain development. Here, we quantified the level of individual brain maturation in children with ADHD using structural neuroimaging and a recently developed machine learning algorithm. More specifically, we compared the BrainAGE index between three groups matched for chronological age (mean ± SD: 11.86 ± 3.25 years): 89 children diagnosed with ADHD, 34 asymptomatic siblings of those children with ADHD, and 21 unrelated healthy control children. Brains of children with ADHD were estimated significantly younger (-0.85 years) than brains of healthy controls (Cohen's d = -0.33; p = 0.028, one-tailed), while there were no significant differences between unaffected siblings and healthy controls. In addition, more severe ADHD symptoms were significantly associated with younger appearing brains. Altogether, these results are in line with the proposed delay of individual brain maturation in children with ADHD. However, given the relatively small sample size (N = 144), the findings should be considered preliminary and need to be confirmed in future studies.
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Affiliation(s)
- Florian Kurth
- School of Psychology, University of Auckland, Auckland, New Zealand
| | - Jennifer G Levitt
- Division of Child and Adolescent Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.,Department of Neurology, Jena University Hospital, Jena, Germany
| | - Jeffry Alger
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Sandra K Loo
- Division of Child and Adolescent Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine L Narr
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States.,Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States
| | - Joseph O'Neill
- Division of Child and Adolescent Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States
| | - Eileen Luders
- School of Psychology, University of Auckland, Auckland, New Zealand.,Laboratory of Neuro Imaging, School of Medicine, University of Southern California, Los Angeles, CA, United States.,Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
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28
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Fu X, Zhang F, Huang M, Zhang L, Guo W. Editorial: Brain and Somatization Symptoms in Psychiatric Disorders, Volume II. Front Psychiatry 2022; 13:881245. [PMID: 35463513 PMCID: PMC9023788 DOI: 10.3389/fpsyt.2022.881245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 03/16/2022] [Indexed: 11/26/2022] Open
Affiliation(s)
- Xiaoya Fu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Fengyu Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Global Clinical and Translational Research Institute, Bethesda, MD, United States.,Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Institute, Beijing, China
| | - Manli Huang
- The Key Laboratory of Mental Disorder's Management, Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lulu Zhang
- Department of Psychiatry, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, China
| | - Wenbin Guo
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.,Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, China
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29
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Kong XJ, Wei Z, Sun B, Tu Y, Huang Y, Cheng M, Yu S, Wilson G, Park J, Feng Z, Vangel M, Kong J, Wan G. Different Eye Tracking Patterns in Autism Spectrum Disorder in Toddler and Preschool Children. Front Psychiatry 2022; 13:899521. [PMID: 35757211 PMCID: PMC9218189 DOI: 10.3389/fpsyt.2022.899521] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/10/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Children with autism spectrum disorder (ASD) have been observed to be associated with fixation abnormality as measured eye tracking, but the dynamics behind fixation patterns across age remain unclear. MATERIALS AND METHODS In this study, we investigated gaze patterns between toddlers and preschoolers with and without ASD while they viewed video clips and still images (i.e., mouth-moving face, biological motion, mouthing face vs. moving object, still face picture vs. objects, and moving toys). RESULTS We found that the fixation time percentage of children with ASD showed significant decrease compared with that of TD children in almost all areas of interest (AOI) except for moving toy (helicopter). We also observed a diagnostic group (ASD vs. TD) and chronological age (Toddlers vs. preschooler) interaction for the eye AOI during the mouth-moving video clip. Support vector machine analysis showed that the classifier could discriminate ASD from TD in toddlers with an accuracy of 80% and could discriminate ASD from TD in preschoolers with an accuracy of 71%. CONCLUSION Our results suggest that toddlers and preschoolers may be associated with both common and distinct fixation patterns. A combination of eye tracking and machine learning methods has the potential to shed light on the development of new early screening/diagnosis methods for ASD.
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Affiliation(s)
- Xue-Jun Kong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Zhen Wei
- Department of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Binbin Sun
- Department of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Yiheng Tu
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Yiting Huang
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Ming Cheng
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Siyi Yu
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Georgia Wilson
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Joel Park
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Zhe Feng
- Department of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Mark Vangel
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Guobin Wan
- Department of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
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30
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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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31
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Zhang J, Kucyi A, Raya J, Nielsen AN, Nomi JS, Damoiseaux JS, Greene DJ, Horovitz SG, Uddin LQ, Whitfield-Gabrieli S. What have we really learned from functional connectivity in clinical populations? Neuroimage 2021; 242:118466. [PMID: 34389443 DOI: 10.1016/j.neuroimage.2021.118466] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/06/2021] [Accepted: 08/09/2021] [Indexed: 02/09/2023] Open
Abstract
Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.
