51
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Schuler M, Mohnke S, Amelung T, Beier KM, Walter M, Ponseti J, Schiffer B, Kruger THC, Walter H. Neural processing associated with Cognitive Empathy in Pedophilia and Child Sexual Offending. Soc Cogn Affect Neurosci 2021; 17:712-722. [PMID: 34907428 PMCID: PMC9340114 DOI: 10.1093/scan/nsab133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 11/01/2021] [Accepted: 12/14/2021] [Indexed: 11/13/2022] Open
Abstract
Behavioral studies found evidence for superior cognitive empathy (CE) in pedophilic men without a history of child sexual offending (P-CSO) compared to pedophilic men with a history of CSO (P+CSO). Functional magnetic resonance imaging (fMRI) studies also point to differences between P-CSO and P+CSO. Neural processing associated with CE has not yet been investigated. Therefore, the present study aimed to explore the neural correlates of CE in subjects with pedophilia with (P+CSO) and without (P-CSO) child sexual offending. 15 P+CSO, 15 P-CSO, and 24 teleiophilic male controls (TC) performed a CE task during fMRI. We observed reduced activation in the left precuneus (Pcu) and increased activation in the left anterior cingulate cortex (ACC) in P-CSO compared to P+CSO. P-CSO also showed stronger connectivity between these regions, which might reflect a top-down modulation of the Pcu by the ACC toward an increased self-focused emotional reaction in social situations. There was also evidence for increased right superior temporal gyrus activation in P-CSO that might constitute a potentially compensatory recruitment due to the dampened Pcu activation. These findings provide first evidence for altered neural processing of CE in P-CSO and underline the importance of addressing CE in pedophilia and CSO in order to uncover processes relevant to effective prevention of child sexual abuse.
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Affiliation(s)
- Miriam Schuler
- Department of Health and Human Sciences, Institute of Sexology and Sexual Medicine
| | - Sebastian Mohnke
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Till Amelung
- Department of Health and Human Sciences, Institute of Sexology and Sexual Medicine
| | - Klaus M Beier
- Department of Health and Human Sciences, Institute of Sexology and Sexual Medicine
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, University Hospital Jena, Jena, Germany.,Department for Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.,Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Jorge Ponseti
- Department for Integrative Psychiatry, Institute of Sexual Medicine and Forensic Psychiatry and Psychotherapy, Kiel University, Medical School, Kiel, Germany
| | - Boris Schiffer
- Division of Forensic Psychiatry, Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL-University Hospital Bochum, Bochum, Germany.,Department of Psychiatry and Psychotherapy, Institute of Forensic Psychiatry, LVR-University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Tillmann H C Kruger
- Divison of Clinical Psychology and Sexual Medicine. Department of Clinical Psychiatry, Social Psychiatry, and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
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52
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Zhang Y, Zhou T, Feng S, Liu X, Wang F, Sha Z, Yu X. A voxel-level brain-wide association study of cortisol at 8 a.m.: Evidence from Cushing's disease. Neurobiol Stress 2021; 15:100414. [PMID: 34786440 PMCID: PMC8578035 DOI: 10.1016/j.ynstr.2021.100414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 10/16/2021] [Accepted: 10/24/2021] [Indexed: 11/28/2022] Open
Abstract
Cortisol, the end product of the hypothalamic–pituitary–adrenal axis, regulates cognitive function and emotion processing. Cushing's disease, which is characterized by a unique excess of cortisol upon clinical diagnosis, serve as an excellent in vivo “hyperexpression” model to investigate the neurobiological mechanisms of cortisol in the human brain. Previous studies have shown the association between cortisol and functional connectivity within an a priori brain network. However, the whole-brain connectivity pattern that accompanies endogenous cortisol variation is still unclear, as are its associated genetic underpinnings. Here, using resting-state functional magnetic resonance imaging in 112 subjects (60 patients with Cushing's disease and 52 healthy subjects), we performed a voxel-level brain-wide association analysis to investigate the functional connectivity pattern associated with a wide variation in cortisol levels at 8 a.m. The results showed that the regions associated with cortisol as of 8 a.m. were primarily distributed in brain functional hubs involved in self-referential processing, such as the medial prefrontal cortex, anterior and posterior cingulate cortex, and caudate. We also found that regions in the middle temporal, inferior parietal and ventrolateral prefrontal cortex, which is important for social communication tasks, and in the visual and supplementary motor cortex, which is involved in primary sensorimotor perception, were adversely affected by excessive cortisol. The connectivity between these regions was also significantly correlated with neuropsychiatric profiles, such anxiety and depression. Finally, combined neuroimaging and transcriptome analysis showed that functional cortisol-sensitive brain variations were significantly coupled to regional expression of glucocorticoid and mineralocorticoid receptors. These findings reveal cortisol-biased functional signatures in the human brain and shed light on the transcriptional regulation constraints on the cortisol-related brain network.
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Affiliation(s)
- Yanyang Zhang
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, PR China
| | - Tao Zhou
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, PR China
| | - Shiyu Feng
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, PR China
| | - Xinyun Liu
- Department of Radiology, The First Medical Center of Chinese PLA General Hospital, Beijing, PR China
| | - Fuyu Wang
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, PR China
| | - Zhiqiang Sha
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xinguang Yu
- Department of Neurosurgery, The First Medical Center of Chinese PLA General Hospital, Beijing, PR China
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53
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Cifre I, Miller Flores MT, Penalba L, Ochab JK, Chialvo DR. Revisiting Nonlinear Functional Brain Co-activations: Directed, Dynamic, and Delayed. Front Neurosci 2021; 15:700171. [PMID: 34712111 PMCID: PMC8546168 DOI: 10.3389/fnins.2021.700171] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 08/23/2021] [Indexed: 12/12/2022] Open
Abstract
The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In the previous study, we first demonstrated that the relatively stronger blood oxygenated level dependent (BOLD) activations contain most of the information relevant to understand functional connectivity, and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this study, we revisit the correlation properties of these epochs to define a measure of nonlinear dynamic directed functional connectivity (nldFC) across regions of interest. We show that the proposed metric provides at once, without extensive numerical complications, directed information of the functional correlations, as well as a measure of temporal lags across regions, overall offering a different and complementary perspective in the analysis of brain co-activation patterns. In this study, we provide further details for the computations of these measures and for a proof of concept based on replicating existing results from an Autistic Syndrome database, and discuss the main features and advantages of the proposed strategy for the study of brain functional correlations.
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Affiliation(s)
- Ignacio Cifre
- Facultat de Psicologia, Ciències de l'Educació i de l'Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain.,Center for Complex Systems and Brain Sciences (CEMSC3), Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Maria T Miller Flores
- Center for Complex Systems and Brain Sciences (CEMSC3), Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Lucia Penalba
- Facultat de Psicologia, Ciències de l'Educació i de l'Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain
| | - Jeremi K Ochab
- Institute of Theoretical Physics and Mark Kac Center for Complex Systems Research, Jagiellonian University, Krakow, Poland
| | - Dante R Chialvo
- Center for Complex Systems and Brain Sciences (CEMSC3), Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Buenos Aires, Argentina.,Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Buenos Aires, Argentina
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54
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Vandewouw MM, Safar K, Mossad SI, Lu J, Lerch JP, Anagnostou E, Taylor MJ. Do shapes have feelings? Social attribution in children with autism spectrum disorder and attention-deficit/hyperactivity disorder. Transl Psychiatry 2021; 11:493. [PMID: 34564704 PMCID: PMC8464598 DOI: 10.1038/s41398-021-01625-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/16/2021] [Accepted: 08/26/2021] [Indexed: 12/14/2022] Open
Abstract
Theory of mind (ToM) deficits are common in children with neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which contribute to their social and cognitive difficulties. The social attribution task (SAT) involves geometrical shapes moving in patterns that depict social interactions and is known to recruit brain regions from the classic ToM network. To better understand ToM in ASD and ADHD children, we examined the neural correlates using the SAT and functional magnetic resonance imaging (fMRI) in a cohort of 200 children: ASD (N = 76), ADHD (N = 74) and typically developing (TD; N = 50) (4-19 years). In the scanner, participants were presented with SAT videos corresponding to social help, social threat, and random conditions. Contrasting social vs. random, the ASD compared with TD children showed atypical activation in ToM brain areas-the middle temporal and anterior cingulate gyri. In the social help vs. social threat condition, atypical activation of the bilateral middle cingulate and right supramarginal and superior temporal gyri was shared across the NDD children, with between-diagnosis differences only being observed in the right fusiform. Data-driven subgrouping identified two distinct subgroups spanning all groups that differed in both their clinical characteristics and brain-behaviour relations with ToM ability.
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Affiliation(s)
- Marlee M Vandewouw
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada.
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada.
- Autism Research Center, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
| | - Kristina Safar
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada
| | - Sarah I Mossad
- Department of Psychology, Hospital for Sick Children, Toronto, ON, Canada
| | - Julie Lu
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada
| | - Jason P Lerch
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Evdokia Anagnostou
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada
- Autism Research Center, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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55
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He S, Duan R, Liu Z, Zhang C, Li T, Wei Y, Ma N, Wang R. Altered functional connectivity is related to impaired cognition in left unilateral asymptomatic carotid artery stenosis patients. BMC Neurol 2021; 21:350. [PMID: 34517833 PMCID: PMC8436468 DOI: 10.1186/s12883-021-02385-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 09/01/2021] [Indexed: 11/10/2022] Open
Abstract
Background Asymptomatic carotid artery stenosis (aCAS) impairs haemodynamic and cognitive functions; however, the relationship between these changes and brain network connectivity remains largely unknown. This study aimed to determine the relationship between functional connectivity and neurocognition in patients with aCAS. Methods We compared functional status in 14 patients with aCAS and 15 healthy controls using resting state functional magnetic resonance imaging sequences. The subjects underwent a full range of neuropsychological tests and a graphical theoretical analysis of their brain networks. Results Compared with controls, patients with aCAS showed significant decline in neuropsychological functions, particularly short-term memory (word-memory, p = .046 and picture-memory, p = .014). Brain network connectivity was lower in patients with aCAS than in the controls, and the decline of functional connectivity in aCAS patients was mainly concentrated in the left and right inferior frontal gyri, temporal lobe, left cingulate gyrus, and hippocampus. Decreased connectivity between various brain regions was significantly correlated with impaired short-term memory. Patients with aCAS showed cognitive impairment independent of known vascular risk factors for vascular cognitive impairment. The cognitive defects were mainly manifested in the short-term memory of words and pictures. Conclusions This study is the first of its kind to identify an association between disruption of functional connections in left carotid stenosis and impairment of short-term memory. The findings suggest that alterations in network connectivity may be an essential mechanism underlying cognitive decline in aCAS patients. Clinical trial registration-URL Unique identifier: 04/06/2019, ChiCTR1900023610. Supplementary Information The online version contains supplementary material available at 10.1186/s12883-021-02385-4.
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Affiliation(s)
- Shihao He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 10070, China
| | - Ran Duan
- Department of Neurosurgery, Peking University International Hospital, Beijing, 102206, China
| | - Ziqi Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 10070, China
| | - Cai Zhang
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
| | - Tian Li
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, 100875, China
| | - Yanchang Wei
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 10070, China
| | - Ning Ma
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 10070, China
| | - Rong Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 10070, China. .,Department of Neurosurgery, Peking University International Hospital, Beijing, 102206, China. .,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, 100069, China.
