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Luo T, Zhang M, Li S, Situ M, Liu P, Wang M, Tao Y, Zhao S, Wang Z, Yang Y, Huang Y. Exome functional risk score and brain connectivity can predict social adaptability outcome of children with autism spectrum disorder in 4 years' follow up. Front Psychiatry 2024; 15:1384134. [PMID: 38818019 PMCID: PMC11137745 DOI: 10.3389/fpsyt.2024.1384134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/29/2024] [Indexed: 06/01/2024] Open
Abstract
Introduction Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder emerging in early childhood, with heterogeneous clinical outcomes across individuals. This study aims to recognize neuroimaging genetic factors associated with outcomes of ASD after a 4-year follow-up. Methods A total of 104 ASD children were included in this study; they underwent clinical assessments, MRI data acquisition, and the whole exome sequencing (WES). Exome functional risk score (EFRS) was calculated based on WES; and two modalities of brain connectivity were constructed based on MRI data, that is functional connectivity (FC) for functional MRI (fMRI), and individual differential structural covariance network (IDSCN) for structural MRI (sMRI), to explore the neuroimaging genetic biomarker of outcomes of ASD children. Results Regression analysis found EFRS predicts social adaptability at the 4-year follow-up (Y = -0.013X + 9.29, p = 0.003). We identified 19 pairs of FC associated with autism symptoms severity at follow-up, 10 pairs of FC and 4 pairs of IDSCN associated with social adaptability at follow-up, and 10 pairs of FC associated with ASD EFRS by support vector regression (SVR). Related brain regions with prognostic predictive effects are mainly distributed in superior frontal gyrus, occipital cortex, temporal cortex, parietal cortex, paracentral lobule, pallidum, and amygdala for FC, and temporal cortex, thalamus, and hippocampus for IDSCN. Mediation model showed that ASD EFRS affects the social communication of ASD children through the mediation of FC between left middle occipital gyrus and left pallidum (RMSEA=0.126, CMIN=80.66, DF=42, p< 0.001, CFI=0.867, AIC=152). Discussion Our findings underscore that both EFRS and brain connectivity can predict social adaptability, and that brain connectivity serving as mediator in the relationship of EFRS and behaviors of ASD, suggesting the intervention targets in the future clinical application.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Yi Huang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
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2
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Li K, Luo X, Zeng Q, Liu X, Li J, Zhong S, Zhang X, Xu X, Wang S, Hong H, Jiaerken Y, Liu Z, Zhao S, Huang P, Zhang M, Chen Y. Gray matter structural covariance networks patterns associated with autopsy-confirmed LATE-NC compared to Alzheimer's disease pathology. Neurobiol Dis 2023; 189:106354. [PMID: 37977431 DOI: 10.1016/j.nbd.2023.106354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 11/14/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Cases with the limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy neuropathologic change (LATE-NC), Alzheimer's disease (AD), and mixed AD+TDP-43 pathology (AD+LATE-NC) share similar symptoms, which makes it a challenge for accurate diagnosis. Exploring the patterns of gray matter structural covariance networks (SCNs) in these three types may help to clarify the underlying mechanism and provide a basis for clinical interventions. METHODS We included ante-mortem MRI data of 10 LATE-NC, 39 AD, and 25 AD+LATE-NC from the ADNI autopsy sample. We used four regions of interest (left posterior cingulate cortex, right entorhinal cortex, frontoinsular and dorsolateral prefrontal cortex) to anchor the default mode network (DMN), salience network (SN), and executive control network (ECN). Finally, we assessed the SCN alternations using a multi-regression model-based linear-interaction analysis. RESULTS Cases with autopsy-confirmed LATE-NC and AD showed increased structural associations involving DMN, ECN, and SN. Cases with AD+LATE-NC showed increased structural association within DMN while decreased structural association between DMN and ECN. The volume of peak clusters showed significant associations with cognition and AD pathology. CONCLUSIONS This study showed different SCN patterns in the cases with LATE-NC, AD, and AD+LATE-NC, and indicated the network disconnection mechanism underlying these three neuropathological progressions. Further, SCN may serve as an effective biomarker to distinguish between different types of dementia.
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Affiliation(s)
- Kaicheng Li
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jixuan Li
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Siyan Zhong
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xinyi Zhang
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Shuyue Wang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Hui Hong
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yerfan Jiaerken
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhirong Liu
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Shuai Zhao
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yanxing Chen
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
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3
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Prigge MBD, Bigler ED, Lange N, Morgan J, Froehlich A, Freeman A, Kellett K, Kane KL, King CK, Taylor J, Dean DC, King JB, Anderson JS, Zielinski BA, Alexander AL, Lainhart JE. Longitudinal Stability of Intellectual Functioning in Autism Spectrum Disorder: From Age 3 Through Mid-adulthood. J Autism Dev Disord 2022; 52:4490-4504. [PMID: 34677753 PMCID: PMC9090201 DOI: 10.1007/s10803-021-05227-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/29/2021] [Indexed: 12/02/2022]
Abstract
Intelligence (IQ) scores are used in educational and vocational planning for individuals with autism spectrum disorder (ASD) yet little is known about the stability of IQ throughout development. We examined longitudinal age-related IQ stability in 119 individuals with ASD (3-36 years of age at first visit) and 128 typically developing controls. Intelligence measures were collected over a 20-year period. In ASD, Full Scale (FSIQ) and Verbal (VIQ) Intelligence started lower in childhood and increased at a greater rate with age relative to the control group. By early adulthood, VIQ and working memory stabilized, whereas nonverbal and perceptual scores continued to change. Our results suggest that in individuals with ASD, IQ estimates may be dynamic in childhood and young adulthood.
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Affiliation(s)
- Molly B D Prigge
- Department of Radiology and Imaging Sciences, Radiology Research, University of Utah, 729 Arapeen Drive, Salt Lake City, UT, 84108, USA.
| | - Erin D Bigler
- Department of Psychology and Neuroscience Center, Brigham Young University, Provo, UT, USA
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
- Department of Neurology, University of California-Davis, Davis, CA, USA
| | - Nicholas Lange
- Department of Psychiatry, Harvard School of Medicine, Boston, MA, USA
| | - Jubel Morgan
- Department of Radiology and Imaging Sciences, Radiology Research, University of Utah, 729 Arapeen Drive, Salt Lake City, UT, 84108, USA
| | - Alyson Froehlich
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
| | - Abigail Freeman
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Kristina Kellett
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Karen L Kane
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Carolyn K King
- Department of Radiology and Imaging Sciences, Radiology Research, University of Utah, 729 Arapeen Drive, Salt Lake City, UT, 84108, USA
| | - June Taylor
- Department of Radiology and Imaging Sciences, Radiology Research, University of Utah, 729 Arapeen Drive, Salt Lake City, UT, 84108, USA
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Jace B King
- Department of Radiology and Imaging Sciences, Radiology Research, University of Utah, 729 Arapeen Drive, Salt Lake City, UT, 84108, USA
| | - Jeff S Anderson
- Department of Radiology and Imaging Sciences, Radiology Research, University of Utah, 729 Arapeen Drive, Salt Lake City, UT, 84108, USA
| | - Brandon A Zielinski
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Janet E Lainhart
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
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Kangarani-Farahani M, Izadi-Najafabadi S, Zwicker JG. How does brain structure and function on MRI differ in children with autism spectrum disorder, developmental coordination disorder, and/or attention deficit hyperactivity disorder? Int J Dev Neurosci 2022; 82:681-715. [PMID: 36084947 DOI: 10.1002/jdn.10228] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 08/22/2022] [Accepted: 09/05/2022] [Indexed: 11/09/2022] Open
Abstract
AIM The purpose of this study was to systematically review the neural similarities and differences in brain structure and function, measured by magnetic resonance imaging (MRI), in children with neurodevelopmental disorders that commonly co-occur to understand if and how they have shared neuronal characteristics. METHOD Using systematic review methodology, the following databases were comprehensively searched: MEDLINE, EMBASE, CINAHL, CENTRAL, PsycINFO, and ProQuest from the earliest record up to December 2021. Inclusion criteria were: (1) peer-reviewed studies, case reports, or theses; (2) children under 18 years of age with at least one of the following neurodevelopmental disorders: autism spectrum disorder (ASD), attention hyperactivity deficit disorder (ADHD), developmental coordination disorder (DCD), and their co-occurrence; (3) studies based on MRI modalities (i.e., structural MRI, diffusion tensor imaging (DTI), and resting-state fMRI). Thirty-one studies that met the inclusion criteria were included for quality assessment by two independent reviewers using the Appraisal tool for Cross-Sectional Studies (AXIS). RESULTS Studies compared brain structure and function of children with DCD and ADHD (n=6), DCD and ASD (n=1), ASD and ADHD (n=17), and various combinations of these co-occurring conditions (n=7). Structural neuroimaging (n=15) was the most commonly reported modality, followed by resting-state (n=8), DTI (n=5), and multi-modalities (n=3). INTERPRETATION Evidence indicated that the neural correlates of the co-occurring conditions were more widespread and distinct compared to a single diagnosis. The majority of findings (77%) suggested that each neurodevelopmental disorder had more distinct neural correlates than shared neural features, suggesting that each disorder is distinct despite commonly co-occurring with each other. As the number of papers examining the co-occurrence of ASD, DCD, and/or ADHD was limited and most findings were not corrected for multiple comparisons, these results must be interpreted with caution.
