101
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Joshi G, Arnold Anteraper S, Patil KR, Semwal M, Goldin RL, Furtak SL, Chai XJ, Saygin ZM, Gabrieli JDE, Biederman J, Whitfield-Gabrieli S. Integration and Segregation of Default Mode Network Resting-State Functional Connectivity in Transition-Age Males with High-Functioning Autism Spectrum Disorder: A Proof-of-Concept Study. Brain Connect 2018; 7:558-573. [PMID: 28942672 DOI: 10.1089/brain.2016.0483] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
The aim of this study is to assess the resting-state functional connectivity (RsFc) profile of the default mode network (DMN) in transition-age males with autism spectrum disorder (ASD). Resting-state blood oxygen level-dependent functional magnetic resonance imaging data were acquired from adolescent and young adult males with high-functioning ASD (n = 15) and from age-, sex-, and intelligence quotient-matched healthy controls (HCs; n = 16). The DMN was examined by assessing the positive and negative RsFc correlations of an average of the literature-based conceptualized major DMN nodes (medial prefrontal cortex [mPFC], posterior cingulate cortex, bilateral angular, and inferior temporal gyrus regions). RsFc data analysis was performed using a seed-driven approach. ASD was characterized by an altered pattern of RsFc in the DMN. The ASD group exhibited a weaker pattern of intra- and extra-DMN-positive and -negative RsFc correlations, respectively. In ASD, the strength of intra-DMN coupling was significantly reduced with the mPFC and the bilateral angular gyrus regions. In addition, the polarity of the extra-DMN correlation with the right hemispheric task-positive regions of fusiform gyrus and supramarginal gyrus was reversed from typically negative to positive in the ASD group. A wide variability was observed in the presentation of the RsFc profile of the DMN in both HC and ASD groups that revealed a distinct pattern of subgrouping using pattern recognition analyses. These findings imply that the functional architecture profile of the DMN is altered in ASD with weaker than expected integration and segregation of DMN RsFc. Future studies with larger sample sizes are warranted.
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Affiliation(s)
- Gagan Joshi
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
- 2 Department of Psychiatry, Harvard Medical School , Boston, Massachusetts
- 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Sheeba Arnold Anteraper
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
- 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Kaustubh R Patil
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
| | - Meha Semwal
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
| | - Rachel L Goldin
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
| | - Stephannie L Furtak
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
| | | | - Zeynep M Saygin
- 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - John D E Gabrieli
- 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, Massachusetts
- 5 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Joseph Biederman
- 1 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital , Boston, Massachusetts
- 2 Department of Psychiatry, Harvard Medical School , Boston, Massachusetts
| | - Susan Whitfield-Gabrieli
- 3 McGovern Institute for Brain Research, Massachusetts Institute of Technology , Cambridge, Massachusetts
- 5 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology , Cambridge, Massachusetts
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102
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Wong E, Anderson JS, Zielinski BA, Fletcher PT. Riemannian Regression and Classification Models of Brain Networks Applied to Autism. CONNECTOMICS IN NEUROIMAGING : SECOND INTERNATIONAL WORKSHOP, CNI 2018, HELD IN CONJUNCTION WITH MICCAI 2018, GRANADA, SPAIN, SEPTEMBER 20, 2018 : PROCEEDINGS. CNI (WORKSHOP) (2ND : 2018 : GRANADA, SPAIN) 2018; 11083:78-87. [PMID: 33345258 DOI: 10.1007/978-3-030-00755-3_9] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Functional connectivity from resting-state functional MRI (rsfMRI) is typically represented as a symmetric positive definite (SPD) matrix. Analysis methods that exploit the Riemannian geometry of SPD matrices appropriately adhere to the positive definite constraint, unlike Euclidean methods. Recently proposed approaches for rsfMRI analysis have achieved high accuracy on public datasets, but are computationally intensive and difficult to interpret. In this paper, we show that we can get comparable results using connectivity matrices under the log-Euclidean and affine-invariant Riemannian metrics with relatively simple and interpretable models. On ABIDE Preprocessed dataset, our methods classify autism versus control subjects with 71.1% accuracy. We also show that Riemannian methods beat baseline in regressing connectome features to subject autism severity scores.
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Affiliation(s)
- Eleanor Wong
- University of Utah, Salt Lake City, UT 84112, USA
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103
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Zhao X, Rangaprakash D, Yuan B, Denney TS, Katz JS, Dretsch MN, Deshpande G. Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning. FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS 2018; 4:25. [PMID: 30393630 PMCID: PMC6214192 DOI: 10.3389/fams.2018.00025] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.
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Affiliation(s)
- Xinyu Zhao
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Quora, Inc., Mountain View, CA, United States
| | - D. Rangaprakash
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Bowen Yuan
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
| | - Thomas S. Denney
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University, University of Alabama at Birmingham, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Jeffrey S. Katz
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University, University of Alabama at Birmingham, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
| | - Michael N. Dretsch
- Human Dimension Division, HQ TRADOC, Fort Eustis, VA, United States
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychology, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Auburn University, University of Alabama at Birmingham, Birmingham, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States
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104
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Harlalka V, Bapi RS, Vinod PK, Roy D. Age, Disease, and Their Interaction Effects on Intrinsic Connectivity of Children and Adolescents in Autism Spectrum Disorder Using Functional Connectomics. Brain Connect 2018; 8:407-419. [PMID: 30009617 DOI: 10.1089/brain.2018.0616] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Brain connectivity analysis has provided crucial insights to pinpoint the differences between autistic and typically developing (TD) children during development. The aim of this study is to investigate the functional connectomics of autism spectrum disorder (ASD) versus TD and underpin the effects of development, disease, and their interactions on the observed atypical brain connectivity patterns. Resting-state functional magnetic resonance imaging (rs-fMRI) from the Autism Brain Imaging Data Exchange (ABIDE) data set, which is stratified into two cohorts: children (9-12 years) and adolescents (13-16 years), is used for the analysis. Differences in various graph theoretical network measures are calculated between ASD and TD in each group. Furthermore, two-factor analysis of variance test is used to study the effect of age, disease, and their interaction on the network measures and the network edges. Furthermore, the differences in connection strength between TD and ASD subjects are assessed using network-based statistics. The results showed that ASD exhibits increased functional integration at the expense of decreased functional segregation. In ASD adolescents, there is a significant decrease in modularity suggesting a less robust modular organization, and an increase in participation coefficient suggesting more random integration and widely distributed connection edges. Furthermore, there is significant hypoconnectivity observed in the adolescent group especially in the default mode network, while the children group shows both hyper- and hypoconnectivity. This study lends support to a model of global atypical connections and further identifies functional networks and areas that are independently affected by age, disease, and their interaction.
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Affiliation(s)
- Vatika Harlalka
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Telangana, India
| | - Raju S Bapi
- Cognitive Science Laboratory, IIIT Hyderabad, Telangana, India.,School of Computer and Information Sciences, University of Hyderabad, Telangana, India
| | | | - Dipanjan Roy
- Cognitive Brain Dynamics Laboratory, National Brain Research Centre, Manesar, India
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105
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Pereira AM, Campos BM, Coan AC, Pegoraro LF, de Rezende TJR, Obeso I, Dalgalarrondo P, da Costa JC, Dreher JC, Cendes F. Differences in Cortical Structure and Functional MRI Connectivity in High Functioning Autism. Front Neurol 2018; 9:539. [PMID: 30042724 PMCID: PMC6048242 DOI: 10.3389/fneur.2018.00539] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Accepted: 06/18/2018] [Indexed: 12/13/2022] Open
Abstract
Autism spectrum disorders (ASD) represent a complex group of neurodevelopmental conditions characterized by deficits in communication and social behaviors. We examined the functional connectivity (FC) of the default mode network (DMN) and its relation to multimodal morphometry to investigate superregional, system-level alterations in a group of 22 adolescents and young adults with high-functioning autism compared to age-, and intelligence quotient-matched 29 healthy controls. The main findings were that ASD patients had gray matter (GM) reduction, decreased cortical thickness and larger cortical surface areas in several brain regions, including the cingulate, temporal lobes, and amygdala, as well as increased gyrification in regions associated with encoding visual memories and areas of the sensorimotor component of the DMN, more pronounced in the left hemisphere. Moreover, patients with ASD had decreased connectivity between the posterior cingulate cortex, and areas of the executive control component of the DMN and increased FC between the anteromedial prefrontal cortex and areas of the sensorimotor component of the DMN. Reduced cortical thickness in the right inferior frontal lobe correlated with higher social impairment according to the scores of the Autism Diagnostic Interview-Revised (ADI-R). Reduced cortical thickness in left frontal regions, as well as an increased cortical thickness in the right temporal pole and posterior cingulate, were associated with worse scores on the communication domain of the ADI-R. We found no association between scores on the restrictive and repetitive behaviors domain of ADI-R with structural measures or FC. The combination of these structural and connectivity abnormalities may help to explain some of the core behaviors in high-functioning ASD and need to be investigated further.
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Affiliation(s)
- Alessandra M. Pereira
- Neuroimaging Laboratory, School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotechnology, University of Campinas, Campinas, Brazil
- Department of Pediatrics, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Brunno M. Campos
- Neuroimaging Laboratory, School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotechnology, University of Campinas, Campinas, Brazil
| | - Ana C. Coan
- Neuroimaging Laboratory, School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotechnology, University of Campinas, Campinas, Brazil
| | - Luiz F. Pegoraro
- Department of Psychiatry, State University of Campinas, Campinas, Brazil
| | - Thiago J. R. de Rezende
- Neuroimaging Laboratory, School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotechnology, University of Campinas, Campinas, Brazil
| | - Ignacio Obeso
- Center for Cognitive Neuroscience, Reward and Decision Making Group, Centre National de la Recherche Scientifique, UMR 5229, Lyon, France
- Centro Integral en Neurociencias A.C., Hospital HM Puerta del Sur en Madrid, Madrid, Spain
| | | | - Jaderson C. da Costa
- Department of Pediatrics, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
- Brain Institute (InsCer), Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Jean-Claude Dreher
- Center for Cognitive Neuroscience, Reward and Decision Making Group, Centre National de la Recherche Scientifique, UMR 5229, Lyon, France
| | - Fernando Cendes
- Neuroimaging Laboratory, School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotechnology, University of Campinas, Campinas, Brazil
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106
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Askari E, Setarehdan SK, Sheikhani A, Mohammadi MR, Teshnehlab M. Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis. Artif Intell Med 2018; 89:40-50. [DOI: 10.1016/j.artmed.2018.05.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2017] [Revised: 05/18/2018] [Accepted: 05/22/2018] [Indexed: 11/26/2022]
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107
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Alterations in resting state connectivity along the autism trait continuum: a twin study. Mol Psychiatry 2018; 23:1659-1665. [PMID: 28761079 DOI: 10.1038/mp.2017.160] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Revised: 05/16/2017] [Accepted: 06/19/2017] [Indexed: 01/03/2023]
Abstract
Autism spectrum disorder (ASD) has been found to be associated with alterations in resting state (RS) functional connectivity, including areas forming the default mode network (DMN) and salience network (SN). However, insufficient control for confounding genetic and environmental influences and other methodological issues limit the generalizability of previous findings. Moreover, it has been hypothesized that ASD might be marked by early hyper-connectivity followed by later hypo-connectivity. To date, only a few studies have explicitly tested age-related influences on RS connectivity alterations in ASD. Using a within-twin pair design (N=150 twins; 8-23 years), we examined altered RS connectivity between core regions of the DMN and SN in relation to autistic trait severity and age in a sample of monozygotic (MZ) and dizygotic (DZ) twins showing typical development, ASD or other neurodevelopmental conditions. Connectivity between core regions of the SN was stronger in twins with higher autistic traits compared to their co-twins. This effect was significant both in the total sample and in MZ twins alone, highlighting the effect of non-shared environmental factors on the link between SN-connectivity and autistic traits. While this link was strongest in children, we did not identify differences between age groups for the SN. In contrast, connectivity between core hubs of the DMN was negatively correlated with autistic traits in adolescents and showed a similar trend in adults but not in children. The results support hypotheses of age-dependent altered RS connectivity in ASD, making altered SN and DMN connectivity promising candidate biomarkers for ASD.