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56
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Accurate assessment of low-function autistic children based on EEG feature fusion. J Clin Neurosci 2021; 90:351-358. [PMID: 34275574 DOI: 10.1016/j.jocn.2021.06.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 05/10/2021] [Accepted: 06/14/2021] [Indexed: 11/22/2022]
Abstract
Autism spectrum disorder (ASD) is a very serious neurodevelopmental disorder and diagnosis mainly depends on the clinical scale, which has a certain degree of subjectivity. It is necessary to make accurate evaluation by objective indicators. In this study, we enrolled 96 children aged from 3 to 6 years: 48 low-function autistic children (38 males and 10 females; mean±SD age: 4.9±1.1 years) and 48 typically developing (TD) children (38 males and 10 females; mean±SD age: 4.9 ± 1.2 years) to participate in our experiment. We investigated to fuse multi-features (entropy, relative power, coherence and bicoherence) to distinguish low-function autistic children and TD children accurately. Minimum redundancy maximum correlation algorithm was used to choose the features and support vector machine was used for classification. Ten-fold cross validation was used to test the accuracy of the model. Better classification result was obtained. We tried to provide a reliable basis for clinical evaluation and diagnosis for ASD.
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57
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Rolls ET, Cheng W, Gilson M, Gong W, Deco G, Lo CYZ, Yang AC, Tsai SJ, Liu ME, Lin CP, Feng J. Beyond the disconnectivity hypothesis of schizophrenia. Cereb Cortex 2021; 30:1213-1233. [PMID: 31381086 DOI: 10.1093/cercor/bhz161] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 06/24/2019] [Accepted: 06/24/2019] [Indexed: 01/01/2023] Open
Abstract
To go beyond the disconnectivity hypothesis of schizophrenia, directed (effective) connectivity was measured between 94 brain regions, to provide evidence on the source of the changes in schizophrenia and a mechanistic model. Effective connectivity (EC) was measured in 180 participants with schizophrenia and 208 controls. For the significantly different effective connectivities in schizophrenia, on average the forward (stronger) effective connectivities were smaller, whereas the backward connectivities tended to be larger. Further, higher EC in schizophrenia was found from the precuneus and posterior cingulate cortex (PCC) to areas such as the parahippocampal, hippocampal, temporal, fusiform, and occipital cortices. These are backward effective connectivities and were positively correlated with the positive symptoms of schizophrenia. Lower effective connectivities were found from temporal and other regions and were negatively correlated with the symptoms, especially the negative and general symptoms. Further, a signal variance parameter was increased for areas that included the parahippocampal gyrus and hippocampus, consistent with the hypothesis that hippocampal overactivity is involved in schizophrenia. This investigation goes beyond the disconnectivity hypothesis by drawing attention to differences in schizophrenia between backprojections and forward connections, with the backward connections from the precuneus and PCC implicated in memory stronger in schizophrenia.
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Affiliation(s)
- Edmund T Rolls
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Oxford Centre for Computational Neuroscience, Oxford OX1 4BH, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433, China
| | - Matthieu Gilson
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona E-08018, Spain and Brain and Cognition, Pompeu Fabra University, Barcelona, Spain
| | - Weikang Gong
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX1 4BH, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona E-08018, Spain and Brain and Cognition, Pompeu Fabra University, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona, 08010, Spain
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China
| | - Albert C Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Mu-En Liu
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11267, Taiwan
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Institute of Neuroscience, National Yang-Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei 11221, Taiwan
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, PR China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,School of Mathematical Sciences, School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, PR China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433, China
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58
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Impaired global efficiency in boys with conduct disorder and high callous unemotional traits. J Psychiatr Res 2021; 138:560-568. [PMID: 33991994 DOI: 10.1016/j.jpsychires.2021.04.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 04/06/2021] [Accepted: 04/25/2021] [Indexed: 11/22/2022]
Abstract
Callous unemotional (CU) traits differentiate subtypes of conduct disorder (CD). It has been suggested that CU traits may be related to topographical irregularities that hinder information integration. To date, there is limited evidence of whether CU traits may be associated with abnormal brain topology. In this study, 43 CD boys with high and low CU trait (CD-HCU, CD-LCU), and 46 healthy controls (HCs) were subjected to resting-state functional magnetic resonance imaging to investigate how CU trait level and conduct problems may be reflected in topological organization. Brain functional networks were constructed and network/nodal properties, including small-world properties and network/nodal efficiency, were calculated. Topological analysis revealed that, compared with HCs, CD-HCU group were characterized by decreased small-worldness (σ), decreased global efficiency, and increased path length (λ). These variables were similar between the CD-LCU and HC groups. Self-reported CU traits in CD patients correlated negatively with global efficiency and positively with λ. Regional analysis revealed diminished nodal efficiency in the right amygdala in the CD-HCU group compared with HCs. The present results suggest that disrupted global efficiency, together with a regional abnormality affecting the amygdala, may contribute to abnormal information processing and integration in adolescents with CD and high CU traits.
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59
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He S, Liu Z, Wei Y, Duan R, Xu Z, Zhang C, Yuan L, Li T, Ma N, Lou X, Liu X, Wang R. Impairments in brain perfusion, executive control network, topological characteristics, and neurocognition in adult patients with asymptomatic Moyamoya disease. BMC Neurosci 2021; 22:35. [PMID: 33980154 PMCID: PMC8117595 DOI: 10.1186/s12868-021-00638-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 04/19/2021] [Indexed: 12/21/2022] Open
Abstract
Background Asymptomatic Moyamoya disease (MMD) impairs hemodynamic and cognitive function. The relationship between these changes, cerebral blood flow (CBF), and network connectivity remains largely unknown. The aim of this study was to increase understanding of the relationship between CBF, functional networks, and neurocognition in adults with asymptomatic MMD. We compared CBF and functional status in 26 patients with MMD and 20 healthy controls using arterial spin labeling and resting state functional magnetic resonance imaging sequences. At the same time, a detailed cognitive test was performed in 15 patients with no cerebral or lumen infarction who were selected by magnetic resonance imaging-T2 FLAIR screening. Results Compared to the controls, the patients showed varying degrees of decline in their computational ability (simple subtraction, p = 0.009; complex subtraction, p = 0.006) and short-term memory (p = 0.042). The asymptomatic MMD group also showed decreased CBF in the left anterior central and left inferior frontal gyri of the island flap with multiple node abnormalities in the brain network and reduced network connectivity. There was a significant association of these changes with cognitive decline in the MMD group. Conclusions In patients with asymptomatic MMD, disturbance of CBF and impaired brain network connections may be important causes of cognitive decline and appear before clinical symptoms. Clinical trial registration-URL: http://www.chictr.org.cn Unique identifier: ChiCTR1900023610 Supplementary Information The online version contains supplementary material available at 10.1186/s12868-021-00638-z.
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Affiliation(s)
- Shihao He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China
| | - Ziqi Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China
| | - Yanchang Wei
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China
| | - Ran Duan
- Department of Neurosurgery, Peking University International Hospital, Beijing, 102206, China
| | - Zongsheng Xu
- Department of Neurosurgery, Peking University International Hospital, Beijing, 102206, China
| | - Cai Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/Mc Govern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Li Yuan
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/Mc Govern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Tian Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/Mc Govern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Ning Ma
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China
| | - Xin Lou
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Xiaoyuan Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China.
| | - Rong Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China. .,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, 100069, China. .,Department of Neurosurgery, Peking University International Hospital, Beijing, 102206, China.
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60
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Chen D, Jia T, Zhang Y, Cao M, Loth E, Lo CYZ, Cheng W, Liu Z, Gong W, Sahakian BJ, Feng J. Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals. Front Hum Neurosci 2021; 15:657857. [PMID: 34025376 PMCID: PMC8134539 DOI: 10.3389/fnhum.2021.657857] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 03/11/2021] [Indexed: 01/01/2023] Open
Abstract
Several previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to partition the high-functioning ASD group (i.e., the ASD discovery group) into subgroups. A support vector machine (SVM) model was trained through the 10-fold strategy to predict Autism Diagnostic Observation Schedule (ADOS) scores within the ASD discovery group (r = 0.30, P < 0.001, n = 260), which was further validated in an independent sample (i.e., the ASD validation group) (r = 0.35, P = 0.031, n = 29). The neuroimage-based partition derived two subgroups representing severe versus mild autistic patients. We identified FCs that show graded changes in strength from ASD-severe, through ASD-mild, to controls, while the same pattern cannot be observed in partitions based on ADOS score. We also identified FCs that are specific for ASD-mild, similar to a partition based on ADOS score. The current study provided multiple pieces of evidence with replication to show that resting-state functional magnetic resonance imaging (rsfMRI) FCs could serve as neural biomarkers in partitioning high-functioning autistic individuals based on their symptom severity and showing advantages over traditional partition based on ADOS score. Our results also indicate a compensatory role for a frontocortical network in patients with mild ASD, indicating potential targets for future clinical treatments.
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Affiliation(s)
- Di Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine, MRC SGDP Centre, IoPPN, King’s College London, London, United Kingdom
| | - Yuning Zhang
- Centre for Population Neuroscience and Precision Medicine, MRC SGDP Centre, IoPPN, King’s College London, London, United Kingdom
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- School of Psychology, University of Southampton, Southampton, United Kingdom
| | - Miao Cao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Eva Loth
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, IoPPN, King’s College London, London, United Kingdom
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Zhaowen Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Weikang Gong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Barbara Jacquelyn Sahakian
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- School of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
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61
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Lian F, Northoff G. The Lost Neural Hierarchy of the Autistic Self-Locked-Out of the Mental Self and Its Default-Mode Network. Brain Sci 2021; 11:574. [PMID: 33946964 PMCID: PMC8145974 DOI: 10.3390/brainsci11050574] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/16/2021] [Accepted: 04/23/2021] [Indexed: 12/12/2022] Open
Abstract
Autism spectrum disorder (ASD) is characterized by a fundamental change in self-awareness including seemingly paradoxical features like increased ego-centeredness and weakened self-referentiality. What is the neural basis of this so-called "self-paradox"? Conducting a meta-analytic review of fMRI rest and task studies, we show that ASD exhibits consistent hypofunction in anterior and posterior midline regions of the default-mode network (DMN) in both rest and task with decreased self-non-self differentiation. Relying on a multilayered nested hierarchical model of self, as recently established (Qin et al. 2020), we propose that ASD subjects cannot access the most upper layer of their self, the DMN-based mental self-they are locked-out of their own DMN and its mental self. This, in turn, results in strong weakening of their self-referentiality with decreases in both self-awareness and self-other distinction. Moreover, this blocks the extension of non-DMN cortical and subcortical regions at the lower layers of the physical self to the DMN-based upper layer of the mental self, including self-other distinction. The ASD subjects remain stuck and restricted to their intero- and exteroceptive selves as manifested in a relative increase in ego-centeredness (as compared to self-referentiality). This amounts to what we describe as "Hierarchical Model of Autistic Self" (HAS), which, characterizing the autistic self in hierarchical and spatiotemporal terms, aligns well with and extends current theories of ASD including predictive coding and weak central coherence.
