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Ehlen F, Roepke S, Klostermann F, Baskow I, Geise P, Belica C, Tiedt HO, Behnia B. Small Semantic Networks in Individuals with Autism Spectrum Disorder Without Intellectual Impairment: A Verbal Fluency Approach. J Autism Dev Disord 2020; 50:3967-3987. [PMID: 32198662 PMCID: PMC7560923 DOI: 10.1007/s10803-020-04457-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Individuals with Autism Spectrum Disorder (ASD) experience a variety of symptoms sometimes including atypicalities in language use. The study explored differences in semantic network organisation of adults with ASD without intellectual impairment. We assessed clusters and switches in verbal fluency tasks ('animals', 'human feature', 'verbs', 'r-words') via curve fitting in combination with corpus-driven analysis of semantic relatedness and evaluated socio-emotional and motor action related content. Compared to participants without ASD (n = 39), participants with ASD (n = 32) tended to produce smaller clusters, longer switches, and fewer words in semantic conditions (no p values survived Bonferroni-correction), whereas relatedness and content were similar. In ASD, semantic networks underlying cluster formation appeared comparably small without affecting strength of associations or content.
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Affiliation(s)
- Felicitas Ehlen
- Department of Neurology, Motor and Cognition Group, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin (CBF), Hindenburgdamm 30, 12203, Berlin, Germany.
- Department of Psychiatry, Jüdisches Krankenhaus Berlin, Heinz-Galinski-Straße 1, 13347, Berlin, Germany.
- Department of Psychiatry, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin (CBF), Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Stefan Roepke
- Department of Psychiatry, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin (CBF), Hindenburgdamm 30, 12203, Berlin, Germany
| | - Fabian Klostermann
- Department of Neurology, Motor and Cognition Group, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin (CBF), Hindenburgdamm 30, 12203, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität Zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
| | - Irina Baskow
- Department of Psychiatry, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin (CBF), Hindenburgdamm 30, 12203, Berlin, Germany
- Department of Psychology, Humboldt-Universität Zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
| | - Pia Geise
- Department of Psychiatry, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin (CBF), Hindenburgdamm 30, 12203, Berlin, Germany
- Universität Potsdam, Am Neuen Palais 10, 14469, Potsdam, Germany
| | - Cyril Belica
- Department of Digital Linguistics, Leibniz-Institut für Deutsche Sprache, R5, 6-13, 68161, Mannheim, Germany
| | - Hannes Ole Tiedt
- Department of Neurology, Motor and Cognition Group, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin (CBF), Hindenburgdamm 30, 12203, Berlin, Germany
| | - Behnoush Behnia
- Department of Psychiatry, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin (CBF), Hindenburgdamm 30, 12203, Berlin, Germany
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Ahmed MR, Zhang Y, Liu Y, Liao H. Single Volume Image Generator and Deep Learning-Based ASD Classification. IEEE J Biomed Health Inform 2020; 24:3044-3054. [DOI: 10.1109/jbhi.2020.2998603] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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153
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Beyond diagnosis: Cross-diagnostic features in canonical resting-state networks in children with neurodevelopmental disorders. NEUROIMAGE-CLINICAL 2020; 28:102476. [PMID: 33201803 PMCID: PMC7649647 DOI: 10.1016/j.nicl.2020.102476] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 10/15/2020] [Accepted: 10/17/2020] [Indexed: 11/24/2022]
Abstract
Resting-state connectivity did not differ across neurodevelopmental disorders. General adaptive function across all participants related to subcortical connectivity. Participants in the same data-driven clusters were highly heterogeneous in diagnosis. Neurobiological similarity and dissimilarity may be seen in beyond-diagnosis categories.
Children with neurodevelopmental disorders (NDDs) share common behavioural manifestations despite distinct categorical diagnostic criteria. Here, we examined canonical resting-state network connectivity in three diagnostic groups (autism spectrum disorder, attention-deficit/hyperactivity disorder and paediatric obsessive–compulsive disorder) and typically developing controls (TD) in a large single-site sample (N = 407), applying diagnosis-based and dimensional approaches to understand underlying neurobiology across NDDs. Each participant’s functional network graphs were computed using five graph metrics. In diagnosis-based comparisons, an analysis of covariance was performed to compare all NDDs to TD, followed by pairwise comparisons between NDDs. In the dimensional approach, participants’ functional network graphs were correlated with continuous behavioural measures, and a data-driven k-means clustering analysis was applied to determine if subgroups of participants were seen, without diagnostic information having been included. In the diagnosis-based comparisons, children with NDDs did not differ significantly from the TD group and the NDD categorical groups also did not differ significantly from each other, across all graph metrics. In the dimensional, diagnostic-independent approach, however, subcortical functional connectivity was significantly correlated with participants’ general adaptive functioning across all participants. The clustering analysis identified an optimal solution of two clusters, and participants assigned in the same data-driven cluster were highly heterogeneous in diagnosis. Neither cluster exclusively contained a specific diagnostic group, nor did NDDs separate cleanly from TDs. Each participant’s distance ratio between the two clusters was significantly correlated with general adaptive functioning, social deficits and attentional problems. Our results suggest the neurobiological similarity and dissimilarity between NDDs need to be investigated beyond DSM/ICD-based, behaviourally-defined diagnostic categories.
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154
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Chien YL, Chao CC, Wu SW, Hsueh HW, Chiu YN, Tsai WC, Gau SSF, Hsieh ST. Small fiber pathology in autism and clinical implications. Neurology 2020; 95:e2697-e2706. [PMID: 33055277 DOI: 10.1212/wnl.0000000000010932] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 07/15/2020] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE To investigate small fiber innervation of the skin and its relationships with clinicometry of autism and peripheral afferents for contact heat-evoked potential (CHEP) and psychophysical measures of thermal thresholds. METHODS We recruited 32 men with autism (26.5 ± 5.9 years) and conducted small fiber assessments of skin biopsy with quantifying intraepidermal nerve fiber (IENF) density, CHEP, quantitative sensory testing, and large fiber physiology of nerve conduction studies. Results were compared with age-matched controls and analyzed with clinical measures of autism. RESULTS Patients with autism showed a lower IENF density than controls (5.53 ± 2.09 vs 11.13 ± 3.49 fibers/mm, p < 0.0001). The IENF density was reduced in 17 (53.1%) men with autism classified as skin denervation group. On psychophysics, 9 (28%) men with autism had elevated thermal thresholds, and the warm threshold of the big toe was negatively correlated with IENF density (p = 0.0073), indicating functional impairments of small fiber sensory nerves. IENF density was negatively correlated with CHEP amplitude in autism (p = 0.003), in contrast to the pattern of positive correlation in controls, indicating different processing of nociceptive afferent in autism. Clinically, IENF density was related to distinct tactile symptom patterns in the skin denervation vs normal innervation group, respectively. Furthermore, IENF density was associated with autistic symptoms measured by the Autism Spectrum Quotient in a U-shaped model (p = 0.014). CONCLUSIONS These observations indicated that a substantial portion of individuals with autism had small fiber pathology, which was associated with tactile and autistic symptoms, providing structural and physiologic evidence for the involvement of peripheral sensory nerves in autism.
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Affiliation(s)
- Yi-Ling Chien
- From the Departments of Psychiatry (Y.-L.C., Y.-N.C., W.-C.T., S.S.-F.G.) and Neurology (C.-C.C., S.-W.W., H.-W.H., S.-T.H.), College of Medicine, National Taiwan University Hospital; and Graduate Institute of Clinical Medicine (Y.-L.C., S.S.-F.G., S.-T.H.), Graduate Institute of Brain and Mind Sciences (S.S.-F.G., S.-T.H.), Department of Anatomy and Cell Biology (S.-T.H.), and Center of Precision Medicine (S.-T.H.), College of Medicine, National Taiwan University, Taipei
| | - Chi-Chao Chao
- From the Departments of Psychiatry (Y.-L.C., Y.-N.C., W.-C.T., S.S.-F.G.) and Neurology (C.-C.C., S.-W.W., H.-W.H., S.-T.H.), College of Medicine, National Taiwan University Hospital; and Graduate Institute of Clinical Medicine (Y.-L.C., S.S.-F.G., S.-T.H.), Graduate Institute of Brain and Mind Sciences (S.S.-F.G., S.-T.H.), Department of Anatomy and Cell Biology (S.-T.H.), and Center of Precision Medicine (S.-T.H.), College of Medicine, National Taiwan University, Taipei
| | - Shao-Wei Wu
- From the Departments of Psychiatry (Y.-L.C., Y.-N.C., W.-C.T., S.S.-F.G.) and Neurology (C.-C.C., S.-W.W., H.-W.H., S.-T.H.), College of Medicine, National Taiwan University Hospital; and Graduate Institute of Clinical Medicine (Y.-L.C., S.S.-F.G., S.-T.H.), Graduate Institute of Brain and Mind Sciences (S.S.-F.G., S.-T.H.), Department of Anatomy and Cell Biology (S.-T.H.), and Center of Precision Medicine (S.-T.H.), College of Medicine, National Taiwan University, Taipei
| | - Hsueh-Wen Hsueh
- From the Departments of Psychiatry (Y.-L.C., Y.-N.C., W.-C.T., S.S.-F.G.) and Neurology (C.-C.C., S.-W.W., H.-W.H., S.-T.H.), College of Medicine, National Taiwan University Hospital; and Graduate Institute of Clinical Medicine (Y.-L.C., S.S.-F.G., S.-T.H.), Graduate Institute of Brain and Mind Sciences (S.S.-F.G., S.-T.H.), Department of Anatomy and Cell Biology (S.-T.H.), and Center of Precision Medicine (S.-T.H.), College of Medicine, National Taiwan University, Taipei
| | - Yen-Nan Chiu
- From the Departments of Psychiatry (Y.-L.C., Y.-N.C., W.-C.T., S.S.-F.G.) and Neurology (C.-C.C., S.-W.W., H.-W.H., S.-T.H.), College of Medicine, National Taiwan University Hospital; and Graduate Institute of Clinical Medicine (Y.-L.C., S.S.-F.G., S.-T.H.), Graduate Institute of Brain and Mind Sciences (S.S.-F.G., S.-T.H.), Department of Anatomy and Cell Biology (S.-T.H.), and Center of Precision Medicine (S.-T.H.), College of Medicine, National Taiwan University, Taipei
| | - Wen-Che Tsai
- From the Departments of Psychiatry (Y.-L.C., Y.-N.C., W.-C.T., S.S.-F.G.) and Neurology (C.-C.C., S.-W.W., H.-W.H., S.-T.H.), College of Medicine, National Taiwan University Hospital; and Graduate Institute of Clinical Medicine (Y.-L.C., S.S.-F.G., S.-T.H.), Graduate Institute of Brain and Mind Sciences (S.S.-F.G., S.-T.H.), Department of Anatomy and Cell Biology (S.-T.H.), and Center of Precision Medicine (S.-T.H.), College of Medicine, National Taiwan University, Taipei
| | - Susan Shur-Fen Gau
- From the Departments of Psychiatry (Y.-L.C., Y.-N.C., W.-C.T., S.S.-F.G.) and Neurology (C.-C.C., S.-W.W., H.-W.H., S.-T.H.), College of Medicine, National Taiwan University Hospital; and Graduate Institute of Clinical Medicine (Y.-L.C., S.S.-F.G., S.-T.H.), Graduate Institute of Brain and Mind Sciences (S.S.-F.G., S.-T.H.), Department of Anatomy and Cell Biology (S.-T.H.), and Center of Precision Medicine (S.-T.H.), College of Medicine, National Taiwan University, Taipei
| | - Sung-Tsang Hsieh
- From the Departments of Psychiatry (Y.-L.C., Y.-N.C., W.-C.T., S.S.-F.G.) and Neurology (C.-C.C., S.-W.W., H.-W.H., S.-T.H.), College of Medicine, National Taiwan University Hospital; and Graduate Institute of Clinical Medicine (Y.-L.C., S.S.-F.G., S.-T.H.), Graduate Institute of Brain and Mind Sciences (S.S.-F.G., S.-T.H.), Department of Anatomy and Cell Biology (S.-T.H.), and Center of Precision Medicine (S.-T.H.), College of Medicine, National Taiwan University, Taipei.