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Affiliation(s)
- Jiahe Zhang
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.
| | - Aaron Kucyi
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Jovicarole Raya
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Jessica S Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI 48202, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA
| | | | - Lucina Q Uddin
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
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Abstract
This article describes the current understanding of the identification, classification, and diagnosis of autism spectrum disorder (ASD) as it relates to the practice of primary care providers. In addition, the most updated information regarding risk factors, as well as effective treatment strategies are provided. Although primary care providers are not typically the experts in ASD treatment, they constitute a critical component of the care team responsible for early identification and intervention initiation for patients with ASD.
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Affiliation(s)
- Ashley Iles
- 215 Central Avenue Suite 100, Louisville, KY 40208, USA.
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33
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Yang D, Zhu X, Yan C, Peng Z, Bagonis M, Laurienti PJ, Styner M, Wu G. Joint hub identification for brain networks by multivariate graph inference. Med Image Anal 2021; 73:102162. [PMID: 34274691 DOI: 10.1016/j.media.2021.102162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/01/2021] [Accepted: 07/02/2021] [Indexed: 11/19/2022]
Abstract
Recent developments in neuroimaging allow us to investigate the structural and functional connectivity between brain regions in vivo. Mounting evidence suggests that hub nodes play a central role in brain communication and neural integration. Such high centrality, however, makes hub nodes particularly susceptible to pathological network alterations and the identification of hub nodes from brain networks has attracted much attention in neuroimaging. Current popular hub identification methods often work in a univariate manner, i.e., selecting the hub nodes one after another based on either heuristic of the connectivity profile at each node or predefined settings of network modules. Since the topological information of the entire network (such as network modules) is not fully utilized, current methods have limited power to identify hubs that link multiple modules (connector hubs) and are biased toward identifying hubs having many connections within the same module (provincial hubs). To address this challenge, we propose a novel multivariate hub identification method. Our method identifies connector hubs as those that partition the network into disconnected components when they are removed from the network. Furthermore, we extend our hub identification method to find the population-based hub nodes from a group of network data. We have compared our hub identification method with existing methods on both simulated and human brain network data. Our proposed method achieves more accurate and replicable discovery of hub nodes and exhibits enhanced statistical power in identifying network alterations related to neurological disorders such as Alzheimer's disease and obsessive-compulsive disorder.
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Affiliation(s)
- Defu Yang
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China; Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | - Xiaofeng Zhu
- School of Natural and Computational Science, Massey University, Auckland, New Zealand
| | - Chenggang Yan
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Ziwen Peng
- Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen, China
| | - Maria Bagonis
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | | | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, USA.
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34
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Braden BB, Wallace GL, Floris DL, Pelphrey KA. Editorial: Sex Differences in the Autistic Brain. Front Integr Neurosci 2021; 15:708368. [PMID: 34211376 PMCID: PMC8239342 DOI: 10.3389/fnint.2021.708368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 05/19/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- B Blair Braden
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Gregory L Wallace
- Department of Speech, Language, and Hearing Sciences, The George Washington University, Washington, DC, United States
| | - Dorothea L Floris
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Kevin A Pelphrey
- Department of Neurology, University of Virginia, Charlottesville, VA, United States
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Alotaibi N, Maharatna K. Classification of Autism Spectrum Disorder From EEG-Based Functional Brain Connectivity Analysis. Neural Comput 2021; 33:1914-1941. [PMID: 34411269 DOI: 10.1162/neco_a_01394] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 02/04/2021] [Indexed: 11/04/2022]
Abstract
Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.