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108
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Beta-adrenergic antagonism modulates functional connectivity in the default mode network of individuals with and without autism spectrum disorder. Brain Imaging Behav 2018; 11:1278-1289. [PMID: 27714553 DOI: 10.1007/s11682-016-9604-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The beta-adrenergic antagonist propranolol benefits some social and communication domains affected in autism spectrum disorder (ASD), and these benefits appear to be associated with increased functional connectivity (FC) in the brain during task performance. FC is implicated in ASD, with the majority of studies suggesting long distance hypo-connectivity combined with regionally specific local hyper-connectivity. The objective in the current investigation was to examine the effect of propranolol on FC at rest and determine whether ASD-specific effects exist. Participants with and without ASD attended three sessions in which propranolol, nadolol (a beta-adrenergic antagonist that does not cross the blood-brain barrier), or placebo were administered. Resting-state fMRI data were acquired, and graph theory techniques were utilized to assess additional aspects of FC. Compared to placebo, propranolol administration was associated with decreased FC in the dorsal medial prefrontal cortex subnetwork of the default mode network and increased FC in the medial temporal lobe subnetwork, regardless of diagnosis. These effects were not seen with nadolol suggesting that the alterations in FC following propranolol administration were not exclusively due to peripheral cardiovascular effects. Thus, beta-adrenergic antagonism can up- or down- regulate FC, depending on the network, and alter coordinated functional activation in the brain. These changes in information processing, as demonstrated by FC, may mediate some of the clinical and behavioral effects of beta-adrenergic antagonism previously reported in patients with ASD.
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109
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The neural circuitry of restricted repetitive behavior: Magnetic resonance imaging in neurodevelopmental disorders and animal models. Neurosci Biobehav Rev 2018; 92:152-171. [PMID: 29802854 DOI: 10.1016/j.neubiorev.2018.05.022] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 04/18/2018] [Accepted: 05/20/2018] [Indexed: 11/23/2022]
Abstract
Restricted, repetitive behaviors (RRBs) are patterns of behavior that exhibit little variation in form and have no obvious function. RRBs although transdiagonstic are a particularly prominent feature of certain neurodevelopmental disorders, yet relatively little is known about the neural circuitry of RRBs. Past work in this area has focused on isolated brain regions and neurotransmitter systems, but implementing a neural circuit approach has the potential to greatly improve understanding of RRBs. Magnetic resonance imaging (MRI) is well-suited to studying the structural and functional connectivity of the nervous system, and is a highly translational research tool. In this review, we synthesize MRI research from both neurodevelopmental disorders and relevant animal models that informs the neural circuitry of RRB. Together, these studies implicate distributed neural circuits between the cortex, basal ganglia, and cerebellum. Despite progress in neuroimaging of RRB, there are many opportunities for conceptual and methodological improvement. We conclude by suggesting future directions for MRI research in RRB, and how such studies can benefit from complementary approaches in neuroscience.
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110
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Bi XA, Zhao J, Xu Q, Sun Q, Wang Z. Abnormal Functional Connectivity of Resting State Network Detection Based on Linear ICA Analysis in Autism Spectrum Disorder. Front Physiol 2018; 9:475. [PMID: 29867534 PMCID: PMC5952255 DOI: 10.3389/fphys.2018.00475] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 04/16/2018] [Indexed: 11/24/2022] Open
Abstract
Some functional magnetic resonance imaging (fMRI) researches in autism spectrum disorder (ASD) patients have shown that ASD patients have significant impairment in brain response. However, few researchers have studied the functional structure changes of the eight resting state networks (RSNs) in ASD patients. Therefore, research on statistical differences of RSNs between 42 healthy controls (HC) and 50 ASD patients has been studied using linear independent component analysis (ICA) in this paper. Our researches showed that there was abnormal functional connectivity (FC) of RSNs in ASD patients. The RSNs with the decreased FC and increased FC in ASD patients included default mode network (DMN), central executive network (CEN), core network (CN), visual network (VN), self-referential network (SRN) compared to HC. The RSNs with the increased FC in ASD patients included auditory network (AN), somato-motor network (SMN). The dorsal attention network (DAN) in ASD patients showed the decreased FC. Our findings indicate that the abnormal FC in RSNs extensively exists in ASD patients. Our results have important contribution for the study of neuro-pathophysiological mechanisms in ASD patients.
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Affiliation(s)
- Xia-An Bi
- College of Mathematics and Computer Science, Hunan Normal University, Changsha, China
| | - Junxia Zhao
- College of Mathematics and Computer Science, Hunan Normal University, Changsha, China
| | - Qian Xu
- College of Mathematics and Computer Science, Hunan Normal University, Changsha, China
| | - Qi Sun
- College of Mathematics and Computer Science, Hunan Normal University, Changsha, China
| | - Zhigang Wang
- College of Mathematics and Computer Science, Hunan Normal University, Changsha, China
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111
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Askari E, Setarehdan SK, Sheikhani A, Mohammadi MR, Teshnehlab M. Designing a model to detect the brain connections abnormalities in children with autism using 3D-cellular neural networks. J Integr Neurosci 2018; 17:391-411. [PMID: 29689730 DOI: 10.3233/jin-180075] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
Abstract
In neuropsychological disorders, the significant abnormalities in the brain connections in some regions are observed. This paper presents a novel model to demonstrate the connections between different regions in children with autism. The proposed model first conducts the wavelet decomposition of electroencephalography signals by wavelet transform then the features are extracted, such as relative energy and entropy. These features are fed to the 3D-cellular neural network model as inputs to indicate the brain connections. The results showed that there are significant differences and abnormalities in the left hemisphere, (p<0.05) at the electrodes AF3, F3, P7, T7 and O1 in alpha band, AF3, F7, T7 and O1 in beta band, T7 and P7 in gamma band for children with autism compared with the control children. Also, the evaluation of the obtained connections values between brain regions indicated that there are more abnormalities in the connectivity of frontal and parietal lobes and the relations of the neighboring regions in all three bands especially in gamma band for autistic children. Evaluation of the analysis demonstrated that alpha frequency band had the best distinction level of 96.6% based on the obtained values of the cellular neural network using support vector machine method.
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Affiliation(s)
- Elham Askari
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. E-mail:
| | - Seyed Kamaledin Setarehdan
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. E-mail:
| | - Ali Sheikhani
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. E-mail:
| | - Mohammad Reza Mohammadi
- Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran. E-mail:
| | - Mohammad Teshnehlab
- Department of Control Engineering, K.N. Toosi University of Technology, Tehran, Iran. E-mail:
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112
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Atherton G, Cross L. Seeing More Than Human: Autism and Anthropomorphic Theory of Mind. Front Psychol 2018; 9:528. [PMID: 29755383 PMCID: PMC5932358 DOI: 10.3389/fpsyg.2018.00528] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Accepted: 03/27/2018] [Indexed: 11/17/2022] Open
Abstract
Theory of mind (ToM) is defined as the process of taking another's perspective. Anthropomorphism can be seen as the extension of ToM to non-human entities. This review examines the literature concerning ToM and anthropomorphism in relation to individuals with Autism Spectrum Disorder (ASD), specifically addressing the questions of how and why those on the spectrum both show an increased interest for anthropomorphism and may even show improved ToM abilities when judging the mental states of anthropomorphic characters. This review highlights that while individuals with ASD traditionally show deficits on a wide range of ToM tests, such as recognizing facial emotions, such ToM deficits may be ameliorated if the stimuli presented is cartoon or animal-like rather than in human form. Individuals with ASD show a greater interest in anthropomorphic characters and process the features of these characters using methods typically reserved for human stimuli. Personal accounts of individuals with ASD also suggest they may identify more closely with animals than other humans. It is shown how the social motivations hypothesized to underlie the anthropomorphizing of non-human targets may lead those on the spectrum to seek social connections and therefore gain ToM experience and expertise amongst unlikely sources.
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Affiliation(s)
- Gray Atherton
- Department of Psychological, Health and Learning Sciences, University of Houston, Houston, TX, United States
- Department of Psychology, School of Science and Technology, Sunway University, Selangor, Malaysia
| | - Liam Cross
- Department of Psychology, School of Science and Technology, Sunway University, Selangor, Malaysia
- Department of Psychology, School of Science, University of Buckingham, Buckingham, United Kingdom
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113
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Xu J, Wang H, Zhang L, Xu Z, Li T, Zhou Z, Zhou Z, Gan Y, Hu Q. Both Hypo-Connectivity and Hyper-Connectivity of the Insular Subregions Associated With Severity in Children With Autism Spectrum Disorders. Front Neurosci 2018; 12:234. [PMID: 29695950 PMCID: PMC5904282 DOI: 10.3389/fnins.2018.00234] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 03/26/2018] [Indexed: 11/13/2022] Open
Abstract
Some studies identified hypo-connectivity, while others showed hyper-connectivity of the insula in the autism spectrum disorders (ASD). These contradictory findings leave open the question of whether and to what extent functional connectivity of the insula is altered and how functional connectivity of the insula is associated with the severity of ASD. A newly emerging insular atlas that comprises multiple functionally differentiated subregions provides a new framework to interpret the functional significance of insular findings and uncover the mechanisms underlying the severity of ASD. Using the new insular atlas, the present study aimed to investigate the distinct functional connectivity of the insular subregions and their associations with ASD severity in a cohort of 49 children with ASD and 33 typically developing (TD) subjects. We found that compared with TD group, the ASD group showed different connectivity patterns in the left ventral agranular insula, right ventral dysgranular and granular insula, and dorsal dysgranular insula, characterized by significant hyper-connectivity and/or hypo-connectivity with special brain regions. Furthermore, both the hypo-connectivity and hyper-connectivity patterns of the insular subregions were significantly associated with the severity of ASD symptoms. Our research demonstrated distinct functional connectivity patterns of the insular subregions and emphasized the importance of the subdivisions within the insula to the potential impact of functional difference in children with ASD. Moreover, these results might help us to better understand the mechanisms underlying the symptoms in children with ASD and might elucidate potential biomarkers for clinical applications.