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155
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Nagabhushan Kalburgi S, Whitten AP, Key AP, Bodfish JW. Children With Autism Produce a Unique Pattern of EEG Microstates During an Eyes Closed Resting-State Condition. Front Hum Neurosci 2020; 14:288. [PMID: 33132865 DOI: 10.3389/fnhum.2020.00288/bibtex] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 06/26/2020] [Indexed: 05/25/2023] Open
Abstract
Although fMRI studies have produced considerable evidence for differences in the spatial connectivity of resting-state brain networks in persons with autism spectrum disorder (ASD) relative to typically developing (TD) peers, little is known about the temporal dynamics of these brain networks in ASD. The aim of this study was to examine the EEG microstate architecture in children with ASD as compared to TD at rest in two separate conditions - eyes-closed (EC) and eyes-open (EO). EEG microstate analysis was performed on resting-state data of 13 ASD and 13 TD children matched on age, gender, and IQ. We found that children with ASD and TD peers produced topographically similar canonical microstates at rest. Group differences in the duration and frequency of these microstates were found primarily in the EC resting-state condition. In line with previous fMRI findings that have reported differences in spatial connectivity within the salience network (previously correlated with the activity of microstate C) in ASD, we found that the duration of activation of microstate C was increased, and the frequency of microstate C was decreased in ASD as compared to TD in EC resting-state. Functionally, these results may be reflective of alterations in interoceptive processes in ASD. These results suggest a unique pattern of EEG microstate architecture in ASD relative to TD during resting-states and also that EEG microstate parameters in ASD are susceptible to differences in resting-state conditions.
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Affiliation(s)
| | | | - Alexandra P Key
- Vanderbilt Kennedy Center, Nashville, TN, United States
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - James W Bodfish
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, United States
- Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Kennedy Center, Nashville, TN, United States
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
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156
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Wang J, Zhang L, Wang Q, Chen L, Shi J, Chen X, Li Z, Shen D. Multi-Class ASD Classification Based on Functional Connectivity and Functional Correlation Tensor via Multi-Source Domain Adaptation and Multi-View Sparse Representation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3137-3147. [PMID: 32305905 DOI: 10.1109/tmi.2020.2987817] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The resting-state functional magnetic resonance imaging (rs-fMRI) reflects functional activity of brain regions by blood-oxygen-level dependent (BOLD) signals. Up to now, many computer-aided diagnosis methods based on rs-fMRI have been developed for Autism Spectrum Disorder (ASD). These methods are mostly the binary classification approaches to determine whether a subject is an ASD patient or not. However, the disease often consists of several sub-categories, which are complex and thus still confusing to many automatic classification methods. Besides, existing methods usually focus on the functional connectivity (FC) features in grey matter regions, which only account for a small portion of the rs-fMRI data. Recently, the possibility to reveal the connectivity information in the white matter regions of rs-fMRI has drawn high attention. To this end, we propose to use the patch-based functional correlation tensor (PBFCT) features extracted from rs-fMRI in white matter, in addition to the traditional FC features from gray matter, to develop a novel multi-class ASD diagnosis method in this work. Our method has two stages. Specifically, in the first stage of multi-source domain adaptation (MSDA), the source subjects belonging to multiple clinical centers (thus called as source domains) are all transformed into the same target feature space. Thus each subject in the target domain can be linearly reconstructed by the transformed subjects. In the second stage of multi-view sparse representation (MVSR), a multi-view classifier for multi-class ASD diagnosis is developed by jointly using both views of the FC and PBFCT features. The experimental results using the ABIDE dataset verify the effectiveness of our method, which is capable of accurately classifying each subject into a respective ASD sub-category.
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157
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Dima DC, Adams R, Linden SC, Baird A, Smith J, Foley S, Perry G, Routley BC, Magazzini L, Drakesmith M, Williams N, Doherty J, van den Bree MBM, Owen MJ, Hall J, Linden DEJ, Singh KD. Electrophysiological network alterations in adults with copy number variants associated with high neurodevelopmental risk. Transl Psychiatry 2020; 10:324. [PMID: 32958742 PMCID: PMC7506525 DOI: 10.1038/s41398-020-00998-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 09/04/2020] [Indexed: 12/20/2022] Open
Abstract
Rare copy number variants associated with increased risk for neurodevelopmental and psychiatric disorders (referred to as ND-CNVs) are characterized by heterogeneous phenotypes thought to share a considerable degree of overlap. Altered neural integration has often been linked to psychopathology and is a candidate marker for potential convergent mechanisms through which ND-CNVs modify risk; however, the rarity of ND-CNVs means that few studies have assessed their neural correlates. Here, we used magnetoencephalography (MEG) to investigate resting-state oscillatory connectivity in a cohort of 42 adults with ND-CNVs, including deletions or duplications at 22q11.2, 15q11.2, 15q13.3, 16p11.2, 17q12, 1q21.1, 3q29, and 2p16.3, and 42 controls. We observed decreased connectivity between occipital, temporal, and parietal areas in participants with ND-CNVs. This pattern was common across genotypes and not exclusively characteristic of 22q11.2 deletions, which were present in a third of our cohort. Furthermore, a data-driven graph theory framework enabled us to successfully distinguish participants with ND-CNVs from unaffected controls using differences in node centrality and network segregation. Together, our results point to alterations in electrophysiological connectivity as a putative common mechanism through which genetic factors confer increased risk for neurodevelopmental and psychiatric disorders.
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Affiliation(s)
- Diana C Dima
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
| | - Rachael Adams
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Stefanie C Linden
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Alister Baird
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Jacqueline Smith
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Gavin Perry
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Bethany C Routley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Lorenzo Magazzini
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Mark Drakesmith
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Nigel Williams
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Joanne Doherty
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Marianne B M van den Bree
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Michael J Owen
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - David E J Linden
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
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158
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Spronk M, Keane BP, Ito T, Kulkarni K, Ji JL, Anticevic A, Cole MW. A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction. Cereb Cortex 2020; 31:547-561. [PMID: 32909037 DOI: 10.1093/cercor/bhaa242] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 07/07/2020] [Accepted: 08/01/2020] [Indexed: 12/13/2022] Open
Abstract
A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations appear to support theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in a broad, whole-brain perspective. Using a graph distance approach-connectome-wide similarity-we found that whole-brain resting-state functional network organization is highly similar across groups of individuals with and without a variety of mental diseases. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease, those differences are informative. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases. Such small network alterations suggest the possibility that small, well-targeted alterations to brain network organization may provide meaningful improvements for a variety of mental disorders.
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Affiliation(s)
- Marjolein Spronk
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Brian P Keane
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA.,Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Kaustubh Kulkarni
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
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159
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Ghahari S, Salehi F, Farahani N, Coben R, Motie Nasrabadi A. Representing Temporal Network based on dDTF of EEG signals in Children with Autism and Healthy Children. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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160
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Mash LE, Keehn B, Linke AC, Liu TT, Helm JL, Haist F, Townsend J, Müller RA. Atypical Relationships Between Spontaneous EEG and fMRI Activity in Autism. Brain Connect 2020; 10:18-28. [PMID: 31884804 DOI: 10.1089/brain.2019.0693] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Autism spectrum disorders (ASDs) have been linked to atypical communication among distributed brain networks. However, despite decades of research, the exact nature of these differences between typically developing (TD) individuals and those with ASDs remains unclear. ASDs have been widely studied using resting-state neuroimaging methods, including both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). However, little is known about how fMRI and EEG measures of spontaneous brain activity are related in ASDs. In the present study, two cohorts of children and adolescents underwent resting-state EEG (n = 38 per group) or fMRI (n = 66 ASD, 57 TD), with a subset of individuals in both the EEG and fMRI cohorts (n = 17 per group). In the EEG cohort, parieto-occipital EEG alpha power was found to be reduced in ASDs. In the fMRI cohort, blood oxygen level-dependent (BOLD) power was regionally increased in right temporal regions and there was widespread overconnectivity between the thalamus and cortical regions in the ASD group relative to the TD group. Finally, multimodal analyses indicated that while TD children showed consistently positive relationships between EEG alpha power and regional BOLD power, these associations were weak or negative in ASDs. These findings suggest atypical links between alpha rhythms and regional BOLD activity in ASDs, possibly implicating neural substrates and processes that coordinate thalamocortical regulation of the alpha rhythm.
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Affiliation(s)
- Lisa E Mash
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Brandon Keehn
- Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, Indiana
| | - Annika C Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Thomas T Liu
- Department of Radiology, Center for Functional MRI, University of California, San Diego, La Jolla, California
| | - Jonathan L Helm
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California.,Department of Psychology, San Diego State University, San Diego, California
| | - Frank Haist
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California.,Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Jeanne Townsend
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California.,Department of Neurosciences, University of California, San Diego, La Jolla, California
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
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161
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Anderson AA, Gropman A, Le Mons C, Stratakis CA, Gandjbakhche AH. Hemodynamics of Prefrontal Cortex in Ornithine Transcarbamylase Deficiency: A Twin Case Study. Front Neurol 2020; 11:809. [PMID: 32922350 PMCID: PMC7456944 DOI: 10.3389/fneur.2020.00809] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 06/29/2020] [Indexed: 11/30/2022] Open
Abstract
Ornithine transcarbamylase deficiency (OTCD) is the most common form of urea cycle disorder characterized by the presence of hyperammonemia (HA). In patients with OTCD, HA is known to cause impairments in domains of executive function and working memory. Monitoring OTCD progression and investigating neurocognitive biomarkers can, therefore, become critical in understanding the underlying brain function in a population with OTCD. We used functional near infrared spectroscopy (fNIRS) to examine the hemodynamics of prefrontal cortex (PFC) in a fraternal twin with and without OTCD. fNIRS is a non-invasive and wearable optical technology that can be used to assess cortical hemodynamics in a realistic clinical setting. We quantified the hemodynamic variations in total-hemoglobin as assessed by fNIRS while subjects performed the N-back working memory (WM) task. Our preliminary results showed that the sibling with OTCD had higher variation in a very low frequency band (<0.03 Hz, related to mechanism of cerebral autoregulation) compared to the control sibling. The difference between these variations was not as prominent in the higher frequency band, indicating the possible role of impaired autoregulation and cognitive function due to presence of HA. We further examined the functional connectivity in PFC, where the OTCD sibling showed lower interhemispheric functional connectivity as the task load increased. Our pilot results are the first to show the utility of fNIRS in monitoring OTCD cortical hemodynamics, indicating the possibility of inefficient neurocognitive function. This study provides a novel insight into the monitoring of OTCD focusing on the contribution of physiological process and neurocognitive function in this population.
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Affiliation(s)
- Afrouz A. Anderson
- National Institutes of Health (NIH), National Institute of Child Health and Human Development, Bethesda, MD, United States
| | - Andrea Gropman
- Children's National Medical Center, Division of Neurogenetics and Neurodevelopmental Pediatrics, Washington, DC, United States
| | - Cynthia Le Mons
- National Urea Cycle Disorders Foundation, Pasadena, CA, United States
| | - Constantine A. Stratakis
- National Institutes of Health (NIH), National Institute of Child Health and Human Development, Bethesda, MD, United States
| | - Amir H. Gandjbakhche
- National Institutes of Health (NIH), National Institute of Child Health and Human Development, Bethesda, MD, United States
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162
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Chen B. A preliminary study of atypical cortical change ability of dynamic whole-brain functional connectivity in autism spectrum disorder. Int J Neurosci 2020; 132:213-225. [PMID: 32762276 DOI: 10.1080/00207454.2020.1806837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVES Designing new objectively diagnostic methods of autism spectrum disorder (ASD) are burning questions. Dynamic functional connectivity (DFC) methodology based on fMRI data are an effective lever to investigate changeability evolution of signal synchronization in macroscopic neural activity patterns. METHODS Embracing the network dynamics concepts, this paper introduces changeability index (C-score)which is focused on time-varying aspects of FCs, and develops a new framework for researching the roots of ASD brains at resting states in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in C-scores of between atypical and typical DFCs of 30 healthy controls (HCs) and 48 ASD patients. RESULTS The abnormities of edge C-scores are found across widespread brain cortex in ASD brains. For whole brain regional C-scores of ASD patients, orbitofrontal middle cortex L, inferior triangular frontal gyrus L, middle occipital gyrus L, postcentral gyrus L, supramarginal L, supramarginal R, cerebellum 8 L, and cerebellum 10 Rare endowed with significantly different C-scores.At brain subsystems level, C-scores in left hemisphere, right hemisphere, top hemisphere, bottom hemisphere, frontal lobe, parietal lobe, occipital lobe, cerebellum sub systems are abnormal in ASD patients. CONCLUSIONS The ASD brains have whole-brain abnormity on widespread regions. Through the strict evidence-based study, it was found that the changeability index (C-score) is a meaningful biological marker to explore cortical activity in ASD.
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Affiliation(s)
- Bo Chen
- School of Science, Hangzhou Dianzi University, Hangzhou, Zhejiang, PR China
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163
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Wylie KP, Tregellas JR, Bear JJ, Legget KT. Autism Spectrum Disorder Symptoms are Associated with Connectivity Between Large-Scale Neural Networks and Brain Regions Involved in Social Processing. J Autism Dev Disord 2020; 50:2765-2778. [PMID: 32006272 PMCID: PMC7377948 DOI: 10.1007/s10803-020-04383-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The neurobiology of autism spectrum disorder remains poorly understood. The present study addresses this knowledge gap by examining the relationship between functional brain connectivity and Autism Diagnostic Observation Schedule (ADOS) scores using publicly available data from the Autism Brain Imaging Data Exchange (ABIDE) database (N = 107). This relationship was tested across all brain voxels, without a priori assumptions, using a novel statistical approach. ADOS scores were primarily associated with decreased connectivity to right temporoparietal junction, right anterior insula, and left fusiform gyrus (p < 0.05, corrected). Seven large-scale brain networks influenced these associations. Findings largely encompassed brain regions involved in processing socially relevant information, highlighting the importance of these processes in autism spectrum disorder.