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Affiliation(s)
- Noura Alotaibi
- Department of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK
| | - Koushik Maharatna
- Department of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK
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36
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Paek AY, Brantley JA, Evans BJ, Contreras-Vidal JL. Concerns in the Blurred Divisions between Medical and Consumer Neurotechnology. IEEE SYSTEMS JOURNAL 2021; 15:3069-3080. [PMID: 35126800 PMCID: PMC8813044 DOI: 10.1109/jsyst.2020.3032609] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Neurotechnology has traditionally been central to the diagnosis and treatment of neurological disorders. While these devices have initially been utilized in clinical and research settings, recent advancements in neurotechnology have yielded devices that are more portable, user-friendly, and less expensive. These improvements allow laypeople to monitor their brain waves and interface their brains with external devices. Such improvements have led to the rise of wearable neurotechnology that is marketed to the consumer. While many of the consumer devices are marketed for innocuous applications, such as use in video games, there is potential for them to be repurposed for medical use. How do we manage neurotechnologies that skirt the line between medical and consumer applications and what can be done to ensure consumer safety? Here, we characterize neurotechnology based on medical and consumer applications and summarize currently marketed uses of consumer-grade wearable headsets. We lay out concerns that may arise due to the similar claims associated with both medical and consumer devices, the possibility of consumer devices being repurposed for medical uses, and the potential for medical uses of neurotechnology to influence commercial markets related to employment and self-enhancement.
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Affiliation(s)
- Andrew Y Paek
- Department of Electrical & Computer Engineering and the IUCRC BRAIN Center at the University of Houston, Houston, TX, USA
| | - Justin A Brantley
- Department of Electrical & Computer Engineering and the IUCRC BRAIN Center at the University of Houston. He is now with the Department of Bioengineering at the University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara J Evans
- Law Center and IUCRC BRAIN Center at the University of Houston. University of Houston, Houston, TX. She is now with the Wertheim College of Engineering and Levin College of Law at the University of Florida, Gainesville, FL, USA
| | - Jose L Contreras-Vidal
- Department of Electrical & Computer Engineering and the IUCRC BRAIN Center at the University of Houston, Houston, TX, USA
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37
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Dor-Ziderman Y, Zeev-Wolf M, Hirsch Klein E, Bar-Oz D, Nitzan U, Maoz H, Segev A, Goldstein A, Koubi M, Mendelovic S, Gvirts H, Bloch Y. High-gamma oscillations as neurocorrelates of ADHD: A MEG crossover placebo-controlled study. J Psychiatr Res 2021; 137:186-193. [PMID: 33684643 DOI: 10.1016/j.jpsychires.2021.02.050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 02/18/2021] [Accepted: 02/22/2021] [Indexed: 10/22/2022]
Abstract
Attention Deficit Hyperactive Disorder (ADHD) is a common neurobehavioral disorder with a significant and pervasive impact on patients' lives. Identifying neurophysiological correlates of ADHD is important for understanding its underlying mechanisms, as well as for improving clinical accuracy beyond cognitive and emotional factors. The present study focuses on finding a diagnostic stable neural correlate based on evaluating MEG resting state frequency bands. Twenty-two ADHD patients and 23 controls adults were blindly randomized to two methylphenidate/placebo evaluation days. On each evaluation day state anxiety was assessed, a 2N-back executive function task was performed, and resting state MEG brain activity was recorded at three timepoints. A frequency-based cluster analysis yielded higher high-gamma power for ADHD over posterior sensors and lower high-gamma power for ADHD over frontal-central sensors. These results were shown to be stable over three measurements, unaffected by methylphenidate treatment, and linked to cognitive accuracy and state anxiety. Furthermore, the differential high-gamma activity evidenced substantial ADHD diagnostic efficacy, comparable to the cognitive and emotional factors. These results indicate that resting state high-gamma activity is a promising, stable, valid and diagnostically-relevant neurocorrelate of ADHD. Due to the evolving understanding both in the cellular and network level of high-gamma oscillations, focusing future studies on this frequency band bears the potential for a better understanding of ADHD, thus advancing the specificity of the evaluation of the disorder and developing new tools for therapy.