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Affiliation(s)
- Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hongwei Wang
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, China
| | - Lu Zhang
- Graduate School of Education, Peking University, Beijing, China
| | - Ziyun Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Tian Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhifeng Zhou
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhenhui Zhou
- Psychological Department, Shenzhen Children's Hospital, Shenzhen, China
| | - Yungen Gan
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, China
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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114
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Dubourg L, Vrticka P, Debbané M, Chambaz L, Eliez S, Schneider M. Neural correlates of socio-emotional perception in 22q11.2 deletion syndrome. J Neurodev Disord 2018; 10:13. [PMID: 29631546 PMCID: PMC5891973 DOI: 10.1186/s11689-018-9232-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 03/23/2018] [Indexed: 01/04/2023] Open
Abstract
Background Social impairments are described as a common feature of the 22q11.2 deletion syndrome (22q11DS). However, the neural correlates underlying these impairments are largely unknown in this population. In this study, we investigated neural substrates of socio-emotional perception. Methods We used event-related functional magnetic resonance imaging (fMRI) to explore neural activity in individuals with 22q11DS and healthy controls during the visualization of stimuli varying in social (social or non-social) or emotional (positive or negative valence) content. Results Neural hyporesponsiveness in regions of the default mode network (inferior parietal lobule, precuneus, posterior and anterior cingulate cortex and frontal regions) in response to social versus non-social images was found in the 22q11DS population compared to controls. A similar pattern of activation for positive and negative emotional processing was observed in the two groups. No correlation between neural activation and social functioning was observed in patients with the 22q11DS. Finally, no social × valence interaction impairment was found in patients. Conclusions Our results indicate atypical neural correlates of social perception in 22q11DS that appear to be independent of valence processing. Abnormalities in the social perception network may lead to social impairments observed in 22q11DS individuals. Electronic supplementary material The online version of this article (10.1186/s11689-018-9232-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lydia Dubourg
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, School of Medicine, University of Geneva, Campus Biotech, Chemin des mines 9, 1202, Geneva, Switzerland.
| | - Pascal Vrticka
- Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Martin Debbané
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, School of Medicine, University of Geneva, Campus Biotech, Chemin des mines 9, 1202, Geneva, Switzerland.,Developmental Clinical Psychology Research Unit, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland.,Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Léa Chambaz
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, School of Medicine, University of Geneva, Campus Biotech, Chemin des mines 9, 1202, Geneva, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, School of Medicine, University of Geneva, Campus Biotech, Chemin des mines 9, 1202, Geneva, Switzerland.,Department of Genetic Medicine and Development, School of Medicine, University of Geneva, Geneva, Switzerland
| | - Maude Schneider
- Center for Contextual Psychiatry, Department of Neurosciences, Research Group Psychiatry, KU Leuven, Leuven, Belgium
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115
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Brodski-Guerniero A, Naumer MJ, Moliadze V, Chan J, Althen H, Ferreira-Santos F, Lizier JT, Schlitt S, Kitzerow J, Schütz M, Langer A, Kaiser J, Freitag CM, Wibral M. Predictable information in neural signals during resting state is reduced in autism spectrum disorder. Hum Brain Mapp 2018; 39:3227-3240. [PMID: 29617056 DOI: 10.1002/hbm.24072] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 03/20/2018] [Accepted: 03/25/2018] [Indexed: 11/12/2022] Open
Abstract
The neurophysiological underpinnings of the nonsocial symptoms of autism spectrum disorder (ASD) which include sensory and perceptual atypicalities remain poorly understood. Well-known accounts of less dominant top-down influences and more dominant bottom-up processes compete to explain these characteristics. These accounts have been recently embedded in the popular framework of predictive coding theory. To differentiate between competing accounts, we studied altered information dynamics in ASD by quantifying predictable information in neural signals. Predictable information in neural signals measures the amount of stored information that is used for the next time step of a neural process. Thus, predictable information limits the (prior) information which might be available for other brain areas, for example, to build predictions for upcoming sensory information. We studied predictable information in neural signals based on resting-state magnetoencephalography (MEG) recordings of 19 ASD patients and 19 neurotypical controls aged between 14 and 27 years. Using whole-brain beamformer source analysis, we found reduced predictable information in ASD patients across the whole brain, but in particular in posterior regions of the default mode network. In these regions, epoch-by-epoch predictable information was positively correlated with source power in the alpha and beta frequency range as well as autocorrelation decay time. Predictable information in precuneus and cerebellum was negatively associated with nonsocial symptom severity, indicating a relevance of the analysis of predictable information for clinical research in ASD. Our findings are compatible with the assumption that use or precision of prior knowledge is reduced in ASD patients.
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Affiliation(s)
| | - Marcus J Naumer
- Institute of Medical Psychology, Faculty of Medicine, Goethe University, Frankfurt am Main, Germany
| | - Vera Moliadze
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany.,Department of Medical Psychology and Medical Sociology, Schleswig-Holstein University Hospital (UKSH), Christian-Albrechts-University, Kiel, Germany
| | - Jason Chan
- Institute of Medical Psychology, Faculty of Medicine, Goethe University, Frankfurt am Main, Germany.,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany.,School of Applied Psychology, University College Cork, Cork, Ireland
| | - Heike Althen
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Fernando Ferreira-Santos
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Education Sciences, University of Porto, Porto, Portugal
| | - Joseph T Lizier
- Complex Systems Research Group and Centre for Complex Systems, Faculty of Engineering & IT, The University of Sydney, New South Wales, 2006, Australia
| | - Sabine Schlitt
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Janina Kitzerow
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Magdalena Schütz
- Institute of Medical Psychology, Faculty of Medicine, Goethe University, Frankfurt am Main, Germany.,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Anne Langer
- Institute of Medical Psychology, Faculty of Medicine, Goethe University, Frankfurt am Main, Germany.,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Jochen Kaiser
- Institute of Medical Psychology, Faculty of Medicine, Goethe University, Frankfurt am Main, Germany
| | - Christine M Freitag
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Michael Wibral
- MEG Unit, Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
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116
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Brain disease, connectivity, plasticity and cognitive therapy: A neurological view of mental disorders. NEUROLOGÍA (ENGLISH EDITION) 2018. [DOI: 10.1016/j.nrleng.2017.02.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
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117
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Chockanathan U, DSouza AM, Abidin AZ, Schifitto G, Wismüller A. Identification and functional characterization of HIV-associated neurocognitive disorders with large-scale Granger causality analysis on resting-state functional MRI. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2018; 10575. [PMID: 30505063 DOI: 10.1117/12.2293888] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Resting-state functional MRI (rs-fMRI), coupled with advanced multivariate time-series analysis methods such as Granger causality, is a promising tool for the development of novel functional connectivity biomarkers of neurologic and psychiatric disease. Recently large-scale Granger causality (lsGC) has been proposed as an alternative to conventional Granger causality (cGC) that extends the scope of robust Granger causal analyses to high-dimensional systems such as the human brain. In this study, lsGC and cGC were comparatively evaluated on their ability to capture neurologic damage associated with HIV-associated neurocognitive disorders (HAND). Functional brain network models were constructed from rs-fMRI data collected from a cohort of HIV+ and HIV - subjects. Graph theoretic properties of the resulting networks were then used to train a support vector machine (SVM) model to predict clinically relevant parameters, such as HIV status and neuropsychometric (NP) scores. For the HIV+ /- classification task, lsGC, which yielded a peak area under the receiver operating characteristic curve (AUC) of 0.83, significantly outperformed cGC, which yielded a peak AUC of 0.61, at all parameter settings tested. For the NP score regression task, lsGC, with a minimum mean squared error (MSE) of 0.75, significantly outperformed cGC, with a minimum MSE of 0.84 (p < 0.001, one-tailed paired t-test). These results show that, at optimal parameter settings, lsGC is better able to capture functional brain connectivity correlates of HAND than cGC. However, given the substantial variation in the performance of the two methods at different parameter settings, particularly for the regression task, improved parameter selection criteria are necessary and constitute an area for future research.
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Affiliation(s)
| | - Adora M DSouza
- Department of Electrical Engineering, University of Rochester, NY, USA
| | - Anas Z Abidin
- Department of Biomedical Engineering, University of Rochester, NY, USA
| | | | - Axel Wismüller
- Department of Biochemistry and Biophysics, University of Rochester, NY, USA.,Department of Electrical Engineering, University of Rochester, NY, USA.,Department of Biomedical Engineering, University of Rochester, NY, USA.,Department of Imaging Sciences, University of Rochester, NY, USA.,Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany
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118
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Noonan MP, Mars RB, Sallet J, Dunbar RIM, Fellows LK. The structural and functional brain networks that support human social networks. Behav Brain Res 2018; 355:12-23. [PMID: 29471028 PMCID: PMC6152579 DOI: 10.1016/j.bbr.2018.02.019] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 11/29/2017] [Accepted: 02/15/2018] [Indexed: 10/26/2022]
Abstract
Social skills rely on a specific set of cognitive processes, raising the possibility that individual differences in social networks are related to differences in specific brain structural and functional networks. Here, we tested this hypothesis with multimodality neuroimaging. With diffusion MRI (DMRI), we showed that differences in structural integrity of particular white matter (WM) tracts, including cingulum bundle, extreme capsule and arcuate fasciculus were associated with an individual's social network size (SNS). A voxel-based morphology analysis demonstrated correlations between gray matter (GM) volume and SNS in limbic and temporal lobe regions. These structural changes co-occured with functional network differences. As a function of SNS, dorsomedial and dorsolateral prefrontal cortex showed altered resting-state functional connectivity with the default mode network (DMN). Finally, we integrated these three complementary methods, interrogating the relationship between social GM clusters and specific WM and resting-state networks (RSNs). Probabilistic tractography seeded in these GM nodes utilized the SNS-related WM pathways. Further, the spatial and functional overlap between the social GM clusters and the DMN was significantly closer than other control RSNs. These integrative analyses provide convergent evidence of the role of specific circuits in SNS, likely supporting the adaptive behavior necessary for success in extensive social environments.
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Affiliation(s)
- M P Noonan
- McGill University, Montreal Neurological Institute, 3801 Rue University, Montreal, H3A 2B4, Quebec, Canada; Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford, OX1 3UD, United Kingdom.
| | - R B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ, Nijmegen, The Netherlands
| | - J Sallet
- Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford, OX1 3UD, United Kingdom
| | - R I M Dunbar
- Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford, OX1 3UD, United Kingdom
| | - L K Fellows
- McGill University, Montreal Neurological Institute, 3801 Rue University, Montreal, H3A 2B4, Quebec, Canada
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119
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Traynor JM, Doyle-Thomas KAR, Hanford LC, Foster NE, Tryfon A, Hyde KL, Anagnostou E, Evans AC, Zwaigenbaum L, Hall GBC. Indices of repetitive behaviour are correlated with patterns of intrinsic functional connectivity in youth with autism spectrum disorder. Brain Res 2018; 1685:79-90. [PMID: 29453959 DOI: 10.1016/j.brainres.2018.02.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 01/11/2018] [Accepted: 02/05/2018] [Indexed: 12/29/2022]
Abstract
The purpose of the current study was to examine how repetitive behaviour in Autism Spectrum Disorder (ASD) is related to intrinsic functional connectivity patterns in a number of large-scale, neural networks. Resting-state fMRI scans from thirty subjects with ASD and thirty-two age-matched, typically developing control subjects were analysed. Seed-to-voxel and ROI-to-ROI functional connectivity analyses were used to examine resting-state connectivity in a number of cortical and subcortical neural networks. Bivariate correlation analysis was performed to examine the relationship between repetitive behaviour scores from the Repetitive Behaviour Scale - Revised and intrinsic functional connectivity in ASD subjects. Compared to control subjects, ASD subjects displayed marked over-connectivity of the thalamus with several cortical sensory processing areas, as well as over-connectivity of the basal ganglia with somatosensory and motor cortices. Within the ASD group, significant correlations were found between functional connectivity patterns and total RBS-R scores as well as one principal component analysis-derived score from the RBS-R. These results suggest that thalamocortical resting-state connectivity is altered in individuals with ASD, and that resting-state functional connectivity is associated with ASD symptomatology.