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Affiliation(s)
- Fuxin Lian
- Institute of Psychological Sciences, School of Education, Hangzhou Normal University, Hangzhou 311121, China;
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4, Canada
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON K1Z 7K4, Canada
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62
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Yao S, Zhou M, Zhang Y, Zhou F, Zhang Q, Zhao Z, Jiang X, Xu X, Becker B, Kendrick KM. Decreased homotopic interhemispheric functional connectivity in children with autism spectrum disorder. Autism Res 2021; 14:1609-1620. [PMID: 33908177 DOI: 10.1002/aur.2523] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 11/11/2022]
Abstract
While several functional and structural changes occur in large-scale brain networks in autism spectrum disorder (ASD), reduced interhemispheric resting-state functional connectivity (rsFC) between homotopic regions may be of particular importance as a biomarker. ASD is an early-onset developmental disorder and neural alterations are often age-dependent. Although there is some evidence for homotopic interhemispheric rsFC alterations in language processing regions in ASD children, wider analyses using large data sets have not been performed. The present study, therefore, conducted a voxel-based homotopic interhemispheric rsFC analysis in 146 ASD and 175 typically developing children under-age 10 and examined associations with symptom severity in the autism brain imaging data exchange data sets. Given the role of corpus callosum (CC) in interhemispheric connectivity and reported CC volume changes in ASD we additionally examined whether there were parallel volumetric changes. Results demonstrated decreased homotopic rsFC in ASD children in the posterior cingulate cortex (PCC) and precuneus of the default mode network, the precentral gyrus of the mirror neuron system, and the caudate of the reward system. Homotopic rsFC of the PCC was associated with symptom severity. Furthermore, although no significant CC volume changes were found in ASD children, there was a significant negative correlation between the anterior CC volumes and homotopic rsFC strengths in the caudate. The present study shows that a reduced pattern of homotopic interhemispheric rsFC in ASD adults/adolescents is already present in children of 5-10 years old and further supports their potential use as a general ASD biomarker. LAY SUMMARY: Homotopic interhemispheric functional connectivity plays an important role in synchronizing activity between the two hemispheres and is altered in adults and adolescents with autism spectrum disorder (ASD). In the present study focused on children with ASD, we have observed a similar pattern of decreased homotopic connectivity, suggesting that alterations in homotopic interhemispheric connectivity may occur early in ASD and be a useful general biomarker across ages.
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Affiliation(s)
- Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Menghan Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Yuan Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Feng Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Qianqian Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Zhongbo Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaolei Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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63
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Roy D, Uddin LQ. Atypical core-periphery brain dynamics in autism. Netw Neurosci 2021; 5:295-321. [PMID: 34189366 PMCID: PMC8233106 DOI: 10.1162/netn_a_00181] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 12/31/2020] [Indexed: 11/06/2022] Open
Abstract
The intrinsic function of the human brain is dynamic, giving rise to numerous behavioral subtypes that fluctuate distinctively at multiple timescales. One of the key dynamical processes that takes place in the brain is the interaction between core-periphery brain regions, which undergoes constant fluctuations associated with developmental time frames. Core-periphery dynamical changes associated with macroscale brain network dynamics span multiple timescales and may lead to atypical behavior and clinical symptoms. For example, recent evidence suggests that brain regions with shorter intrinsic timescales are located at the periphery of brain networks (e.g., sensorimotor hand, face areas) and are implicated in perception and movement. On the contrary, brain regions with longer timescales are core hub regions. These hubs are important for regulating interactions between the brain and the body during self-related cognition and emotion. In this review, we summarize a large body of converging evidence derived from time-resolved fMRI studies in autism to characterize atypical core-periphery brain dynamics and how they relate to core and contextual sensory and cognitive profiles.
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Affiliation(s)
- Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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64
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Wang Y, Zhan G, Cai Z, Jiao B, Zhao Y, Li S, Luo A. Vagus nerve stimulation in brain diseases: Therapeutic applications and biological mechanisms. Neurosci Biobehav Rev 2021; 127:37-53. [PMID: 33894241 DOI: 10.1016/j.neubiorev.2021.04.018] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 04/12/2021] [Accepted: 04/18/2021] [Indexed: 12/21/2022]
Abstract
Brain diseases, including neurodegenerative, cerebrovascular and neuropsychiatric diseases, have posed a deleterious threat to human health and brought a great burden to society and the healthcare system. With the development of medical technology, vagus nerve stimulation (VNS) has been approved by the Food and Drug Administration (FDA) as an alternative treatment for refractory epilepsy, refractory depression, cluster headaches, and migraines. Furthermore, current evidence showed promising results towards the treatment of more brain diseases, such as Parkinson's disease (PD), autistic spectrum disorder (ASD), traumatic brain injury (TBI), and stroke. Nonetheless, the biological mechanisms underlying the beneficial effects of VNS in brain diseases remain only partially elucidated. This review aims to delve into the relevant preclinical and clinical studies and update the progress of VNS applications and its potential mechanisms underlying the biological effects in brain diseases.
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Affiliation(s)
- Yue Wang
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Gaofeng Zhan
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Ziwen Cai
- Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Bo Jiao
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Yilin Zhao
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Shiyong Li
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Ailin Luo
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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65
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Nair A, Jalal R, Liu J, Tsang T, McDonald NM, Jackson L, Ponting C, Jeste SS, Bookheimer SY, Dapretto M. Altered Thalamocortical Connectivity in 6-Week-Old Infants at High Familial Risk for Autism Spectrum Disorder. Cereb Cortex 2021; 31:4191-4205. [PMID: 33866373 DOI: 10.1093/cercor/bhab078] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 03/11/2021] [Accepted: 03/12/2021] [Indexed: 12/14/2022] Open
Abstract
Converging evidence from neuroimaging studies has revealed altered connectivity in cortical-subcortical networks in youth and adults with autism spectrum disorder (ASD). Comparatively little is known about the development of cortical-subcortical connectivity in infancy, before the emergence of overt ASD symptomatology. Here, we examined early functional and structural connectivity of thalamocortical networks in infants at high familial risk for ASD (HR) and low-risk controls (LR). Resting-state functional connectivity and diffusion tensor imaging data were acquired in 52 6-week-old infants. Functional connectivity was examined between 6 cortical seeds-prefrontal, motor, somatosensory, temporal, parietal, and occipital regions-and bilateral thalamus. We found significant thalamic-prefrontal underconnectivity, as well as thalamic-occipital and thalamic-motor overconnectivity in HR infants, relative to LR infants. Subsequent structural connectivity analyses also revealed atypical white matter integrity in thalamic-occipital tracts in HR infants, compared with LR infants. Notably, aberrant connectivity indices at 6 weeks predicted atypical social development between 9 and 36 months of age, as assessed with eye-tracking and diagnostic measures. These findings indicate that thalamocortical connectivity is disrupted at both the functional and structural level in HR infants as early as 6 weeks of age, providing a possible early marker of risk for ASD.
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Affiliation(s)
- Aarti Nair
- Department of Psychology, School of Behavioral Health, Loma Linda University, Loma Linda, CA 92354, USA
| | - Rhideeta Jalal
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Janelle Liu
- Interdepartmental Neuroscience Program, University of California, Los Angeles, CA 90095, USA
| | - Tawny Tsang
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
| | - Nicole M McDonald
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Lisa Jackson
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Carolyn Ponting
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
| | - Shafali S Jeste
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
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66
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Node Centrality Measures Identify Relevant Structural MRI Features of Subjects with Autism. Brain Sci 2021; 11:brainsci11040498. [PMID: 33919984 PMCID: PMC8071038 DOI: 10.3390/brainsci11040498] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 11/29/2022] Open
Abstract
Autism spectrum disorders (ASDs) are a heterogeneous group of neurodevelopmental conditions characterized by impairments in social interaction and communication and restricted patterns of behavior, interests, and activities. Although the etiopathogenesis of idiopathic ASD has not been fully elucidated, compelling evidence suggests an interaction between genetic liability and environmental factors in producing early alterations of structural and functional brain development that are detectable by magnetic resonance imaging (MRI) at the group level. This work shows the results of a network-based approach to characterize not only variations in the values of the extracted features but also in their mutual relationships that might reflect underlying brain structural differences between autistic subjects and healthy controls. We applied a network-based analysis on sMRI data from the Autism Brain Imaging Data Exchange I (ABIDE-I) database, containing 419 features extracted with FreeSurfer software. Two networks were generated: one from subjects with autistic disorder (AUT) (DSM-IV-TR), and one from typically developing controls (TD), adopting a subsampling strategy to overcome class imbalance (235 AUT, 418 TD). We compared the distribution of several node centrality measures and observed significant inter-class differences in averaged centralities. Moreover, a single-node analysis allowed us to identify the most relevant features that distinguished the groups.
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67
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Yao S, Becker B, Kendrick KM. Reduced Inter-hemispheric Resting State Functional Connectivity and Its Association With Social Deficits in Autism. Front Psychiatry 2021; 12:629870. [PMID: 33746796 PMCID: PMC7969641 DOI: 10.3389/fpsyt.2021.629870] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 02/11/2021] [Indexed: 02/06/2023] Open
Abstract
Autism spectrum disorder (ASD) is an early onset developmental disorder which persists throughout life and is increasing in prevalence over the last few decades. Given its early onset and variable cognitive and emotional functional impairments, it is generally challenging to assess ASD individuals using task-based behavioral and functional MRI paradigms. Consequently, resting state functional MRI (rs-fMRI) has become a key approach for examining ASD-associated neural alterations and revealed functional alterations in large-scale brain networks relative to typically developing (TD) individuals, particularly those involved in social-cognitive and affective processes. Recent progress suggests that alterations in inter-hemispheric resting state functional connectivity (rsFC) between regions in the 2 brain hemispheres, particularly homotopic ones, may be of great importance. Here we have reviewed neuroimaging studies examining inter-hemispheric rsFC abnormities in ASD and its associations with symptom severity. As an index of inter-hemispheric functional connectivity, we have additionally reviewed previous studies on corpus callosum (CC) volumetric and fiber changes in ASD. There are converging findings on reduced inter-hemispheric (including homotopic) rsFC in large-scale brain networks particularly in posterior hubs of the default mode network, reduced volumes in the anterior and posterior CC, and on decreased FA and increased MD or RD across CC subregions. Associations between the strength of inter-hemispheric rsFC and social impairments in ASD together with their classification performance in distinguishing ASD subjects from TD controls across ages suggest that the strength of inter-hemispheric rsFC may be a more promising biomarker for assisting in ASD diagnosis than abnormalities in either brain wide rsFC or brain structure.
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Affiliation(s)
- Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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68
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Bathelt J, Geurts HM. Difference in default mode network subsystems in autism across childhood and adolescence. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2021; 25:556-565. [PMID: 33246376 PMCID: PMC7874372 DOI: 10.1177/1362361320969258] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
LAY ABSTRACT Neuroimaging research has identified a network of brain regions that are more active when we daydream compared to when we are engaged in a task. This network has been named the default mode network. Furthermore, differences in the default mode network are the most consistent findings in neuroimaging research in autism. Recent studies suggest that the default mode network is composed of subnetworks that are tied to different functions, namely memory and understanding others' minds. In this study, we investigated if default mode network differences in autism are related to specific subnetworks of the default mode network and if these differences change across childhood and adolescence. Our results suggest that the subnetworks of the default mode network are less differentiated in autism in middle childhood compared to neurotypicals. By late adolescence, the default mode network subnetwork organisation was similar in the autistic and neurotypical groups. These findings provide a foundation for future studies to investigate if this developmental pattern relates to improvements in the integration of memory and social understanding as autistic children grow up.