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Affiliation(s)
- Yair Dor-Ziderman
- Gonda Brain Research Center, Bar Ilan University, Ramat-Gan, Israel; Edmond J. Safra Brain Research Center, University of Haifa, Haifa, Israel
| | - Maor Zeev-Wolf
- Department of Education & Zlotowski Center for Neuroscience, Ben Gurion University of the Negev, Israel
| | | | - Dor Bar-Oz
- The Emotion-Cognition Research Center, Shalvata Mental Health Care Center, Hod-Hasharon, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Uriel Nitzan
- The Emotion-Cognition Research Center, Shalvata Mental Health Care Center, Hod-Hasharon, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Hagai Maoz
- The Emotion-Cognition Research Center, Shalvata Mental Health Care Center, Hod-Hasharon, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Aviv Segev
- The Emotion-Cognition Research Center, Shalvata Mental Health Care Center, Hod-Hasharon, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Abraham Goldstein
- Gonda Brain Research Center, Bar Ilan University, Ramat-Gan, Israel; Department of Psychology, Bar Ilan University, Ramat-Gan, Israel
| | - May Koubi
- The Emotion-Cognition Research Center, Shalvata Mental Health Care Center, Hod-Hasharon, Israel; Child and Adolescent Outpatient Clinic, Shalvata Mental Health Care Center, Hod-Hasharon, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Shlomo Mendelovic
- The Emotion-Cognition Research Center, Shalvata Mental Health Care Center, Hod-Hasharon, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Hila Gvirts
- Department of Behavioral Sciences and Psychology, Ariel University, Ariel, Israel
| | - Yuval Bloch
- The Emotion-Cognition Research Center, Shalvata Mental Health Care Center, Hod-Hasharon, Israel; Child and Adolescent Outpatient Clinic, Shalvata Mental Health Care Center, Hod-Hasharon, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
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38
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Harikumar A, Evans DW, Dougherty CC, Carpenter KL, Michael AM. A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder. Brain Connect 2021; 11:253-263. [PMID: 33403915 PMCID: PMC8112713 DOI: 10.1089/brain.2020.0865] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD.
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Affiliation(s)
- Amritha Harikumar
- Department of Psychiatry, Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
- Address correspondence to: Amritha Harikumar, Department of Psychological Sciences, Rice University, 6566 Main St, BRC 780B, Houston, TX 77030, USA
| | - David W. Evans
- Department of Psychology, Bucknell University, Lewisburg, Pennsylvania, USA
| | - Chase C. Dougherty
- Department of Psychiatry, Penn State College of Medicine, Hershey, Pennsylvania, USA
| | - Kimberly L.H. Carpenter
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA
| | - Andrew M. Michael
- Department of Psychiatry and Behavioral Sciences, Duke Institute for Brain Science, Duke University, Durham, North Carolina, USA
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39
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Behavioural Measures of Infant Activity but Not Attention Associate with Later Preschool ADHD Traits. Brain Sci 2021; 11:brainsci11050524. [PMID: 33919004 PMCID: PMC8143002 DOI: 10.3390/brainsci11050524] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/15/2021] [Accepted: 03/18/2021] [Indexed: 11/16/2022] Open
Abstract
Mapping infant neurocognitive differences that precede later ADHD-related behaviours is critical for designing early interventions. In this study, we investigated (1) group differences in a battery of measures assessing aspects of attention and activity level in infants with and without a family history of ADHD or related conditions (ASD), and (2) longitudinal associations between the infant measures and preschool ADHD traits at 3 years. Participants (N = 151) were infants with or without an elevated likelihood for ADHD (due to a family history of ADHD and/or ASD). A multi-method assessment protocol was used to assess infant attention and activity level at 10 months of age that included behavioural, cognitive, physiological and neural measures. Preschool ADHD traits were measured at 3 years of age using the Child Behaviour Checklist (CBCL) and the Child Behaviour Questionnaire (CBQ). Across a broad range of measures, we found no significant group differences in attention or activity level at 10 months between infants with and without a family history of ADHD or ASD. However, parent and observer ratings of infant activity level at 10 months were positively associated with later preschool ADHD traits at 3 years. Observable behavioural differences in activity level (but not attention) may be apparent from infancy in children who later develop elevated preschool ADHD traits.