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Affiliation(s)
- Melika Kangarani-Farahani
- Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, Canada.,BC Children's Hospital Research Institute, Vancouver, Canada
| | - Sara Izadi-Najafabadi
- Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, Canada.,BC Children's Hospital Research Institute, Vancouver, Canada
| | - Jill G Zwicker
- BC Children's Hospital Research Institute, Vancouver, Canada.,Department of Occupational Science & Occupational Therapy, University of British Columbia, Vancouver, Canada.,Department of Pediatrics, University of British Columbia, Vancouver, Canada.,CanChild Centre for Childhood Disability Research, Hamilton, Canada
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5
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Zielinski BA, Andrews DS, Lee JK, Solomon M, Rogers SJ, Heath B, Nordahl CW, Amaral DG. Sex-dependent structure of socioemotional salience, executive control, and default mode networks in preschool-aged children with autism. Neuroimage 2022; 257:119252. [PMID: 35500808 PMCID: PMC11107798 DOI: 10.1016/j.neuroimage.2022.119252] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 03/12/2022] [Accepted: 04/16/2022] [Indexed: 12/26/2022] Open
Abstract
The structure of large-scale intrinsic connectivity networks is atypical in adolescents diagnosed with autism spectrum disorder (ASD or autism). However, the degree to which alterations occur in younger children, and whether these differences vary by sex, is unknown. We utilized structural magnetic resonance imaging (MRI) data from a sex- and age- matched sample of 122 autistic and 122 typically developing (TD) children (2-4 years old) to investigate differences in underlying network structure in preschool-aged autistic children within three large scale intrinsic connectivity networks implicated in ASD: the Socioemotional Salience, Executive Control, and Default Mode Networks. Utilizing structural covariance MRI (scMRI), we report network-level differences in autistic versus TD children, and further report preliminary findings of sex-dependent differences within network topology.
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Affiliation(s)
- Brandon A Zielinski
- Departments of Pediatrics and Neurology, University of Utah School of Medicine, University of Utah, Salt Lake City, UT, USA.
| | - Derek S Andrews
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Joshua K Lee
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Marjorie Solomon
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Sally J Rogers
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Brianna Heath
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - Christine Wu Nordahl
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
| | - David G Amaral
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioral Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA
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6
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Sader M, Williams JHG, Waiter GD. A meta-analytic investigation of grey matter differences in anorexia nervosa and autism spectrum disorder. EUROPEAN EATING DISORDERS REVIEW 2022; 30:560-579. [PMID: 35526083 PMCID: PMC9543727 DOI: 10.1002/erv.2915] [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: 03/17/2022] [Accepted: 04/21/2022] [Indexed: 11/11/2022]
Abstract
Recent research reports Anorexia Nervosa (AN) to be highly dependent upon neurobiological function. Some behaviours, particularly concerning food selectivity are found in populations with both Autism Spectrum Disorder (ASD) and AN, and there is a proportionally elevated number of anorexic patients exhibiting symptoms of ASD. We performed a systematic review of structural MRI literature with the aim of identifying common structural neural correlates common to both AN and ASD. Across 46 ASD publications, a meta‐analysis of volumetric differences between ASD and healthy controls revealed no consistently affected brain regions. Meta‐analysis of 23 AN publications revealed increased volume within the orbitofrontal cortex and medial temporal lobe, and adult‐only AN literature revealed differences within the genu of the anterior cingulate cortex. The changes are consistent with alterations in flexible reward‐related learning and episodic memory reported in neuropsychological studies. There was no structural overlap between ASD and AN. Findings suggest no consistent neuroanatomical abnormality associated with ASD, and evidence is lacking to suggest that reported behavioural similarities between those with AN and ASD are due to neuroanatomical structural similarities. Findings related to neuroanatomical structure in AN/ASD demonstrate overlap and require revisiting. Meta‐analytic findings show structural increase/decrease versus healthy controls (LPFC/MTL/OFC) in AN, but no clusters found in ASD. The neuroanatomy associated with ASD is inconsistent, but findings in AN reflect condition‐related impairment in executive function and sociocognitive behaviours.
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Affiliation(s)
- Michelle Sader
- Translational Neuroscience, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Justin H G Williams
- Translational Neuroscience, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Gordon D Waiter
- Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
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7
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Wang Y, Hu D, Wu Z, Wang L, Huang W, Li G. Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2 years. Cereb Cortex 2022; 32:3786-3798. [PMID: 35034115 PMCID: PMC9433424 DOI: 10.1093/cercor/bhab448] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 10/18/2021] [Accepted: 11/01/2021] [Indexed: 01/19/2023] Open
Abstract
Converging evidence supports that a collection of brain regions is functionally or anatomically abnormal in autistic subjects. Structural covariance networks (SCNs) representing patterns of coordinated regional maturation are widely used to study abnormalities associated with neurodisorders. However, the possible developmental changes of SCNs in autistic individuals during the first 2 postnatal years, which features dynamic development and can potentially serve as biomarkers, remain unexplored. To fill this gap, for the first time, SCNs of cortical thickness and surface area were constructed and investigated in infants at high familial risk for autism and typically developing infants in this study. Group differences of SCNs emerge at 12 months of age in surface area. By 24 months of age, the autism group shows significantly increased integration, decreased segregation, and decreased small-worldness, compared with controls. The SCNs of surface area are deteriorated and shifted toward randomness in autistic infants. The abnormal brain regions changed during development, and the group differences of the left lateral occipital cortex become more prominent with age. These results indicate that autism has more significant influences on coordinated development of surface area than that of cortical thickness and the occipital cortex maybe an important biomarker of autism during infancy.
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Affiliation(s)
- Ya Wang
- National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China,Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Wenhua Huang
- Address correspondence to Wenhua Huang, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, 11th floor, Southern Medical University, Guangzhou 510515, China. ; Gang Li, The University of North Carolina at Chapel Hill, Bioinformatics Building #3104, Chapel Hill, NC 27599.
| | - Gang Li
- Address correspondence to Wenhua Huang, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, 11th floor, Southern Medical University, Guangzhou 510515, China. ; Gang Li, The University of North Carolina at Chapel Hill, Bioinformatics Building #3104, Chapel Hill, NC 27599.
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8
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Liu M, Li B, Hu D. Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review. Front Neurosci 2021; 15:697870. [PMID: 34602966 PMCID: PMC8480393 DOI: 10.3389/fnins.2021.697870] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/09/2021] [Indexed: 01/01/2023] Open
Abstract
Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD.
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Affiliation(s)
- Meijie Liu
- Engineering Training Center, Xi'an University of Science and Technology, Xi'an, China.,College of Missile Engineering, Rocket Force University of Engineering, Xi'an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
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Benkarim O, Paquola C, Park BY, Hong SJ, Royer J, Vos de Wael R, Lariviere S, Valk S, Bzdok D, Mottron L, C Bernhardt B. Connectivity alterations in autism reflect functional idiosyncrasy. Commun Biol 2021; 4:1078. [PMID: 34526654 PMCID: PMC8443598 DOI: 10.1038/s42003-021-02572-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 08/17/2021] [Indexed: 02/08/2023] Open
Abstract
Autism spectrum disorder (ASD) is commonly understood as an alteration of brain networks, yet case-control analyses against typically-developing controls (TD) have yielded inconsistent results. Here, we devised a novel approach to profile the inter-individual variability in functional network organization and tested whether such idiosyncrasy contributes to connectivity alterations in ASD. Studying a multi-centric dataset with 157 ASD and 172 TD, we obtained robust evidence for increased idiosyncrasy in ASD relative to TD in default mode, somatomotor and attention networks, but also reduced idiosyncrasy in lateral temporal cortices. Idiosyncrasy increased with age and significantly correlated with symptom severity in ASD. Furthermore, while patterns of functional idiosyncrasy were not correlated with ASD-related cortical thickness alterations, they co-localized with the expression patterns of ASD risk genes. Notably, we could demonstrate that patterns of atypical idiosyncrasy in ASD closely overlapped with connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between inter-individual heterogeneity in autism and functional signatures. Our findings provide novel biomarkers to study atypical brain development and may consolidate prior research findings on the variable nature of connectome level anomalies in autism.
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Affiliation(s)
- Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sara Lariviere
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sofie Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- INM-7, FZ Jülich, Jülich, Germany
| | - Danilo Bzdok
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Laurent Mottron
- Centre de recherche du CIUSSSNIM et Département de Psychiatrie, Université de Montréal, Montreal, QC, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
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10
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Elalfy MS, Ibrahim AS, Ibrahim GS, Hussein HMAG, Mohammed HGE, Ebeid FSE. Hidden brain iron content in sickle cell disease: impact on neurocognitive functions. Eur J Pediatr 2021; 180:2677-2686. [PMID: 34236515 DOI: 10.1007/s00431-021-04189-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/31/2021] [Accepted: 06/25/2021] [Indexed: 11/29/2022]
Abstract
Children with sickle cell disease (SCD) are at a high risk for neurocognitive impairment. We aim to quantitatively measure cerebral tissue R2* to investigate the brain iron deposition in children and young adults with SCD in comparison to beta thalassemia major (BTM) and healthy controls and evaluate its impact on neurocognitive functions in patients with SCD. Thirty-two SCD, fifteen BTM, and eleven controls were recruited. Multi-echo fast-gradient echo sequence brain MRI was performed, and brain R2* values of both caudate and thalamic regions were calculated. SCD patients were examined for the neurocognitive functions. SCD had high iron overload 0.30 ± 0.12 mg/kg/day. 68.9% of SCD had under-threshold IQ, 12.5% had moderate to severe anxiety, and 60.8% had depression. There were no differences between SCD, BTM, and controls in brain MRI except that left thalamus R2* higher in BTM than both SCD and controls (p = 0.032). Mean right caudate R2* was higher in female than male (p = 0.044). No significant association between brain R2* and LIC or heart R2* values in SCD. Left caudate R2* directly correlate with age and HbS%, and negatively correlate with HbA% while right thalamus R2* negatively correlate with transfusion index and among SCD patients.Conclusion: Neurocognitive dysfunction in SCD could not be explained solely by brain iron overload. What is Known: • Children with sickle cell disease are at great risk of brain damage due to their irregularly shaped red blood cells that can interrupt blood flow to the brain. • There are a number of factors that have negative brain effects that result in learning difficulties, and this not only due to increase brain iron content. What is New: • Assessment of quantitative brain iron content using MRI R2* in children and young adults with SCD in comparison to beta thalassemia major and healthy controls. • Impact of brain iron content on neurocognitive functions of children and young adults with SCD.