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Affiliation(s)
- Korey P Wylie
- Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, Fitzsimons Building, Mail Stop F546, 13001 East 17th Place, Aurora, CO, 80045, USA
| | - Jason R Tregellas
- Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, Fitzsimons Building, Mail Stop F546, 13001 East 17th Place, Aurora, CO, 80045, USA
- Research Service, Rocky Mountain Regional VA Medical Center, Eastern Colorado Health System, 1700 N. Wheeling St., Aurora, CO, 80045, USA
| | - Joshua J Bear
- Department of Pediatrics, Section of Neurology, Children's Hospital Colorado, 13123 East 16th Avenue, Aurora, CO, 80045, USA
- Department of Pediatrics, Section of Neurology, University of Colorado School of Medicine, Anschutz Medical Campus, 13123 East 16th Avenue, Aurora, CO, 80045, USA
| | - Kristina T Legget
- Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, Fitzsimons Building, Mail Stop F546, 13001 East 17th Place, Aurora, CO, 80045, USA.
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164
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Shalev I, Uzefovsky F. Empathic disequilibrium in two different measures of empathy predicts autism traits in neurotypical population. Mol Autism 2020; 11:59. [PMID: 32660537 PMCID: PMC7359469 DOI: 10.1186/s13229-020-00362-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 07/02/2020] [Indexed: 01/10/2023] Open
Abstract
Background Features of autism spectrum conditions (ASC) are normally distributed within the population, giving rise to the notion of the autism spectrum. One of the hallmark features of ASC is difficulties in social communication, which relies heavily on our ability to empathize with others. Empathy comprises of both cognitive (CE) and emotional (EE) components that, together, allow us to understand another’s emotions and be affected by them appropriately, while maintaining a self-other distinction. Although CE and EE depend on distinct neural and developmental trajectories, it was suggested that the two empathic capacities can influence, balance, and regulate each other. Previous findings regarding the role of emotional and cognitive empathy in ASC have been mixed. Therefore, our study aimed to investigate whether the intra-personal empathy imbalance between the cognitive and emotional components, a measure we termed empathic disequilibrium (ED), can be associated with autism traits at the neurotypical range. Methods Participants were 671 young-adults at the neurotypical range who self-reported their empathy, assessed using two highly validated questionnaires—the Interpersonal Reactivity Index and the Empathy Quotient, autism traits using the Autism-Spectrum Quotient, and the related traits, alexithymia, and systemizing. Results Controlling for the total empathy score, greater ED was found to be positively correlated with autism traits. Specifically, autism traits were found to be elevated in groups of individuals with relatively higher EE than CE, underscoring their imbalance. Conclusions Our study offers a novel perspective on the understanding of the social difficulties associated with autism tendencies in the general population and has potentially important clinical implications for understanding of ASC. We also propose a novel characterization of autism tendencies based on the imbalance between EE and CE, which we term ED, as opposed to examining EE and CE separately.
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Affiliation(s)
- Ido Shalev
- Department of Psychology Ben Gurion University of the Negev, 84105, Beersheba, Israel.,Zlotowski Center for Neuroscience Ben Gurion University of the Negev, Beersheba, Israel
| | - Florina Uzefovsky
- Department of Psychology Ben Gurion University of the Negev, 84105, Beersheba, Israel. .,Zlotowski Center for Neuroscience Ben Gurion University of the Negev, Beersheba, Israel.
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165
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Triana AM, Glerean E, Saramäki J, Korhonen O. Effects of spatial smoothing on group-level differences in functional brain networks. Netw Neurosci 2020; 4:556-574. [PMID: 32885115 PMCID: PMC7462426 DOI: 10.1162/netn_a_00132] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 02/20/2020] [Indexed: 12/19/2022] Open
Abstract
Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum disorder and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0–32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different. For thresholded networks, larger smoothing kernels lead to more similar networks, although this depends on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the region of interest (ROI) size, but varies with link length. The effects of spatial smoothing are diverse, nontrivial, and difficult to predict. This has important consequences: The choice of smoothing kernel affects the observed network differences. Spatial smoothing is a preprocessing tool commonly applied to reduce the amount of noise in functional magnetic resonance imaging (fMRI) data. However, smoothing is known to affect the outcomes of functional brain network analysis at the level of individual subjects in undesired ways. Here, we investigate how spatial smoothing affects the observed differences in brain network structure between subject groups. Using fMRI data from two clinical populations and healthy controls, we show that the between-group differences in network structure depend on the amount of spatial smoothing applied during preprocessing in a nontrivial way. The optimal level of spatial smoothing is difficult to define and probably depends on a set of analysis parameters. Therefore, we recommend applying spatial smoothing only after careful consideration.
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Affiliation(s)
- Ana María Triana
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Jari Saramäki
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Onerva Korhonen
- Université de Lille, CNRS, UMR 9193 - SCALab - Sciences Cognitives et Sciences Affectives, Lille, France
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166
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Machine learning methods for brain network classification: Application to autism diagnosis using cortical morphological networks. J Neurosci Methods 2020; 343:108799. [PMID: 32574639 DOI: 10.1016/j.jneumeth.2020.108799] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 05/26/2020] [Accepted: 05/27/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND Autism spectrum disorder (ASD) affects the brain connectivity at different levels. Nonetheless, non-invasively distinguishing such effects using magnetic resonance imaging (MRI) remains very challenging to machine learning diagnostic frameworks due to ASD heterogeneity. So far, existing network neuroscience works mainly focused on functional (derived from functional MRI) and structural (derived from diffusion MRI) brain connectivity, which might not directly capture relational morphological changes between brain regions. Indeed, machine learning (ML) studies for ASD diagnosis using morphological brain networks derived from conventional T1-weighted MRI are very scarce. NEW METHOD To fill this gap, we leverage crowdsourcing by organizing a Kaggle competition to build a pool of machine learning pipelines for neurological disorder diagnosis with application to ASD diagnosis using cortical morphological networks derived from T1-weighted MRI. RESULTS During the competition, participants were provided with a training dataset and only allowed to check their performance on a public test data. The final evaluation was performed on both public and hidden test datasets based on accuracy, sensitivity, and specificity metrics. Teams were ranked using each performance metric separately and the final ranking was determined based on the mean of all rankings. The first-ranked team achieved 70% accuracy, 72.5% sensitivity, and 67.5% specificity, where the second-ranked team achieved 63.8%, 62.5%, 65% respectively. CONCLUSION Leveraging participants to design ML diagnostic methods within a competitive machine learning setting has allowed the exploration and benchmarking of wide spectrum of ML methods for ASD diagnosis using cortical morphological networks.
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167
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Byrge L, Kennedy DP. Accurate prediction of individual subject identity and task, but not autism diagnosis, from functional connectomes. Hum Brain Mapp 2020; 41:2249-2262. [PMID: 32150312 PMCID: PMC7268028 DOI: 10.1002/hbm.24943] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 01/27/2020] [Accepted: 01/28/2020] [Indexed: 12/24/2022] Open
Abstract
Despite enthusiasm about the potential for using fMRI-based functional connectomes in the development of biomarkers for autism spectrum disorder (ASD), the literature is full of negative findings-failures to distinguish ASD functional connectomes from those of typically developing controls (TD)-and positive findings that are inconsistent across studies. Here, we report on a new study designed to either better differentiate ASD from TD functional connectomes-or, alternatively, to refine our understanding of the factors underlying the current state of affairs. We scanned individuals with ASD and controls both at rest and while watching videos with social content. Using multiband fMRI across repeat sessions, we improved both data quantity and scanning duration by collecting up to 2 hr of data per individual. This is about 50 times the typical number of temporal samples per individual in ASD fcMRI studies. We obtained functional connectomes that were discriminable, allowing for near-perfect individual identification regardless of diagnosis, and equally reliable in both groups. However, contrary to what one might expect, we did not consistently or robustly observe in the ASD group either reductions in similarity to TD functional connectivity (FC) patterns or shared atypical FC patterns. Accordingly, FC-based predictions of diagnosis group achieved accuracy levels around chance. However, using the same approaches to predict scan type (rest vs. video) achieved near-perfect accuracy. Our findings suggest that neither the limitations of resting state as a "task," data resolution, data quantity, or scan duration can be considered solely responsible for failures to differentiate ASD from TD functional connectomes.
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Affiliation(s)
- Lisa Byrge
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndiana
| | - Daniel P. Kennedy
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonIndiana
- Cognitive Science ProgramIndiana UniversityBloomingtonIndiana
- Program in NeuroscienceIndiana UniversityBloomingtonIndiana
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168
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Rinat S, Izadi-Najafabadi S, Zwicker JG. Children with developmental coordination disorder show altered functional connectivity compared to peers. Neuroimage Clin 2020; 27:102309. [PMID: 32590334 PMCID: PMC7320316 DOI: 10.1016/j.nicl.2020.102309] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 02/06/2023]
Abstract
Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder that affects a child's ability to learn motor skills and participate in self-care, educational, and leisure activities. The cause of DCD is unknown, but evidence suggests that children with DCD have atypical brain structure and function. Resting-state MRI assesses functional connectivity by identifying brain regions that have parallel activation during rest. As only a few studies have examined functional connectivity in this population, our objective was to compare whole-brain resting-state functional connectivity of children with DCD and typically-developing children. Using Independent Component Analysis (ICA), we compared functional connectivity of 8-12 year old children with DCD (N = 35) and typically-developing children (N = 23) across 19 networks, controlling for age and sex. Children with DCD demonstrate altered functional connectivity between the sensorimotor network and the posterior cingulate cortex (PCC), precuneus, and the posterior middle temporal gyrus (pMTG) (p < 0.0001). Previous evidence suggests the PCC acts as a link between functionally distinct networks. Our results indicate that ineffective communication between the sensorimotor network and the PCC might play a role in inefficient motor learning seen in DCD. The pMTG acts as hub for action-related information and processing, and its involvement could explain some of the functional difficulties seen in DCD. This study increases our understanding of the neurological differences that characterize this common motor disorder.
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Affiliation(s)
- Shie Rinat
- Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, Canada; BC Children's Hospital Research Institute, Vancouver, Canada
| | - Sara Izadi-Najafabadi
- Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, Canada; BC Children's Hospital Research Institute, Vancouver, Canada
| | - Jill G Zwicker
- BC Children's Hospital Research Institute, Vancouver, Canada; Department of Occupational Science & Occupational Therapy, University of British Columbia, Vancouver, Canada; Department of Pediatrics, University of British Columbia, Vancouver, Canada; Sunny Hill Health Centre for Children, Vancouver, Canada; CanChild Centre for Childhood Disability Research, Hamilton, Canada.
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169
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Wenderski W, Wang L, Krokhotin A, Walsh JJ, Li H, Shoji H, Ghosh S, George RD, Miller EL, Elias L, Gillespie MA, Son EY, Staahl BT, Baek ST, Stanley V, Moncada C, Shipony Z, Linker SB, Marchetto MCN, Gage FH, Chen D, Sultan T, Zaki MS, Ranish JA, Miyakawa T, Luo L, Malenka RC, Crabtree GR, Gleeson JG. Loss of the neural-specific BAF subunit ACTL6B relieves repression of early response genes and causes recessive autism. Proc Natl Acad Sci U S A 2020; 117:10055-10066. [PMID: 32312822 PMCID: PMC7211998 DOI: 10.1073/pnas.1908238117] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Synaptic activity in neurons leads to the rapid activation of genes involved in mammalian behavior. ATP-dependent chromatin remodelers such as the BAF complex contribute to these responses and are generally thought to activate transcription. However, the mechanisms keeping such "early activation" genes silent have been a mystery. In the course of investigating Mendelian recessive autism, we identified six families with segregating loss-of-function mutations in the neuronal BAF (nBAF) subunit ACTL6B (originally named BAF53b). Accordingly, ACTL6B was the most significantly mutated gene in the Simons Recessive Autism Cohort. At least 14 subunits of the nBAF complex are mutated in autism, collectively making it a major contributor to autism spectrum disorder (ASD). Patient mutations destabilized ACTL6B protein in neurons and rerouted dendrites to the wrong glomerulus in the fly olfactory system. Humans and mice lacking ACTL6B showed corpus callosum hypoplasia, indicating a conserved role for ACTL6B in facilitating neural connectivity. Actl6b knockout mice on two genetic backgrounds exhibited ASD-related behaviors, including social and memory impairments, repetitive behaviors, and hyperactivity. Surprisingly, mutation of Actl6b relieved repression of early response genes including AP1 transcription factors (Fos, Fosl2, Fosb, and Junb), increased chromatin accessibility at AP1 binding sites, and transcriptional changes in late response genes associated with early response transcription factor activity. ACTL6B loss is thus an important cause of recessive ASD, with impaired neuron-specific chromatin repression indicated as a potential mechanism.