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69
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Wang K, Li K, Niu X. Altered Functional Connectivity in a Triple-Network Model in Autism With Co-occurring Attention Deficit Hyperactivity Disorder. Front Psychiatry 2021; 12:736755. [PMID: 34925086 PMCID: PMC8674431 DOI: 10.3389/fpsyt.2021.736755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: This study aimed to explore alterations in functional connectivity (FC) within and between default mode network (DMN), central executive network, and salience network in autism spectrum disorder (ASD) with co-occurring attention deficit hyperactivity disorder (ADHD). Method: A total of 135 individuals' date of the Autism Brain Imaging Data Exchange II was used to compare the ASD+ADHD group with the ASD group in relation to the abnormal within-network and between-network connectivity of the ASD group relative to the TD group; consequently, the correlation analysis between abnormal FC and behavior was performed. Results: The ASD+ADHD group exhibited decreased within-network connectivity in the precuneus of the ventral DMN compared with the ASD group. Among the three groups, the ASD+ADHD group showed lower connectivity, whereas the ASD group had higher connectivity than the TD group, although the effect of the separate post hoc test was not significant. Meanwhile, the ASD+ADHD group showed increased between-network connectivity between the ventral DMN and dorsal DMN and between the ventral DMN and left executive control network, compared with the ASD and TD groups. Conclusion: Dysfunction of DMN in the "triple-network model" is the core evidence for ASD with co-occurring ADHD.
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Affiliation(s)
- Kai Wang
- Department of Pediatrics, First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
| | - Ke Li
- Department of Child Healthcare, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Xiaoyu Niu
- Department of Pediatrics, First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
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70
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Sexually dimorphic neuroanatomical differences relate to ASD-relevant behavioral outcomes in a maternal autoantibody mouse model. Mol Psychiatry 2021; 26:7530-7537. [PMID: 34290368 PMCID: PMC8776898 DOI: 10.1038/s41380-021-01215-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/15/2021] [Accepted: 06/24/2021] [Indexed: 02/07/2023]
Abstract
Immunoglobulin G (IgG) autoantibodies reactive to fetal brain proteins in mothers of children with ASD have been described by several groups. To understand their pathologic significance, we developed a mouse model of maternal autoantibody related ASD (MAR-ASD) utilizing the peptide epitopes from human autoantibody reactivity patterns. Male and female offspring prenatally exposed to the salient maternal autoantibodies displayed robust deficits in social interactions and increased repetitive self-grooming behaviors as juveniles and adults. In the present study, neuroanatomical differences in adult MAR-ASD and control offspring were assessed via high-resolution ex vivo magnetic resonance imaging (MRI) at 6 months of age. Of interest, MAR-ASD mice displayed significantly larger total brain volume and of the 159 regions examined, 31 were found to differ significantly in absolute volume (mm3) at an FDR of <5%. Specifically, the absolute volumes of several white matter tracts, cortical regions, and basal nuclei structures were significantly increased in MAR-ASD animals. These phenomena were largely driven by female MAR-ASD offspring, as no significant differences were seen with either absolute or relative regional volume in male MAR-ASD mice. However, structural covariance analysis suggests network-level desynchronization in brain volume in both male and female MAR-ASD mice. Additionally, preliminary correlational analysis with behavioral data relates that volumetric increases in numerous brain regions of MAR-ASD mice were correlated with social interaction and repetitive self-grooming behaviors in a sex-specific manner. These results demonstrate significant sex-specific effects in brain size, regional relationships, and behavior for offspring prenatally exposed to MAR-ASD autoantibodies relative to controls.
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71
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Zhan Y, Wei J, Liang J, Xu X, He R, Robbins TW, Wang Z. Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model. Am J Psychiatry 2021; 178:65-76. [PMID: 32539526 DOI: 10.1176/appi.ajp.2020.19101091] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Psychiatric disorders commonly comprise comorbid symptoms, such as autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD), raising controversies over accurate diagnosis and overlap of their neural underpinnings. The authors used noninvasive neuroimaging in humans and nonhuman primates to identify neural markers associated with DSM-5 diagnoses and quantitative measures of symptom severity. METHODS Resting-state functional connectivity data obtained from both wild-type and methyl-CpG binding protein 2 (MECP2) transgenic monkeys were used to construct monkey-derived classifiers for diagnostic classification in four human data sets (ASD: Autism Brain Imaging Data Exchange [ABIDE-I], N=1,112; ABIDE-II, N=1,114; ADHD-200 sample: N=776; OCD local institutional database: N=186). Stepwise linear regression models were applied to examine associations between functional connections of monkey-derived classifiers and dimensional symptom severity of psychiatric disorders. RESULTS Nine core regions prominently distributed in frontal and temporal cortices were identified in monkeys and used as seeds to construct the monkey-derived classifier that informed diagnostic classification in human autism. This same set of core regions was useful for diagnostic classification in the OCD cohort but not the ADHD cohort. Models based on functional connections of the right ventrolateral prefrontal cortex with the left thalamus and right prefrontal polar cortex predicted communication scores of ASD patients and compulsivity scores of OCD patients, respectively. CONCLUSIONS The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity. These findings may inform future development of machine-learning models for psychiatric disorders and may improve the accuracy and speed of clinical assessments.
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Affiliation(s)
- Yafeng Zhan
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Jianze Wei
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Jian Liang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Xiu Xu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Ran He
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Trevor W Robbins
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
| | - Zheng Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai (Zhan, Wang); University of Chinese Academy of Sciences, Beijing (Zhan, Wang); School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing (Wei); Institute of Automation, Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing (Wei, Liang, He); Department of Child Health Care, Children's Hospital of Fudan University, Shanghai (Xu); Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, U.K. (Robbins); Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai (Robbins); Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai (Wang); and Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China (Wang)
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72
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Pua EPK, Thomson P, Yang JYM, Craig JM, Ball G, Seal M. Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders. Cereb Cortex 2021; 31:681-693. [PMID: 32959054 DOI: 10.1093/cercor/bhaa252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 08/06/2020] [Accepted: 08/07/2020] [Indexed: 12/18/2022] Open
Abstract
The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case-control pairs (ASD: n = 100, mean age = 11.43 years, IQ = 110.58; controls: n = 100, mean age = 11.43 years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.
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Affiliation(s)
- Emmanuel Peng Kiat Pua
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville VIC 3010, Australia.,Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Medicine, Austin Health, University of Melbourne, Parkville VIC 3010, Australia
| | - Phoebe Thomson
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia
| | - Joseph Yuan-Mou Yang
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Neurosurgery, Neuroscience Advanced Clinical Imaging Suite (NACIS), The Royal Children's Hospital, Parkville VIC 3052, Australia
| | - Jeffrey M Craig
- Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia.,Molecular Epidemiology, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Centre for Molecular and Medical Research, School of Medicine, Deakin University, Geelong VIC 3220, Australia
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia
| | - Marc Seal
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia
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73
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Lanka P, Rangaprakash D, Dretsch MN, Katz JS, Denney TS, Deshpande G. Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. Brain Imaging Behav 2020; 14:2378-2416. [PMID: 31691160 PMCID: PMC7198352 DOI: 10.1007/s11682-019-00191-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large heterogeneous populations, we investigated four disorders: Autism spectrum disorder (N = 988), Attention deficit hyperactivity disorder (N = 930), Post-traumatic stress disorder (N = 87) and Alzheimer's disease (N = 132). We applied 18 different machine learning classifiers (based on diverse principles) wherein the training/validation and the hold-out test data belonged to samples with the same diagnosis but differing in either the age range or the acquisition site. Our results indicate that overfitting can be a huge problem in heterogeneous datasets, especially with fewer samples, leading to inflated measures of accuracy that fail to generalize well to the general clinical population. Further, different classifiers tended to perform well on different datasets. In order to address this, we propose a consensus-classifier by combining the predictive power of all 18 classifiers. The consensus-classifier was less sensitive to unmatched training/validation and holdout test data. Finally, we combined feature importance scores obtained from all classifiers to infer the discriminative ability of connectivity features. The functional connectivity patterns thus identified were robust to the classification algorithm used, age and acquisition site differences, and had diagnostic predictive ability in addition to univariate statistically significant group differences between the groups. A MATLAB toolbox called Machine Learning in NeuroImaging (MALINI), which implements all the 18 different classifiers along with the consensus classifier is available from Lanka et al. (2019) The toolbox can also be found at the following URL: https://github.com/pradlanka/malini .
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Affiliation(s)
- Pradyumna Lanka
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Department of Psychological Sciences, University of California Merced, Merced, CA, USA
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Departments of Radiology and Biomedical Engineering, Northwestern University, Chicago, IL, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA
- US Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McCord, WA, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA.
- Department of Psychology, Auburn University, Auburn, AL, USA.
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA.
- Center for Neuroscience, Auburn University, Auburn, AL, USA.
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA.
- Department of Psychiatry, National Institute of Mental and Neurosciences, Bangalore, India.
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74
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Kotila A, Järvelä M, Korhonen V, Loukusa S, Hurtig T, Ebeling H, Kiviniemi V, Raatikainen V. Atypical Inter-Network Deactivation Associated With the Posterior Default-Mode Network in Autism Spectrum Disorder. Autism Res 2020; 14:248-264. [PMID: 33206471 DOI: 10.1002/aur.2433] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/27/2020] [Accepted: 10/28/2020] [Indexed: 12/13/2022]
Abstract
Previous studies have suggested that atypical deactivation of functional brain networks contributes to the complex cognitive and behavioral profile associated with autism spectrum disorder (ASD). However, these studies have not considered the temporal dynamics of deactivation mechanisms between the networks. In this study, we examined (a) mutual deactivation and (b) mutual activation-deactivation (i.e., anticorrelated) time-lag patterns between resting-state networks (RSNs) in young adults with ASD (n = 20) and controls (n = 20) by applying the recently defined dynamic lag analysis (DLA) method, which measures time-lag variations peak-by-peak between the networks. In order to achieve temporally accurate lag patterns, the brain imaging data was acquired with a fast functional magnetic resonance imaging (fMRI) sequence (TR = 100 ms). Group-level independent component analysis was used to identify 16 RSNs for the DLA. We found altered mutual deactivation timings in ASD in (a) three of the deactivated and (b) two of the transiently anticorrelated (activated-deactivated) RSN pairs, which survived the strict threshold for significance of surrogate data. Of the significant RSN pairs, 80% included the posterior default-mode network (DMN). We propose that temporally altered deactivation mechanisms, including timings and directionality, between the posterior DMN and RSNs mediating processing of socially relevant information may contribute to the ASD phenotype. LAY SUMMARY: To understand autistic traits on a neural level, we examined temporal fluctuations in information flow between brain regions in young adults with autism spectrum disorder (ASD) and controls. We used a fast neuroimaging procedure to investigate deactivation mechanisms between brain regions. We found that timings and directionality of communication between certain brain regions were temporally altered in ASD, suggesting atypical deactivation mechanisms associated with the posterior default-mode network.