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11
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Qian L, Li Y, Wang Y, Wang Y, Cheng X, Li C, Cui X, Jiao G, Ke X. Shared and Distinct Topologically Structural Connectivity Patterns in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder. Front Neurosci 2021; 15:664363. [PMID: 34177449 PMCID: PMC8226092 DOI: 10.3389/fnins.2021.664363] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 05/10/2021] [Indexed: 12/04/2022] Open
Abstract
Background Previous neuroimaging studies have described shared and distinct neurobiological mechanisms between autism spectrum disorders (ASDs) and attention-deficit/hyperactivity disorder (ADHD). However, little is known about the similarities and differences in topologically structural connectivity patterns between the two disorders. Methods Diffusion tensor imaging (DTI) and deterministic tractography were used to construct the brain white matter (WM) structural networks of children and adolescents (age range, 6–16 years); 31 had ASD, 34 had ADHD, and 30 were age- and sex-matched typically developing (TD) individuals. Then, graph theoretical analysis was performed to investigate the alterations in the global and node-based properties of the WM structural networks in these groups. Next, measures of ASD traits [Social Responsiveness Scale (SRS)] and ADHD traits (Swanson, Nolan, and Pelham, version IV scale, SNAP-IV) were correlated with the alterations to determine the functional significance of such changes. Results First, there were no significant differences in the global network properties among the three groups; moreover, compared with that of the TD group, nodal degree (Ki) of the right amygdala (AMYG.R) and right parahippocampal gyrus (PHG.R) were found in both the ASD and ADHD groups. Also, the ASD and ADHD groups shared four additional hubs, including the left middle temporal gyrus (MTG.L), left superior temporal gyrus (STG.L), left postcentral gyrus (PoCG.L), and right middle frontal gyrus (MFG.R) compared with the TD group. Moreover, the ASD and ADHD groups exhibited no significant differences regarding regional connectivity characteristics. Second, the ADHD group showed significantly increased nodal betweenness centrality (Bi) of the left hippocampus (HIP.L) compared with the ASD group; also, compared with the ADHD group, the ASD group lacked the left anterior cingulate gyrus (ACG.L) as a hub. Last, decreased nodal efficiency (Enodal) of the AMYG.R, Ki of the AMYG.R, and Ki of the PHG.R were associated with higher SRS scores in the ASD group. Decreased Ki of the PHG.R was associated with higher SRS scores in the full sample, whereas decreased Bi of the PHG.R was associated with lower oppositional defiance subscale scores of the SNAP-IV in the ADHD group, and decreased Bi of the HIP.L was associated with lower inattention subscale scores of the SNAP-IV in the full sample. Conclusion From the perspective of the topological properties of brain WM structural networks, ADHD and ASD have both shared and distinct features. More interestingly, some shared and distinct topological properties of WM structures are related to the core symptoms of these disorders.
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Affiliation(s)
- Lu Qian
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
| | - Yun Li
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Yao Wang
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Yue Wang
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Xin Cheng
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Chunyan Li
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Xiwen Cui
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Gongkai Jiao
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
| | - Xiaoyan Ke
- Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing, China
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12
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Liloia D, Mancuso L, Uddin LQ, Costa T, Nani A, Keller R, Manuello J, Duca S, Cauda F. Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence. Neuroimage Clin 2021; 30:102583. [PMID: 33618237 PMCID: PMC7903137 DOI: 10.1016/j.nicl.2021.102583] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/15/2020] [Accepted: 01/30/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical brain anatomy and connectivity. Graph-theoretical methods have mainly been applied to detect altered patterns of white matter tracts and functional brain activation in individuals with ASD. The network topology of gray matter (GM) abnormalities in ASD remains relatively unexplored. METHODS An innovative meta-connectomic analysis on voxel-based morphometry data (45 experiments, 1,786 subjects with ASD) was performed in order to investigate whether GM variations can develop in a distinct pattern of co-alteration across the brain. This pattern was then compared with normative profiles of structural and genetic co-expression maps. Graph measures of centrality and clustering were also applied to identify brain areas with the highest topological hierarchy and core sub-graph components within the co-alteration network observed in ASD. RESULTS Individuals with ASD exhibit a distinctive and topologically defined pattern of GM co-alteration that moderately follows the structural connectivity constraints. This was not observed with respect to the pattern of genetic co-expression. Hub regions of the co-alteration network were mainly left-lateralized, encompassing the precuneus, ventral anterior cingulate, and middle occipital gyrus. Regions of the default mode network appear to be central in the topology of co-alterations. CONCLUSION These findings shed new light on the pathobiology of ASD, suggesting a network-level dysfunction among spatially distributed GM regions. At the same time, this study supports pathoconnectomics as an insightful approach to better understand neuropsychiatric disorders.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy.
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
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13
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Guo X, Duan X, Suckling J, Wang J, Kang X, Chen H, Biswal BB, Cao J, He C, Xiao J, Huang X, Wang R, Han S, Fan YS, Guo J, Zhao J, Wu L, Chen H. Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain. Cereb Cortex 2021; 31:1500-1510. [PMID: 33123725 DOI: 10.1093/cercor/bhaa304] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/09/2020] [Accepted: 09/14/2020] [Indexed: 12/16/2022] Open
Abstract
Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2-7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.
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Affiliation(s)
- Xiaonan Guo
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xujun Duan
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB21 5EF, UK
| | - Jia Wang
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin 150086, China
| | - Xiaodong Kang
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCM, Sichuan Bayi Rehabilitation Center, Chengdu 611135, China
| | - Heng Chen
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,School of Medicine, Guizhou University, Guiyang 550025, China
| | - Bharat B Biswal
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Jing Cao
- Affiliated Sichuan Provincial Rehabilitation Hospital of Chengdu University of TCM, Sichuan Bayi Rehabilitation Center, Chengdu 611135, China
| | - Changchun He
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jinming Xiao
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xinyue Huang
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Runshi Wang
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Shaoqiang Han
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yun-Shuang Fan
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jing Guo
- MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jingping Zhao
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Lijie Wu
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin 150086, China
| | - Huafu Chen
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.,MOE Key Lab for Neuroinformation; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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14
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Yankowitz LD, Yerys BE, Herrington JD, Pandey J, Schultz RT. Dissociating regional gray matter density and gray matter volume in autism spectrum condition. NEUROIMAGE: CLINICAL 2021; 32:102888. [PMID: 34911194 PMCID: PMC8633367 DOI: 10.1016/j.nicl.2021.102888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 10/18/2021] [Accepted: 11/16/2021] [Indexed: 12/21/2022] Open
Abstract
Local gray matter structure can be separated into density and volume. Gray matter density increased and volume decreased with age in adolescence. Increased gray matter density but reduced volume in girls relative to boys. Autism is associated with increased gray matter volume, but not density. Regions with increased volume align with prior studies in autistic youth.
Background Despite decades of research, there is continued uncertainty regarding whether autism is associated with a specific profile of gray matter (GM) structure. This inconsistency may stem from the widespread use of voxel-based morphometry (VBM) methods that combine indices of GM density and GM volume. If GM density or volume, but not both, prove different in autism, the traditional VBM approach of combining the two indices may obscure the difference. The present study measures GM density and volume separately to examine whether autism is associated with alterations in GM volume, density, or both. Methods Differences in MRI-based GM density and volume were examined in 6–25 year-olds with a diagnosis of autism spectrum disorder (n = 213, 80.8% male, IQ 47–154) and a typically developing (TD) sample (n = 190, 71.6% male, IQ 67–155). High-resolution T1-weighted anatomical images were collected on a single MRI scanner. Regional density and volume were estimated via whole-brain parcellation comprised of 1625 parcels. Parcel-wise analyses were conducted using generalized additive models while controlling the false discovery rate (FDR, q < 0.05). Volume differences in the 68-region Desikan-Killiany atlas derived from Freesurfer were also examined, to establish the generalizability of findings across methods. Results No density differences were observed between the autistic and TD groups, either in individual parcels or whole brain mean density. Increased volume was observed in autism compared to the TD group when controlling for Age, Sex, and IQ, both at the level of the whole brain (total volume) and in 45 parcels (2.8% of total parcels). Parcels with greater volume included inferior, middle, and superior temporal gyrus, inferior and superior frontal gyrus, precuneus, and fusiform gyrus. Converging evidence from a standard Freesurfer pipeline also identified greater volume in a number of overlapping regions. Limitations The method for determining “density” is not a direct measure of neuronal density, and this study cannot reveal underlying cellular differences. While this study represents possibly the largest single-site sample of its kind, it is underpowered to detect very small differences. Conclusions These results provide compelling evidence that autism is associated with regional GM volumetric differences, which are more prominent than density differences. This underscores the importance of examining volume and density separately, and suggests that direct measures of volume (e.g. region-of-interest or tensor-based morphometry approaches) may be more sensitive to autism-relevant differences in neuroanatomy than concentration/density-based approaches.