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40
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Adelhöfer N, Bluschke A, Roessner V, Beste C. The dynamics of theta-related pro-active control and response inhibition processes in AD(H)D. NEUROIMAGE-CLINICAL 2021; 30:102609. [PMID: 33711621 PMCID: PMC7970141 DOI: 10.1016/j.nicl.2021.102609] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 02/07/2021] [Accepted: 02/17/2021] [Indexed: 11/01/2022]
Abstract
Impulsivity and deficits in response inhibition are hallmarks of attention-deficit(-hyperactivity) disorder (AD(H)D), can cause severe problems in daily functioning, and are thus of high clinical relevance. Traditionally, research to elucidate associated neural correlates has intensively, but also quite selectively examined mechanisms during response inhibition in various tasks. Doing so, in-between trial periods or periods prior to the response inhibition process, where no information relevant to inhibitory control is presented, have been neglected. Yet, these periods may nevertheless reveal relevant information. In the present study, using a case-control cross-sectional design, we take a more holistic approach, examining the inter-relation of pre-trial and within-trial periods in a Go/Nogo task with a focus on EEG theta band activity. Applying EEG beamforming methods, we show that the dynamics between pre-trial (pro-active) and within-trial (inhibition-related) control processes significantly differ between AD(H)D subtypes. We show that response inhibition, and differences between AD(H)D subtypes, exhibit distinct patterns of (at least) three factors: (i) strength of pre-trial (pro-active control) theta-band activity, (ii) the inter-relation of pro-active control and inhibition-relation theta band activity and (iii) the functional neuroanatomical region active during theta-related pro-active control processes. This multi-factorial pattern is captured by AD(H)D subtype clinical symptom clusters. The study provides a first hint that novel cognitive-neurophysiological facets of AD(H)D may be relevant to distinguish AD(H)D subtypes.
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Affiliation(s)
- Nico Adelhöfer
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany
| | - Annet Bluschke
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany
| | - Veit Roessner
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany.
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41
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Wang LJ, Lin LC, Lee SY, Wu CC, Chou WJ, Hsu CF, Tseng HH, Lin WC. l-Cystine is associated with the dysconnectivity of the default-mode network and salience network in attention-deficit/hyperactivity disorder. Psychoneuroendocrinology 2021; 125:105105. [PMID: 33338922 DOI: 10.1016/j.psyneuen.2020.105105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/04/2020] [Accepted: 12/06/2020] [Indexed: 11/16/2022]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Distributed dysconnectivity within both the default-mode network (DMN) and the salience network (SN) has been observed in ADHD. L-cystine may serve as a neuroprotective molecule and signaling pathway, as well as a biomarker of ADHD. The purpose of this study was to explore whether differential brain network connectivity is associated with peripheral L-cystine levels in ADHD patients. We recruited a total of 31 drug-naïve patients with ADHD (mean age: 10.4 years) and 29 healthy controls (mean age: 10.3 years) that underwent resting state functional magnetic resonance imaging scans. Functional connectomes were generated for each subject, and we examined the cross-sectional group difference in functional connectivity (FC) within and between DMN and SN. L-cystine plasma levels were determined using high-performance chemical isotope labeling (CIL)-based liquid chromatography-mass spectrometry (LC-MS). Compared to the control group, the ADHD group showed decreased FC of dorsal DMN (p = 0.031), as well as decreased FC of precuneus-post SN (p = 0.006) and ventral DMN-post SN (p = 0.001). The plasma L-cystine levels of the ADHD group were significantly higher than in the control group (p = 0.002). Furthermore, L-cystine levels were negatively correlated with FC of precuneus-post SN (r = -0.404, p = 0.045) and ventral DMN-post SN (r = -0.540, p = 0.007). The findings suggest that decreased synergies of DMN and SN may serve as neurobiomarkers for ADHD, while L-cystine may be involved in the pathophysiology of network dysconnectivity. Future studies on the molecular mechanism of the cystine-glutamate system in brain network connectivity are warranted.
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Affiliation(s)
- Liang-Jen Wang
- Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Liang-Chun Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Taiwan; Chang Gung University College of Medicine, Taiwan
| | - Sheng-Yu Lee
- Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Psychiatry, College of Medicine, Graduate Institute of Medicine, School of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chih-Ching Wu
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan; Department of Otolaryngology-Head & Neck Surgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan; Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan; Research Center for Emerging Viral Infections, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Wen-Jiun Chou
- Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chia-Fen Hsu
- Division of Clinical Psychology, Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan, Taiwan; Department of Child Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Huai-Hsuan Tseng
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Wei-Che Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Taiwan; Chang Gung University College of Medicine, Taiwan.