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Affiliation(s)
- J M Traynor
- McMaster University, Department of Psychology, Neuroscience & Behaviour, Hamilton, Ontario, Canada
| | - K A R Doyle-Thomas
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, University of Toronto, Toronto, Ontario, Canada
| | - L C Hanford
- McMaster University, Department of Psychology, Neuroscience & Behaviour, Hamilton, Ontario, Canada
| | - N E Foster
- International Laboratory for Brain Music and Sound (BRAMS), University of Montreal, Montreal, Quebec, Canada; Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - A Tryfon
- International Laboratory for Brain Music and Sound (BRAMS), University of Montreal, Montreal, Quebec, Canada; Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - K L Hyde
- International Laboratory for Brain Music and Sound (BRAMS), University of Montreal, Montreal, Quebec, Canada; Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - E Anagnostou
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, University of Toronto, Toronto, Ontario, Canada; Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada
| | - A C Evans
- Montreal Neurological Institute, Montreal, Quebec, Canada
| | - L Zwaigenbaum
- Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
| | - G B C Hall
- McMaster University, Department of Psychology, Neuroscience & Behaviour, Hamilton, Ontario, Canada.
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120
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Picci G, Gotts SJ, Scherf KS. A theoretical rut: revisiting and critically evaluating the generalized under/over-connectivity hypothesis of autism. Dev Sci 2018; 19:524-49. [PMID: 27412228 DOI: 10.1111/desc.12467] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Accepted: 05/28/2016] [Indexed: 11/29/2022]
Abstract
In 2004, two papers proposed that pervasive functional under-connectivity (Just et al., ) or a trade-off between excessive local connectivity at the cost of distal under-connectivity (Belmonte et al., ) characterizes atypical brain organization in autism. Here, we take stock of the most recent and rigorous functional and structural connectivity findings with a careful eye toward evaluating the extent to which they support these original hypotheses. Indeed, the empirical data do not support them. From rsfMRI studies in adolescents and adults, there is an emerging consensus regarding long-range functional connections indicating cortico-cortical under-connectivity, specifically involving the temporal lobes, combined with subcortical-cortical over-connectivity. In contrast, there is little to no consensus regarding local functional connectivity or findings from task-based functional connectivity studies. The structural connectivity data suggest that white matter tracts are pervasively weak, particularly in the temporal lobe. Together, these findings are revealing how deeply complex the story is regarding atypical neural network organization in autism. In other words, distance and strength of connectivity as individual factors or as interacting factors do not consistently explain the patterns of atypical neural connectivity in autism. Therefore, we make several methodological recommendations and highlight developmental considerations that will help researchers in the field cultivate new hypotheses about the nature and mechanisms of potentially aberrant functional and structural connectivity in autism.
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Affiliation(s)
- Giorgia Picci
- Department of Psychology, Pennsylvania State University, USA
| | - Stephen J Gotts
- Department of Psychology, Pennsylvania State University, USA
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121
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Sampaio A, Moreira PS, Osório A, Magalhães R, Vasconcelos C, Férnandez M, Carracedo A, Alegria J, Gonçalves ÓF, Soares JM. Altered functional connectivity of the default mode network in Williams syndrome: a multimodal approach. Dev Sci 2018; 19:686-95. [PMID: 27412230 DOI: 10.1111/desc.12443] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 03/24/2016] [Indexed: 11/30/2022]
Abstract
Resting state brain networks are implicated in a variety of relevant brain functions. Importantly, abnormal patterns of functional connectivity (FC) have been reported in several neurodevelopmental disorders. In particular, the Default Mode Network (DMN) has been found to be associated with social cognition. We hypothesize that the DMN may be altered in Williams syndrome (WS), a neurodevelopmental genetic disorder characterized by an unique cognitive and behavioral phenotype. In this study, we assessed the architecture of the DMN using fMRI in WS patients and typically developing matched controls (sex and age) in terms of FC and volumetry of the DMN. Moreover, we complemented the analysis with a functional connectome approach. After excluding participants due to movement artifacts (n = 3), seven participants with WS and their respective matched controls were included in the analyses. A decreased FC between the DMN regions was observed in the WS group when compared with the typically developing group. Specifically, we found a decreased FC in a posterior hub of the DMN including the precuneus, calcarine and the posterior cingulate of the left hemisphere. The functional connectome approach showed a focalized and global increased FC connectome in the WS group. The reduced FC of the posterior hub of the DMN in the WS group is consistent with immaturity of the brain FC patterns and may be associated with the singularity of their visual spatial phenotype.
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Affiliation(s)
- Adriana Sampaio
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Portugal
| | - Pedro Silva Moreira
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Ana Osório
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Portugal.,Social and Cognitive Neuroscience Lab, Post-Graduate Program on Developmental Disorders - Center for Biological and Health Sciences, Mackenzie Presbyterian University, São Paulo, Brazil
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | | | - Montse Férnandez
- Genetic Molecular Unit, Galician Public Foundation of Genomic Medicine, University of Santiago de Compostela, Spain
| | - Angel Carracedo
- Genetic Molecular Unit, Galician Public Foundation of Genomic Medicine, University of Santiago de Compostela, Spain
| | - Joana Alegria
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Portugal
| | - Óscar F Gonçalves
- Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Portugal.,Spaulding Neuromodulation Center, Department of Physical Medicine & Rehabilitation, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, USA.,Department of Applied Psychology, Bouvé College of Health Sciences, Northeastern University, USA
| | - José Miguel Soares
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Portugal.,ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
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122
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Venkatraghavan L, Bharadwaj S, Wourms V, Tan A, Jurkiewicz MT, Mikulis DJ, Crawley AP. Brain Resting-State Functional Connectivity Is Preserved Under Sevoflurane Anesthesia in Patients with Pervasive Developmental Disorders: A Pilot Study. Brain Connect 2018; 7:250-257. [PMID: 28443736 DOI: 10.1089/brain.2016.0448] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Functional connectivity studies play a huge role in understanding the relationship between the network connections and the behavioral phenotype of patients with pervasive developmental disorders (PDD). Some patients with PDD may not be able to tolerate the imaging procedure while they are awake, and, hence, they often need general anesthesia. General anesthesia is a confounding factor in functional imaging studies due to its effect on the functional connectivity. The objective of this study is to look at the resting-state functional connectivity (RS-FC) under sevoflurane anesthesia in patients with PDDs. Thirteen adults with PDD scheduled for magnetic resonance imaging (MRI) of the brain under general anesthesia were recruited for the study. Resting-state functional MRI (fMRI) scans were acquired at 1 minimum alveolar concentration (MAC) of sevoflurane. Spontaneous blood oxygenation level-dependent fluctuations were measured, and a seed-voxel analysis was done to identify the resting-state networks. Subjects' data were compared with data from 16 nonanesthetized healthy controls. Six networks (default mode network [DMN], executive control network [ECN], salience network [SN], auditory, visual, and sensorimotor) were investigated. At 1 MAC sevoflurane anesthesia, RS-FC was preserved in all the networks. Secondary analysis of connectivity showed a decrease in connectivity within the thalamus and an increase in DMN-ECN and DMN-SN cross-network connectivity in the anesthetized patient group compared to healthy controls. Previous reports suggested that even mild levels of anesthesia could reduce overall fluctuation levels in the major brain. However, our results provide strong evidence that most networks can sustain detectable levels of activity in patients with PDDs even under deep levels of anesthesia.
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Affiliation(s)
- Lakshmikumar Venkatraghavan
- 1 Department of Anesthesia and Pain Management, Toronto Western Hospital, University Health Network, University of Toronto , Toronto, Canada
| | - Suparna Bharadwaj
- 1 Department of Anesthesia and Pain Management, Toronto Western Hospital, University Health Network, University of Toronto , Toronto, Canada
| | - Vincent Wourms
- 2 Department of Anesthesia, University of Manitoba , Winnipeg, Canada
| | - Audrey Tan
- 3 Department of Anesthesia, St. George's Hospital NHS Foundation Trust , London, United Kingdom
| | - Michael T Jurkiewicz
- 4 Division of Neuroradiology, Department of Radiology, Hospital of the University of Pennsylvania , Philadelphia, Pennsylvania
| | - David J Mikulis
- 5 Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, University of Toronto , Toronto, Canada
| | - Adrian P Crawley
- 5 Joint Department of Medical Imaging and the Functional Neuroimaging Laboratory, University Health Network, University of Toronto , Toronto, Canada
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123
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Beker S, Foxe JJ, Molholm S. Ripe for solution: Delayed development of multisensory processing in autism and its remediation. Neurosci Biobehav Rev 2018; 84:182-192. [PMID: 29162518 PMCID: PMC6389331 DOI: 10.1016/j.neubiorev.2017.11.008] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 11/09/2017] [Accepted: 11/13/2017] [Indexed: 12/24/2022]
Abstract
Difficulty integrating inputs from different sensory sources is commonly reported in individuals with Autism Spectrum Disorder (ASD). Accumulating evidence consistently points to altered patterns of behavioral reactions and neural activity when individuals with ASD observe or act upon information arriving through multiple sensory systems. For example, impairments in the integration of seen and heard speech appear to be particularly acute, with obvious implications for interpersonal communication. Here, we explore the literature on multisensory processing in autism with a focus on developmental trajectories. While much remains to be understood, some consistent observations emerge. Broadly, sensory integration deficits are found in children with an ASD whereas these appear to be much ameliorated, or even fully recovered, in older teenagers and adults on the spectrum. This protracted delay in the development of multisensory processing raises the possibility of applying early intervention strategies focused on multisensory integration, to accelerate resolution of these functions. We also consider how dysfunctional cross-sensory oscillatory neural communication may be one key pathway to impaired multisensory processing in ASD.
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Affiliation(s)
- Shlomit Beker
- The Sheryl and Daniel R. Tishman Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, United States; Rose F. Kennedy Intellectual and Developmental Disabilities Research Center (IDDRC), Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, United States
| | - John J Foxe
- The Sheryl and Daniel R. Tishman Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, United States; Rose F. Kennedy Intellectual and Developmental Disabilities Research Center (IDDRC), Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, United States; The Ernest J. Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, United States
| | - Sophie Molholm
- The Sheryl and Daniel R. Tishman Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, United States; Rose F. Kennedy Intellectual and Developmental Disabilities Research Center (IDDRC), Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, United States; The Ernest J. Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, United States.