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15
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Muller AM, Pennington DL, Meyerhoff DJ. Substance-Specific and Shared Gray Matter Signatures in Alcohol, Opioid, and Polysubstance Use Disorder. Front Psychiatry 2021; 12:795299. [PMID: 35115969 PMCID: PMC8803650 DOI: 10.3389/fpsyt.2021.795299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/27/2021] [Indexed: 11/13/2022] Open
Abstract
Substance use disorders (SUD) have been shown to be associated with gray matter (GM) loss, particularly in the frontal cortex. However, unclear is to what degree these regional GM alterations are substance-specific or shared across different substances, and if these regional GM alterations are independent of each other or the result of system-level processes at the intrinsic connectivity network level. The T1 weighted MRI data of 65 treated patients with alcohol use disorder (AUD), 27 patients with opioid use disorder (OUD) on maintenance therapy, 21 treated patients with stimulant use disorder comorbid with alcohol use disorder (polysubstance use disorder patients, PSU), and 21 healthy controls were examined via data-driven vertex-wise and voxel-wise GM analyses. Then, structural covariance analyses and open-access fMRI database analyses were used to map the cortical thinning patterns found in the three SUD groups onto intrinsic functional systems. Among AUD and OUD, we identified both common cortical thinning in right anterior brain regions as well as SUD-specific regional GM alterations that were not present in the PSU group. Furthermore, AUD patients had not only the most extended regional thinning but also significantly smaller subcortical structures and cerebellum relative to controls, OUD and PSU individuals. The system-level analyses revealed that AUD and OUD showed cortical thinning in several functional systems. In the AUD group the default mode network was clearly most affected, followed by the salience and executive control networks, whereas the salience and somatomotor network were highlighted as critical for understanding OUD. Structural brain alterations in groups with different SUDs are largely unique in their spatial extent and functional network correlates.
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Affiliation(s)
- Angela M Muller
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.,VA Advanced Imaging Research Center (VAARC), San Francisco VA Medical Center, San Francisco, CA, United States
| | - David L Pennington
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, United States.,San Francisco Veterans Affairs Health Care System (SFVAHCS), San Francisco, CA, United States
| | - Dieter J Meyerhoff
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.,VA Advanced Imaging Research Center (VAARC), San Francisco VA Medical Center, San Francisco, CA, United States
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16
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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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17
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Khundrakpam BS, Lewis JD, Jeon S, Kostopoulos P, Itturia Medina Y, Chouinard-Decorte F, Evans AC. Exploring Individual Brain Variability during Development based on Patterns of Maturational Coupling of Cortical Thickness: A Longitudinal MRI Study. Cereb Cortex 2020; 29:178-188. [PMID: 29228120 DOI: 10.1093/cercor/bhx317] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Indexed: 12/29/2022] Open
Abstract
Structural covariance has recently emerged as a tool to study brain connectivity in health and disease. The main assumption behind the phenomenon of structural covariance is that changes in brain structure during development occur in a coordinated fashion. However, no study has yet explored the correlation of structural brain changes within individuals across development. Here, we used longitudinal magnetic resonance imaging scans from 141 normally developing children and adolescents (scanned 3 times) to introduce a novel subject-based maturational coupling approach. For each subject, maturational coupling was defined as similarity in the trajectory of cortical thickness (across the time points) between any two cortical regions. Our approach largely captured features seen in population-based structural covariance, and confirmed strong maturational coupling between homologous and near-neighbor cortical regions. Stronger maturational coupling among several homologous regions was observed for females compared to males, possibly indicating greater interhemispheric connectivity in females. Developmental changes in maturational coupling within the default-mode network (DMN) aligned with developmental changes in structural and functional DMN connectivity. Our findings indicate that patterns of maturational coupling within individuals may provide mechanistic explanation for the phenomenon of structural covariance, and allow investigation of individual brain variability with respect to cognition and disease vulnerability.
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Affiliation(s)
- Budhachandra S Khundrakpam
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Ludmer Centre for NeuroInformatics and Mental Health, McGill University, Montreal, Quebec, Canada
| | - John D Lewis
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Ludmer Centre for NeuroInformatics and Mental Health, McGill University, Montreal, Quebec, Canada
| | - Seun Jeon
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Ludmer Centre for NeuroInformatics and Mental Health, McGill University, Montreal, Quebec, Canada
| | - Penelope Kostopoulos
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Ludmer Centre for NeuroInformatics and Mental Health, McGill University, Montreal, Quebec, Canada
| | - Yasser Itturia Medina
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Ludmer Centre for NeuroInformatics and Mental Health, McGill University, Montreal, Quebec, Canada
| | - François Chouinard-Decorte
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Ludmer Centre for NeuroInformatics and Mental Health, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Ludmer Centre for NeuroInformatics and Mental Health, McGill University, Montreal, Quebec, Canada
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18
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Gupta S, Rajapakse JC, Welsch RE. Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder. Neuroimage Clin 2020; 25:102186. [PMID: 32000101 PMCID: PMC7042673 DOI: 10.1016/j.nicl.2020.102186] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 01/08/2020] [Accepted: 01/13/2020] [Indexed: 11/30/2022]
Abstract
Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We argue that weak connections in brain functional networks lead to misclassification of brain regions as hubs. In order to resolve this, we propose a new measure called ambivert degree that considers the node's degree as well as connection weights in order to identify nodes with both high degree and high connection weights as hubs. Using resting-state functional MRI scans from the Human Connectome Project, we show that ambivert degree identifies brain hubs that are not only crucial but also invariable across subjects. We hypothesize that nodal measures based on ambivert degree can be effectively used to classify patients from healthy controls for diseases that are known to have widespread hub disruption. Using patient data for Alzheimer's Disease and Autism Spectrum Disorder, we show that the hubs in the patient and healthy groups are very different for both the diseases and deep feedforward neural networks trained on nodal hub features lead to a significantly higher classification accuracy with significantly fewer trainable weights compared to using functional connectivity features. Thus, the ambivert degree improves identification of crucial brain hubs in healthy subjects and can be used as a diagnostic feature to detect neurological diseases characterized by hub disruption.
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Affiliation(s)
- Sukrit Gupta
- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
| | - Jagath C Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore.
| | - Roy E Welsch
- MIT Center for Statistics and Data Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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19
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Green RR, Bigler ED, Froehlich A, Prigge MBD, Zielinski BA, Travers BG, Anderson JS, Alexander A, Lange N, Lainhart JE. Beery VMI and Brain Volumetric Relations in Autism Spectrum Disorder. JOURNAL OF PEDIATRIC NEUROPSYCHOLOGY 2019; 5:77-84. [PMID: 32953403 PMCID: PMC7497806 DOI: 10.1007/s40817-019-00069-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Revised: 07/20/2019] [Accepted: 08/02/2019] [Indexed: 11/29/2022]
Abstract
Although diminished proficiency on tasks that require visual-motor integration (VMI) has been reported in individuals with autism spectrum disorder (ASD), very few studies have examined the association between VMI performance and neuroanatomical regions of interest (ROI) involved in motor and perceptual functioning. To address these issues, the current study included an all-male sample of 41 ASD (ages 3-23 years) and 27 typically developing (TD) participants (ages 5-26 years) who completed the Beery-Buktenica Developmental Test of Visual-Motor Integration (Beery VMI) as part of a comprehensive neuropsychological battery. All participants underwent 3.0 T magnetic resonance imaging (MRI) with image quantification (FreeSurfer software v5.3). The groups were statistically matched on age, handedness, and intracranial volume (ICV). ASD participants performed significantly lower on VMI and IQ measures compared with the TD group. VMI performance was significantly correlated with FSIQ and PIQ in the TD group only. No pre-defined neuroanatomical ROIs were significantly different between groups. Significant correlations were observed in the TD group between VMI and total precentral gyrus gray matter volume (r = .51, p = .006) and total frontal lobe gray matter volume (r = .46, p = .017). There were no significant ROI correlations with Beery VMI performance in ASD participants. At the group level, despite ASD participants exhibiting reduced visuomotor abilities, no systematic relation with motor or sensory-perceptual ROIs was observed. In the TD group, results were consistent with the putative role of the precentral gyrus in motor control along with frontal involvement in planning, organization, and execution monitoring, all essential for VMI performance. Given that similar associations between VMI and ROIs were not observed in those with ASD, neurodevelopment in ASD group participants may not follow homogenous patterns making correlations in these brain regions unlikely to be observed.
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Affiliation(s)
- Ryan R. Green
- Department of Psychology, Brigham Young University, 1001 SWKT, Provo, UT 84602, USA
| | - Erin D. Bigler
- Department of Psychology, Brigham Young University, 1001 SWKT, Provo, UT 84602, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Alyson Froehlich
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | | | - Brandon A. Zielinski
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Brittany G. Travers
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI, USA
- Occupational Therapy Program, University of Wisconsin-Madison, Madison, WI 53705, USA
| | | | - Andrew Alexander
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Nicholas Lange
- Departments of Psychiatry and Biostatistics, Harvard University, Boston, MA, USA
- Neurostatistics Laboratory, McLean Hospital, Belmont, MA, USA
| | - Janet E. Lainhart
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
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20
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Li K, Luo X, Zeng Q, Huang P, Shen Z, Xu X, Xu J, Wang C, Zhou J, Zhang M. Gray matter structural covariance networks changes along the Alzheimer's disease continuum. Neuroimage Clin 2019; 23:101828. [PMID: 31029051 PMCID: PMC6484365 DOI: 10.1016/j.nicl.2019.101828] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 04/01/2019] [Accepted: 04/15/2019] [Indexed: 11/24/2022]
Abstract
Alzheimer's disease (AD) has a long neuropathological accumulation phase before the onset of dementia. Such AD neuropathological deposition between neurons impairs the synaptic communication, resulting in networks disorganization. Our study aimed to explore the evolution patterns of gray matter structural covariance networks (SCNs) along AD continuum. Based on the AT(N) (i.e., Amyloid/Tau/Neurodegeneration) pathological classification system, we classified subjects into four groups using cerebrospinal fluid amyloid-beta1-42 (A) and phosphorylated tau protein181 (T). We identified 101 subjects with normal AD biomarkers (A-T-), 40 subjects with Alzheimer's pathologic change (A + T-), 101 subjects with biological AD (A + T+) and 91 AD with dementia (demented subjects with A + T+). We used four regions of interest to anchor default mode network (DMN, medial temporal subsystem and midline core subsystem), salience network (SN) and executive control network (ECN). Finally, we used a multi-regression model-based linear-interaction analysis to assess the SCN changes. Along the disease progression, DMN and SN showed increased structural association at the early stage while decreased structural association at the late stage. Moreover, ECN showed progressively increased structural association as AD neuropathological profiles progress. In conclusion, this study found the dynamic trajectory of SCNs changes along the AD continuum and support the network disconnection hypothesis underlying AD neuropathological progression. Further, SCN may potentially serve as an effective AD biomarker.