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Affiliation(s)
- Aija Kotila
- Research Unit of Logopedics, the Faculty of Humanities, University of Oulu, Oulu, Finland
| | - Matti Järvelä
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Vesa Korhonen
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Soile Loukusa
- Research Unit of Logopedics, the Faculty of Humanities, University of Oulu, Oulu, Finland
| | - Tuula Hurtig
- Research Unit of Clinical Neuroscience, Psychiatry, University of Oulu, Oulu, Finland.,Clinic of Child Psychiatry, Oulu University Hospital and PEDEGO Research Unit, University of Oulu, Oulu, Finland
| | - Hanna Ebeling
- Clinic of Child Psychiatry, Oulu University Hospital and PEDEGO Research Unit, University of Oulu, Oulu, Finland
| | - Vesa Kiviniemi
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
| | - Ville Raatikainen
- Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Medical Research Center (MRC), Oulu University Hospital, Oulu, Finland
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75
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Zheng W, Zhao Z, Zhang Z, Liu T, Zhang Y, Fan J, Wu D. Developmental pattern of the cortical topology in high-functioning individuals with autism spectrum disorder. Hum Brain Mapp 2020; 42:660-675. [PMID: 33085836 PMCID: PMC7814766 DOI: 10.1002/hbm.25251] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/24/2020] [Accepted: 10/07/2020] [Indexed: 12/15/2022] Open
Abstract
A number of studies have indicated alterations of brain morphology in individuals with autism spectrum disorder (ASD); however, how ASD influences the topological organization of the brain cortex at different developmental stages is not yet well characterized. In this study, we used structural images of 492 high‐functioning participants in the Autism Brain Imaging Data Exchange database acquired from 17 international imaging centers, including 75 autistic children (age 7–11 years), 91 adolescents with ASD (age 12–17 years), and 80 young adults with ASD (age 18–29 years), and 246 typically developing controls (TDCs) that were age, gender, handedness, and full‐scale IQ matched. Cortical thickness (CT) and surface area (SA) were extracted and the covariance between cortical regions across participants were treated as a network to examine developmental patterns of the cortical topological organization at different stages. A center‐paired resampling strategy was developed to control the center bias during the permutation test. Compared with the TDCs, network of SA (but not CT) of individuals with ASD showed reduced small‐worldness in childhood, and the network hubs were reorganized in the adulthood such that hubs inclined to connect with nonhub nodes and demonstrated more dispersed spatial distribution. Furthermore, the SA network of the ASD cohort exhibited increased segregation of the inferior parietal lobule and prefrontal cortex, and insular‐opercular cortex in all three age groups, resulting in the emergence of two unique modules in the autistic brain. Our findings suggested that individuals with ASD may undergo remarkable remodeling of the cortical topology from childhood to adulthood, which may be associated with the altered social and cognitive functions in ASD.
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Affiliation(s)
- Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhe Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Jin Fan
- Department of Psychology, Queens College, The City University of New York, New York, New York, USA
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
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76
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Brain Functional Network in Chronic Asymptomatic Carotid Artery Stenosis and Occlusion: Changes and Compensation. Neural Plast 2020; 2020:9345602. [PMID: 33029129 PMCID: PMC7530486 DOI: 10.1155/2020/9345602] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 09/09/2020] [Indexed: 11/17/2022] Open
Abstract
Asymptomatic carotid artery stenosis (CAS) and occlusion (CAO) disrupt cerebral hemodynamics. There are few studies on the brain network changes and compensation associated with the progression from chronic CAS to CAO. In the current study, our goal is to improve the understanding of the specific abnormalities and compensatory phenomena associated with the functional connection in patients with CAS and CAO. In this prospective study, 27 patients with CAO, 29 patients with CAS, and 15 healthy controls matched for age, sex, education, handedness, and risk factors underwent neuropsychological testing and resting-state functional magnetic resonance (rs-fMRI) imaging simultaneously; graph theoretical analysis of brain networks was performed to determine the relationship between changes in brain network connectivity and the progression from internal CAS to CAO. The global properties of the brain network assortativity (p = 0.002), hierarchy (p = 0.002), network efficiency (p = 0.011), and small-worldness (p = 0.009) were significantly more abnormal in the CAS group than in the control and CAO groups. In patients with CAS and CAO, the nodal efficiency of key nodes in multiple brain regions decreased, while the affected hemisphere lost many key functional connections. In this study, we found that patients with CAS showed grade reconstruction, invalid connections, and other phenomena that impaired the efficiency of information transmission in the brain network. A compensatory functional connection in the contralateral cerebral hemisphere of patients with CAS and CAO may be an important mechanism that maintains clinical asymptomatic performance. This study not only reveals the compensation mechanism of cerebral hemisphere ischemia but also validates previous explanations for brain function connectivity, which can help provide interventions in advance and reduce the impairment of higher brain functions. This trial is registered with Clinical Trial Registration-URL http://www.chictr.org.cn and Unique identifier ChiCTR1900023610.
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77
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Dryburgh E, McKenna S, Rekik I. Predicting full-scale and verbal intelligence scores from functional Connectomic data in individuals with autism Spectrum disorder. Brain Imaging Behav 2020; 14:1769-1778. [PMID: 31055763 PMCID: PMC7572331 DOI: 10.1007/s11682-019-00111-w] [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] [Indexed: 12/20/2022]
Abstract
Decoding how intelligence is engrained in the human brain construct is vital in the understanding of particular neurological disorders. While the majority of existing studies focus on characterizing intelligence in neurotypical (NT) brains, investigating how neural correlates of intelligence scores are altered by atypical neurodevelopmental disorders, such as Autism Spectrum Disorders (ASD), is almost absent. To help fill this gap, we use a connectome-based predictive model (CPM) to predict intelligence scores from functional connectome data, derived from resting-state functional magnetic resonance imaging (rsfMRI). The utilized model learns how to select the most significant positive and negative brain connections, independently, to predict the target intelligence scores in NT and ASD populations, respectively. In the first step, using leave-one-out cross-validation we train a linear regressor robust to outliers to identify functional brain connections that best predict the target intelligence score (p - value < 0.01). Next, for each training subject, positive (respectively negative) connections are summed to produce single-subject positive (respectively negative) summary values. These are then paired with the target training scores to train two linear regressors: (a) a positive model which maps each positive summary value to the subject score, and (b) a negative model which maps each negative summary value to the target score. In the testing stage, by selecting the same connections for the left-out testing subject, we compute their positive and negative summary values, which are then fed to the trained negative and positive models for predicting the target score. This framework was applied to NT and ASD populations independently to identify significant functional connections coding for full-scale and verbal intelligence quotients in the brain.
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Affiliation(s)
- Elizabeth Dryburgh
- BASIRA Lab, CVIP Group, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Stephen McKenna
- CVIP Group, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Islem Rekik
- BASIRA Lab, CVIP Group, Computing, School of Science and Engineering, University of Dundee, Dundee, UK.
- Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey.
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78
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Constantin L, Poulsen RE, Scholz LA, Favre-Bulle IA, Taylor MA, Sun B, Goodhill GJ, Vanwalleghem GC, Scott EK. Altered brain-wide auditory networks in a zebrafish model of fragile X syndrome. BMC Biol 2020; 18:125. [PMID: 32938458 PMCID: PMC7493858 DOI: 10.1186/s12915-020-00857-6] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 08/26/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Loss or disrupted expression of the FMR1 gene causes fragile X syndrome (FXS), the most common monogenetic form of autism in humans. Although disruptions in sensory processing are core traits of FXS and autism, the neural underpinnings of these phenotypes are poorly understood. Using calcium imaging to record from the entire brain at cellular resolution, we investigated neuronal responses to visual and auditory stimuli in larval zebrafish, using fmr1 mutants to model FXS. The purpose of this study was to model the alterations of sensory networks, brain-wide and at cellular resolution, that underlie the sensory aspects of FXS and autism. RESULTS Combining functional analyses with the neurons' anatomical positions, we found that fmr1-/- animals have normal responses to visual motion. However, there were several alterations in the auditory processing of fmr1-/- animals. Auditory responses were more plentiful in hindbrain structures and in the thalamus. The thalamus, torus semicircularis, and tegmentum had clusters of neurons that responded more strongly to auditory stimuli in fmr1-/- animals. Functional connectivity networks showed more inter-regional connectivity at lower sound intensities (a - 3 to - 6 dB shift) in fmr1-/- larvae compared to wild type. Finally, the decoding capacities of specific components of the ascending auditory pathway were altered: the octavolateralis nucleus within the hindbrain had significantly stronger decoding of auditory amplitude while the telencephalon had weaker decoding in fmr1-/- mutants. CONCLUSIONS We demonstrated that fmr1-/- larvae are hypersensitive to sound, with a 3-6 dB shift in sensitivity, and identified four sub-cortical brain regions with more plentiful responses and/or greater response strengths to auditory stimuli. We also constructed an experimentally supported model of how auditory information may be processed brain-wide in fmr1-/- larvae. Our model suggests that the early ascending auditory pathway transmits more auditory information, with less filtering and modulation, in this model of FXS.
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Affiliation(s)
- Lena Constantin
- Queensland Brain Institute, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Rebecca E Poulsen
- Queensland Brain Institute, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Leandro A Scholz
- Queensland Brain Institute, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Itia A Favre-Bulle
- Queensland Brain Institute, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
- School of Mathematics and Physics, The University of Queensland, Brisbane, 4072, Australia
| | - Michael A Taylor
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Biao Sun
- Queensland Brain Institute, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Geoffrey J Goodhill
- Queensland Brain Institute, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
- School of Mathematics and Physics, The University of Queensland, Brisbane, 4072, Australia
| | - Gilles C Vanwalleghem
- Queensland Brain Institute, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
| | - Ethan K Scott
- Queensland Brain Institute, The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
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79
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Niego A, Benítez-Burraco A. Autism and Williams syndrome: truly mirror conditions in the socio-cognitive domain? INTERNATIONAL JOURNAL OF DEVELOPMENTAL DISABILITIES 2020; 68:399-415. [PMID: 35937179 PMCID: PMC9351567 DOI: 10.1080/20473869.2020.1817717] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 08/25/2020] [Accepted: 08/27/2020] [Indexed: 06/15/2023]
Abstract
Autism Spectrum Disorders (ASD) and Williams Syndrome (WS) are frequently characterized as mirror conditions in the socio-cognitive domain, with ASD entailing restrictive social interests and with WS exhibiting hypersociability. In this review paper, we examine in detail the strong points and deficits of people with ASD or WS in the socio-cognitive domain and show that both conditions also share some common features. Moreover, we explore the neurobiological basis of the social profile of ASD and WS and found a similar mixture of common affected areas and condition-specific impaired regions. We discuss these findings under the hypothesis of a continuum of the socio-cognitive abilities in humans.