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124
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Demirtaş M, Deco G. Computational Models of Dysconnectivity in Large-Scale Resting-State Networks. COMPUTATIONAL PSYCHIATRY 2018. [DOI: 10.1016/b978-0-12-809825-7.00004-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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125
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Andrews DS, Marquand A, Ecker C, McAlonan G. Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder. Curr Top Behav Neurosci 2018; 40:413-436. [PMID: 29626339 DOI: 10.1007/7854_2018_47] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction and communication, as well as repetitive and restrictive behaviours. The etiological and phenotypic complexity of ASD has so far hindered the development of clinically useful biomarkers for the condition. Neuroimaging studies have been valuable in establishing a biological basis for ASD. Increasingly, neuroimaging has been combined with 'machine learning'-based pattern classification methods to make individual diagnostic predictions. Moving forward, the hope is that these techniques may not only facilitate the diagnostic process but may also aid in fractionating the ASD phenotype into more biologically homogeneous sub-groups, with defined pathophysiology, predictable outcomes and/or responses to targeted treatments and/or interventions. This review chapter will first introduce 'machine learning' and pattern recognition methods in general, with a focus on their application to diagnostic classification. It will highlight why such approaches to biomarker discovery may have advantages over more conventional analytical methods. Magnetic resonance imaging (MRI) findings of atypical brain structure, function and connectivity in ASD will be briefly reviewed before we describe how pattern recognition has been applied to generate predictive models for ASD. Last, we will discuss some limitations and pitfalls of pattern recognition analyses in ASD and consider how the field can advance beyond the prediction of binary outcomes.
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Affiliation(s)
- Derek Sayre Andrews
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioural Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA.,Department of Forensic and Neurodevelopmental Sciences, The Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Andre Marquand
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.,Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Christine Ecker
- Department of Forensic and Neurodevelopmental Sciences, The Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Universitätsklinikum Frankfurt am Main, Goethe-University Frankfurt am Main, Frankfurt, Germany
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Sciences, The Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. .,South London and Maudsley NHS Foundation Trust, London, UK.
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126
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Qin B, Wang L, Zhang Y, Cai J, Chen J, Li T. Enhanced Topological Network Efficiency in Preschool Autism Spectrum Disorder: A Diffusion Tensor Imaging Study. Front Psychiatry 2018; 9:278. [PMID: 29997534 PMCID: PMC6030375 DOI: 10.3389/fpsyt.2018.00278] [Citation(s) in RCA: 18] [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: 02/24/2018] [Accepted: 06/07/2018] [Indexed: 12/27/2022] Open
Abstract
Background: The functional mechanism behind autism spectrum disorder (ASD) is not clear, but it is related to a brain connectivity disorder. Previous studies have found that functional brain connectivity of ASD is linked to both increased connections and weakened connections, and the inconsistencies in functional brain connectivity may be related to age. The functional connectivity in adolescents and adults with ASD is generally less than in age-matched controls; functional connectivity in younger children with the disorder appears to be higher. As the basis of the functional network, the structural network is less studied. This study intends to further study the pathogenesis of ASD by analyzing the white matter network of ASD preschool children. Materials and Methods: In this study, Diffusion Tensor Imaging (DTI) was used to scan preschool children (aged 2-6 years, 39 children with ASD, 19 children as controls), and graph theory was used for analysis. Result: Enhanced topological network efficiency was found in the preschool children with ASD. A higher nodal efficiency was found in the left precuneus, thalamus, and bilateral superior parietal cortex, and the nodal efficiency of the left precuneus was positively associated with the severity of ASD. Conclusion: Our research shows the white matter network efficiency of preschoolers with ASD. It supports the theory of excessive early brain growth in ASD, and it shows left brain lateralization. It opens the way for new research perspectives of children with ASD.
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Affiliation(s)
- Bin Qin
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Longlun Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yun Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Jinhua Cai
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Chen
- Children Nutrition Research Center, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Chongqing, China
| | - Tingyu Li
- Children Nutrition Research Center, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Chongqing, China
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127
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Linke AC, Jao Keehn RJ, Pueschel EB, Fishman I, Müller RA. Children with ASD show links between aberrant sound processing, social symptoms, and atypical auditory interhemispheric and thalamocortical functional connectivity. Dev Cogn Neurosci 2018; 29:117-126. [PMID: 28223033 PMCID: PMC5664206 DOI: 10.1016/j.dcn.2017.01.007] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 11/04/2016] [Accepted: 01/06/2017] [Indexed: 01/31/2023] Open
Abstract
Autism spectrum disorder (ASD) is a complex and prevalent neurodevelopmental disorder characterized by social and communicative deficits, as well as repetitive behaviors and atypical sensitivity to sensory stimulation. Alterations in network connectivity are widely recognized, but their interplay with social and sensory symptoms remains largely unclear. Here, functional magnetic resonance imaging and diagnostic and behavioral assessments were used in a cohort of children and adolescents with ASD (n=40) and matched typically developing (TD, n=38) controls to examine the relation between auditory processing, interhemispheric and thalamocortical network connectivity, and social-behavioral symptom severity. We found that atypical processing of sounds was related to social, cognitive, and communicative impairments. Additionally, severity of sensory processing deficits and lower verbal IQ were related to reduced interhemispheric connectivity of auditory cortices in ASD. Increased connectivity between the thalamus and auditory cortex in ASD, however, was associated with reduced cognitive and behavioral symptomatology, suggesting that thalamocortical overconnectivity might reflect a compensatory mechanism in ASD. These findings provide novel evidence for links between auditory sensory deficits and impairments in social interaction and communication.
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Affiliation(s)
- Annika C Linke
- Brain Development Imaging Laboratory, Department of Psychology, San Diego State University, 6363 Alvarado CT, Suite #200, San Diego, CA, 92120, USA.
| | - R Joanne Jao Keehn
- Brain Development Imaging Laboratory, Department of Psychology, San Diego State University, 6363 Alvarado CT, Suite #200, San Diego, CA, 92120, USA.
| | - Ellyn B Pueschel
- Brain Development Imaging Laboratory, Department of Psychology, San Diego State University, 6363 Alvarado CT, Suite #200, San Diego, CA, 92120, USA
| | - Inna Fishman
- Brain Development Imaging Laboratory, Department of Psychology, San Diego State University, 6363 Alvarado CT, Suite #200, San Diego, CA, 92120, USA
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratory, Department of Psychology, San Diego State University, 6363 Alvarado CT, Suite #200, San Diego, CA, 92120, USA
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128
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Lajiness-O'Neill R, Brennan JR, Moran JE, Richard AE, Flores AM, Swick C, Goodcase R, Andersen T, McFarlane K, Rusiniak K, Kovelman I, Wagley N, Ugolini M, Albright J, Bowyer SM. Patterns of altered neural synchrony in the default mode network in autism spectrum disorder revealed with magnetoencephalography (MEG): Relationship to clinical symptomatology. Autism Res 2017; 11:434-449. [PMID: 29251830 DOI: 10.1002/aur.1908] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 11/05/2017] [Accepted: 11/28/2017] [Indexed: 01/02/2023]
Abstract
Disrupted neural synchrony may be a primary electrophysiological abnormality in autism spectrum disorders (ASD), altering communication between discrete brain regions and contributing to abnormalities in patterns of connectivity within identified neural networks. Studies exploring brain dynamics to comprehensively characterize and link connectivity to large-scale cortical networks and clinical symptoms are lagging considerably. Patterns of neural coherence within the Default Mode Network (DMN) and Salience Network (SN) during resting state were investigated in 12 children with ASD (MAge = 9.2) and 13 age and gender-matched neurotypicals (NT) (MAge = 9.3) with magnetoencephalography. Coherence between 231 brain region pairs within four frequency bands (theta (4-7 Hz), alpha, (8-12 Hz), beta (13-30 Hz), and gamma (30-80 Hz)) was calculated. Relationships between neural coherence and social functioning were examined. ASD was characterized by lower synchronization across all frequencies, reaching clinical significance in the gamma band. Lower gamma synchrony between fronto-temporo-parietal regions was observed, partially consistent with diminished default mode network (DMN) connectivity. Lower gamma coherence in ASD was evident in cross-hemispheric connections between: angular with inferior/middle frontal; middle temporal with middle/inferior frontal; and within right-hemispheric connections between angular, middle temporal, and inferior/middle frontal cortices. Lower gamma coherence between left angular and left superior frontal, right inferior/middle frontal, and right precuneus and between right angular and inferior/middle frontal cortices was related to lower social/social-communication functioning. Results suggest a pattern of lower gamma band coherence in a subset of regions within the DMN in ASD (angular and middle temporal cortical areas) related to lower social/social-communicative functioning. Autism Res 2018, 11: 434-449. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY Communication between different areas of the brain was observed in children with ASD and neurotypical children while awake, but not working on a task. Magnetoencephalography was used to measure tiny magnetic fields naturally generated via brain activity. The brains of children with ASD showed less communication between areas that are important for social information processing compared to the brains of neurotypical children. The amount of communication between these areas was associated with social and social communication difficulties.
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Affiliation(s)
- Renée Lajiness-O'Neill
- Eastern Michigan University, Ypsilanti, Michigan.,Center for Human Growth and Development, University of Michigan, Ann Arbor, Michigan
| | | | | | | | | | - Casey Swick
- Eastern Michigan University, Ypsilanti, Michigan
| | | | | | | | | | - Ioulia Kovelman
- Center for Human Growth and Development, University of Michigan, Ann Arbor, Michigan.,Department of Psychology, Ann Arbor, Michigan
| | - Neelima Wagley
- Center for Human Growth and Development, University of Michigan, Ann Arbor, Michigan.,Department of Psychology, Ann Arbor, Michigan
| | | | | | - Susan M Bowyer
- University of Massachusetts, Amherst, Massachusetts.,Wayne State University, Detroit, Michigan.,Oakland University, Rochester, Michigan
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129
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Cardon GJ, Hepburn S, Rojas DC. Structural Covariance of Sensory Networks, the Cerebellum, and Amygdala in Autism Spectrum Disorder. Front Neurol 2017; 8:615. [PMID: 29230189 PMCID: PMC5712069 DOI: 10.3389/fneur.2017.00615] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 11/03/2017] [Indexed: 11/13/2022] Open
Abstract
Sensory dysfunction is a core symptom of autism spectrum disorder (ASD), and abnormalities with sensory responsivity and processing can be extremely debilitating to ASD patients and their families. However, relatively little is known about the underlying neuroanatomical and neurophysiological factors that lead to sensory abnormalities in ASD. Investigation into these aspects of ASD could lead to significant advancements in our general knowledge about ASD, as well as provide targets for treatment and inform diagnostic procedures. Thus, the current study aimed to measure the covariation of volumes of brain structures (i.e., structural magnetic resonance imaging) that may be involved in abnormal sensory processing, in order to infer connectivity of these brain regions. Specifically, we quantified the structural covariation of sensory-related cerebral cortical structures, in addition to the cerebellum and amygdala by computing partial correlations between the structural volumes of these structures. These analyses were performed in participants with ASD (n = 36), as well as typically developing peers (n = 32). Results showed decreased structural covariation between sensory-related cortical structures, especially between the left and right cerebral hemispheres, in participants with ASD. In contrast, these same participants presented with increased structural covariation of structures in the right cerebral hemisphere. Additionally, sensory-related cerebral structures exhibited decreased structural covariation with functionally identified cerebellar networks. Also, the left amygdala showed significantly increased structural covariation with cerebral structures related to visual processing. Taken together, these results may suggest several patterns of altered connectivity both within and between cerebral cortices and other brain structures that may be related to sensory processing.