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Affiliation(s)
- Kaicheng Li
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Xiao Luo
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Qingze Zeng
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Peiyu Huang
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Zhujing Shen
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Xiaojun Xu
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Jingjing Xu
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Chao Wang
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Jiong Zhou
- Department of Neurology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Minming Zhang
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China.
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21
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Palande S, Jose V, Zielinski B, Anderson J, Fletcher PT, Wang B. Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference. Brain Connect 2018; 9:13-21. [PMID: 30543119 DOI: 10.1089/brain.2018.0604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
A large body of evidence relates autism with abnormal structural and functional brain connectivity. Structural covariance magnetic resonance imaging (scMRI) is a technique that maps brain regions with covarying gray matter densities across subjects. It provides a way to probe the anatomical structure underlying intrinsic connectivity networks (ICNs) through analysis of gray matter signal covariance. In this article, we apply topological data analysis in conjunction with scMRI to explore network-specific differences in the gray matter structure in subjects with autism versus age-, gender-, and IQ-matched controls. Specifically, we investigate topological differences in gray matter structure captured by structural correlation graphs derived from three ICNs strongly implicated in autism, namely the salience network, default mode network, and executive control network. By combining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism.
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Affiliation(s)
- Sourabh Palande
- 1 Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah.,2 School of Computing, University of Utah, Salt Lake City, Utah
| | - Vipin Jose
- 2 School of Computing, University of Utah, Salt Lake City, Utah
| | - Brandon Zielinski
- 3 Department of Pediatrics, University of Utah, Salt Lake City, Utah.,4 Department of Neurology, University of Utah, Salt Lake City, Utah
| | - Jeffrey Anderson
- 5 Department of Radiology, University of Utah, Salt Lake City, Utah
| | - P Thomas Fletcher
- 1 Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah.,2 School of Computing, University of Utah, Salt Lake City, Utah
| | - Bei Wang
- 1 Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah.,2 School of Computing, University of Utah, Salt Lake City, Utah
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22
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A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective. Int J Dev Neurosci 2018; 71:68-82. [DOI: 10.1016/j.ijdevneu.2018.08.010] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 08/28/2018] [Accepted: 08/28/2018] [Indexed: 11/19/2022] Open
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23
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Yee Y, Fernandes DJ, French L, Ellegood J, Cahill LS, Vousden DA, Spencer Noakes L, Scholz J, van Eede MC, Nieman BJ, Sled JG, Lerch JP. Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity. Neuroimage 2018; 179:357-372. [PMID: 29782994 DOI: 10.1016/j.neuroimage.2018.05.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 04/13/2018] [Accepted: 05/10/2018] [Indexed: 12/14/2022] Open
Abstract
An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings. Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed. We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.
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Affiliation(s)
- Yohan Yee
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada.
| | - Darren J Fernandes
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Leon French
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Jacob Ellegood
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Lindsay S Cahill
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Dulcie A Vousden
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | | | - Jan Scholz
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Matthijs C van Eede
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Brian J Nieman
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - John G Sled
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Jason P Lerch
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
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24
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Bethlehem RAI, Romero-Garcia R, Mak E, Bullmore ET, Baron-Cohen S. Structural Covariance Networks in Children with Autism or ADHD. Cereb Cortex 2018. [PMID: 28633299 PMCID: PMC5903412 DOI: 10.1093/cercor/bhx135] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Background While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable heterogeneity in their results. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics. Method Graph theory was used to analyse topological properties of structural covariance networks across both conditions and relative to a neurotypical (NT; n = 87) group using data from the ABIDE (autism; n = 62) and ADHD-200 datasets (ADHD; n = 69). Regional cortical thickness was used to construct the structural covariance networks. This was analysed in a theoretical framework examining potential differences in long and short-range connectivity, with a specific focus on relation between central graph measures and cortical thickness. Results We found convergence between autism and ADHD, where both conditions show an overall decrease in CT covariance with increased Euclidean distance between centroids compared with a NT population. The 2 conditions also show divergence. Namely, there is less modular overlap between the 2 conditions than there is between each condition and the NT group. The ADHD group also showed reduced cortical thickness and lower degree in hub regions than the autism group. Lastly, the ADHD group also showed reduced wiring costs compared with the autism groups. Conclusions Our results indicate a need for taking an integrated approach when considering highly comorbid conditions such as autism and ADHD. Furthermore, autism and ADHD both showed alterations in the relation between inter-regional covariance and centroid distance, where both groups show a steeper decline in covariance as a function of distance. The 2 groups also diverge on modular organization, cortical thickness of hub regions and wiring cost of the covariance network. Thus, on some network features the groups are distinct, yet on others there is convergence.
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Affiliation(s)
- R A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK.,Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - R Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - E Mak
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - E T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK.,MRC/Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 3EB, UK.,Immuno-psychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK
| | - S Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK.,CLASS Clinic, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB21 5EF, UK
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25
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Krongold M, Cooper C, Bray S. Modular Development of Cortical Gray Matter Across Childhood and Adolescence. Cereb Cortex 2018; 27:1125-1136. [PMID: 26656727 DOI: 10.1093/cercor/bhv307] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Brain maturation across childhood and adolescence is characterized by cortical thickness (CT) and volume contraction, and early expansion of surface area (SA). These processes occur asynchronously across the cortical surface, with functional, topographic, and network-based organizing principles proposed to account for developmental patterns. Characterizing regions undergoing synchronized development can help determine whether "maturational networks" overlap with well-described functional networks, and whether they are targeted by neurodevelopmental and psychiatric disorders. In the present study, we modeled changes with age in CT, SA, and volume from 335 typically developing subjects in the NIH MRI study of normal brain development, with 262 followed longitudinally for a total of 724 scans. Vertices showing similar maturation between 5 and 22 years were grouped together using data-driven clustering. Patterns of CT development distinguished sensory and motor regions from association regions, and were vastly different from SA patterns, which separated anterior from posterior regions. Developmental modules showed little similarity to networks derived from resting-state functional connectivity. Our findings present a novel perspective on maturational changes across the cortex, showing that several proposed organizing principles of cortical development co-exist, albeit in different structural parameters, and enable visualization of developmental trends occurring in parallel at remote cortical sites.
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Affiliation(s)
- Mark Krongold
- Child and Adolescent Imaging Research Program, Alberta Children's Hospital, Calgary, AB, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, Calgary, AB, Canada T3B 6A8.,Biomedical Engineering Graduate Program
| | - Cassandra Cooper
- Child and Adolescent Imaging Research Program, Alberta Children's Hospital, Calgary, AB, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, Calgary, AB, Canada T3B 6A8
| | - Signe Bray
- Child and Adolescent Imaging Research Program, Alberta Children's Hospital, Calgary, AB, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, Calgary, AB, Canada T3B 6A8.,Department of Radiology, Cumming School of Medicine.,Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada T2N 1N4
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26
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Chang CC, Chang YT, Huang CW, Tsai SJ, Hsu SW, Huang SH, Lee CC, Chang WN, Lui CC, Lien CY. Associations of Bcl-2 rs956572 genotype groups in the structural covariance network in early-stage Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2018; 10:17. [PMID: 29422088 PMCID: PMC5806294 DOI: 10.1186/s13195-018-0344-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Accepted: 01/16/2018] [Indexed: 12/30/2022]
Abstract
Background Alzheimer’s disease (AD) is a complex neurodegenerative disease, and genetic differences may mediate neuronal degeneration. In humans, a single-nucleotide polymorphism in the B-cell chronic lymphocytic leukemia/lymphoma-2 (Bcl-2) gene, rs956572, has been found to significantly modulate Bcl-2 protein expression in the brain. The Bcl-2 AA genotype has been associated with reduced Bcl-2 levels and lower gray matter volume in healthy populations. We hypothesized that different Bcl-2 genotype groups may modulate large-scale brain networks that determine neurobehavioral test scores. Methods Gray matter structural covariance networks (SCNs) were constructed in 104 patients with AD using T1-weighted magnetic resonance imaging with seed-based correlation analysis. The patients were stratified into two genotype groups on the basis of Bcl-2 expression (G carriers, n = 76; A homozygotes, n = 28). Four SCNs characteristic of AD were constructed from seeds in the default mode network, salience network, and executive control network, and cognitive test scores served as the major outcome factor. Results For the G carriers, influences of the SCNs were observed mostly in the default mode network, of which the peak clusters anchored by the posterior cingulate cortex seed determined the cognitive test scores. In contrast, genetic influences in the A homozygotes were found mainly in the executive control network, and both the dorsolateral prefrontal cortex seed and the interconnected peak clusters were correlated with the clinical scores. Despite a small number of cases, the A homozygotes showed greater covariance strength than the G carriers among all four SCNs. Conclusions Our results suggest that the Bcl-2 rs956572 polymorphism is associated with different strengths of structural covariance in AD that determine clinical outcomes. The greater covariance strength in the four SCNs shown in the A homozygotes suggests that different Bcl-2 polymorphisms play different modulatory roles. Electronic supplementary material The online version of this article (10.1186/s13195-018-0344-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chiung-Chih Chang
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123 Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan.