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Affiliation(s)
- Wendy Wenderski
- Department of Pathology, Stanford Medical School, Palo Alto, CA 94305
- Department of Genetics, Stanford Medical School, Palo Alto, CA 94305
- Department of Developmental Biology, Stanford Medical School, Palo Alto, CA 94305
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
| | - Lu Wang
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92037
- Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92037
- Rady Children's Institute of Genomic Medicine, University of California San Diego, La Jolla, CA 92037
| | - Andrey Krokhotin
- Department of Pathology, Stanford Medical School, Palo Alto, CA 94305
- Department of Genetics, Stanford Medical School, Palo Alto, CA 94305
- Department of Developmental Biology, Stanford Medical School, Palo Alto, CA 94305
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
| | - Jessica J Walsh
- Nancy Pritztker Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford Medical School, Palo Alto, CA 94305
| | - Hongjie Li
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
- Department of Biology, Stanford University, Palo Alto, CA 94305
| | - Hirotaka Shoji
- Division of Systems Medical Science, Institute for Comprehensive Medical Science, Fujita Health University, 470-1192 Toyoake, Aichi, Japan
| | - Shereen Ghosh
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92037
- Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92037
- Rady Children's Institute of Genomic Medicine, University of California San Diego, La Jolla, CA 92037
| | - Renee D George
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92037
- Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92037
- Rady Children's Institute of Genomic Medicine, University of California San Diego, La Jolla, CA 92037
| | - Erik L Miller
- Department of Pathology, Stanford Medical School, Palo Alto, CA 94305
- Department of Genetics, Stanford Medical School, Palo Alto, CA 94305
- Department of Developmental Biology, Stanford Medical School, Palo Alto, CA 94305
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
| | - Laura Elias
- Department of Pathology, Stanford Medical School, Palo Alto, CA 94305
- Department of Genetics, Stanford Medical School, Palo Alto, CA 94305
- Department of Developmental Biology, Stanford Medical School, Palo Alto, CA 94305
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
| | | | - Esther Y Son
- Department of Pathology, Stanford Medical School, Palo Alto, CA 94305
- Department of Genetics, Stanford Medical School, Palo Alto, CA 94305
- Department of Developmental Biology, Stanford Medical School, Palo Alto, CA 94305
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
| | - Brett T Staahl
- Department of Pathology, Stanford Medical School, Palo Alto, CA 94305
- Department of Genetics, Stanford Medical School, Palo Alto, CA 94305
- Department of Developmental Biology, Stanford Medical School, Palo Alto, CA 94305
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
| | - Seung Tae Baek
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92037
- Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92037
- Rady Children's Institute of Genomic Medicine, University of California San Diego, La Jolla, CA 92037
| | - Valentina Stanley
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92037
- Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92037
- Rady Children's Institute of Genomic Medicine, University of California San Diego, La Jolla, CA 92037
| | - Cynthia Moncada
- Department of Pathology, Stanford Medical School, Palo Alto, CA 94305
- Department of Genetics, Stanford Medical School, Palo Alto, CA 94305
- Department of Developmental Biology, Stanford Medical School, Palo Alto, CA 94305
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
| | - Zohar Shipony
- Department of Pathology, Stanford Medical School, Palo Alto, CA 94305
- Department of Genetics, Stanford Medical School, Palo Alto, CA 94305
- Department of Developmental Biology, Stanford Medical School, Palo Alto, CA 94305
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
| | - Sara B Linker
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA 92037
| | - Maria C N Marchetto
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA 92037
| | - Fred H Gage
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA 92037
| | - Dillon Chen
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92037
- Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92037
- Rady Children's Institute of Genomic Medicine, University of California San Diego, La Jolla, CA 92037
| | - Tipu Sultan
- Department of Pediatric Neurology, Institute of Child Health, Children Hospital Lahore, 54000 Lahore, Pakistan
| | - Maha S Zaki
- Clinical Genetics Department, Human Genetics and Genome Research Division, National Research Centre, 12311 Cairo, Egypt
| | | | - Tsuyoshi Miyakawa
- Division of Systems Medical Science, Institute for Comprehensive Medical Science, Fujita Health University, 470-1192 Toyoake, Aichi, Japan
| | - Liqun Luo
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
- Department of Biology, Stanford University, Palo Alto, CA 94305
| | - Robert C Malenka
- Nancy Pritztker Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford Medical School, Palo Alto, CA 94305
| | - Gerald R Crabtree
- Department of Pathology, Stanford Medical School, Palo Alto, CA 94305;
- Department of Genetics, Stanford Medical School, Palo Alto, CA 94305
- Department of Developmental Biology, Stanford Medical School, Palo Alto, CA 94305
- Howard Hughes Medical Institute, Stanford University, Palo Alto, CA 94305
| | - Joseph G Gleeson
- Department of Neuroscience, University of California San Diego, La Jolla, CA 92037;
- Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92037
- Rady Children's Institute of Genomic Medicine, University of California San Diego, La Jolla, CA 92037
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170
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Kozhemiako N, Nunes AS, Vakorin V, Iarocci G, Ribary U, Doesburg SM. Alterations in Local Connectivity and Their Developmental Trajectories in Autism Spectrum Disorder: Does Being Female Matter? Cereb Cortex 2020; 30:5166-5179. [PMID: 32368779 DOI: 10.1093/cercor/bhaa109] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/31/2020] [Accepted: 04/05/2020] [Indexed: 01/10/2023] Open
Abstract
Autism spectrum disorder (ASD) is diagnosed more often in males with a ratio of 1:4 females/males. This bias is even stronger in neuroimaging studies. There is a growing evidence suggesting that local connectivity and its developmental trajectory is altered in ASD. Here, we aim to investigate how local connectivity and its age-related trajectories vary with ASD in both males and females. We used resting-state fMRI data from the ABIDE I and II repository: males (n = 102) and females (n = 92) with ASD, and typically developing males (n = 104) and females (n = 92) aged between 6 and 26. Local connectivity was quantified as regional homogeneity. We found increases in local connectivity in participants with ASD in the somatomotor and limbic networks and decreased local connectivity within the default mode network. These alterations were more pronounced in females with ASD. In addition, the association between local connectivity and ASD symptoms was more robust in females. Females with ASD had the most distinct developmental trajectories of local connectivity compared with other groups. Overall, our findings of more pronounced local connectivity alterations in females with ASD could indicate a greater etiological load for an ASD diagnosis in this group congruent with the female protective effect hypothesis.
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Affiliation(s)
- Nataliia Kozhemiako
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Adonay S Nunes
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Vasily Vakorin
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.,Behavioural and Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.,Fraser Health, British Columbia Health Authority, Surrey, BC V3T 5X3, Canada
| | - Grace Iarocci
- Department of Psychology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Urs Ribary
- Behavioural and Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.,Department of Psychology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.,Department of Pediatrics and Psychiatry, University of British Columbia, Vancouver, BC V6T 2A1, Canada
| | - Sam M Doesburg
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.,Behavioural and Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
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171
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Lawrence KE, Hernandez LM, Bowman HC, Padgaonkar NT, Fuster E, Jack A, Aylward E, Gaab N, Van Horn JD, Bernier RA, Geschwind DH, McPartland JC, Nelson CA, Webb SJ, Pelphrey KA, Green SA, Bookheimer SY, Dapretto M. Sex Differences in Functional Connectivity of the Salience, Default Mode, and Central Executive Networks in Youth with ASD. Cereb Cortex 2020; 30:5107-5120. [PMID: 32350530 DOI: 10.1093/cercor/bhaa105] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 03/10/2020] [Accepted: 03/30/2020] [Indexed: 02/07/2023] Open
Abstract
Autism spectrum disorder (ASD) is associated with the altered functional connectivity of 3 neurocognitive networks that are hypothesized to be central to the symptomatology of ASD: the salience network (SN), default mode network (DMN), and central executive network (CEN). Due to the considerably higher prevalence of ASD in males, however, previous studies examining these networks in ASD have used primarily male samples. It is thus unknown how these networks may be differentially impacted among females with ASD compared to males with ASD, and how such differences may compare to those observed in neurotypical individuals. Here, we investigated the functional connectivity of the SN, DMN, and CEN in a large, well-matched sample of girls and boys with and without ASD (169 youth, ages 8-17). Girls with ASD displayed greater functional connectivity between the DMN and CEN than boys with ASD, whereas typically developing girls and boys differed in SN functional connectivity only. Together, these results demonstrate that youth with ASD exhibit altered sex differences in these networks relative to what is observed in typical development, and highlight the importance of considering sex-related biological factors and participant sex when characterizing the neural mechanisms underlying ASD.
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Affiliation(s)
- Katherine E Lawrence
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Leanna M Hernandez
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Hilary C Bowman
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Namita T Padgaonkar
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Emily Fuster
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Allison Jack
- Autism & Neurodevelopmental Disorders Institute, The George Washington University, Washington, DC 20052, USA.,Dept. of Pharmacology & Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC 20052, USA
| | - Elizabeth Aylward
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington 98195, USA
| | - Nadine Gaab
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Cambridge, MA 02138, USA.,Harvard Graduate School of Education, Cambridge, MA 02138, USA
| | - John D Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA
| | - Raphael A Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Daniel H Geschwind
- Department of Neurology and Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - James C McPartland
- Department of Pediatrics, Yale School of Medicine, New Haven, CT 06520, USA.,Child Study Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Charles A Nelson
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Cambridge, MA 02138, USA
| | - Sara J Webb
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington 98195, USA.,Center on Child Health, Behavior, and Development, Seattle Children's Research Institute, Seattle, Washington 98195, USA
| | - Kevin A Pelphrey
- Department of Neurology, University of Virginia, Charlottesville, VA 22904, USA
| | - Shulamite A Green
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
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172
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Leming M, Górriz JM, Suckling J. Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks. Int J Neural Syst 2020; 30:2050012. [DOI: 10.1142/s0129065720500124] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the “black box problem”). In this paper, we train a convolutional neural network (CNN) with the largest multi-source, functional MRI (fMRI) connectomic dataset ever compiled, consisting of 43,858 datapoints. We apply this model to a cross-sectional comparison of autism spectrum disorder (ASD) versus typically developing (TD) controls that has proved difficult to characterize with inferential statistics. To contextualize these findings, we additionally perform classifications of gender and task versus rest. Employing class-balancing to build a training set, we trained [Formula: see text] modified CNNs in an ensemble model to classify fMRI connectivity matrices with overall AUROCs of 0.6774, 0.7680, and 0.9222 for ASD versus TD, gender, and task versus rest, respectively. Additionally, we aim to address the black box problem in this context using two visualization methods. First, class activation maps show which functional connections of the brain our models focus on when performing classification. Second, by analyzing maximal activations of the hidden layers, we were also able to explore how the model organizes a large and mixed-center dataset, finding that it dedicates specific areas of its hidden layers to processing different covariates of data (depending on the independent variable analyzed), and other areas to mix data from different sources. Our study finds that deep learning models that distinguish ASD from TD controls focus broadly on temporal and cerebellar connections, with a particularly high focus on the right caudate nucleus and paracentral sulcus.
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Affiliation(s)
- Matthew Leming
- Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain & Mind Sciences, Forvie Site, Robinson Way, Cambridge, CB20SZ, UK
| | - Juan Manuel Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Avenida del Hospicio, E-18071 Granada, Spain
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain & Mind Sciences, Forvie Site, Robinson Way, Cambridge, CB20SZ, UK
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173
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Rolison MJ, Naples AJ, Rutherford HJV, McPartland JC. The Presence of Another Person Influences Oscillatory Cortical Dynamics During Dual Brain EEG Recording. Front Psychiatry 2020; 11:246. [PMID: 32362842 PMCID: PMC7180176 DOI: 10.3389/fpsyt.2020.00246] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 03/12/2020] [Indexed: 11/13/2022] Open
Abstract
Humans are innately social creatures and the social environment strongly influences brain development. As such, the human brain is primed for and sensitive to social information even in the absence of explicit task or instruction. In this study, we examined the influence of different levels of interpersonal proximity on resting state brain activity and its association with social cognition. We measured EEG in pairs of 13 typically developing (TD) adults seated in separate rooms, in the same room back-to-back, and in the same room facing each other. Interpersonal proximity modulated broadband EEG power from 4-55 Hz and individual differences in self-reported social cognition modulated these effects in the beta and gamma frequency bands. These findings provide novel insight into the influence of social environment on brain activity and its association with social cognition through dual-brain EEG recording and demonstrate the importance of using interactive methods to study the human brain.