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Affiliation(s)
- Garrett J Cardon
- Department of Psychology, Colorado State University, Fort Collins, CO, United States
| | - Susan Hepburn
- Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO, United States
| | - Donald C Rojas
- Department of Psychology, Colorado State University, Fort Collins, CO, United States
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130
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Henry TR, Dichter GS, Gates K. Age and Gender Effects on Intrinsic Connectivity in Autism Using Functional Integration and Segregation. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 3:414-422. [PMID: 29735152 DOI: 10.1016/j.bpsc.2017.10.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 10/29/2017] [Accepted: 10/30/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND The objective of this study was to examine intrinsic whole-brain functional connectivity in autism spectrum disorder (ASD) using the framework of functional segregation and integration. Emphasis was given to potential gender and developmental effects as well as identification of specific networks that may contribute to the global results. METHODS We leveraged an open data resource (N = 1587) of resting-state functional magnetic resonance imaging data in the Autism Brain Imaging Data Exchange (ABIDE) initiative, combining data from more than 2100 unique cross-sectional datasets in ABIDE I and ABIDE II collected at different sites. Modularity and global efficiency were utilized to assess functional segregation and integration, respectively. A meta-analytic approach for handling site differences was used. The effects of age, gender, and diagnostic category on segregation and integration were assessed using linear regression. RESULTS Modularity decreased nonlinearly in the ASD group with age, as evidenced by an increase and then decrease over development. Global efficiency had an opposite relationship with age by first decreasing and then increasing in the ASD group. Both modularity and global efficiency remained largely stable in the typically developing control group during development, representing a significantly different effect than seen in the ASD group. Age effects on modularity were localized to the somatosensory network. Finally, a marginally significant interaction between age, gender, and diagnostic category was found for modularity. CONCLUSIONS Our results support prior work that suggested a quadratic effect of age on brain development in ASD, while providing new insights about the specific characteristics of developmental and gender effects on intrinsic connectivity in ASD.
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Affiliation(s)
- Teague Rhine Henry
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
| | - Gabriel S Dichter
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Kathleen Gates
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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131
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Duan X, Chen H, He C, Long Z, Guo X, Zhou Y, Uddin LQ, Chen H. Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism. Prog Neuropsychopharmacol Biol Psychiatry 2017; 79:434-441. [PMID: 28779909 DOI: 10.1016/j.pnpbp.2017.07.027] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Revised: 07/28/2017] [Accepted: 07/29/2017] [Indexed: 12/17/2022]
Abstract
Although evidence is accumulating that autism spectrum disorder (ASD) is associated with disruption of functional connections between and within brain networks, it remains largely unknown whether these abnormalities are related to specific frequency bands. To address this question, network contingency analysis was performed on brain functional connectomes obtained from 213 adolescent participants across nine sites in the Autism Brain Imaging Data Exchange (ABIDE) multisite sample, to determine the disrupted connections between and within seven major cortical networks in adolescents with ASD at Slow-5, Slow-4 and Slow-3 frequency bands and further assess whether the aberrant intra- and inter-network connectivity varied as a function of ASD symptoms. Overall under-connectivity within and between large-scale intrinsic networks in ASD was revealed across the three frequency bands. Specifically, decreased connectivity strength within the default mode network (DMN), between DMN and visual network (VN), ventral attention network (VAN), and between dorsal attention network (DAN) and VAN was observed in the lower frequency band (slow-5, slow-4), while decreased connectivity between limbic network (LN) and frontal-parietal network (FPN) was observed in the higher frequency band (slow-3). Furthermore, weaker connectivity within and between specific networks correlated with poorer communication and social interaction skills in the slow-5 band, uniquely. These results demonstrate intrinsic under-connectivity within and between multiple brain networks within predefined frequency bands in ASD, suggesting that frequency-related properties underlie abnormal brain network organization in the disorder.
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Affiliation(s)
- Xujun Duan
- Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, PR China.
| | - Heng Chen
- Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Changchun He
- Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Zhiliang Long
- Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Xiaonan Guo
- Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Yuanyue Zhou
- Mental Health Center, Zhejiang University School of Medicine, Hangzhou Seventh People's Hospital, Hangzhou, PR China
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, United States
| | - Huafu Chen
- Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, PR China.
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132
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Linke AC, Olson L, Gao Y, Fishman I, Müller RA. Psychotropic medication use in autism spectrum disorders may affect functional brain connectivity. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 2:518-527. [PMID: 29104944 PMCID: PMC5667652 DOI: 10.1016/j.bpsc.2017.06.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Prescription of psychotropic medications is common in autism spectrum disorders (ASDs), either off-label or to treat comorbid conditions such as ADHD or depression. Psychotropic medications are intended to alter brain function. Yet, studies investigating the functional brain organization in ASDs rarely take medication usage into account. This could explain some of the inconsistent findings of atypical brain network connectivity reported in the autism literature. METHODS The current study tested whether functional connectivity patterns, as assessed with functional magnetic resonance imaging (fMRI), differed in a cohort of 49 children and adolescents with ASDs based on psychotropic medication status, and in comparison with 50 matched typically developing (TD) participants. Twenty-five participants in the ASD group (51%) reported current psychotropic medication usage, including stimulants, antidepressants, antipsychotics, and anxiolytics. Age, IQ, head motion, and ASD symptom severity did not differ between groups. Whole-brain functional connectivity between 132 regions of interest was assessed. RESULTS Different functional connectivity patterns were identified in the ASD group taking psychotropic medications (ASD-on), as compared to the TD group and the ASD subgroup not using psychotropic medications (ASD-none). The ASD-on group showed distinct underconnectivity between the cerebellum and basal ganglia but cortico-cortical overconnectivity compared to the TD group. Cortical underconnectivity relative to the TD pattern, on the other hand, was pronounced in the ASD-none group. CONCLUSIONS These results suggest that psychotropic medications may affect functional connectivity, and that medication status should be taken into consideration when studying brain function in autism.
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Affiliation(s)
- Annika C. Linke
- Brain Development Imaging Laboratory, Department of Psychology San Diego State University, 6363 Alvarado CT, Suite 200, San Diego, CA 92120, USA
| | - Lindsay Olson
- Brain Development Imaging Laboratory, Department of Psychology San Diego State University, 6363 Alvarado CT, Suite 200, San Diego, CA 92120, USA
- San Diego State University/UC San Diego Joint Doctoral Program in Clinical Psychology, 6363 Alvarado CT, Suite 103, San Diego, CA 92120, USA
| | - Yangfeifei Gao
- Brain Development Imaging Laboratory, Department of Psychology San Diego State University, 6363 Alvarado CT, Suite 200, San Diego, CA 92120, USA
- San Diego State University/UC San Diego Joint Doctoral Program in Clinical Psychology, 6363 Alvarado CT, Suite 103, San Diego, CA 92120, USA
| | - Inna Fishman
- Brain Development Imaging Laboratory, Department of Psychology San Diego State University, 6363 Alvarado CT, Suite 200, San Diego, CA 92120, USA
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratory, Department of Psychology San Diego State University, 6363 Alvarado CT, Suite 200, San Diego, CA 92120, USA
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133
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An altered scaffold for information processing: Cognitive control development in adolescents with autism. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 2:464-475. [PMID: 28924621 DOI: 10.1016/j.bpsc.2017.06.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
We investigated how cognitive neuroscientific studies during the last decade have advanced understanding of cognitive control from adolescence to young adulthood in individuals with autism spectrum disorder (ASD). To do so, we conducted a selective review of the larger structural, resting state, and diffusion imaging studies of brain regions and networks related to cognitive control that have been conducted since 2007 in individuals with ASD and typical development (TYP) ages 10 to 30 years that examined how these regions and networks support behavioral and task-based fMRI performance on tasks assessing cognitive control during this period. Longitudinal structural studies reveal overgrowth of the anterior cingulate (ACC) and slower white matter development in the parietal cortex in adolescents with ASD versus TYP. Cross-sectional studies of the salience, executive control and default mode resting state functional connectivity networks, which mediate cognitive control, demonstrate patterns of connectivity that differ from TYP through adolescence. Finally, white matter tracts underlying these control-related brain regions continue to show reduced diffusion properties compared to TYP. It is thus not surprising that cognitive control tasks performance improves less during adolescence in ASD versus TYP. This review illustrates that a cognitive neuroscientific approach produces insights about the mechanisms of persistent cognitive control deficits in individuals with ASD from adolescence into young adulthood not apparent with neuropsychological methods alone, and draws attention to the great need for longitudinal studies of this period in those with ASD. Further investigation of ACC and fronto-parietal neural circuits may help specify pathophysiology and treatment options.
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134
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Padmanabhan A, Lynch CJ, Schaer M, Menon V. The Default Mode Network in Autism. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 2:476-486. [PMID: 29034353 PMCID: PMC5635856 DOI: 10.1016/j.bpsc.2017.04.004] [Citation(s) in RCA: 154] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Autism spectrum disorder (ASD) is characterized by deficits in social communication and interaction. Since its discovery as a major functional brain system, the default mode network (DMN) has been implicated in a number of psychiatric disorders, including ASD. Here we review converging multimodal evidence for DMN dysfunction in the context of specific components of social cognitive dysfunction in ASD: 'self-referential processing' - the ability to process social information relative to oneself and 'theory of mind' or 'mentalizing' - the ability to infer the mental states such as beliefs, intentions, and emotions of others. We show that altered functional and structural organization of the DMN, and its atypical developmental trajectory, are prominent neurobiological features of ASD. We integrate findings on atypical cytoarchitectonic organization and imbalance in excitatory-inhibitory circuits, which alter local and global brain signaling, to scrutinize putative mechanisms underlying DMN dysfunction in ASD. Our synthesis of the extant literature suggests that aberrancies in key nodes of the DMN and their dynamic functional interactions contribute to atypical integration of information about the self in relation to 'other', as well as impairments in the ability to flexibly attend to socially relevant stimuli. We conclude by highlighting open questions for future research.