| | - Ya-Ting Chang
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123 Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan
| | - Chi-Wei Huang
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123 Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan
| | - Shih-Jen Tsai
- Psychiatric Department, Taipei Veterans General Hospital, Taipei, Taiwan.,Psychiatric Division, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Shih-Wei Hsu
- Department of Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Shu-Hua Huang
- Department of Nuclear Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chen-Chang Lee
- Department of Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Wen-Neng Chang
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123 Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan
| | - Chun-Chung Lui
- Division of Medical Imaging, E-Da Cancer Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Chia-Yi Lien
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123 Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan
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27
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Lin PH, Tsai SJ, Huang CW, Mu-En L, Hsu SW, Lee CC, Chen NC, Chang YT, Lan MY, Chang CC. Dose-dependent genotype effects of BDNF Val66Met polymorphism on default mode network in early stage Alzheimer's disease. Oncotarget 2018; 7:54200-54214. [PMID: 27494844 PMCID: PMC5342335 DOI: 10.18632/oncotarget.11027] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 07/22/2016] [Indexed: 01/09/2023] Open
Abstract
In humans, brain-derived neurotrophic factor (BDNF) has been shown to play a pivotal role in neurocognition, and its gene contains a functional polymorphism (Val66Met) that may explain individual differences in brain volume and memory-related activity.In this study, we enrolled 186 Alzheimer's disease (AD) patients who underwent 3D T1 magnetic resonance imaging, and explored the gray matter (GM) structural covariance networks (SCN). The patients were divided into three groups according to their genotype: Met/Met (n = 45), Val/Met (n = 86) and Val/Val (n = 55). Seed-based analysis was performed focusing on four SCN networks. Neurobehavioral scores served as the major outcome factor.Only peak cluster volumes of default mode medial temporal lobe network showed significant genotype interactions, of which the interconnected peak clusters showed dose-dependent genotype effects. There were also significant correlations between the cognitive test scores and interconnected-cluster volumes, especially in the orbitofrontal cortex.These findings support the hypothesis that BDNF rs6265 polymorphisms modulate entorhinal cortex-interconnected clusters and the valine allele was associated with stronger structural covariance patterns that determined the cognitive outcomes.
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Affiliation(s)
- Pin-Hsuan Lin
- Department of Health and Beauty, Shu-Zen College of Medicine and Management, Kaohsiung, Taiwan
| | - Shih-Jen Tsai
- Psychiatric Department of Taipei Veterans General Hospital, Taipei, Taiwan.,Psychiatric Division, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Chi-Wei Huang
- Department of Health and Beauty, Shu-Zen College of Medicine and Management, Kaohsiung, Taiwan.,Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Liu Mu-En
- Psychiatric Division, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Shih-Wei Hsu
- Department of Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chen-Chang Lee
- Department of Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Nai-Ching Chen
- Department of Health and Beauty, Shu-Zen College of Medicine and Management, Kaohsiung, Taiwan.,Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Ya-Ting Chang
- Department of Health and Beauty, Shu-Zen College of Medicine and Management, Kaohsiung, Taiwan.,Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Min-Yu Lan
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chiung-Chih Chang
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
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28
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Scheinost D, Kwon SH, Lacadie C, Vohr BR, Schneider KC, Papademetris X, Constable RT, Ment LR. Alterations in Anatomical Covariance in the Prematurely Born. Cereb Cortex 2018; 27:534-543. [PMID: 26494796 DOI: 10.1093/cercor/bhv248] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Preterm (PT) birth results in long-term alterations in functional and structural connectivity, but the related changes in anatomical covariance are just beginning to be explored. To test the hypothesis that PT birth alters patterns of anatomical covariance, we investigated brain volumes of 25 PTs and 22 terms at young adulthood using magnetic resonance imaging. Using regional volumetrics, seed-based analyses, and whole brain graphs, we show that PT birth is associated with reduced volume in bilateral temporal and inferior frontal lobes, left caudate, left fusiform, and posterior cingulate for prematurely born subjects at young adulthood. Seed-based analyses demonstrate altered patterns of anatomical covariance for PTs compared with terms. PTs exhibit reduced covariance with R Brodmann area (BA) 47, Broca's area, and L BA 21, Wernicke's area, and white matter volume in the left prefrontal lobe, but increased covariance with R BA 47 and left cerebellum. Graph theory analyses demonstrate that measures of network complexity are significantly less robust in PTs compared with term controls. Volumes in regions showing group differences are significantly correlated with phonological awareness, the fundamental basis for reading acquisition, for the PTs. These data suggest both long-lasting and clinically significant alterations in the covariance in the PTs at young adulthood.
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Affiliation(s)
- Dustin Scheinost
- Department of Diagnostic Radiology.,Magnetic Resonance Research Center, New Haven, CT 06520-8043, USA
| | | | | | - Betty R Vohr
- Department of Pediatrics, Warren Alpert Brown Medical School, Providence, RI, USA
| | | | | | | | - Laura R Ment
- Department of Pediatrics.,Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
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29
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Stoodley CJ, D'Mello AM, Ellegood J, Jakkamsetti V, Liu P, Nebel MB, Gibson JM, Kelly E, Meng F, Cano CA, Pascual JM, Mostofsky SH, Lerch JP, Tsai PT. Altered cerebellar connectivity in autism and cerebellar-mediated rescue of autism-related behaviors in mice. Nat Neurosci 2017; 20:1744-1751. [PMID: 29184200 PMCID: PMC5867894 DOI: 10.1038/s41593-017-0004-1] [Citation(s) in RCA: 197] [Impact Index Per Article: 28.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 09/11/2017] [Indexed: 01/12/2023]
Abstract
Cerebellar abnormalities, particularly in Right Crus I (RCrusI), are consistently reported in autism spectrum disorders (ASD). Although RCrusI is functionally connected with ASD-implicated circuits, the contribution of RCrusI dysfunction to ASD remains unclear. Here neuromodulation of RCrusI in neurotypical humans resulted in altered functional connectivity with the inferior parietal lobule, and children with ASD showed atypical functional connectivity in this circuit. Atypical RCrusI-inferior parietal lobule structural connectivity was also evident in the Purkinje neuron (PN) TscI ASD mouse model. Additionally, chemogenetically mediated inhibition of RCrusI PN activity in mice was sufficient to generate ASD-related social, repetitive, and restricted behaviors, while stimulation of RCrusI PNs rescued social impairment in the PN TscI ASD mouse model. Together, these studies reveal important roles for RCrusI in ASD-related behaviors. Further, the rescue of social behaviors in an ASD mouse model suggests that investigation of the therapeutic potential of cerebellar neuromodulation in ASD may be warranted.
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Affiliation(s)
- Catherine J Stoodley
- Department of Psychology and Center for Behavioral Neuroscience, American University, Washington, DC, USA.
| | - Anila M D'Mello
- Department of Psychology and Center for Behavioral Neuroscience, American University, Washington, DC, USA
| | - Jacob Ellegood
- Toronto Mouse Imaging Centre, Hospital for Sick Kids, Toronto, Canada
| | - Vikram Jakkamsetti
- The Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Pei Liu
- The Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Jennifer M Gibson
- The Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Elyza Kelly
- The Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Fantao Meng
- The Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Christopher A Cano
- The Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Juan M Pascual
- The Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Jason P Lerch
- Toronto Mouse Imaging Centre, Hospital for Sick Kids, Toronto, Canada
| | - Peter T Tsai
- The Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
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Lalani SJ, Duffield TC, Trontel HG, Bigler ED, Abildskov TJ, Froehlich A, Prigge MBD, Travers BG, Anderson JS, Zielinski BA, Alexander A, Lange N, Lainhart JE. Auditory attention in autism spectrum disorder: An exploration of volumetric magnetic resonance imaging findings. J Clin Exp Neuropsychol 2017; 40:502-517. [PMID: 29072106 DOI: 10.1080/13803395.2017.1373746] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Studies have shown that individuals with autism spectrum disorder (ASD) tend to perform significantly below typically developing individuals on standardized measures of attention, even when controlling for IQ. The current study sought to examine within ASD whether anatomical correlates of attention performance differed between those with average to above-average IQ (AIQ group) and those with low-average to borderline ability (LIQ group) as well as in comparison to typically developing controls (TDC). Using automated volumetric analyses, we examined regional volume of classic attention areas including the superior frontal gyrus, anterior cingulate cortex, and precuneus in ASD AIQ (n = 38) and LIQ (n = 18) individuals along with 30 TDC. Auditory attention performance was assessed using subtests of the Test of Memory and Learning (TOMAL) compared among the groups and then correlated with regional brain volumes. Analyses revealed group differences in attention. The three groups did not differ significantly on any auditory attention-related brain volumes; however, trends toward significant size-attention function interactions were observed. Negative correlations were found between the volume of the precuneus and auditory attention performance for the AIQ ASD group, indicating larger volume related to poorer performance. Implications for general attention functioning and dysfunctional neural connectivity in ASD are discussed.