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174
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Barnett BR, Casey CP, Torres-Velázquez M, Rowley PA, Yu JPJ. Convergent brain microstructure across multiple genetic models of schizophrenia and autism spectrum disorder: A feasibility study. Magn Reson Imaging 2020; 70:36-42. [PMID: 32298718 DOI: 10.1016/j.mri.2020.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/27/2020] [Accepted: 04/08/2020] [Indexed: 11/17/2022]
Abstract
Neuroimaging studies of psychiatric illness have revealed a broad spectrum of structural and functional perturbations that have been attributed in part to the complex genetic heterogeneity underpinning these disorders. These perturbations have been identified in both preclinical genetic models and in patients when compared to control populations, but recent work has also demonstrated strong evidence for genetic, molecular, and structural convergence of several psychiatric diseases. We explored potential similarities in neural microstructure in preclinical genetic models of ASD (Fmr1, Nrxn1, Pten) and schizophrenia (Disc1 svΔ2) and in age- and sex-matched control animals with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Our findings demonstrate a convergence in brain microstructure across these four genetic models with both tract-based and region-of-interest based analyses, which continues to buttress an emerging understanding of converging neural microstructure in psychiatric disease.
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Affiliation(s)
- Brian R Barnett
- Neuroscience Training Program, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Cameron P Casey
- Neuroscience Training Program, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Maribel Torres-Velázquez
- Department of Biomedical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Paul A Rowley
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - John-Paul J Yu
- Neuroscience Training Program, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Biomedical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.
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175
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Yang J, Gohel S, Vachha B. Current methods and new directions in resting state fMRI. Clin Imaging 2020; 65:47-53. [PMID: 32353718 DOI: 10.1016/j.clinimag.2020.04.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 03/24/2020] [Accepted: 04/08/2020] [Indexed: 12/12/2022]
Abstract
Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a key component of investigations of neurocognitive and psychiatric behaviors. Over the past two decades, several methods and paradigms have been adopted to utilize and interpret data from resting-state fluctuations in the brain. These findings have increased our understanding of changes in many disease states. As the amount of resting state data available for research increases with big datasets and data-sharing projects, it is important to review the established traditional analysis methods and recognize areas where research methodology can be adapted to better accommodate the scale and complexity of rsfcMRI analysis. In this paper, we review established methods of analysis as well as areas that have been receiving increasing attention such as dynamic rsfcMRI, independent vector analysis, multiband rsfcMRI and network of networks.
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Affiliation(s)
- Jackie Yang
- NYU Grossman School of Medicine, 550 1(st) Avenue, New York, NY 10016, USA
| | - Suril Gohel
- Department of Health Informatics, Rutgers University School of Health Professions, 65 Bergen Street, Newark, NJ 07107, USA
| | - Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA.
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176
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Martínez K, Martínez-García M, Marcos-Vidal L, Janssen J, Castellanos FX, Pretus C, Villarroya Ó, Pina-Camacho L, Díaz-Caneja CM, Parellada M, Arango C, Desco M, Sepulcre J, Carmona S. Sensory-to-Cognitive Systems Integration Is Associated With Clinical Severity in Autism Spectrum Disorder. J Am Acad Child Adolesc Psychiatry 2020; 59:422-433. [PMID: 31260788 DOI: 10.1016/j.jaac.2019.05.033] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 05/16/2019] [Accepted: 06/25/2019] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Impaired multisensory integration in autism spectrum disorder (ASD) may arise from functional dysconnectivity among brain systems. Our study examines the functional connectivity integration between primary modal sensory regions and heteromodal processing cortex in ASD, and whether abnormalities in network integration relate to clinical severity. METHOD We studied a sample of 55 high-functioning ASD and 64 healthy control (HC) male children and adolescents (total n = 119, age range 7-18 years). Stepwise functional connectivity analysis (SFC) was applied to resting state functional magnetic resonance images (rsfMRI) to characterize the connectivity paths that link primary sensory cortices to higher-order brain cognitive functional circuits and to relate alterations in functional connectivity integration with three clinical scales: Social Communication Questionnaire, Social Responsiveness Scale, and Vineland Adaptive Behavior Scales. RESULTS HC displayed typical functional connectivity transitions from primary sensory systems to association areas, but the ASD group showed altered patterns of multimodal sensory integration to heteromodal systems. Specifically, compared to the HC group, the ASD group showed the following: (1) hyperconnectivity in the visual cortex at initial link step distances; (2) hyperconnectivity between sensory unimodal regions and regions of the default mode network; and (3) hypoconnectivity between sensory unimodal regions and areas of the fronto-parietal and attentional networks. These patterns of hyper- and hypoconnectivity were associated with increased clinical severity in ASD. CONCLUSION Networkwise reorganization in high-functioning ASD individuals affects strategic regions of unimodal-to-heteromodal cortical integration predicting clinical severity. In addition, SFC analysis appears to be a promising approach for studying the neural pathophysiology of multisensory integration deficits in ASD.
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Affiliation(s)
- Kenia Martínez
- Centro De Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón, Hospital General Universitario Gregorio Marañón, Madrid, Spain.
| | - Magdalena Martínez-García
- Centro De Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Unidad de Medicina y Cirugía Experimental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Luis Marcos-Vidal
- Unidad de Medicina y Cirugía Experimental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Joost Janssen
- Centro De Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands
| | - Francisco X Castellanos
- Hassenfeld Children's Hospital at NYU Langone, New York, NY and the Division of Clinical Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York
| | - Clara Pretus
- Unitat de Recerca en Neurociència Cognitiva, Departament de Psiquiatria i Medicina Legal, Universitat Autònoma de Barcelona, Barcelona, Spain, and Fundació IMIM (Institut Mar d'Investigacions Mèdiques), Barcelona, Spain
| | - Óscar Villarroya
- Unitat de Recerca en Neurociència Cognitiva, Departament de Psiquiatria i Medicina Legal, Universitat Autònoma de Barcelona, Barcelona, Spain, and Fundació IMIM (Institut Mar d'Investigacions Mèdiques), Barcelona, Spain
| | - Laura Pina-Camacho
- Centro De Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Hospital General Universitario Gregorio Marañón, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain; Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Covadonga M Díaz-Caneja
- Centro De Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Hospital General Universitario Gregorio Marañón, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Mara Parellada
- Centro De Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Hospital General Universitario Gregorio Marañón, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Celso Arango
- Centro De Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Hospital General Universitario Gregorio Marañón, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Manuel Desco
- Centro De Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Unidad de Medicina y Cirugía Experimental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Jorge Sepulcre
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA
| | - Susanna Carmona
- Centro De Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Unidad de Medicina y Cirugía Experimental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Universidad Carlos III, Madrid, Spain
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177
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Wills P, Meyer FG. Metrics for graph comparison: A practitioner's guide. PLoS One 2020; 15:e0228728. [PMID: 32050004 PMCID: PMC7015405 DOI: 10.1371/journal.pone.0228728] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 01/22/2020] [Indexed: 11/18/2022] Open
Abstract
Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work.
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Affiliation(s)
- Peter Wills
- Department of Applied Mathematics, University of Colorado at Boulder, Boulder, CO, United States of America
| | - François G. Meyer
- Department of Applied Mathematics, University of Colorado at Boulder, Boulder, CO, United States of America
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178
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Kercher C, Azinfar L, Dinalankara DMR, Takahashi TN, Miles JH, Yao G. A longitudinal study of pupillary light reflex in 6- to 24-month children. Sci Rep 2020; 10:1205. [PMID: 31988320 PMCID: PMC6985190 DOI: 10.1038/s41598-020-58254-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 01/13/2020] [Indexed: 01/08/2023] Open
Abstract
Pupillary light reflex (PLR) is an involuntary response where the pupil size changes with luminance. Studies have shown that PLR response was altered in children with autism spectrum disorders (ASDs) and other neurological disorders. However, PLR in infants and toddlers is still understudied. We conducted a longitudinal study to investigate PLR in children of 6-24 months using a remote pupillography device. The participants are categorized into two groups. The 'high risk' (HR) group includes children with one or more siblings diagnosed with ASDs; whereas the 'low risk' (LR) group includes children without an ASD diagnosis in the family history. The participants' PLR was measured every six months until the age of 24 months. The results indicated a significant age effect in multiple PLR parameters including resting pupil radius, minimal pupil radius, relative constriction, latency, and response time. In addition, the HR group had a significantly larger resting and minimal pupil size than the LR group. The experimental data acquired in this study revealed not only general age-related PLR changes in infants and toddlers, but also different PLRs in children with a higher risk of ASD.
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Affiliation(s)
- Clare Kercher
- Department of Biomedical, Biological & Chemical Engineering, University of Missouri, Columbia, MO, 65211, USA
| | - Leila Azinfar
- Department of Biomedical, Biological & Chemical Engineering, University of Missouri, Columbia, MO, 65211, USA
| | - Dinalankara M R Dinalankara
- Department of Biomedical, Biological & Chemical Engineering, University of Missouri, Columbia, MO, 65211, USA
- Department of Computer Engineering, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - T Nicole Takahashi
- Thompson Center for Autism and Neurodevelopmental Disorders, University of Missouri, Columbia, MO, 65211, USA
| | - Judith H Miles
- Thompson Center for Autism and Neurodevelopmental Disorders, University of Missouri, Columbia, MO, 65211, USA
| | - Gang Yao
- Department of Biomedical, Biological & Chemical Engineering, University of Missouri, Columbia, MO, 65211, USA.
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179
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Abstract
BACKGROUND Brain regions are functionally diverse, and a given region may engage in a variety of tasks. This functional diversity of brain regions may be one factor that has prevented the finding of consistent biomarkers for brain disorders such as autism spectrum disorder (ASD). Thus, methods to characterise brain regions would help to determine how functional abnormalities contribute to affected behaviours. AIMS As the first illustration of the meta-analytic behavioural profiling procedure, we evaluated how the regions with disrupted connectivity in ASD contributed to various behaviours. METHOD Connectivity abnormalities were determined from a published degree centrality group comparison based on functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange. Using BrainMap's database of task-based neuroimaging studies, behavioural profiles were created for abnormally connected regions by relating these regions to tasks activating them. RESULTS Hyperconnectivity in ASD brains was significantly related to memory, attention, reasoning, social, execution and speech behaviours. Hypoconnectivity was related to vision, execution and speech behaviours. CONCLUSIONS The procedure outlines the first clinical neuroimaging application of a behavioural profiling method that estimates the functional diversity of brain regions, allowing for the relation of abnormal functional connectivity to diagnostic criteria. Behavioural profiling and the computational insights it provides can facilitate better understanding of the functional manifestations of various disorders, including ASD.
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Affiliation(s)
- William Snyder
- Student, Program in Neuroscience, Bucknell University; Research Assistant, Autism and Developmental Medicine Institute, Geisinger, USA
| | - Vanessa Troiani
- Assistant Professor, Autism and Developmental Medicine Institute, Geisinger; and Department of Basic Sciences, Geisinger Commonwealth School of Medicine, USA
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180
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Bathelt J, Koolschijn PC, Geurts HM. Age-variant and age-invariant features of functional brain organization in middle-aged and older autistic adults. Mol Autism 2020; 11:9. [PMID: 31993112 PMCID: PMC6977283 DOI: 10.1186/s13229-020-0316-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 01/12/2020] [Indexed: 11/21/2022] Open
Abstract
Background The majority of research effort into autism has been dedicated to understanding mechanisms during early development. As a consequence, research on the broader life course of an autism spectrum condition (ASC) has largely been neglected and almost nothing is known about ASC beyond middle age. Differences in brain connectivity that arise during early development may be maintained across the lifespan and may play protective or detrimental roles in older age. Method This study explored age-related differences in functional connectivity across middle and older age in clinically diagnosed autistic adults (n = 44, 30-73 years) and in an age-matched typical comparison group (n = 45). Results The results indicated parallel age-related associations in ASC and typical aging for the local efficiency and connection strength of the default mode network and for the segregation of the frontoparietal control network. In contrast, group differences in visual network connectivity are compatible with a safeguarding interpretation of less age-related decline in brain function in ASC. This divergence was mirrored in different associations between visual network connectivity and reaction time variability in the ASC and comparison group. Limitations The study is cross-sectional and may be affected by cohort effects. As all participants received their autism diagnosis in adulthood, this might hinder generalizability. Conclusion These results highlight the complexity of aging in ASC with both parallel and divergent trajectories across different aspects of functional network organization.