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42
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Wang LJ, Chou WJ, Tsai CS, Lee MJ, Lee SY, Hsu CW, Hsueh PC, Wu CC. Novel plasma metabolite markers of attention-deficit/hyperactivity disorder identified using high-performance chemical isotope labelling-based liquid chromatography-mass spectrometry. World J Biol Psychiatry 2021; 22:139-148. [PMID: 32351159 DOI: 10.1080/15622975.2020.1762930] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
OBJECTIVES Metabolites are the intermediate and final products of biological processes and ultimately reflect the responses of these processes to genetic regulation and environmental perturbations, including those involved in attention deficit/hyperactivity disorder (ADHD). METHODS We identified a quantitative profile of plasma metabolites in 58 ADHD patients (mean age 9.0 years, 77.6% males) and 38 healthy control subjects (mean age 10.2 years, 55.3% males) using the high-performance chemical isotope labelling (CIL)-based liquid chromatography-mass spectrometry (LC-MS). Using a volcano plot and orthogonal projections to latent structure-discriminant analysis (OPLS-DA), we determined nine metabolites with differentially expressed levels in ADHD plasma samples. RESULTS Compared to the control group, the plasma levels of guanosine, O-phosphoethanolamine, phenyl-leucine, hypoxanthine, 4-aminohippuric acid, 5-hydroxylysine, and L-cystine appeared increased in the ADHD patients, whilegentisic acid and tryptophyl-phenylalanine were down-regulated in the patients with ADHD. We found that the plasma levels of all nine metabolites were able to discriminate the ADHD group from the control group. Levels of O-phosphoethanolamine, 4-aminohippuric acid, 5-hydroxylysine, L-cystine, tryptophyl-phenylalanine, and gentisic acid were significantly correlated with clinical ADHD symptoms. CONCLUSIONS This study is the first to use the CIL-based LC-MS to profile ADHD plasma metabolites, and we identified nine novel metabolite biomarkers of ADHD.
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Affiliation(s)
- Liang-Jen Wang
- Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Wen-Jiun Chou
- Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Ching-Shu Tsai
- Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Min-Jing Lee
- Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Sheng-Yu Lee
- Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Department of Psychiatry, School of Medicine, and Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chia-Wei Hsu
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Pei-Chun Hsueh
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chih-Ching Wu
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan.,Department of Otolaryngology-Head & Neck Surgery, Linkuo Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan.,Research Center for Emerging Viral Infections, College of Medicine, Chang Gung University, Taoyuan, Taiwan
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43
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Bjørklund G, Doşa MD, Maes M, Dadar M, Frye RE, Peana M, Chirumbolo S. The impact of glutathione metabolism in autism spectrum disorder. Pharmacol Res 2021; 166:105437. [PMID: 33493659 DOI: 10.1016/j.phrs.2021.105437] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 12/31/2020] [Accepted: 01/12/2021] [Indexed: 12/14/2022]
Abstract
This paper reviews the potential role of glutathione (GSH) in autism spectrum disorder (ASD). GSH plays a key role in the detoxification of xenobiotics and maintenance of balance in intracellular redox pathways. Recent data showed that imbalances in the GSH redox system are an important factor in the pathophysiology of ASD. Furthermore, ASD is accompanied by decreased concentrations of reduced GSH in part caused by oxidation of GSH into glutathione disulfide (GSSG). GSSG can react with protein sulfhydryl (SH) groups, thereby causing proteotoxic stress and other abnormalities in SH-containing enzymes in the brain and blood. Moreover, alterations in the GSH metabolism via its effects on redox-independent mechanisms are other processes associated with the pathophysiology of ASD. GSH-related regulation of glutamate receptors such as the N-methyl-D-aspartate receptor can contribute to glutamate excitotoxicity. Synergistic and antagonistic interactions between glutamate and GSH can result in neuronal dysfunction. These interactions can involve transcription factors of the immune pathway, such as activator protein 1 and nuclear factor (NF)-κB, thereby interacting with neuroinflammatory mechanisms, ultimately leading to neuronal damage. Neuronal apoptosis and mitochondrial dysfunction are recently outlined as significant factors linking GSH impairments with the pathophysiology of ASD. Moreover, GSH regulates the methylation of DNA and modulates epigenetics. Existing data support a protective role of the GSH system in ASD development. Future research should focus on the effects of GSH redox signaling in ASD and should explore new therapeutic approaches by targeting the GSH system.