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Affiliation(s)
- Sanam J Lalani
- a Department of Psychology , Brigham Young University , Provo , UT , USA
| | - Tyler C Duffield
- a Department of Psychology , Brigham Young University , Provo , UT , USA
| | - Haley G Trontel
- a Department of Psychology , Brigham Young University , Provo , UT , USA
| | - Erin D Bigler
- a Department of Psychology , Brigham Young University , Provo , UT , USA.,b Neuroscience Center , Brigham Young University , Provo , UT , USA.,c Department of Psychology , University of Utah , Salt Lake City , UT , USA.,d Department of Pediatrics , University of Utah , Salt Lake City , UT , USA
| | - Tracy J Abildskov
- a Department of Psychology , Brigham Young University , Provo , UT , USA
| | - Alyson Froehlich
- c Department of Psychology , University of Utah , Salt Lake City , UT , USA
| | - Molly B D Prigge
- d Department of Pediatrics , University of Utah , Salt Lake City , UT , USA
| | - Brittany G Travers
- e Waisman Laboratory for Brain Imaging and Behavior , University of Wisconsin-Madison , Madison , WI , USA.,f Department of Kinesiology , University of Wisconsin-Madison , Madison , WI , USA
| | - Jeffrey S Anderson
- g Department of Radiology , University of Utah , Salt Lake City , UT , USA
| | - Brandon A Zielinski
- d Department of Pediatrics , University of Utah , Salt Lake City , UT , USA.,h Department of Neurology, School of Medicine , University of Utah , Salt Lake City , UT , USA
| | - Andrew Alexander
- e Waisman Laboratory for Brain Imaging and Behavior , University of Wisconsin-Madison , Madison , WI , USA.,i Department of Medical Physics , University of Wisconsin-Madison , Madison , WI , USA.,j Department of Psychiatry , University of Wisconsin-Madison , Madison , WI , USA
| | - Nicholas Lange
- k Department of Psychiatry , Harvard Medical School , Boston , MA , USA.,l Neurostatistics Laboratory , McLean Hospital , Belmont , MA , USA
| | - Janet E Lainhart
- e Waisman Laboratory for Brain Imaging and Behavior , University of Wisconsin-Madison , Madison , WI , USA.,j Department of Psychiatry , University of Wisconsin-Madison , Madison , WI , USA
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31
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Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference. ACTA ACUST UNITED AC 2017. [PMID: 30135962 DOI: 10.1007/978-3-319-67159-8_12] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
A large body of evidence relates autism with abnormal structural and functional brain connectivity. Structural covariance MRI (scMRI) is a technique that maps brain regions with covarying gray matter density across subjects. It provides a way to probe the anatomical structures underlying intrinsic connectivity networks (ICNs) through the analysis of the gray matter signal covariance. In this paper, we apply topological data analysis in conjunction with scMRI to explore network-specific differences in the gray matter structure in subjects with autism versus age-, gender- and IQ-matched controls. Specifically, we investigate topological differences in gray matter structures captured by structural covariance networks (SCNs) derived from three ICNs strongly implicated in autism, namely, the salience network (SN), the default mode network (DMN) and the executive control network (ECN). By combining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism, from SCNs derived from SN and ECN. These differences in brain architecture are consistent with direct structural analysis using scMRI (Zielinski et al. 2012).
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Padmanabhan A, Lynch CJ, Schaer M, Menon V. The Default Mode Network in Autism. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 2:476-486. [PMID: 29034353 PMCID: PMC5635856 DOI: 10.1016/j.bpsc.2017.04.004] [Citation(s) in RCA: 154] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Autism spectrum disorder (ASD) is characterized by deficits in social communication and interaction. Since its discovery as a major functional brain system, the default mode network (DMN) has been implicated in a number of psychiatric disorders, including ASD. Here we review converging multimodal evidence for DMN dysfunction in the context of specific components of social cognitive dysfunction in ASD: 'self-referential processing' - the ability to process social information relative to oneself and 'theory of mind' or 'mentalizing' - the ability to infer the mental states such as beliefs, intentions, and emotions of others. We show that altered functional and structural organization of the DMN, and its atypical developmental trajectory, are prominent neurobiological features of ASD. We integrate findings on atypical cytoarchitectonic organization and imbalance in excitatory-inhibitory circuits, which alter local and global brain signaling, to scrutinize putative mechanisms underlying DMN dysfunction in ASD. Our synthesis of the extant literature suggests that aberrancies in key nodes of the DMN and their dynamic functional interactions contribute to atypical integration of information about the self in relation to 'other', as well as impairments in the ability to flexibly attend to socially relevant stimuli. We conclude by highlighting open questions for future research.
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Affiliation(s)
- Aarthi Padmanabhan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA
| | | | - Marie Schaer
- University of Geneva, Department of Psychiatry, Geneva, Switzerland
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA
- Program in Neuroscience, Stanford University School of Medicine, Stanford, CA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
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Chang Y, Hsu S, Tsai S, Chang Y, Huang C, Liu M, Chen N, Chang W, Hsu J, Lee C, Chang C. Genetic effect of MTHFR C677T polymorphism on the structural covariance network and white-matter integrity in Alzheimer's disease. Hum Brain Mapp 2017; 38:3039-3051. [PMID: 28342207 PMCID: PMC6866732 DOI: 10.1002/hbm.23572] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 02/07/2017] [Accepted: 03/03/2017] [Indexed: 11/10/2022] Open
Abstract
The 677 C to T transition in the MTHFR gene is a genetic determinant for hyperhomocysteinemia. We investigated whether this polymorphism modulates gray matter (GM) structural covariance networks independently of white-matter integrity in patients with Alzheimer's disease (AD). GM structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed-based analysis. The patients were divided into two genotype groups: C homozygotes (n = 73) and T carriers (n = 62). Using diffusion tensor imaging and white-matter parcellation, 11 fiber bundle integrities were compared between the two genotype groups. Cognitive test scores were the major outcome factors. The T carriers had higher homocysteine levels, lower posterior cingulate cortex GM volume, and more clusters in the dorsal medial lobe subsystem showing stronger covariance strength. Both posterior cingulate cortex seed and interconnected peak cluster volumes predicted cognitive test scores, especially in the T carriers. There were no between-group differences in fiber tract diffusion parameters. The MTHFR 677T polymorphism modulates posterior cingulate cortex-anchored structural covariance strength independently of white matter integrities. Hum Brain Mapp 38:3039-3051, 2017. © 2017 The Authors Human Brain Mapping Published Wiley by Periodicals, Inc.
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Affiliation(s)
- Yu‐Tzu Chang
- Division of NephrologyDepartment of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung UniversityTainanTaiwan
| | - Shih‐Wei Hsu
- Department of RadiologyKaohsiung Chang Gung Memorial Hospital, Chang Gung University College of MedicineKaohsiungTaiwan
| | - Shih‐Jen Tsai
- Psychiatric Department of Taipei Veterans General HospitalTaipeiTaiwan
- Psychiatric DivisionSchool of Medicine, National Yang‐Ming UniversityTaipeiTaiwan
| | - Ya‐Ting Chang
- Department of NeurologyCognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of MedicineKaohsiungTaiwan
| | - Chi‐Wei Huang
- Department of NeurologyCognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of MedicineKaohsiungTaiwan
| | - Mu‐En Liu
- Psychiatric Department of Taipei Veterans General HospitalTaipeiTaiwan
| | - Nai‐Ching Chen
- Department of NeurologyCognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of MedicineKaohsiungTaiwan
| | - Wen‐Neng Chang
- Department of NeurologyCognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of MedicineKaohsiungTaiwan
| | - Jung‐Lung Hsu
- Section of Dementia and Cognitive ImpairmentDepartment of Neurology, Chang Gung Memorial HospitalLinkouTaiwan
| | - Chen‐Chang Lee
- Department of RadiologyKaohsiung Chang Gung Memorial Hospital, Chang Gung University College of MedicineKaohsiungTaiwan
| | - Chiung‐Chih Chang
- Department of NeurologyCognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of MedicineKaohsiungTaiwan
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Sharda M, Foster NEV, Tryfon A, Doyle-Thomas KAR, Ouimet T, Anagnostou E, Evans AC, Zwaigenbaum L, Lerch JP, Lewis JD, Hyde KL. Language Ability Predicts Cortical Structure and Covariance in Boys with Autism Spectrum Disorder. Cereb Cortex 2017; 27:1849-1862. [PMID: 26891985 DOI: 10.1093/cercor/bhw024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
There is significant clinical heterogeneity in language and communication abilities of individuals with Autism Spectrum Disorders (ASD). However, no consistent pathology regarding the relationship of these abilities to brain structure has emerged. Recent developments in anatomical correlation-based approaches to map structural covariance networks (SCNs), combined with detailed behavioral characterization, offer an alternative for studying these relationships. In this study, such an approach was used to study the integrity of SCNs of cortical thickness and surface area associated with language and communication, in 46 high-functioning, school-age children with ASD compared with 50 matched, typically developing controls (all males) with IQ > 75. Findings showed that there was alteration of cortical structure and disruption of fronto-temporal cortical covariance in ASD compared with controls. Furthermore, in an analysis of a subset of ASD participants, alterations in both cortical structure and covariance were modulated by structural language ability of the participants, but not communicative function. These findings indicate that structural language abilities are related to altered fronto-temporal cortical covariance in ASD, much more than symptom severity or cognitive ability. They also support the importance of better characterizing ASD samples while studying brain structure and for better understanding individual differences in language and communication abilities in ASD.