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Affiliation(s)
- Joe Bathelt
- Dutch Autism & ADHD Research Center, Brain & Cognition, Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WS Amsterdam, Netherlands
| | - P. Cédric Koolschijn
- Dutch Autism & ADHD Research Center, Brain & Cognition, Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WS Amsterdam, Netherlands
| | - Hilde M. Geurts
- Dutch Autism & ADHD Research Center, Brain & Cognition, Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WS Amsterdam, Netherlands
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181
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Lin HY, Ni HC, Tseng WYI, Gau SSF. Characterizing intrinsic functional connectivity in relation to impaired self-regulation in intellectually able male youth with autism spectrum disorder. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2020; 24:1201-1216. [DOI: 10.1177/1362361319888104] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
While a considerable number of youth with autism spectrum disorder exhibit impaired self-regulation (dysregulation), little is known about the neural correlates of dysregulation in autism spectrum disorder. In a sample of intellectually able boys with autism spectrum disorder (further categorized as those with and without dysregulation) and typically developing boys (aged 7–17 years), we conducted a multivariate connectome-wide association study to examine the intrinsic functional connectivity with resting-state functional magnetic resonance imaging. Dysregulation was defined by the sum of Attention, Aggression, and Anxiety/Depression subscales on the Child Behavior Checklist. We identified that both categorical and dimensional neural correlates of dysregulation in youth with autism spectrum disorder involved atypical connectivity among the components of multiple brain networks, especially between those subserving sensorimotor processing and salience encoding, beyond higher-level cognitive control circuitries. Interaction within the attention network might serve as autism spectrum disorder–specific neural correlates underpinning dysregulation. Our results highlight that the inter-individual variability in dysregulation might contribute to the inconsistency in the neuroimaging literature of autism spectrum disorder. Collectively, the present findings provide evidence to suggest that dysregulation might be considered as both categorical and dimensional moderators to parse heterogeneity of autism spectrum disorder.Lay AbstractImpaired self-regulation (i.e., dysregulation in affective, behavioral, and cognitive control), is commonly present in young people with autism spectrum disorder (ASD). However, little is known about what is happening in people’s brains when self-regulation is impaired in young people with ASD. We used a technique called functional MRI (which measures brain activity by detecting changes associated with blood flow) at a resting state (when participants are not asked to do anything) to research this in intellectually able young people with ASD. We found that brains with more connections, especially between regions involved in sensorimotor processing and regions involved in the processes that enable peoples to focus their attention on the most pertinent features from the sensory environment (salience processing), were related to more impaired self-regulation in young people with and without ASD. We also found that impaired self-regulation was related to increased communication within the brain system involved in voluntary orienting attention to a sensory cue (the dorsal attention network) in young people with ASD. These results highlight how different people have different degrees of dysregulation, which has been largely overlooked in the earlier brain imaging reports on ASD. This might contribute to understanding some of the inconsistencies in the existing published literature on this topic.
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Affiliation(s)
- Hsiang-Yuan Lin
- National Taiwan University Hospital
- National Taiwan University
| | - Hsing-Chang Ni
- National Taiwan University
- Chang Gung Memorial Hospital at Linkou
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182
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Gupta S, Rajapakse JC, Welsch RE. Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder. Neuroimage Clin 2020; 25:102186. [PMID: 32000101 PMCID: PMC7042673 DOI: 10.1016/j.nicl.2020.102186] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 01/08/2020] [Accepted: 01/13/2020] [Indexed: 11/30/2022]
Abstract
Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We argue that weak connections in brain functional networks lead to misclassification of brain regions as hubs. In order to resolve this, we propose a new measure called ambivert degree that considers the node's degree as well as connection weights in order to identify nodes with both high degree and high connection weights as hubs. Using resting-state functional MRI scans from the Human Connectome Project, we show that ambivert degree identifies brain hubs that are not only crucial but also invariable across subjects. We hypothesize that nodal measures based on ambivert degree can be effectively used to classify patients from healthy controls for diseases that are known to have widespread hub disruption. Using patient data for Alzheimer's Disease and Autism Spectrum Disorder, we show that the hubs in the patient and healthy groups are very different for both the diseases and deep feedforward neural networks trained on nodal hub features lead to a significantly higher classification accuracy with significantly fewer trainable weights compared to using functional connectivity features. Thus, the ambivert degree improves identification of crucial brain hubs in healthy subjects and can be used as a diagnostic feature to detect neurological diseases characterized by hub disruption.
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Affiliation(s)
- Sukrit Gupta
- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
| | - Jagath C Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore.
| | - Roy E Welsch
- MIT Center for Statistics and Data Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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183
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Lord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T, Jones EJH, Jones RM, Pickles A, State MW, Taylor JL, Veenstra-VanderWeele J. Autism spectrum disorder. Nat Rev Dis Primers 2020; 6:5. [PMID: 31949163 PMCID: PMC8900942 DOI: 10.1038/s41572-019-0138-4] [Citation(s) in RCA: 572] [Impact Index Per Article: 143.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/26/2019] [Indexed: 12/27/2022]
Abstract
Autism spectrum disorder is a construct used to describe individuals with a specific combination of impairments in social communication and repetitive behaviours, highly restricted interests and/or sensory behaviours beginning early in life. The worldwide prevalence of autism is just under 1%, but estimates are higher in high-income countries. Although gross brain pathology is not characteristic of autism, subtle anatomical and functional differences have been observed in post-mortem, neuroimaging and electrophysiological studies. Initially, it was hoped that accurate measurement of behavioural phenotypes would lead to specific genetic subtypes, but genetic findings have mainly applied to heterogeneous groups that are not specific to autism. Psychosocial interventions in children can improve specific behaviours, such as joint attention, language and social engagement, that may affect further development and could reduce symptom severity. However, further research is necessary to identify the long-term needs of people with autism, and treatments and the mechanisms behind them that could result in improved independence and quality of life over time. Families are often the major source of support for people with autism throughout much of life and need to be considered, along with the perspectives of autistic individuals, in both research and practice.
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Affiliation(s)
- Catherine Lord
- Departments of Psychiatry and School of Education, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Traolach S Brugha
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Tony Charman
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Guillaume Dumas
- Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France
| | | | - Emily J H Jones
- Centre for Brain & Cognitive Development, University of London, London, UK
| | - Rebecca M Jones
- The Sackler Institute for Developmental Psychobiology, New York, NY, USA
- The Center for Autism and the Developing Brain, White Plains, NY, USA
| | - Andrew Pickles
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Matthew W State
- Department of Psychiatry, Langley Porter Psychiatric Institute and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Julie Lounds Taylor
- Department of Pediatrics and Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
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184
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He Y, Byrge L, Kennedy DP. Nonreplication of functional connectivity differences in autism spectrum disorder across multiple sites and denoising strategies. Hum Brain Mapp 2020; 41:1334-1350. [PMID: 31916675 PMCID: PMC7268009 DOI: 10.1002/hbm.24879] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 10/25/2019] [Accepted: 11/19/2019] [Indexed: 12/24/2022] Open
Abstract
A rapidly growing number of studies on autism spectrum disorder (ASD) have used resting‐state fMRI to identify alterations of functional connectivity, with the hope of identifying clinical biomarkers or underlying neural mechanisms. However, results have been largely inconsistent across studies, and there remains a pressing need to determine the primary factors influencing replicability. Here, we used resting‐state fMRI data from the Autism Brain Imaging Data Exchange to investigate two potential factors: denoising strategy and data site (which differ in terms of sample, data acquisition, etc.). We examined the similarity of both group‐averaged functional connectomes and group‐level differences (ASD vs. control) across 33 denoising pipelines and four independently‐acquired datasets. The group‐averaged connectomes were highly consistent across pipelines (r = 0.92 ± 0.06) and sites (r = 0.88 ± 0.02). However, the group differences, while still consistent within site across pipelines (r = 0.76 ± 0.12), were highly inconsistent across sites regardless of choice of denoising strategies (r = 0.07 ± 0.04), suggesting lack of replication may be strongly influenced by site and/or cohort differences. Across‐site similarity remained low even when considering the data at a large‐scale network level or when considering only the most significant edges. We further show through an extensive literature survey that the parameters chosen in the current study (i.e., sample size, age range, preprocessing methods) are quite representative of the published literature. These results highlight the importance of examining replicability in future studies of ASD, and, more generally, call for extra caution when interpreting alterations in functional connectivity across groups of individuals.
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Affiliation(s)
- Ye He
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
| | - Lisa Byrge
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
| | - Daniel P Kennedy
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana.,Cognitive Science Program, Indiana University, Bloomington, Indiana.,Program in Neuroscience, Indiana University, Bloomington, Indiana
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185
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Khosla M, Jamison K, Ngo GH, Kuceyeski A, Sabuncu MR. Machine learning in resting-state fMRI analysis. Magn Reson Imaging 2019; 64:101-121. [PMID: 31173849 PMCID: PMC6875692 DOI: 10.1016/j.mri.2019.05.031] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 05/20/2019] [Accepted: 05/21/2019] [Indexed: 12/13/2022]
Abstract
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.
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Affiliation(s)
- Meenakshi Khosla
- School of Electrical and Computer Engineering, Cornell University, United States of America
| | - Keith Jamison
- Radiology, Weill Cornell Medical College, United States of America
| | - Gia H Ngo
- School of Electrical and Computer Engineering, Cornell University, United States of America
| | - Amy Kuceyeski
- Radiology, Weill Cornell Medical College, United States of America; Brain and Mind Research Institute, Weill Cornell Medical College, United States of America
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, United States of America; Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University, United States of America.
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186
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Jia Y, Gu H. Sample Entropy Combined with the K-Means Clustering Algorithm Reveals Six Functional Networks of the Brain. ENTROPY 2019. [PMCID: PMC7514501 DOI: 10.3390/e21121156] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Identifying brain regions contained in brain functional networks and functions of brain functional networks is of great significance in understanding the complexity of the human brain. The 160 regions of interest (ROIs) in the human brain determined by the Dosenbach’s template have been divided into six functional networks with different functions. In the present paper, the complexity of the human brain is characterized by the sample entropy (SampEn) of dynamic functional connectivity (FC) which is obtained by analyzing the resting-state functional magnetic resonance imaging (fMRI) data acquired from healthy participants. The 160 ROIs are clustered into six clusters by applying the K-means clustering algorithm to the SampEn of dynamic FC as well as the static FC which is also obtained by analyzing the resting-state fMRI data. The six clusters obtained from the SampEn of dynamic FC and the static FC show very high overlap and consistency ratios with the six functional networks. Furthermore, for four of six clusters, the overlap ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC, and for five of six clusters, the consistency ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC. The results show that the combination of machine learning methods and the FC obtained using the blood oxygenation level-dependent (BOLD) signals can identify the functional networks of the human brain, and nonlinear dynamic characteristics of the FC are more effective than the static characteristics of the FC in identifying brain functional networks and the complexity of the human brain.
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Affiliation(s)
- Yanbing Jia
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471000, China;
| | - Huaguang Gu
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
- Correspondence:
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187
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Rolls ET, Zhou Y, Cheng W, Gilson M, Deco G, Feng J. Effective connectivity in autism. Autism Res 2019; 13:32-44. [PMID: 31657138 DOI: 10.1002/aur.2235] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 10/02/2019] [Accepted: 10/03/2019] [Indexed: 11/06/2022]
Abstract
The aim was to go beyond functional connectivity, by measuring in the first large-scale study differences in effective, that is directed, connectivity between brain areas in autism compared to controls. Resting-state functional magnetic resonance imaging was analyzed from the Autism Brain Imaging Data Exchange (ABIDE) data set in 394 people with autism spectrum disorder and 473 controls, and effective connectivity (EC) was measured between 94 brain areas. First, in autism, the middle temporal gyrus and other temporal areas had lower effective connectivities to the precuneus and cuneus, and these were correlated with the Autism Diagnostic Observational Schedule total, communication, and social scores. This lower EC from areas implicated in face expression analysis and theory of mind to the precuneus and cuneus implicated in the sense of self may relate to the poor understanding of the implications of face expression inputs for oneself in autism, and to the reduced theory of mind. Second, the hippocampus and amygdala had higher EC to the middle temporal gyrus in autism, and these are thought to be back projections based on anatomical evidence and are weaker than in the other direction. This may be related to increased retrieval of recent and emotional memories in autism. Third, some prefrontal cortex areas had higher EC with each other and with the precuneus and cuneus. Fourth, there was decreased EC from the temporal pole to the ventromedial prefrontal cortex, and there was evidence for lower activity in the ventromedial prefrontal cortex, a brain area implicated in emotion-related decision-making. Autism Res 2020, 13: 32-44. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: To understand autism spectrum disorders better, it may be helpful to understand whether brain systems cause effects on each other differently in people with autism. In this first large-scale neuroimaging investigation of effective connectivity in people with autism, it is shown that parts of the temporal lobe involved in facial expression identification and theory of mind have weaker effects on the precuneus and cuneus implicated in the sense of self. This may relate to the poor understanding of the implications of face expression inputs for oneself in autism, and to the reduced theory of mind.