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Affiliation(s)
- Amy Niego
- Faculty of Philology, University of Seville, Seville, Spain
| | - Antonio Benítez-Burraco
- Department of Spanish, Linguistics, and Theory of Literature (Linguistics), Faculty of Philology, University of Seville, Seville, Spain
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80
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Piras IS, Picinelli C, Iennaco R, Baccarin M, Castronovo P, Tomaiuolo P, Cucinotta F, Ricciardello A, Turriziani L, Nanetti L, Mariotti C, Gellera C, Lintas C, Sacco R, Zuccato C, Cattaneo E, Persico AM. Huntingtin gene CAG repeat size affects autism risk: Family-based and case-control association study. Am J Med Genet B Neuropsychiatr Genet 2020; 183:341-351. [PMID: 32652810 DOI: 10.1002/ajmg.b.32806] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 04/20/2020] [Accepted: 05/04/2020] [Indexed: 11/10/2022]
Abstract
The Huntingtin (HTT) gene contains a CAG repeat in exon 1, whose expansion beyond 39 repeats consistently leads to Huntington's disease (HD), whereas normal-to-intermediate alleles seemingly modulate brain structure, function and behavior. The role of the CAG repeat in Autism Spectrum Disorder (ASD) was investigated applying both family-based and case-control association designs, with the SCA3 repeat as a negative control. Significant overtransmission of "long" CAG alleles (≥17 repeats) to autistic children and of "short" alleles (≤16 repeats) to their unaffected siblings (all p < 10-5 ) was observed in 612 ASD families (548 simplex and 64 multiplex). Surprisingly, both 193 population controls and 1,188 neurological non-HD controls have significantly lower frequencies of "short" CAG alleles compared to 185 unaffected siblings and higher rates of "long" alleles compared to 548 ASD patients from the same families (p < .05-.001). The SCA3 CAG repeat displays no association. "Short" HTT alleles seemingly exert a protective effect from clinically overt autism in families carrying a genetic predisposition for ASD, while "long" alleles may enhance autism risk. Differential penetrance of autism-inducing genetic/epigenetic variants may imply atypical developmental trajectories linked to HTT functions, including excitation/inhibition imbalance, cortical neurogenesis and apoptosis, neuronal migration, synapse formation, connectivity and homeostasis.
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Affiliation(s)
- Ignazio Stefano Piras
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, Arizona, USA
| | - Chiara Picinelli
- Mafalda Luce Center for Pervasive Developmental Disorders, Milan, Italy
| | - Raffaele Iennaco
- Department of Biosciences, Università degli Studi di Milano, Milan, Italy.,Istituto Nazionale di Genetica Molecolare "Romeo ed Enrica Invernizzi", Milan, Italy
| | - Marco Baccarin
- Mafalda Luce Center for Pervasive Developmental Disorders, Milan, Italy
| | - Paola Castronovo
- Mafalda Luce Center for Pervasive Developmental Disorders, Milan, Italy
| | - Pasquale Tomaiuolo
- Interdepartmental Program "Autism 0-90", "Gaetano Martino" University Hospital, University of Messina, Messina, Italy
| | - Francesca Cucinotta
- Interdepartmental Program "Autism 0-90", "Gaetano Martino" University Hospital, University of Messina, Messina, Italy
| | - Arianna Ricciardello
- Interdepartmental Program "Autism 0-90", "Gaetano Martino" University Hospital, University of Messina, Messina, Italy
| | - Laura Turriziani
- Interdepartmental Program "Autism 0-90", "Gaetano Martino" University Hospital, University of Messina, Messina, Italy
| | - Lorenzo Nanetti
- Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Caterina Mariotti
- Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Cinzia Gellera
- Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Carla Lintas
- Unit of Child and Adolescent NeuroPsychiatry & Laboratory of Molecular Psychiatry and Neurogenetics, University Campus Bio-Medico, Rome, Italy
| | - Roberto Sacco
- Unit of Child and Adolescent NeuroPsychiatry & Laboratory of Molecular Psychiatry and Neurogenetics, University Campus Bio-Medico, Rome, Italy
| | - Chiara Zuccato
- Department of Biosciences, Università degli Studi di Milano, Milan, Italy.,Istituto Nazionale di Genetica Molecolare "Romeo ed Enrica Invernizzi", Milan, Italy
| | - Elena Cattaneo
- Department of Biosciences, Università degli Studi di Milano, Milan, Italy.,Istituto Nazionale di Genetica Molecolare "Romeo ed Enrica Invernizzi", Milan, Italy
| | - Antonio M Persico
- Interdepartmental Program "Autism 0-90", "Gaetano Martino" University Hospital, University of Messina, Messina, Italy
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81
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Wang H, Rolls ET, Du X, Du J, Yang D, Li J, Li F, Cheng W, Feng J. Severe nausea and vomiting in pregnancy: psychiatric and cognitive problems and brain structure in children. BMC Med 2020; 18:228. [PMID: 32867775 PMCID: PMC7460800 DOI: 10.1186/s12916-020-01701-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 07/08/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Two studies have suggested that severe prolonged nausea and vomiting during pregnancy is associated with emotional and behavioral problems in offspring, with smaller sample size and short-term follow-up. Moreover, little information is available on the role of the brain structure in the associations. METHODS In a US-based cohort, the association was investigated between severe prolonged nausea and vomiting in pregnancy (extending after the second trimester and termed SNVP), psychiatric and cognitive problems, and brain morphology, from the Adolescent Brain Cognitive Development (ABCD) study, from 10,710 children aged 9-11 years. We validated the emotional including psychiatric findings using the Danish National Cohort Study with 2,092,897 participants. RESULTS SNVP was significantly associated with emotional and psychiatric problems (t = 8.89, Cohen's d = 0.172, p = 6.9 × 10-19) and reduced global cognitive performance (t = - 4.34, d = - 0.085, p = 1.4 × 10-5) in children. SNVP was associated with low cortical area and volume, especially in the cingulate cortex, precuneus, and superior medial prefrontal cortex. These lower cortical areas and volumes significantly mediated the relation between SNVP and the psychiatric and cognitive problems in children. In the Danish National Cohort, severe nausea and vomiting in pregnancy were significantly associated with increased risks of behavioral and emotional disorders in children (hazard ratio, 1.24; 95% confidence interval, 1.16-1.33). CONCLUSIONS SNVP is strongly associated with psychiatric and cognitive problems in children, with mediation by brain structure. These associations highlight the clinical importance and potential benefits of the treatment of SNVP, which could reduce the risk of psychiatric disorder in the next generation.
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Affiliation(s)
- Hui Wang
- Department of Developmental and Behavioral Pediatric & Child Primary Care/MOE-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-inspired intelligence, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
- Oxford Centre for Computational Neuroscience, Oxford, OX1 4BH, UK
| | - Xiujuan Du
- Department of Developmental and Behavioral Pediatric & Child Primary Care/MOE-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingnan Du
- Institute of Science and Technology for Brain-inspired intelligence, Fudan University, Shanghai, China
| | - Dexin Yang
- Institute of Science and Technology for Brain-inspired intelligence, Fudan University, Shanghai, China
| | - Jiong Li
- Department of Developmental and Behavioral Pediatric & Child Primary Care/MOE-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus N, Denmark
| | - Fei Li
- Department of Developmental and Behavioral Pediatric & Child Primary Care/MOE-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
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82
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Kubota M, Fujino J, Tei S, Takahata K, Matsuoka K, Tagai K, Sano Y, Yamamoto Y, Shimada H, Takado Y, Seki C, Itahashi T, Aoki YY, Ohta H, Hashimoto RI, Zhang MR, Suhara T, Nakamura M, Takahashi H, Kato N, Higuchi M. Binding of Dopamine D1 Receptor and Noradrenaline Transporter in Individuals with Autism Spectrum Disorder: A PET Study. Cereb Cortex 2020; 30:6458-6468. [DOI: 10.1093/cercor/bhaa211] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 06/26/2020] [Accepted: 07/14/2020] [Indexed: 11/13/2022] Open
Abstract
Abstract
Although previous studies have suggested the involvement of dopamine (DA) and noradrenaline (NA) neurotransmissions in the autism spectrum disorder (ASD) pathophysiology, few studies have examined these neurotransmissions in individuals with ASD in vivo. Here, we investigated DA D1 receptor (D1R) and noradrenaline transporter (NAT) binding in adults with ASD (n = 18) and neurotypical controls (n = 20) by utilizing two different PET radioligands, [11C]SCH23390 and (S,S)-[18F]FMeNER-D2, respectively. We found no significant group differences in DA D1R (striatum, anterior cingulate cortex, and temporal cortex) or NAT (thalamus and pons) binding. However, in the ASD group, there were significant negative correlations between DA D1R binding (striatum, anterior cingulate cortex and temporal cortex) and the “attention to detail” subscale score of the Autism Spectrum Quotient. Further, there was a significant positive correlation between DA D1R binding (temporal cortex) and emotion perception ability assessed by the neurocognitive battery. Associations of NAT binding with empathic abilities and executive function were found in controls, but were absent in the ASD group. Although a lack of significant group differences in binding might be partly due to the heterogeneity of ASD, our results indicate that central DA and NA function might play certain roles in the clinical characteristics of ASD.
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Affiliation(s)
- Manabu Kubota
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Junya Fujino
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Shisei Tei
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
- Institute of Applied Brain Sciences, Waseda University, Saitama 359-1192, Japan
- School of Human and Social Sciences, Tokyo International University, Saitama 350-1198, Japan
| | - Keisuke Takahata
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Kiwamu Matsuoka
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
| | - Kenji Tagai
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
| | - Yasunori Sano
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Yasuharu Yamamoto
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Hitoshi Shimada
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
| | - Yuhei Takado
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
| | - Chie Seki
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
| | - Yuta Y Aoki
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
| | - Haruhisa Ohta
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
- Department of Psychiatry, School of Medicine, Showa University, Tokyo 157-8577, Japan
| | - Ryu-ichiro Hashimoto
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
- Department of Language Sciences, Graduate School of Humanities, Tokyo Metropolitan University, Tokyo 192-0397, Japan
| | - Ming-Rong Zhang
- Department of Radiopharmaceuticals Development, National Institute of Radiological Sciences, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
| | - Tetsuya Suhara
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
| | - Motoaki Nakamura
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
- Kanagawa Psychiatric Center, Yokohama, Kanagawa 233-0006, Japan
| | - Hidehiko Takahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Nobumasa Kato
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo 157-8577, Japan
| | - Makoto Higuchi
- Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba 263-8555, Japan
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83
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Matsubara T, Kusano K, Tashiro T, Ukai K, Uehara K. Deep Generative Model of Individual Variability in fMRI Images of Psychiatric Patients. IEEE Trans Biomed Eng 2020; 68:592-605. [PMID: 32746057 DOI: 10.1109/tbme.2020.3008707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Neuroimaging techniques, such as the resting-state functional magnetic resonance imaging (fMRI), have been investigated to find objective biomarkers of neuro-logical and psychiatric disorders. Objective biomarkers potentially provide a refined diagnosis and quantitative measurements of the effects of treatment. However, fMRI images are sensitive to individual variability, such as functional topography and personal attributes. Suppressing the irrelevant individual variability is crucial for finding objective biomarkers for multiple subjects. Herein, we propose a structured generative model based on deep learning (i.e., a deep generative model) that considers such individual variability. The proposed model builds a joint distribution of (preprocessed) fMRI images, state (with or without a disorder), and individual variability. It can thereby discriminate individual variability from the subject's state. Experimental results demonstrate that the proposed model can diagnose unknown subjects with greater accuracy than conventional approaches. Moreover, the diagnosis is fairer to gender and state, because the proposed model extracts subject attributes (age, gender, and scan site) in an unsupervised manner.