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Affiliation(s)
- Geir Bjørklund
- Council for Nutritional and Environmental Medicine (CONEM), Toften 24, 8610, Mo i Rana, Norway.
| | - Monica Daniela Doşa
- Department of Pharmacology, Faculty of Medicine, Ovidius University of Constanta, Campus, 900470, Constanta, Romania.
| | - Michael Maes
- Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Impact Research Center, Deakin University, Geelong, Australia
| | - Maryam Dadar
- Razi Vaccine and Serum Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Richard E Frye
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, USA; Department of Child Health, University of Arizona College of Medicine, Phoenix, AZ, USA
| | | | - Salvatore Chirumbolo
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy; CONEM Scientific Secretary, Verona, Italy
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44
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Graña M, Silva M. Impact of Machine Learning Pipeline Choices in Autism Prediction From Functional Connectivity Data. Int J Neural Syst 2021; 31:2150009. [PMID: 33472548 DOI: 10.1142/s012906572150009x] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction, and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this paper, we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines. Specifically, we consider six brain parcellation definitions, five methods for functional connectivity matrix construction, six feature extraction/selection approaches, and nine classifier building algorithms. We report the prediction performance sensitivity to each of these choices, as well as the best results that are comparable with the state of the art.
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Affiliation(s)
- Manuel Graña
- Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain
| | - Moises Silva
- Universidad Mayor de San Andres, La Paz, Bolivia
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45
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Uddin LQ. Brain Mechanisms Supporting Flexible Cognition and Behavior in Adolescents With Autism Spectrum Disorder. Biol Psychiatry 2021; 89:172-183. [PMID: 32709415 PMCID: PMC7677208 DOI: 10.1016/j.biopsych.2020.05.010] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 05/11/2020] [Accepted: 05/12/2020] [Indexed: 02/08/2023]
Abstract
Cognitive flexibility enables appropriate responses to a changing environment and is associated with positive life outcomes. Adolescence, with its increased focus on transitioning to independent living, presents particular challenges for youths with autism spectrum disorder (ASD) who often struggle to behave in a flexible way when faced with challenges. This review focuses on brain mechanisms underlying the development of flexible cognition during adolescence and how these neural systems are affected in ASD. Neuroimaging studies of task switching and set-shifting provide evidence for atypical lateral frontoparietal and midcingulo-insular network activation during cognitive flexibility task performance in individuals with ASD. Recent work also examines how intrinsic brain network dynamics support flexible cognition. These dynamic functional connectivity studies provide evidence for alterations in the number of transitions between brain states, as well as hypervariability of functional connections in adolescents with ASD. Future directions for the field include addressing issues related to measurement of cognitive flexibility using a combination of metrics with ecological and construct validity. Heterogeneity of executive function ability in ASD must also be parsed to determine which individuals will benefit most from targeted training to improve flexibility. The influence of pubertal hormones on brain network development and cognitive maturation in adolescents with ASD is another area requiring further exploration. Finally, the intriguing possibility that bilingualism might be associated with preserved cognitive flexibility in ASD should be further examined. Addressing these open questions will be critical for future translational neuroscience investigations of cognitive and behavioral flexibility in adolescents with ASD.
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Affiliation(s)
- Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, and the Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida.
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46
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Marshall E, Nomi JS, Dirks B, Romero C, Kupis L, Chang C, Uddin LQ. Coactivation pattern analysis reveals altered salience network dynamics in children with autism spectrum disorder. Netw Neurosci 2020; 4:1219-1234. [PMID: 33409437 PMCID: PMC7781614 DOI: 10.1162/netn_a_00163] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/29/2020] [Indexed: 12/17/2022] Open
Abstract
Brain connectivity studies of autism spectrum disorder (ASD) have historically relied on static measures of functional connectivity. Recent work has focused on identifying transient configurations of brain activity, yet several open questions remain regarding the nature of specific brain network dynamics in ASD. We used a dynamic coactivation pattern (CAP) approach to investigate the salience/midcingulo-insular (M-CIN) network, a locus of dysfunction in ASD, in a large multisite resting-state fMRI dataset collected from 172 children (ages 6–13 years; n = 75 ASD; n = 138 male). Following brain parcellation by using independent component analysis, dynamic CAP analyses were conducted and k-means clustering was used to determine transient activation patterns of the M-CIN. The frequency of occurrence of different dynamic CAP brain states was then compared between children with ASD and typically developing (TD) children. Dynamic brain configurations characterized by coactivation of the M-CIN with central executive/lateral fronto-parietal and default mode/medial fronto-parietal networks appeared less frequently in children with ASD compared with TD children. This study highlights the utility of time-varying approaches for studying altered M-CIN function in prevalent neurodevelopmental disorders. We speculate that altered M-CIN dynamics in ASD may underlie the inflexible behaviors commonly observed in children with the disorder. Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with altered patterns of functional brain connectivity. Little is currently known about how moment-to-moment brain dynamics differ in children with ASD and typically developing (TD) children. Altered functional integrity of the midcingulo-insular network (M-CIN) has been implicated in the neurobiology of ASD. Here we use a novel coactivation analysis approach applied to a large sample of resting-state fMRI data collected from children with ASD and TD children to demonstrate altered patterns of M-CIN dynamics in children with the disorder. We speculate that these atypical patterns of brain dynamics may underlie behavioral inflexibility in ASD.