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Affiliation(s)
- Aarthi Padmanabhan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA
| | | | - Marie Schaer
- University of Geneva, Department of Psychiatry, Geneva, Switzerland
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA
- Program in Neuroscience, Stanford University School of Medicine, Stanford, CA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
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135
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Yerys BE, Herrington JD, Satterthwaite TD, Guy L, Schultz RT, Bassett DS. Globally weaker and topologically different: resting-state connectivity in youth with autism. Mol Autism 2017; 8:39. [PMID: 28770039 PMCID: PMC5530457 DOI: 10.1186/s13229-017-0156-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 06/28/2017] [Indexed: 01/18/2023] Open
Abstract
Background There is a lack of agreement about functional connectivity differences in individuals with autism spectrum disorder (ASD). Studies using absolute strength have found reduced connectivity, while those using relative strength––a measure of system topology––reveal mostly enhanced connectivity. We hypothesized that mixed findings may be driven by the metric of functional connectivity. Methods Resting-state echo planar 3 T functional magnetic resonance imaging scans were acquired on a Siemens Verio Scanner from 6 to 17-year-old youth with ASD (n = 81) and a matched typically developing control group (n = 82). All functional time series data were preprocessed using a confound regression procedure that has been previously validated in large-scale developmental datasets. It has also been shown to be highly effective at reducing the influence of motion artifact on connectivity data. We extracted time series data from a 333-node parcellation scheme, which was previously mapped to 13 functional systems. A Pearson’s correlation was calculated and transformed to Fisher’s z between every pair of nodes to create a weighted 333 × 333 adjacency matrix. Mean absolute functional connectivity strength was the mean Fisher’s z of the matrix. Relative functional connectivity was corrected for individual differences in mean absolute functional connectivity (i.e., each connection in the matrix was divided by their mean z), and functional connectivity was evaluated within and across each of the functional networks in the parcellation scheme. Results Absolute functional connectivity strength was lower in ASD, and lower functional connectivity was correlated with greater ASD symptom severity. Relative functional connectivity was higher for the ASD group in the ventral attention and retrosplenial-temporal systems, with lower cross-system functional connectivity between the ventral attention and somatomotor-mouth systems. Functional connectivity within the ventral attention and retro-splenial systems correlated significantly with ASD symptom severity. Conclusions Within a context of globally weaker functional connectivity, youth with ASD have an atypical topology of brain systems that support social perception and communication. This study clarifies the mixed results reported previously and demonstrates that the functional connectivity metric influences the observed direction of functional connectivity differences for individuals with ASD. Electronic supplementary material The online version of this article (doi:10.1186/s13229-017-0156-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Benjamin E Yerys
- Center for Autism Research and Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, 3535 Market Street, Ste 860, Philadelphia, PA 19104 USA.,Department of Psychiatry, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA USA
| | - John D Herrington
- Center for Autism Research and Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, 3535 Market Street, Ste 860, Philadelphia, PA 19104 USA.,Department of Psychiatry, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA USA
| | - Lisa Guy
- Center for Autism Research and Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, 3535 Market Street, Ste 860, Philadelphia, PA 19104 USA.,Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Robert T Schultz
- Center for Autism Research and Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, 3535 Market Street, Ste 860, Philadelphia, PA 19104 USA.,Department of Psychiatry, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA USA.,Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA USA
| | - Danielle S Bassett
- Departments of Bioengineering and Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
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136
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Funakoshi Y, Harada M, Otsuka H, Mori K, Ito H, Iwanaga T. Default mode network abnormalities in children with autism spectrum disorder detected by resting-state functional magnetic resonance imaging. THE JOURNAL OF MEDICAL INVESTIGATION 2017; 63:204-8. [PMID: 27644559 DOI: 10.2152/jmi.63.204] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
PURPOSE The purpose of this study was to investigate changes in the functional connectivity of the default mode network (DMN) in normal aging and in children with autistic spectrum disorder (ASD) by using resting-state functional magnetic resonance imaging (rsfMRI) and independent component analysis. METHODS Thirty-one healthy controls (HC) in four age groups (1-3, 4-8, 20-29, and 50-59 years) and 14 childhood ASD cases (1-8 years of age) were examined by rsfMRI echo-planar imaging on a clinical 3-T MRI scanner. Imaging of all children (1-8 years) was conducted under sedation, while adults were scanned in the awake state with eyes closed. RESULTS The regions of DMN functional connectivity in the bilateral inferior parietal lobule and posterior cingulate cortex were smaller in HC children than in HC adults, and smaller in the ASD group than in the HC children. CONCLUSION It is possible to observe developmental and pathological changes in the DMN by rsfMRI. Reduced DMN functional connectivity in children may be a useful biomarker for ASD diagnosis. J. Med. Invest. 63: 204-208, August, 2016.
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Affiliation(s)
- Yasuhiro Funakoshi
- Department of Medical Imaging, Institute of Biomedical Sciences, University of Tokushima Graduate School
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137
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Kana RK, Sartin EB, Stevens C, Deshpande HD, Klein C, Klinger MR, Klinger LG. Neural networks underlying language and social cognition during self-other processing in Autism spectrum disorders. Neuropsychologia 2017; 102:116-123. [PMID: 28619530 DOI: 10.1016/j.neuropsychologia.2017.06.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 04/07/2017] [Accepted: 06/10/2017] [Indexed: 10/19/2022]
Abstract
The social communication impairments defining autism spectrum disorders (ASD) may be built upon core deficits in perspective-taking, language processing, and self-other representation. Self-referential processing entails the ability to incorporate self-awareness, self-judgment, and self-memory in information processing. Very few studies have examined the neural bases of integrating self-other representation and semantic processing in individuals with ASD. The main objective of this functional MRI study is to examine the role of language and social brain networks in self-other processing in young adults with ASD. Nineteen high-functioning male adults with ASD and 19 age-sex-and-IQ-matched typically developing (TD) control participants made "yes" or "no" judgments of whether an adjective, presented visually, described them (self) or their favorite teacher (other). Both ASD and TD participants showed significantly increased activity in the medial prefrontal cortex (MPFC) during self and other processing relative to letter search. Analyses of group differences revealed significantly reduced activity in left inferior frontal gyrus (LIFG), and left inferior parietal lobule (LIPL) in ASD participants, relative to TD controls. ASD participants also showed significantly weaker functional connectivity of the anterior cingulate cortex (ACC) with several brain areas while processing self-related words. The LIFG and IPL are important regions functionally at the intersection of language and social roles; reduced recruitment of these regions in ASD participants may suggest poor level of semantic and social processing. In addition, poor connectivity of the ACC may suggest the difficulty in meeting the linguistic and social demands of this task in ASD. Overall, this study provides new evidence of the altered recruitment of the neural networks underlying language and social cognition in ASD.
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Affiliation(s)
- Rajesh K Kana
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Emma B Sartin
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Carl Stevens
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | - Mark R Klinger
- Department of Allied Health Sciences, University of North Carolina, Chapel Hill, NC, USA
| | - Laura Grofer Klinger
- Department of Psychiatry, TEACCH Autism Program, University of North Carolina, Chapel Hill, NC, USA
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138
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Thompson WH, Brantefors P, Fransson P. From static to temporal network theory: Applications to functional brain connectivity. Netw Neurosci 2017; 1:69-99. [PMID: 29911669 PMCID: PMC5988396 DOI: 10.1162/netn_a_00011] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 03/29/2017] [Indexed: 11/25/2022] Open
Abstract
Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain's network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.
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Affiliation(s)
| | - Per Brantefors
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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139
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Licari MK, Reynolds JE. Understanding Performance Variability in Developmental Coordination Disorder: What Does It All Mean? CURRENT DEVELOPMENTAL DISORDERS REPORTS 2017. [DOI: 10.1007/s40474-017-0112-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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140
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Sadeghi M, Khosrowabadi R, Bakouie F, Mahdavi H, Eslahchi C, Pouretemad H. Screening of autism based on task-free fMRI using graph theoretical approach. Psychiatry Res Neuroimaging 2017; 263:48-56. [PMID: 28324694 DOI: 10.1016/j.pscychresns.2017.02.004] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Revised: 01/30/2017] [Accepted: 02/09/2017] [Indexed: 01/07/2023]
Abstract
Studies on autism spectrum disorder (ASD) have indicated several dysfunctions in the structure, and functional organization of the brain. However, findings have not been established as a general diagnostic tool yet. In this regard, current study proposed an automatic screening method for recognition of ASDs from healthy controls (HCs) based on their brain functional abnormalities. In this paradigm, brain functional networks of 60 adolescent and young adult males (29 ASDs and 31 HCs) were estimated from subjects' task-free fMRI data. Then, autism screening was developed based on characteristics of the functional networks using the following steps: A) local and global parameters of the brain functional network were calculated using graph theory. B) network parameters of the ASDs were statistically compared to the HCs. C) significantly altered parameters were used as input features of the screening system. D) performance of the system was verified using various classification techniques. The support vector machine showed superiority to others with an accuracy of 92%. Subsequently, reliability of the results was examined using an independent dataset including 20 ASDs and 20 HCs. Our findings suggest that local parameters of the brain functional network, despite the individual variability, can potentially be used for autism screening.
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Affiliation(s)
- Masoumeh Sadeghi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran; Department of Computer Sciences, Faculty of Mathematics, Shahid Beheshti University, Tehran, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran.
| | - Fatemeh Bakouie
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Hoda Mahdavi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Changiz Eslahchi
- Department of Computer Sciences, Faculty of Mathematics, Shahid Beheshti University, Tehran, Iran; School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Hamidreza Pouretemad
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran; Faculty of Psychology and Educational Sciences, Shahid Beheshti University, Tehran, Iran
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141
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142
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Lubrini G, Martín-Montes A, Díez-Ascaso O, Díez-Tejedor E. Brain disease, connectivity, plasticity and cognitive therapy: A neurological view of mental disorders. Neurologia 2017; 33:187-191. [PMID: 28454990 DOI: 10.1016/j.nrl.2017.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 02/03/2017] [Accepted: 02/07/2017] [Indexed: 10/19/2022] Open
Abstract
INTRODUCTION Our conception of the mind-brain relationship has evolved from the traditional idea of dualism to current evidence that mental functions result from brain activity. This paradigm shift, combined with recent advances in neuroimaging, has led to a novel definition of brain functioning in terms of structural and functional connectivity. The purpose of this literature review is to describe the relationship between connectivity, brain lesions, cerebral plasticity, and functional recovery. DEVELOPMENT Assuming that brain function results from the organisation of the entire brain in networks, brain dysfunction would be a consequence of altered brain network connectivity. According to this approach, cognitive and behavioural impairment following brain damage result from disrupted functional organisation of brain networks. However, the dynamic and versatile nature of these circuits makes recovering brain function possible. Cerebral plasticity allows for functional reorganisation leading to recovery, whether spontaneous or resulting from cognitive therapy, after brain disease. CONCLUSIONS Current knowledge of brain connectivity and cerebral plasticity provides new insights into normal brain functioning, the mechanisms of brain damage, and functional recovery, which in turn serve as the foundations of cognitive therapy.
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Affiliation(s)
- G Lubrini
- Servicio de Neurología, Centro de Ictus, Hospital Universitario La Paz, Universidad Autónoma de Madrid, Área de Neurociencias, Instituto de Investigación IdiPAZ, Madrid, España
| | - A Martín-Montes
- Servicio de Neurología, Centro de Ictus, Hospital Universitario La Paz, Universidad Autónoma de Madrid, Área de Neurociencias, Instituto de Investigación IdiPAZ, Madrid, España
| | - O Díez-Ascaso
- Servicio de Neurología, Centro de Ictus, Hospital Universitario La Paz, Universidad Autónoma de Madrid, Área de Neurociencias, Instituto de Investigación IdiPAZ, Madrid, España
| | - E Díez-Tejedor
- Servicio de Neurología, Centro de Ictus, Hospital Universitario La Paz, Universidad Autónoma de Madrid, Área de Neurociencias, Instituto de Investigación IdiPAZ, Madrid, España.