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Affiliation(s)
- Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry, UK.,Oxford Centre for Computational Neuroscience, Oxford, UK.,Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yunyi Zhou
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Barcelona, Spain
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, UK.,Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
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188
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Li Y, Zhu Y, Nguchu BA, Wang Y, Wang H, Qiu B, Wang X. Dynamic Functional Connectivity Reveals Abnormal Variability and Hyper‐connected Pattern in Autism Spectrum Disorder. Autism Res 2019; 13:230-243. [DOI: 10.1002/aur.2212] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 09/08/2019] [Accepted: 09/10/2019] [Indexed: 12/19/2022]
Affiliation(s)
- Yu Li
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
| | - Yuying Zhu
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
- School of Information Engineering, Southwest University of Science and Technology Mianyang China
| | | | - Yanming Wang
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
| | - Huijuan Wang
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
| | - Bensheng Qiu
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
| | - Xiaoxiao Wang
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
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189
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Yang G, Shcheglovitov A. Probing disrupted neurodevelopment in autism using human stem cell-derived neurons and organoids: An outlook into future diagnostics and drug development. Dev Dyn 2019; 249:6-33. [PMID: 31398277 DOI: 10.1002/dvdy.100] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 07/23/2019] [Accepted: 07/31/2019] [Indexed: 12/11/2022] Open
Abstract
Autism spectrum disorders (ASDs) represent a spectrum of neurodevelopmental disorders characterized by impaired social interaction, repetitive or restrictive behaviors, and problems with speech. According to a recent report by the Centers for Disease Control and Prevention, one in 68 children in the US is diagnosed with ASDs. Although ASD-related diagnostics and the knowledge of ASD-associated genetic abnormalities have improved in recent years, our understanding of the cellular and molecular pathways disrupted in ASD remains very limited. As a result, no specific therapies or medications are available for individuals with ASDs. In this review, we describe the neurodevelopmental processes that are likely affected in the brains of individuals with ASDs and discuss how patient-specific stem cell-derived neurons and organoids can be used for investigating these processes at the cellular and molecular levels. Finally, we propose a discovery pipeline to be used in the future for identifying the cellular and molecular deficits and developing novel personalized therapies for individuals with idiopathic ASDs.
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Affiliation(s)
- Guang Yang
- Department of Neurobiology and Anatomy, University of Utah, Salt Lake City, Utah.,Neuroscience Graduate Program, University of Utah, Salt Lake City, Utah
| | - Alex Shcheglovitov
- Department of Neurobiology and Anatomy, University of Utah, Salt Lake City, Utah.,Neuroscience Graduate Program, University of Utah, Salt Lake City, Utah
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190
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Decreased interhemispheric functional connectivity rather than corpus callosum volume as a potential biomarker for autism spectrum disorder. Cortex 2019; 119:258-266. [DOI: 10.1016/j.cortex.2019.05.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 02/03/2019] [Accepted: 05/03/2019] [Indexed: 01/06/2023]
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191
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Dubourg L, Vrticka P, Pouillard V, Eliez S, Schneider M. Divergent default mode network connectivity during social perception in 22q11.2 deletion syndrome. Psychiatry Res Neuroimaging 2019; 291:9-17. [PMID: 31344628 DOI: 10.1016/j.pscychresns.2019.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 07/09/2019] [Accepted: 07/12/2019] [Indexed: 02/05/2023]
Abstract
AIM The 22q11.2 deletion (22q11DS) syndrome is a neurogenetic condition marked by social dysfunction. A major network involved in social cognition is the default mode network (DMN). To date, no study has investigated DMN functional connectivity during socio-cognitive paradigms in 22q11DS. METHOD We used the psychophysiological analysis (PPI) to investigate functional connectivity of the DMN during social perception in 22 participants with 22q11DS and 22 healthy controls. Association between DMN connectivity and prodromal symptoms was also examined. RESULTS 22q11DS patients exhibited stronger connectivity between the inferior parietal lobule (IPL) and the posterior cingulate cortex (PCC)/precuneus as well as lower connectivity between the precuneus and middle/superior frontal regions compared to controls. Association between IPL-PCC/precuneus connectivity and negative symptoms was also found in individuals with 22q11DS. CONCLUSION Our results point to (1) divergent DMN connectivity in patients with 22q11DS compared to controls; (2) association between DMN connectivity and negative symptom severity in patients. Results support the role of the DMN in social deficits of the 22q11DS population.
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Affiliation(s)
- Lydia Dubourg
- Developmental Imaging and Psychopathology Lab, Department of Psychiatry, School of Medicine, University of Geneva, Geneva, Switzerland.
| | - Pascal Vrticka
- Department of Social Neuroscience, Max Planck Institute for Human Cognitive and brain Sciences, Leipzig, Germany
| | - Virginie Pouillard
- Developmental Imaging and Psychopathology Lab, Department of Psychiatry, School of Medicine, University of Geneva, Geneva, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Lab, Department of Psychiatry, School of Medicine, University of Geneva, Geneva, Switzerland; Department of Genetic Medicine and Development, School of Medicine, University of Geneva, Geneva, Switzerland
| | - Maude Schneider
- Developmental Imaging and Psychopathology Lab, Department of Psychiatry, School of Medicine, University of Geneva, Geneva, Switzerland
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192
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Takeda Y, Itahashi T, Sato MA, Yamashita O. Estimating repetitive spatiotemporal patterns from many subjects' resting-state fMRIs. Neuroimage 2019; 203:116182. [PMID: 31525496 DOI: 10.1016/j.neuroimage.2019.116182] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 08/20/2019] [Accepted: 09/10/2019] [Indexed: 01/06/2023] Open
Abstract
Recently, we proposed a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data (SpatioTemporal Pattern estimation, STeP) (Takeda et al., 2016). From such resting-state data as functional MRI (fMRI), STeP can estimate several spatiotemporal patterns and their onsets even if they are overlapping. Nowadays, a growing number of resting-state data are publicly available from such databases as the Autism Brain Imaging Data Exchange (ABIDE), which promote a better understanding of resting-state brain activities. In this study, we extend STeP to make it applicable to such big databases, thus proposing the method we call BigSTeP. From many subjects' resting-state data, BigSTeP estimates spatiotemporal patterns that are common across subjects (common spatiotemporal patterns) as well as the corresponding spatiotemporal patterns in each subject (subject-specific spatiotemporal patterns). After verifying the performance of BigSTeP by simulation tests, we applied it to over 1,000 subjects' resting-state fMRIs (rsfMRIs) obtained from ABIDE I. This revealed two common spatiotemporal patterns and the corresponding subject-specific spatiotemporal patterns. The common spatiotemporal patterns included spatial patterns resembling the default mode (DMN), sensorimotor, auditory, and visual networks, suggesting that these networks are time-locked with each other. We compared the subject-specific spatiotemporal patterns between autism spectrum disorder (ASD) and typically developed (TD) groups. As a result, significant differences were concentrated at a specific time in a pattern, when the DMN exhibited large positive activity. This suggests that the differences are context-dependent, that is, the differences in fMRI activities between ASDs and TDs do not always occur during the resting state but tend to occur when the DMN exhibits large positive activity. All of these results demonstrate the usefulness of BigSTeP in extracting inspiring hypotheses from big databases in a data-driven way.
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Affiliation(s)
- Yusuke Takeda
- Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan.
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, 6-11-11 Kita-karasuyama, Setagaya-ku, Tokyo, 157-8577, Japan
| | - Masa-Aki Sato
- Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
| | - Okito Yamashita
- Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
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193
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A Protocol for Sedation Free MRI and PET Imaging in Adults with Autism Spectrum Disorder. J Autism Dev Disord 2019; 49:3036-3044. [PMID: 31004246 DOI: 10.1007/s10803-019-04010-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Imaging technologies such as positron emission tomography (PET) and magnetic resonance imaging (MRI) present unparalleled opportunities to investigate the neural basis of autism spectrum disorder (ASD). However, challenges such as deficits in social interaction, anxiety around new experiences, impaired language abilities, and hypersensitivity to sensory stimuli make participating in neuroimaging studies challenging for individuals with ASD. In this commentary, we describe the existent training protocols for preparing individuals with ASD for PET/MRI scans and our own experience developing a training protocol to facilitate the inclusion of low-functioning adults with ASD in PET-MRI studies. We hope to raise awareness of the need for more information exchange between research groups about lessons learned in this context in order to include the entire disease spectrum in neuroimaging studies.
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194
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Guo X, Simas T, Lai MC, Lombardo MV, Chakrabarti B, Ruigrok ANV, Bullmore ET, Baron-Cohen S, Chen H, Suckling J. Enhancement of indirect functional connections with shortest path length in the adult autistic brain. Hum Brain Mapp 2019; 40:5354-5369. [PMID: 31464062 PMCID: PMC6864892 DOI: 10.1002/hbm.24777] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 06/23/2019] [Accepted: 08/18/2019] [Indexed: 12/30/2022] Open
Abstract
Autism is a neurodevelopmental condition characterized by atypical brain functional organization. Here we investigated the intrinsic indirect (semi‐metric) connectivity of the functional connectome associated with autism. Resting‐state functional magnetic resonance imaging scans were acquired from 65 neurotypical adults (33 males/32 females) and 61 autistic adults (30 males/31 females). From functional connectivity networks, semi‐metric percentages (SMPs) were calculated to assess the proportion of indirect shortest functional pathways at global, hemisphere, network, and node levels. Group comparisons were then conducted to ascertain differences between autism and neurotypical control groups. Finally, the strength and length of edges were examined to explore the patterns of semi‐metric connections associated with autism. Compared with neurotypical controls, autistic adults displayed significantly higher SMP at all spatial scales, similar to prior observations in adolescents. Differences were primarily in weaker, longer‐distance edges in the majority between networks. However, no significant diagnosis‐by‐sex interaction effects were observed on global SMP. These findings suggest increased indirect functional connectivity in the autistic brain is persistent from adolescence to adulthood and is indicative of reduced functional network integration.
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Affiliation(s)
- Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Tiago Simas
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Meng-Chuan Lai
- Centre for Addiction and Mental Health and the Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, Canada.,Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Michael V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Italian Institute of Technology, Rovereto, Italy
| | - Bhismadev Chakrabarti
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK
| | - Amber N V Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
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195
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Eill A, Jahedi A, Gao Y, Kohli JS, Fong CH, Solders S, Carper RA, Valafar F, Bailey BA, Müller RA. Functional Connectivities Are More Informative Than Anatomical Variables in Diagnostic Classification of Autism. Brain Connect 2019; 9:604-612. [PMID: 31328535 DOI: 10.1089/brain.2019.0689] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Machine learning techniques have been implemented to reveal brain features that distinguish people with autism spectrum disorders (ASDs) from typically developing (TD) peers. However, it remains unknown whether different neuroimaging modalities are equally informative for diagnostic classification. We combined anatomical magnetic resonance imaging (aMRI), diffusion weighted imaging (DWI), and functional connectivity MRI (fcMRI) using conditional random forest (CRF) for supervised learning to compare how informative each modality was in diagnostic classification. In-house data (N = 93) included 47 TD and 46 ASD participants, matched on age, motion, and nonverbal IQ. Four main analyses consistently indicated that fcMRI variables were significantly more informative than anatomical variables from aMRI and DWI. This was found (1) when the top 100 variables from CRF (run separately in each modality) were combined for multimodal CRF; (2) when only 19 top variables reaching >67% accuracy in each modality were combined in multimodal CRF; and (3) when the large number of initial variables (before dimension reduction) potentially biasing comparisons in favor of fcMRI was reduced using a less granular region of interest scheme. Consistent superiority of fcMRI was even found (4) when 100 variables per modality were randomly selected, removing any such potential bias. Greater informative value of functional than anatomical modalities may relate to the nature of fcMRI data, reflecting more closely behavioral condition, which is also the basis of diagnosis, whereas brain anatomy may be more reflective of neurodevelopmental history.