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Affiliation(s)
- Emily Marshall
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Bryce Dirks
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Celia Romero
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Lauren Kupis
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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Neuroimaging Markers of Risk and Pathways to Resilience in Autism Spectrum Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 6:200-210. [PMID: 32839155 DOI: 10.1016/j.bpsc.2020.06.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 06/04/2020] [Accepted: 06/28/2020] [Indexed: 01/22/2023]
Abstract
Autism spectrum disorder is a complex, heterogeneous neurodevelopmental condition of largely unknown etiology. This heterogeneity of symptom presentation, combined with high rates of comorbidity with other developmental disorders and a lack of reliable biomarkers, makes diagnosing and evaluating life outcomes for individuals with autism spectrum disorder a challenge. We review the growing literature on neuroimaging-based biomarkers of risk for the development of autism and explore evidence for resilience in some autistic individuals. The current literature suggests that neuroimaging during early infancy, in combination with prebirth and early genetic studies, is a promising tool for identifying biomarkers of risk, while studies of gene expression and DNA methylation have provided some key insights into mechanisms of resilience. With genetics and the environment contributing to both risk for the development of autism spectrum disorder and conditions for resilience, additional studies are needed to understand how risk and resilience interact mechanistically, whereby factors of risk may engender conditions for adaptation. Future studies should prioritize longitudinal designs in global cohorts, with the involvement of the autism community as partners in research to help identify domains of functioning that hold value and importance to the community.
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Vargason T, Grivas G, Hollowood-Jones KL, Hahn J. Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements. Semin Pediatr Neurol 2020; 34:100803. [PMID: 32446437 PMCID: PMC7248126 DOI: 10.1016/j.spen.2020.100803] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.
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Affiliation(s)
- Troy Vargason
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Genevieve Grivas
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Kathryn L Hollowood-Jones
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Juergen Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY; Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY.
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50
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Feczko E, Fair DA. Methods and Challenges for Assessing Heterogeneity. Biol Psychiatry 2020; 88:9-17. [PMID: 32386742 PMCID: PMC8404882 DOI: 10.1016/j.biopsych.2020.02.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/30/2019] [Accepted: 02/07/2020] [Indexed: 01/14/2023]
Abstract
The widely acknowledged homogeneity assumption limits progress in refining clinical diagnosis, understanding mechanisms, and developing new treatments for mental health disorders. This homogeneity assumption drives both a comorbidity and a heterogeneity problem, where two different approaches tackle the problems. One, a unifying approach, tackles the comorbidity problem by assuming that a single general psychopathology factor underlies multiple disorders. Another, a multifactorial approach, tackles the heterogeneity problem by assuming that disorders comprise multiple subtypes driven by multiple discrete factors. We show how each of these approaches can make useful contributions to mental health-related research and clinical practice. For example, the unifying approach can develop a rapid assessment tool that may be clinically valuable for triaging cases. The multifactorial approach can reveal subtypes that are differentially responsive to treatments and highlight distinct mechanisms leading to similar phenotypes. Because both approaches tackle different problems, both have different limitations. We describe the statistical frameworks that incorporate and adjudicate between both approaches (e.g., the bifactor model, normative modeling, and the functional random forest). Such frameworks can identify whether sets of disorders are more affected by heterogeneity or comorbidity. Therefore, future studies that incorporate such frameworks can provide further insight into the nature of psychopathology.
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Affiliation(s)
- Eric Feczko
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon.
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon; Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon
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