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143
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Igelström KM, Webb TW, Graziano MSA. Functional Connectivity Between the Temporoparietal Cortex and Cerebellum in Autism Spectrum Disorder. Cereb Cortex 2017; 27:2617-2627. [PMID: 27073219 DOI: 10.1093/cercor/bhw079] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
The neural basis of autism spectrum disorder (ASD) is not yet understood. ASD is marked by social deficits and is strongly associated with cerebellar abnormalities. We studied the organization and cerebellar connectivity of the temporoparietal junction (TPJ), an area that plays a crucial role in social cognition. We applied localized independent component analysis to resting-state fMRI data from autistic and neurotypical adolescents to yield an unbiased parcellation of the bilateral TPJ into 11 independent components (ICs). A comparison between neurotypical and autistic adolescents showed that the organization of the TPJ was not significantly altered in ASD. Second, we used the time courses of the TPJ ICs as spatially unbiased "seeds" for a functional connectivity analysis applied to voxels within the cerebellum. We found that the cerebellum contained a fine-grained, lateralized map of the TPJ. The connectivity of the TPJ subdivisions with cerebellar zones showed one striking difference in ASD. The right dorsal TPJ showed markedly less connectivity with the left Crus II. Disturbed cerebellar input to this key region for cognition and multimodal integration may contribute to social deficits in ASD. The findings might also suggest that the right TPJ and/or left Crus II are potential targets for noninvasive brain stimulation therapies.
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Affiliation(s)
- Kajsa M Igelström
- Princeton Neuroscience Institute.,Department of Psychology, Princeton University, Princeton, NJ 08544, USA
| | - Taylor W Webb
- Princeton Neuroscience Institute.,Department of Psychology, Princeton University, Princeton, NJ 08544, USA
| | - Michael S A Graziano
- Princeton Neuroscience Institute.,Department of Psychology, Princeton University, Princeton, NJ 08544, USA
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144
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Chen S, Xing Y, Kang J. Latent and Abnormal Functional Connectivity Circuits in Autism Spectrum Disorder. Front Neurosci 2017; 11:125. [PMID: 28377688 PMCID: PMC5359252 DOI: 10.3389/fnins.2017.00125] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 02/28/2017] [Indexed: 12/22/2022] Open
Abstract
Autism spectrum disorder (ASD) is associated with disrupted brain networks. Neuroimaging techniques provide noninvasive methods of investigating abnormal connectivity patterns in ASD. In the present study, we compare functional connectivity networks in people with ASD with those in typical controls, using neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE) project. Specifically, we focus on the characteristics of intrinsic functional connectivity based on data collected by resting-state functional magnetic resonance imaging (rs-fMRI). Our aim was to identify disrupted brain connectivity patterns across all networks, instead of in individual edges, by using advanced statistical methods. Unlike many brain connectome studies, in which networks are prespecified before the edge connectivity in each network is compared between clinical groups, we detected the latent differentially expressed networks automatically. Our network-level analysis identified abnormal connectome networks that (i) included a high proportion of edges that were differentially expressed between people with ASD and typical controls; and (ii) showed highly-organized graph topology. These findings provide new insight into the study of the underlying neuropsychiatric mechanism of ASD.
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Affiliation(s)
- Shuo Chen
- Department of Epidemiology and Biostatistics, University of Maryland College Park, MD USA
| | - Yishi Xing
- Department of Epidemiology and Biostatistics, University of Maryland College Park, MD USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan Ann Arbor, MI, USA
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145
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Havlík M. From Anomalies to Essential Scientific Revolution? Intrinsic Brain Activity in the Light of Kuhn's Philosophy of Science. Front Syst Neurosci 2017; 11:7. [PMID: 28293181 PMCID: PMC5328955 DOI: 10.3389/fnsys.2017.00007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 02/13/2017] [Indexed: 01/04/2023] Open
Abstract
The first step toward a modern understanding of fMRI resting brain activity was made by Bharat Biswal in 1995. This surprising, and at first rejected, discovery is now associated with many resting state networks, notably the famous default mode network (DMN). Resting state activity and DMN significantly reassessed our traditional beliefs and conventions about the functioning of the brain. For the majority of the twentieth century, neuroscientists assumed that the brain is mainly the "reactive engine" to the environment operating mostly through stimulation. This "reactive convention" was very influential and convenient for the goals of twentieth century neuroscience-non-invasive functional localization based on stimulation. Largely unchallenged, "reactive convention" determined the direction of scientific research for a long time and became the "reactive paradigm" of the twentieth century. Resting state activity brought knowledge that was quite different of the "reactive paradigm." Current research of the DMN, probably the best known resting state network, leads to entirely new observations and conclusions, which were not achievable from the perspective of the "reactive paradigm." This shift from reactive activity to resting state activity of the brain is accompanied by an important question: "Can resting state activity be considered a scientific revolution and the new paradigm of neuroscience, or is it only significant for one branch of neuroscience, such as fMRI?"
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Affiliation(s)
- Marek Havlík
- Department of Applied Neurosciences and Brain Imaging, National Institute of Mental HealthKlecany, Czechia
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146
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Vidal MC, Sato JR, Balardin JB, Takahashi DY, Fujita A. ANOCVA in R: A Software to Compare Clusters between Groups and Its Application to the Study of Autism Spectrum Disorder. Front Neurosci 2017; 11:16. [PMID: 28174516 PMCID: PMC5258722 DOI: 10.3389/fnins.2017.00016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 01/09/2017] [Indexed: 11/22/2022] Open
Abstract
Understanding how brain activities cluster can help in the diagnosis of neuropsychological disorders. Thus, it is important to be able to identify alterations in the clustering structure of functional brain networks. Here, we provide an R implementation of Analysis of Cluster Variability (ANOCVA), which statistically tests (1) whether a set of brain regions of interest (ROI) are equally clustered between two or more populations and (2) whether the contribution of each ROI to the differences in clustering is significant. To illustrate the usefulness of our method and software, we apply the R package in a large functional magnetic resonance imaging (fMRI) dataset composed of 896 individuals (529 controls and 285 diagnosed with ASD—autism spectrum disorder) collected by the ABIDE (The Autism Brain Imaging Data Exchange) Consortium. Our analysis show that the clustering structure of controls and ASD subjects are different (p < 0.001) and that specific brain regions distributed in the frontotemporal, sensorimotor, visual, cerebellar, and brainstem systems significantly contributed (p < 0.05) to this differential clustering. These findings suggest an atypical organization of domain-specific function brain modules in ASD.
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Affiliation(s)
- Maciel C Vidal
- Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo São Paulo, Brazil
| | - João R Sato
- Center of Mathematics, Computation, and Cognition, Universidade Federal do ABC Santo André, Brazil
| | | | - Daniel Y Takahashi
- Deparment of Psychology and Princeton Neuroscience Institute, Princeton University Princeton, NJ, USA
| | - André Fujita
- Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo São Paulo, Brazil
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147
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Weak functional connectivity in the human fetal brain prior to preterm birth. Sci Rep 2017; 7:39286. [PMID: 28067865 PMCID: PMC5221666 DOI: 10.1038/srep39286] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 11/21/2016] [Indexed: 12/21/2022] Open
Abstract
It has been suggested that neurological problems more frequent in those born preterm are expressed prior to birth, but owing to technical limitations, this has been difficult to test in humans. We applied novel fetal resting-state functional MRI to measure brain function in 32 human fetuses in utero and found that systems-level neural functional connectivity was diminished in fetuses that would subsequently be born preterm. Neural connectivity was reduced in a left-hemisphere pre-language region, and the degree to which connectivity of this left language region extended to right-hemisphere homologs was positively associated with the time elapsed between fMRI assessment and delivery. These results provide the first evidence that altered functional connectivity in the preterm brain is identifiable before birth. They suggest that neurodevelopmental disorders associated with preterm birth may result from neurological insults that begin in utero.
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148
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Guo X, Chen H, Long Z, Duan X, Zhang Y, Chen H. Atypical developmental trajectory of local spontaneous brain activity in autism spectrum disorder. Sci Rep 2017; 7:39822. [PMID: 28057930 PMCID: PMC5216408 DOI: 10.1038/srep39822] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Accepted: 11/28/2016] [Indexed: 01/06/2023] Open
Abstract
Autism spectrum disorder (ASD) is marked by atypical trajectory of brain maturation, yet the developmental abnormalities in brain function remain unclear. The current study examined the effect of age on amplitude of low-frequency fluctuations (ALFF) in ASD and typical controls (TC) using a cross-sectional design. We classified all the participants into three age cohorts: child (<11 years, 18ASD/20TC), adolescent (11-18 years, 28ASD/26TC) and adult (≥18 years, 18ASD/18TC). Two-way analysis of variance (ANOVA) was performed to ascertain main effects and interaction effects on whole brain ALFF maps. Results exhibited significant main effect of diagnosis in ASD with decreased ALFF in the right precuneus and left middle occipital gyrus during all developmental stages. Significant diagnosis-by-age interaction was observed in the medial prefrontal cortex (mPFC) with ALFF lowered in autistic children but highered in autistic adolescents and adults. Specifically, remarkable quadratic change of ALFF with increasing age in mPFC presented in TC group was absent in ASD. Additionally, abnormal ALFF values in diagnosis-related brain regions predicted the social deficits in ASD. Our findings indicated aberrant developmental patterns of spontaneous brain activity associated with social deficits in ASD and highlight the crucial role of the default mode network in the development of disease.
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Affiliation(s)
- Xiaonan Guo
- Center for Information in BioMedicine, Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Heng Chen
- Center for Information in BioMedicine, Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Zhiliang Long
- Center for Information in BioMedicine, Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Xujun Duan
- Center for Information in BioMedicine, Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Youxue Zhang
- Center for Information in BioMedicine, Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Huafu Chen
- Center for Information in BioMedicine, Key laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China
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149
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Hull JV, Dokovna LB, Jacokes ZJ, Torgerson CM, Irimia A, Van Horn JD. Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review. Front Psychiatry 2017; 7:205. [PMID: 28101064 PMCID: PMC5209637 DOI: 10.3389/fpsyt.2016.00205] [Citation(s) in RCA: 265] [Impact Index Per Article: 37.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 12/13/2016] [Indexed: 11/18/2022] Open
Abstract
Ongoing debate exists within the resting-state functional MRI (fMRI) literature over how intrinsic connectivity is altered in the autistic brain, with reports of general over-connectivity, under-connectivity, and/or a combination of both. Classifying autism using brain connectivity is complicated by the heterogeneous nature of the condition, allowing for the possibility of widely variable connectivity patterns among individuals with the disorder. Further differences in reported results may be attributable to the age and sex of participants included, designs of the resting-state scan, and to the analysis technique used to evaluate the data. This review systematically examines the resting-state fMRI autism literature to date and compares studies in an attempt to draw overall conclusions that are presently challenging. We also propose future direction for rs-fMRI use to categorize individuals with autism spectrum disorder, serve as a possible diagnostic tool, and best utilize data-sharing initiatives.
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Affiliation(s)
- Jocelyn V. Hull
- Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Lisa B. Dokovna
- Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Zachary J. Jacokes
- Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Carinna M. Torgerson
- Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - John Darrell Van Horn
- Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
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150
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Subbaraju V, Suresh MB, Sundaram S, Narasimhan S. Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging : A spatial filtering approach. Med Image Anal 2017; 35:375-389. [DOI: 10.1016/j.media.2016.08.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Revised: 07/28/2016] [Accepted: 08/15/2016] [Indexed: 10/21/2022]
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