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84
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Heather Hsu CC, Rolls ET, Huang CC, Chong ST, Zac Lo CY, Feng J, Lin CP. Connections of the Human Orbitofrontal Cortex and Inferior Frontal Gyrus. Cereb Cortex 2020; 30:5830-5843. [PMID: 32548630 DOI: 10.1093/cercor/bhaa160] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/27/2020] [Accepted: 05/20/2020] [Indexed: 12/19/2022] Open
Abstract
The direct connections of the orbitofrontal cortex (OFC) were traced with diffusion tractography imaging and statistical analysis in 50 humans, to help understand better its roles in emotion and its disorders. The medial OFC and ventromedial prefrontal cortex have direct connections with the pregenual and subgenual parts of the anterior cingulate cortex; all of which are reward-related areas. The lateral OFC (OFClat) and its closely connected right inferior frontal gyrus (rIFG) have direct connections with the supracallosal anterior cingulate cortex; all of which are punishment or nonreward-related areas. The OFClat and rIFG also have direct connections with the right supramarginal gyrus and inferior parietal cortex, and with some premotor cortical areas, which may provide outputs for the OFClat and rIFG. Another key finding is that the ventromedial prefrontal cortex shares with the medial OFC especially strong outputs to the nucleus accumbens and olfactory tubercle, which comprise the ventral striatum, whereas the other regions have more widespread outputs to the striatum. Direct connections of the OFC and IFG were with especially the temporal pole part of the temporal lobe. The left IFG, which includes Broca's area, has direct connections with the left angular and supramarginal gyri.
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Affiliation(s)
- Chih-Chin Heather Hsu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 11221, Taiwan
| | - Edmund T Rolls
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Chu-Chung Huang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Shin Tai Chong
- Institute of Neuroscience, National Yang-Ming University, Taipei 11221, Taiwan
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Oxford Centre for Computational Neuroscience, Oxford, UK.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, 200433, China
| | - Ching-Po Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 11221, Taiwan.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China.,Institute of Neuroscience, National Yang-Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei 11221, Taiwan
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85
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Tang S, Sun N, Floris DL, Zhang X, Di Martino A, Yeo BTT. Reconciling Dimensional and Categorical Models of Autism Heterogeneity: A Brain Connectomics and Behavioral Study. Biol Psychiatry 2020; 87:1071-1082. [PMID: 31955916 DOI: 10.1016/j.biopsych.2019.11.009] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/15/2019] [Accepted: 11/04/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND Heterogeneity in autism spectrum disorder (ASD) has hindered the development of biomarkers, thus motivating subtyping efforts. Most subtyping studies divide individuals with ASD into nonoverlapping (categorical) subgroups. However, continuous interindividual variation in ASD suggests that there is a need for a dimensional approach. METHODS A Bayesian model was employed to decompose resting-state functional connectivity (RSFC) of individuals with ASD into multiple abnormal RSFC patterns, i.e., categorical subtypes, henceforth referred to as "factors." Importantly, the model allowed each individual to express one or more factors to varying degrees (dimensional subtyping). The model was applied to 306 individuals with ASD (5.2-57 years of age) from two multisite repositories. Post hoc analyses associated factors with symptoms and demographics. RESULTS Analyses yielded three factors with dissociable whole-brain hypo- and hyper-RSFC patterns. Most participants expressed multiple (categorical) factors, suggestive of a mosaic of subtypes within individuals. All factors shared abnormal RSFC involving the default mode network, but the directionality (hypo- or hyper-RSFC) differed across factors. Factor 1 was associated with core ASD symptoms. Factors 1 and 2 were associated with distinct comorbid symptoms. Older male participants preferentially expressed factor 3. Factors were robust across control analyses and were not associated with IQ or head motion. CONCLUSIONS There exist at least three ASD factors with dissociable whole-brain RSFC patterns, behaviors, and demographics. Heterogeneous default mode network hypo- and hyper-RSFC across the factors might explain previously reported inconsistencies. The factors differentiated between core ASD and comorbid symptoms-a less appreciated domain of heterogeneity in ASD. These factors are coexpressed in individuals with ASD with different degrees, thus reconciling categorical and dimensional perspectives of ASD heterogeneity.
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Affiliation(s)
- Siyi Tang
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, National University of Singapore, Singapore, Republic of Singapore; Department of Electrical Engineering, Stanford University, Stanford, California
| | - Nanbo Sun
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, National University of Singapore, Singapore, Republic of Singapore
| | - Dorothea L Floris
- Donders Center for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Xiuming Zhang
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Adriana Di Martino
- Autism and Social Cognition Center, Child Mind Institute, New York, New York
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, National University of Singapore, Singapore, Republic of Singapore; Centre for Cognitive Neuroscience, Duke-National University of Singapore Medical School, Singapore, Republic of Singapore; National University of Singapore Graduate School for Integrative Sciences and Engineering, Singapore, Republic of Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.
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86
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Liu Z, Rolls ET, Liu Z, Zhang K, Yang M, Du J, Gong W, Cheng W, Dai F, Wang H, Ugurbil K, Zhang J, Feng J. Brain annotation toolbox: exploring the functional and genetic associations of neuroimaging results. Bioinformatics 2020; 35:3771-3778. [PMID: 30854545 DOI: 10.1093/bioinformatics/btz128] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 01/25/2019] [Accepted: 02/20/2019] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and identify the roots of psychiatric diseases. However, the results from most neuroimaging studies, i.e. activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. RESULTS We describe a brain annotation toolbox that generates functional and genetic annotations for neuroimaging results. The voxel-level functional description from the Neurosynth database and gene expression profile from the Allen Human Brain Atlas are used to generate functional/genetic information for region-level neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and genetic similarity network are highly correlated for major brain atlases. One application of brain annotation toolbox is to help provide functional/genetic annotations for newly discovered regions with unknown functions, e.g. the 97 new regions identified in the Human Connectome Project. Importantly, this toolbox can help understand differences between psychiatric patients and controls, and this is demonstrated using schizophrenia and autism data, for which the functional and genetic annotations for the neuroimaging changes in patients are consistent with each other and help interpret the results. AVAILABILITY AND IMPLEMENTATION BAT is implemented as a free and open-source MATLAB toolbox and is publicly available at http://123.56.224.61:1313/post/bat. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhaowen Liu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry, UK.,Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Zhi Liu
- The School of Information Science and Engineering, Shandong University, Jinan, China
| | - Kai Zhang
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Ming Yang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Jingnan Du
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Weikang Gong
- 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
| | - Fei Dai
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Jie Zhang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Shanghai, China.,Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Shanghai, China
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87
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Lo W, Li X, Hoskinson K, McNally K, Chung M, Lee J, Wang J, Lu ZL, Yeates K. Pediatric Stroke Impairs Theory of Mind Performance. J Child Neurol 2020; 35:228-234. [PMID: 31775563 DOI: 10.1177/0883073819887590] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
AIM This pilot study explored whether childhood stroke impairs performance on theory of mind (ToM) tasks and whether ToM task performance correlates with resting state connectivity in brain regions linked with social cognition. METHOD We performed a case-control study of 10 children with stroke and 10 age- and gender-matched controls. They completed 2 ToM tasks, and resting state connectivity was measured with functional magnetic resonance imaging (MRI). RESULTS Children with stroke performed worse than controls on conative ToM tasks. Resting state connectivity in the central executive network was significantly higher and connectivity between right and left inferior parietal lobules was significantly decreased in children with stroke. Resting state activity and ToM performance were not significantly correlated. INTERPRETATION Childhood stroke results in poorer performance on specific ToM tasks. Stroke is associated with changes in resting state connectivity in networks linked with social cognition including ToM. Although the basis for these changes in connectivity is not well understood, these results may provide preliminary insights into potential mechanisms affecting social cognition after stroke. The findings suggest that further study of the effect of childhood stroke on network connectivity may yield insights as to how stroke affects cognitive functions in children.
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Affiliation(s)
- Warren Lo
- Departments of Pediatrics and Neurology The Ohio State University and Nationwide Children's Hospital, Columbus, OH
| | - Xiangrui Li
- Department of Psychology, The Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH
| | - Kristen Hoskinson
- Department of Pediatrics, The Ohio State University, and the Center for Biobehavioral Health, The Research Institute at Nationwide Children's Hospital, Columbus, OH
| | - Kelly McNally
- Department of Pediatrics, The Ohio State University, and Nationwide Children's Hospital, Columbus, OH
| | - Melissa Chung
- Department of Pediatrics, The Ohio State University, and Nationwide Children's Hospital, Columbus, OH
| | - JoEllen Lee
- Departments of Pediatrics, College of Nursing, The Ohio State University, Nationwide Children's Hospital, Columbus, OH
| | - Ji Wang
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - Zhong-Lin Lu
- Center for Neural Science and Department of Psychology, New York University, New York, NY
| | - Keith Yeates
- Departments of Psychology, Pediatrics, and Clinical Neurosciences, University of Calgary, Alberta, Canada
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88
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Domain-general and domain-preferential neural correlates underlying empathy towards physical pain, emotional situation and emotional faces: An ALE meta-analysis. Neuropsychologia 2020; 137:107286. [DOI: 10.1016/j.neuropsychologia.2019.107286] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 11/07/2019] [Accepted: 11/25/2019] [Indexed: 01/10/2023]
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89
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Gotts SJ, Gilmore AW, Martin A. Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics. Neuroimage 2020; 205:116289. [PMID: 31629827 PMCID: PMC6919311 DOI: 10.1016/j.neuroimage.2019.116289] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 10/12/2019] [Accepted: 10/16/2019] [Indexed: 12/11/2022] Open
Abstract
One of the most controversial practices in resting-state fMRI functional connectivity studies is whether or not to regress out the global average brain signal (GS) during artifact removal. Some groups have argued that it is absolutely essential to regress out the GS in order to fully remove head motion, respiration, and other global imaging artifacts. Others have argued that removing the GS distorts the resulting correlation matrices and inappropriately alters the results of group comparisons and relationships to behavior. At the core of this argument is the assessment of dimensionality in terms of the number of brain networks with uncorrelated time series. If the dimensionality is high, then the distortions due to GS removal could be effectively negligible. In the current paper, we examine the dimensionality of resting-state fMRI data using principal component analyses (PCA) and network clustering analyses. In two independent datasets (Set 1: N = 62, Set 2: N = 32), scree plots of the eigenvalues level off at or prior to 10 principal components, with prominent elbows at 3 and 7 components. While network clustering analyses have previously demonstrated that numerous networks can be distinguished with high thresholding of the voxel-wise correlation matrices, lower thresholding reveals a lower-dimensional hierarchical structure, with the first prominent branch at 2 networks (corresponding to the previously described "task-positive"/"task-negative" distinction) and further stable subdivisions at 4, 7 and 17. Since inter-correlated time series within these larger branches do not cancel to zero when averaged, the hierarchical nature of the correlation structure results in low effective dimensionality. Consistent with this, partial correlation analyses revealed that network-specific variance remains present in the GS at each level of the hierarchy, accounting for at least 14-18% of the overall GS variance in each dataset. These results demonstrate that GS regression is expected to remove substantial portions of network-specific brain signals along with artifacts, not simply whole-brain signals corresponding to arousal levels. We highlight alternative means of controlling for residual global artifacts when not removing the GS.