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Affiliation(s)
- Aina Eill
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Department of Bioinformatics and Medical Informatics, San Diego State University, San Diego, California
| | - Afrooz Jahedi
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Computational Science Research Center, San Diego State University, San Diego, California
| | - Yangfeifei Gao
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Jiwandeep S Kohli
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Christopher H Fong
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Seraphina Solders
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Ruth A Carper
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Faramarz Valafar
- Department of Bioinformatics and Medical Informatics, San Diego State University, San Diego, California
| | - Barbara A Bailey
- Department of Mathematics and Statistics, San Diego State University, San Diego, California
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
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196
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Kahathuduwa CN, West B, Mastergeorge A. Effects of Overweight or Obesity on Brain Resting State Functional Connectivity of Children with Autism Spectrum Disorder. J Autism Dev Disord 2019; 49:4751-4760. [DOI: 10.1007/s10803-019-04187-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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197
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Lake EMR, Finn ES, Noble SM, Vanderwal T, Shen X, Rosenberg MD, Spann MN, Chun MM, Scheinost D, Constable RT. The Functional Brain Organization of an Individual Allows Prediction of Measures of Social Abilities Transdiagnostically in Autism and Attention-Deficit/Hyperactivity Disorder. Biol Psychiatry 2019; 86:315-326. [PMID: 31010580 PMCID: PMC7311928 DOI: 10.1016/j.biopsych.2019.02.019] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 02/01/2019] [Accepted: 02/02/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND Autism spectrum disorder and attention-deficit/hyperactivity disorder (ADHD) are associated with complex changes as revealed by functional magnetic resonance imaging. To date, neuroimaging-based models are not able to characterize individuals with sufficient sensitivity and specificity. Further, although evidence shows that ADHD traits occur in individuals with autism spectrum disorder, and autism spectrum disorder traits in individuals with ADHD, the neurofunctional basis of the overlap is undefined. METHODS Using individuals from the Autism Brain Imaging Data Exchange and ADHD-200, we apply a data-driven, subject-level approach, connectome-based predictive modeling, to resting-state functional magnetic resonance imaging data to identify brain-behavior associations that are predictive of symptom severity. We examine cross-diagnostic commonalities and differences. RESULTS Using leave-one-subject-out and split-half analyses, we define networks that predict Social Responsiveness Scale, Autism Diagnostic Observation Schedule, and ADHD Rating Scale scores and confirm that these networks generalize to novel subjects. Networks share minimal overlap of edges (<2%) but some common regions of high hubness (Brodmann areas 10, 11, and 21, cerebellum, and thalamus). Further, predicted Social Responsiveness Scale scores for individuals with ADHD are linked to ADHD symptoms, supporting the hypothesis that brain organization relevant to autism spectrum disorder severity shares a component associated with attention in ADHD. Predictive connections and high-hubness regions are found within a wide range of brain areas and across conventional networks. CONCLUSIONS An individual's functional connectivity profile contains information that supports dimensional, nonbinary classification in autism spectrum disorder and ADHD. Furthermore, we can determine disorder-specific and shared neurofunctional pathology using our method.
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Affiliation(s)
- Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut.
| | - Emily S Finn
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, Maryland
| | - Stephanie M Noble
- Interdepartmental Neuroscience Program, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Tamara Vanderwal
- Yale Child Study Center, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Monica D Rosenberg
- Department of Psychology, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Psychology, University of Chicago, Chicago, Illinois
| | - Marisa N Spann
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, New York
| | - Marvin M Chun
- Interdepartmental Neuroscience Program, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Psychology, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Neurobiology, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut; Interdepartmental Neuroscience Program, Yale School of Medicine, Yale University, New Haven, Connecticut; Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, Connecticut
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198
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Saaybi S, AlArab N, Hannoun S, Saade M, Tutunji R, Zeeni C, Shbarou R, Hourani R, Boustany RM. Pre- and Post-therapy Assessment of Clinical Outcomes and White Matter Integrity in Autism Spectrum Disorder: Pilot Study. Front Neurol 2019; 10:877. [PMID: 31456741 PMCID: PMC6701406 DOI: 10.3389/fneur.2019.00877] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 07/29/2019] [Indexed: 11/13/2022] Open
Abstract
Objective: This pilot study aims to identify white matter (WM) tract abnormalities in Autism Spectrum Disorders (ASD) toddlers and pre-schoolers by Diffusion Tensor Imaging (DTI), and to correlate imaging findings with clinical improvement after early interventional and Applied Behavior Analysis (ABA) therapies by Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP). Methods: DTI scans were performed on 17 ASD toddlers/pre-schoolers and seven age-matched controls. Nine ASD patients had follow-up MRI 12 months following early intervention and ABA therapy. VB-MAPP was assessed and compared at diagnosis, 6 and 12 months after therapies. Tract-Based Spatial Statistics (TBSS) was used to measure fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial (RD) diffusivity. Results: VB-MAPP scores improved at 6 and 12 months after early intervention and ABA therapy compared to scores at baseline. TBSS analysis showed significant FA decrease and/or RD increase in ASD patients before therapy vs. controls in inferior fronto-occipital fasciculi, uncinate fasciculi, left superior fronto-occipital fasciculus, forceps minor, left superior fronto-occipital fasciculus, right superior longitudinal fasciculus, corona radiate bilaterally, and left external capsule. A significantly FA increase in 21 tracts and ROIs is reported in post- vs. pre-therapy DTI analysis. Conclusion: DTI findings highlighted ASD patient WM abnormalities at diagnosis and confirmed the benefits of 12 months of early intervention and ABA therapy on clinical and neuro imaging outcomes.
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Affiliation(s)
- Stephanie Saaybi
- Division of Pediatric Neurology, Departments of Pediatrics and Adolescent Medicine/Biochemistry and Molecular Genetics, American University of Beirut Medical Center, Beirut, Lebanon.,Department of Pediatrics and Adolescent Medicine, MedStar Georgetown University Hospital, Washington, DC, United States
| | - Natally AlArab
- Division of Neuroradiology, Department of Diagnostic Radiology, American University of Beirut Medical Center, Beirut, Lebanon
| | - Salem Hannoun
- Faculty of Medicine, Nehme and Therese Tohme Multiple Sclerosis Center, American University of Beirut Medical Center, Beirut, Lebanon.,Faculty of Medicine, Abu-Haidar Neuroscience Institute, American University of Beirut Medical Center, Beirut, Lebanon
| | - Maritherese Saade
- AUBMC Special Kids Clinic (ASKC), American University of Beirut Medical Center, Beirut, Lebanon
| | - Rayyan Tutunji
- Division of Neuroradiology, Department of Diagnostic Radiology, American University of Beirut Medical Center, Beirut, Lebanon.,Cognition and Behaviour, Department of Cognitive Neuroscience, Donders Institute for Brain, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Carine Zeeni
- Division of Neuro-Anesthesia, Department of Anesthesia, American University of Beirut Medical Center, Beirut, Lebanon
| | - Rolla Shbarou
- Division of Pediatric Neurology, Departments of Pediatrics and Adolescent Medicine/Biochemistry and Molecular Genetics, American University of Beirut Medical Center, Beirut, Lebanon
| | - Roula Hourani
- Division of Neuroradiology, Department of Diagnostic Radiology, American University of Beirut Medical Center, Beirut, Lebanon
| | - Rose-Mary Boustany
- Division of Pediatric Neurology, Departments of Pediatrics and Adolescent Medicine/Biochemistry and Molecular Genetics, American University of Beirut Medical Center, Beirut, Lebanon.,AUBMC Special Kids Clinic (ASKC), American University of Beirut Medical Center, Beirut, Lebanon
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199
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Trait and state patterns of basolateral amygdala connectivity at rest are related to endogenous testosterone and aggression in healthy young women. Brain Imaging Behav 2019; 13:564-576. [PMID: 29744800 DOI: 10.1007/s11682-018-9884-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The steroid hormone testosterone (T) has been suggested to influence reactive aggression upon its action on the basolateral amygdala (BLA), a key brain region for threat detection. However, it is unclear whether T modulates resting-state functional connectivity (rsFC) of the BLA, and whether this predicts subsequent aggressive behavior. Aggressive interactions themselves, which often induce changes in T concentrations, could further alter BLA rsFC, but this too remains untested. Here we investigated the effect of endogenous T on rsFC of the BLA at baseline as well as after an aggressive encounter, and whether this was related to behavioral aggression in healthy young women (n = 39). Pre-scan T was negatively correlated with basal rsFC between BLA and left superior temporal gyrus (STG; p < .001, p < .05 Family-Wise Error [FWE] cluster-level corrected), which in turn was associated with increased aggression (r = .37, p = .020). BLA-STG coupling at rest might thus underlie hostile readiness in low-T women. In addition, connectivity between the BLA and the right superior parietal lobule (SPL), a brain region involved in higher-order perceptual processes, was reduced in aggressive participants (p < .001, p < .05 FWE cluster-level corrected). On the other hand, post-task increases in rsFC between BLA and medial orbitofrontal cortex (mOFC) were linked to reduced aggression (r = -.36, p = .023), consistent with the established notion that the mOFC regulates amygdala activity in order to curb aggressive impulses. Finally, competition-induced changes in T were associated with increased coupling between the BLA and the right lateral OFC (p < .001, p < .05 FWE cluster-level corrected), but this effect was unrelated to aggression. We thus identified connectivity patterns that prospectively predict aggression in women, and showed how aggressive interactions in turn impact these neural systems.
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200
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Domínguez-Iturza N, Lo AC, Shah D, Armendáriz M, Vannelli A, Mercaldo V, Trusel M, Li KW, Gastaldo D, Santos AR, Callaerts-Vegh Z, D'Hooge R, Mameli M, Van der Linden A, Smit AB, Achsel T, Bagni C. The autism- and schizophrenia-associated protein CYFIP1 regulates bilateral brain connectivity and behaviour. Nat Commun 2019; 10:3454. [PMID: 31371726 PMCID: PMC6672001 DOI: 10.1038/s41467-019-11203-y] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 06/20/2019] [Indexed: 12/26/2022] Open
Abstract
Copy-number variants of the CYFIP1 gene in humans have been linked to autism spectrum disorders (ASD) and schizophrenia (SCZ), two neuropsychiatric disorders characterized by defects in brain connectivity. Here, we show that CYFIP1 plays an important role in brain functional connectivity and callosal functions. We find that Cyfip1-heterozygous mice have reduced functional connectivity and defects in white matter architecture, similar to phenotypes found in patients with ASD, SCZ and other neuropsychiatric disorders. Cyfip1-deficient mice also present decreased myelination in the callosal axons, altered presynaptic function, and impaired bilateral connectivity. Finally, Cyfip1 deficiency leads to abnormalities in motor coordination, sensorimotor gating and sensory perception, which are also known neuropsychiatric disorder-related symptoms. These results show that Cyfip1 haploinsufficiency compromises brain connectivity and function, which might explain its genetic association to neuropsychiatric disorders.
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Affiliation(s)
- Nuria Domínguez-Iturza
- Department of Fundamental Neurosciences, University of Lausanne, 1005, Lausanne, Switzerland
- Department of Human Genetics KU Leuven, VIB Center for Brain & Disease Research, 3000, Leuven, Belgium
| | - Adrian C Lo
- Department of Fundamental Neurosciences, University of Lausanne, 1005, Lausanne, Switzerland
| | - Disha Shah
- Department of Biomedical Sciences, Bio-Imaging Laboratory, University of Antwerp, 2610, Antwerp, Belgium
- Department of Neuroscience KU Leuven, VIB Center for Brain & Disease Research, 3000, Leuven, Belgium
| | - Marcelo Armendáriz
- Department of Neurosciences, Laboratory of Neuro- and Psychophysiology, KU Leuven, 3000, Leuven, Belgium
| | - Anna Vannelli
- Department of Fundamental Neurosciences, University of Lausanne, 1005, Lausanne, Switzerland
| | - Valentina Mercaldo
- Department of Fundamental Neurosciences, University of Lausanne, 1005, Lausanne, Switzerland
| | - Massimo Trusel
- Department of Fundamental Neurosciences, University of Lausanne, 1005, Lausanne, Switzerland
| | - Ka Wan Li
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, 1081, Amsterdam, The Netherlands
| | - Denise Gastaldo
- Department of Fundamental Neurosciences, University of Lausanne, 1005, Lausanne, Switzerland
| | - Ana Rita Santos
- Department of Human Genetics KU Leuven, VIB Center for Brain & Disease Research, 3000, Leuven, Belgium
- VIB Discovery Sciences, Bioincubator, 3001, Heverlee, Belgium
| | - Zsuzsanna Callaerts-Vegh
- Faculty of Psychology and Educational Sciences, KU Leuven, Laboratory of Biological Psychology, 3000, Leuven, Belgium
| | - Rudi D'Hooge
- Faculty of Psychology and Educational Sciences, KU Leuven, Laboratory of Biological Psychology, 3000, Leuven, Belgium
| | - Manuel Mameli
- Department of Fundamental Neurosciences, University of Lausanne, 1005, Lausanne, Switzerland
| | - Annemie Van der Linden
- Department of Biomedical Sciences, Bio-Imaging Laboratory, University of Antwerp, 2610, Antwerp, Belgium
| | - August B Smit
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, 1081, Amsterdam, The Netherlands
| | - Tilmann Achsel
- Department of Fundamental Neurosciences, University of Lausanne, 1005, Lausanne, Switzerland
- Department of Human Genetics KU Leuven, VIB Center for Brain & Disease Research, 3000, Leuven, Belgium
| | - Claudia Bagni
- Department of Fundamental Neurosciences, University of Lausanne, 1005, Lausanne, Switzerland.
- Department of Human Genetics KU Leuven, VIB Center for Brain & Disease Research, 3000, Leuven, Belgium.
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133, Rome, Italy.
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