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Affiliation(s)
- Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Adrian W Gilmore
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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90
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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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91
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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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92
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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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93
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Wang W, Zhang X, Li L. Common reducing subspace model and network alternation analysis. Biometrics 2019; 75:1109-1120. [DOI: 10.1111/biom.13099] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 05/22/2019] [Indexed: 12/21/2022]
Affiliation(s)
- Wenjing Wang
- Department of Statistics Florida State University Tallahassee Florida
| | - Xin Zhang
- Department of Statistics Florida State University Tallahassee Florida
| | - Lexin Li
- Department of Biostatistics and Epidemiology University of California Berkeley California
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94
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SAKADE Y, YAMANAKA K, SOUMIYA H, FURUKAWA S, FUKUMITSU H. Exposure to valproic acid during middle to late-stage corticogenesis induces learning and social behavioral abnormalities with attention deficit/hyperactivity in adult mice. Biomed Res 2019; 40:179-188. [DOI: 10.2220/biomedres.40.179] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Yuki SAKADE
- Laboratory of Molecular Biology, Department of Biofunctional Analysis, Gifu Pharmaceutical University
| | - Kumiko YAMANAKA
- Laboratory of Molecular Biology, Department of Biofunctional Analysis, Gifu Pharmaceutical University
| | - Hitomi SOUMIYA
- Laboratory of Molecular Biology, Department of Biofunctional Analysis, Gifu Pharmaceutical University
| | - Shoei FURUKAWA
- Laboratory of Molecular Biology, Department of Biofunctional Analysis, Gifu Pharmaceutical University
| | - Hidefumi FUKUMITSU
- Laboratory of Molecular Biology, Department of Biofunctional Analysis, Gifu Pharmaceutical University
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95
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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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96
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Wang Z, Zhang T, Liu J, Wang H, Lu T, Jia M, Zhang D, Wang L, Li J. Family-based association study of ZNF804A polymorphisms and autism in a Han Chinese population. BMC Psychiatry 2019; 19:159. [PMID: 31122238 PMCID: PMC6533675 DOI: 10.1186/s12888-019-2144-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 05/06/2019] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Autism is a complex neurodevelopmental disorder with high heritability. Zinc finger protein 804A (ZNF804A) was suggested to play important roles in neurodevelopment. Previous studies indicated that single-nucleotide polymorphism (SNP) rs1344706 in ZNF804A was strongly associated with schizophrenia and might influence social interaction. Only one study explored the significance of ZNF804A polymorphisms in autism, which discovered that rs7603001 was nominally associated with autism. Moreover, no previous study investigated the association between ZNF804A and autism in a Han Chinese population. Here, we investigated whether these two SNPs (rs1344706 and rs7603001) in ZNF804A contribute to the risk of autism in a Han Chinese population. METHODS We performed a family-based association study in 640 Han Chinese autism trios. Sanger sequencing was used for sample genotyping. Then, single marker association analyses were conducted using the family-based association test (FBAT) program. RESULTS No significant association was found between the two SNPs (rs1344706 and rs7603001) in ZNF804A and autism (P > 0.05). CONCLUSIONS Our findings suggested that rs1344706 and rs7603001 in ZNF804A might not be associated with autism in a Han Chinese population.
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Affiliation(s)
- Ziqi Wang
- 0000 0004 1798 0615grid.459847.3Peking University Sixth Hospital, No. 51, Hua Yuan Bei Road, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37Peking University Institute of Mental Health, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191 China ,0000 0004 1798 0615grid.459847.3National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Tian Zhang
- 0000 0004 1798 0615grid.459847.3Peking University Sixth Hospital, No. 51, Hua Yuan Bei Road, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37Peking University Institute of Mental Health, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191 China ,0000 0004 1798 0615grid.459847.3National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Jing Liu
- 0000 0004 1798 0615grid.459847.3Peking University Sixth Hospital, No. 51, Hua Yuan Bei Road, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37Peking University Institute of Mental Health, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191 China ,0000 0004 1798 0615grid.459847.3National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Han Wang
- 0000 0004 1798 0615grid.459847.3Peking University Sixth Hospital, No. 51, Hua Yuan Bei Road, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37Peking University Institute of Mental Health, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191 China ,0000 0004 1798 0615grid.459847.3National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Tianlan Lu
- 0000 0004 1798 0615grid.459847.3Peking University Sixth Hospital, No. 51, Hua Yuan Bei Road, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37Peking University Institute of Mental Health, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191 China ,0000 0004 1798 0615grid.459847.3National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Meixiang Jia
- 0000 0004 1798 0615grid.459847.3Peking University Sixth Hospital, No. 51, Hua Yuan Bei Road, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37Peking University Institute of Mental Health, Beijing, 100191 China ,0000 0001 2256 9319grid.11135.37NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191 China ,0000 0004 1798 0615grid.459847.3National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Dai Zhang
- Peking University Sixth Hospital, No. 51, Hua Yuan Bei Road, Beijing, 100191, China. .,Peking University Institute of Mental Health, Beijing, 100191, China. .,NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China. .,National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China. .,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China. .,PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China.
| | - Lifang Wang
- Peking University Sixth Hospital, No. 51, Hua Yuan Bei Road, Beijing, 100191, China. .,Peking University Institute of Mental Health, Beijing, 100191, China. .,NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China. .,National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Jun Li
- Peking University Sixth Hospital, No. 51, Hua Yuan Bei Road, Beijing, 100191, China. .,Peking University Institute of Mental Health, Beijing, 100191, China. .,NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China. .,National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
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97
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Multifaceted Integration: Memory for Faces Is Subserved by Widespread Connections between Visual, Memory, Auditory, and Social Networks. J Neurosci 2019; 39:4976-4985. [PMID: 31036762 DOI: 10.1523/jneurosci.0217-19.2019] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 04/04/2019] [Accepted: 04/05/2019] [Indexed: 01/07/2023] Open
Abstract
Our ability to recognize others by their facial features is at the core of human social interaction, yet this ability varies widely within the general population, ranging from developmental prosopagnosia to "super-recognizers". Previous work has focused mainly on the contribution of neural activity within the well described face network to this variance. However, given the nature of face memory in everyday life, and the social context in which it takes place, we were interested in exploring how the collaboration between different networks outside the face network in humans (measured through resting state connectivity) affects face memory performance. Fifty participants (men and women) were scanned with fMRI. Our data revealed that although the nodes of the face-processing network were tightly coupled at rest, the strength of these connections did not predict face memory performance. Instead, face recognition memory was dependent on multiple connections between these face patches and regions of the medial temporal lobe memory system (including the hippocampus), and the social processing system. Moreover, this network was selective for memory for faces, and did not predict memory for other visual objects (cars). These findings suggest that in the general population, variability in face memory is dependent on how well the face processing system interacts with other processing networks, with interaction among the face patches themselves accounting for little of the variance in memory ability.SIGNIFICANCE STATEMENT Our ability to recognize and remember faces is one of the pillars of human social interaction. Face recognition however is a very complex skill, requiring specialized neural resources in visual cortex, as well as memory, identity, and social processing, all of which are inherent in our real-world experience of faces. Yet in the general population, people vary greatly in their face memory abilities. Here we show that in the neural domain this variability is underpinned by the integration of visual, memory and social circuits, with the strength of the connections between these circuits directly linked to face recognition ability.
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98
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Noriega G. Restricted, Repetitive, and Stereotypical Patterns of Behavior in Autism-an fMRI Perspective. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1139-1148. [PMID: 31021772 DOI: 10.1109/tnsre.2019.2912416] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The main objective of this paper is to determine whether resting-state fMRI can identify functional connectivity differences between individuals with autism who experience severe issues with restricted, repetitive, and stereotypical behaviors, those who experience only mild issues, and controls. We use resting-state fMRI data from the ABIDE-I preprocessed repository, with participants grouped according to their ADI-R Restricted, Repetitive, and Stereotyped Patterns of Behavior Subscore. Three processing methods are used for analysis. A time-correlation approach establishes a basic baseline, and we introduce a method based on sliding time windows, with means across time adjusted to consider the fraction of time the correlation measure is above/below average. We complement these with a band-limited coherence approach. For completeness, preprocessing schemes with and without global signal regression are considered. Our results are in line with recent ones which find both over- and under-connectivities in the autistic brain. We find that there are indeed significant differences in connectivity between various regions that differentiate between ASD subjects with severe stereotypical/restrictive behavior issues, those with only mild issues, and controls. Interestingly, for some regions, the "signature" of subjects in the milder of the ASD groups appears to be distinct (i.e., over- or under-connected) relative to both the more severe ASD group and the controls.
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99
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Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study. Diagnostics (Basel) 2019; 9:diagnostics9010032. [PMID: 30901848 PMCID: PMC6468479 DOI: 10.3390/diagnostics9010032] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 03/04/2019] [Accepted: 03/08/2019] [Indexed: 11/17/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurological and developmental disorder whose late diagnosis is based on subjective tests. In seeking for earlier diagnosis, we aimed to find objective biomarkers via analysis of resting-state functional MRI (rs-fMRI) images obtained from the Autism Brain Image Data Exchange (ABIDE) database. Thus, we estimated brain functional connectivity (FC) between pairs of regions as the statistical dependence between their neural-related blood-oxygen-level-dependent (BOLD) signals. We compared FC of individuals with ASD and healthy controls, matched by age and intelligence quotient (IQ), and split into three age groups (50 children, 98 adolescents, and 32 adults), from a developmental perspective. After estimating the correlation, we observed hypoconnectivities in children and adolescents with ASD between regions belonging to the default mode network (DMN). Concretely, in children, FC decreased between the left middle temporal gyrus and right frontal pole (p = 0.0080), and between the left orbitofrontal cortex and right superior frontal gyrus (p = 0.0144). In adolescents, this decrease was observed between bilateral postcentral gyri (p = 0.0012), and between the right precuneus and right middle temporal gyrus (p = 0.0236). These results help to gain a better understanding of the involved regions on autism and its connection with the affected superior cognitive brain functions.
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100
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Seok JW, Cheong C. Dynamic Causal Modeling of Effective Connectivity During Anger Experience in Healthy Young Men: 7T Magnetic Resonance Imaging Study. Adv Cogn Psychol 2019; 15:52-62. [PMID: 32537036 PMCID: PMC7278524 DOI: 10.5709/acp-0256-7] [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] [Indexed: 01/22/2023] Open
Abstract
Little is known about how anger-associated brain regions integrate and modulate external input. Therefore, we investigated the neural connectivity architecture of anger processing using a dynamic causal modeling (DCM) approach. Thirteen subjects underwent functional magnetic resonance imaging (fMRI) while viewing anger-inducing film clips. Conventional fMRI and DCM analyses were conducted to identify a dominant connectivity model. Viewing anger-inducing film clips led to activation in the left superior temporal gyrus, left insula, and left orbitofrontal cortex (OFC). The results of a group-level comparison of eight connectivity models based on conventional fMRI findings showed superiority of the model including reciprocal effective connectivities between the left insula, left superior temporal gyrus, and left orbitofrontal gyrus and bottom-up connectivity from the left superior temporal gyrus to the left orbitofrontal gyrus. Positive coupling effects were identified for connectivities between the left superior temporal gyrus and left insula and the left superior temporal gyrus and left OFC. A negative coupling effect was identified for connectivity between the left OFC and left insula. In conclusion, we propose a model of effective connectivity associated with the anger experience based on dynamic causal modeling. The findings have implications for various psychiatric disorders related to abnormalities in anger processing.
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Affiliation(s)
- Ji-Woo Seok
- Department of Counseling Psychology, Honam University, Kwangju, South Korea
- Bioimaging Research Team, Korea Basic Science Institute, Cheongju, South Korea
| | - Chaejoon Cheong
- Bioimaging Research Team, Korea Basic Science Institute, Cheongju, South Korea
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