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Affiliation(s)
- Megha Sharda
- International Laboratory for Brain Music and Sound Research (BRAMS), Université de Montréal, Montréal, Quebec, CanadaH2V 2J2
| | - Nicholas E V Foster
- International Laboratory for Brain Music and Sound Research (BRAMS), Université de Montréal, Montréal, Quebec, CanadaH2V 2J2
| | - Ana Tryfon
- International Laboratory for Brain Music and Sound Research (BRAMS), Université de Montréal, Montréal, Quebec, Canada H2V 2J2.,Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, Quebec, Canada H3A 2B4
| | | | - Tia Ouimet
- International Laboratory for Brain Music and Sound Research (BRAMS), Université de Montréal, Montréal, Quebec, CanadaH2V 2J2
| | | | - Alan C Evans
- Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, Quebec, CanadaH3A 2B4
| | | | - Jason P Lerch
- The Hospital for Sick Children, University of Toronto, Toronto, Ontario, CanadaM5T 3H7
| | - John D Lewis
- Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, Quebec, CanadaH3A 2B4
| | - Krista L Hyde
- International Laboratory for Brain Music and Sound Research (BRAMS), Université de Montréal, Montréal, Quebec, Canada H2V 2J2.,Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, Quebec, Canada H3A 2B4
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35
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Huang CW, Hsu SW, Tsai SJ, Chen NC, Liu ME, Lee CC, Huang SH, Chang WN, Chang YT, Tsai WC, Chang CC. Genetic effect of interleukin-1 beta (C-511T) polymorphism on the structural covariance network and white matter integrity in Alzheimer's disease. J Neuroinflammation 2017; 14:12. [PMID: 28100246 PMCID: PMC5242022 DOI: 10.1186/s12974-017-0791-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 01/04/2017] [Indexed: 11/17/2022] Open
Abstract
Background Inflammatory processes play a pivotal role in the degenerative process of Alzheimer’s disease. In humans, a biallelic (C/T) polymorphism in the promoter region (position-511) (rs16944) of the interleukin-1 beta gene has been significantly associated with differences in the secretory capacity of interleukin-1 beta. In this study, we investigated whether this functional polymorphism mediates the brain networks in patients with Alzheimer’s disease. Methods We enrolled a total of 135 patients with Alzheimer’s disease (65 males, 70 females), and investigated their gray matter structural covariance networks using 3D T1 magnetic resonance imaging and their white matter macro-structural integrities using fractional anisotropy. The patients were classified into two genotype groups: C-carriers (n = 108) and TT-carriers (n = 27), and the structural covariance networks were constructed using seed-based analysis focusing on the default mode network medial temporal or dorsal medial subsystem, salience network and executive control network. Neurobehavioral scores were used as the major outcome factors for clinical correlations. Results There were no differences between the two genotype groups in the cognitive test scores, seed, or peak cluster volumes and white matter fractional anisotropy. The covariance strength showing C-carriers > TT-carriers was the entorhinal-cingulum axis. There were two peak clusters (Brodmann 6 and 10) in the salience network and four peak clusters (superior prefrontal, precentral, fusiform, and temporal) in the executive control network that showed C-carriers < TT-carriers in covariance strength. The salience network and executive control network peak clusters in the TT group and the default mode network peak clusters in the C-carriers strongly predicted the cognitive test scores. Conclusions Interleukin-1 beta C-511 T polymorphism modulates the structural covariance strength on the anterior brain network and entorhinal-interconnected network which were independent of the white matter tract integrity. Depending on the specific C-511 T genotype, different network clusters could predict the cognitive tests. Electronic supplementary material The online version of this article (doi:10.1186/s12974-017-0791-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chi-Wei Huang
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, #123, Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan
| | - Shih-Wei Hsu
- Department of Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Niaosung, Kaohsiung, Taiwan
| | - Shih-Jen Tsai
- Psychiatric Department of Taipei Veterans General Hospital, Taipei, Taiwan.,Psychiatric Division, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Nai-Ching Chen
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, #123, Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan
| | - Mu-En Liu
- Psychiatric Department of Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chen-Chang Lee
- Department of Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Niaosung, Kaohsiung, Taiwan
| | - Shu-Hua Huang
- Department of Nuclear Medicine, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Niaosung, Kaohsiung, Taiwan
| | - Weng-Neng Chang
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, #123, Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan
| | - Ya-Ting Chang
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, #123, Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan
| | - Wan-Chen Tsai
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, #123, Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan
| | - Chiung-Chih Chang
- Department of Neurology, Cognition and Aging Center, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, #123, Ta-Pei Road, Niaosung, Kaohsiung County, 833, Taiwan.
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Sivapalan S, Aitchison KJ. Neurological Structure Variations in Individuals with Autism Spectrum Disorder: a Review. ACTA ACUST UNITED AC 2016. [DOI: 10.5455/bcp.20140903110206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
| | - Katherine J. Aitchison
- Departments of Psychiatry and Medical Genetics, University of Alberta, Edmonton, AB, T6G 2E1
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37
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Bergman NJ. Hypothesis on supine sleep, sudden infant death syndrome reduction and association with increasing autism incidence. World J Clin Pediatr 2016; 5:330-342. [PMID: 27610351 PMCID: PMC4978628 DOI: 10.5409/wjcp.v5.i3.330] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 05/26/2016] [Accepted: 06/03/2016] [Indexed: 02/05/2023] Open
Abstract
AIM: To identify a hypothesis on: Supine sleep, sudden infant death syndrome (SIDS) reduction and association with increasing autism incidence.
METHODS: Literature was searched for autism spectrum disorder incidence time trends, with correlation of change-points matching supine sleep campaigns. A mechanistic model expanding the hypothesis was constructed based on further review of epidemiological and other literature on autism.
RESULTS: In five countries (Denmark, United Kingdom, Australia, Israel, United States) with published time trends of autism, change-points coinciding with supine sleep campaigns were identified. The model proposes that supine sleep does not directly cause autism, but increases the likelihood of expression of a subset of autistic criteria in individuals with genetic susceptibility, thereby specifically increasing the incidence of autism without intellectual disability.
CONCLUSION: Supine sleep is likely a physiological stressor, that does reduce SIDS, but at the cost of impact on emotional and social development in the population, a portion of which will be susceptible to, and consequently express autism. A re-evaluation of all benefits and harms of supine sleep is warranted. If the SIDS mechanism proposed and autism model presented can be verified, the research agenda may be better directed, in order to further decrease SIDS, and reduce autism incidence.
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38
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Anatomical imbalance between cortical networks in autism. Sci Rep 2016; 6:31114. [PMID: 27484308 PMCID: PMC4971490 DOI: 10.1038/srep31114] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 07/10/2016] [Indexed: 02/06/2023] Open
Abstract
Influential psychological models of autism spectrum disorder (ASD) have proposed that this prevalent developmental disorder results from impairment of global (integrative) information processing and overload of local (sensory) information. However, little neuroanatomical evidence consistent with this account has been reported. Here, we examined relative grey matter volumes (rGMVs) between three cortical networks, how they changed with age, and their relationship with core symptomatology. Using public neuroimaging data of high-functioning ASD males and age-/sex-/IQ-matched controls, we first identified age-associated atypical increases in rGMVs of the regions of two sensory systems (auditory and visual networks), and an age-related aberrant decrease in rGMV of a task-control system (fronto-parietal network, FPN) in ASD children. While the enlarged rGMV of the auditory network in ASD adults was associated with the severity of autistic socio-communicational core symptom, that of the visual network was instead correlated with the severity of restricted and repetitive behaviours in ASD. Notably, the atypically decreased rGMV of FPN predicted both of the two core symptoms. These findings suggest that disproportionate undergrowth of a task-control system (FPN) may be a common anatomical basis for the two ASD core symptoms, and relative overgrowth of the two different sensory systems selectively compounds the distinct symptoms.
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Dean DC, Travers BG, Adluru N, Tromp DP, Destiche DJ, Samsin D, Prigge MB, Zielinski BA, Fletcher PT, Anderson JS, Froehlich AL, Bigler ED, Lange N, Lainhart JE, Alexander AL. Investigating the Microstructural Correlation of White Matter in Autism Spectrum Disorder. Brain Connect 2016; 6:415-33. [PMID: 27021440 PMCID: PMC4913512 DOI: 10.1089/brain.2015.0385] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
White matter microstructure forms a complex and dynamical system that is critical for efficient and synchronized brain function. Neuroimaging findings in children with autism spectrum disorder (ASD) suggest this condition is associated with altered white matter microstructure, which may lead to atypical macroscale brain connectivity. In this study, we used diffusion tensor imaging measures to examine the extent that white matter tracts are interrelated within ASD and typical development. We assessed the strength of inter-regional white matter correlations between typically developing and ASD diagnosed individuals. Using hierarchical clustering analysis, clustering patterns of the pairwise white matter correlations were constructed and revealed to be different between the two groups. Additionally, we explored the use of graph theory analysis to examine the characteristics of the patterns formed by inter-regional white matter correlations and compared these properties between ASD and typical development. We demonstrate that the ASD sample has significantly less coherence in white matter microstructure across the brain compared to that in the typical development sample. The ASD group also presented altered topological characteristics, which may implicate less efficient brain networking in ASD. These findings highlight the potential of graph theory based network characteristics to describe the underlying networks as measured by diffusion magnetic resonance imaging and furthermore indicates that ASD may be associated with altered brain network characteristics. Our findings are consistent with those of a growing number of studies and hypotheses that have suggested disrupted brain connectivity in ASD.
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Affiliation(s)
- Douglas C. Dean
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin
| | - Brittany G. Travers
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin
- Occupational Therapy Program, Department of Kinesiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin
| | - Do P.M. Tromp
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin
| | | | - Danica Samsin
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin
| | - Molly B. Prigge
- Department of Radiology, University of Utah, Salt Lake City, Utah
- Department of Pediatrics, University of Utah and Primary Children's Medical Center, Salt Lake City, Utah
| | - Brandon A. Zielinski
- Department of Pediatrics, University of Utah and Primary Children's Medical Center, Salt Lake City, Utah
- Department of Neurology, University of Utah, Salt Lake City, Utah
| | - P. Thomas Fletcher
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah
- School of Computing, University of Utah, Salt Lake City, Utah
| | - Jeffrey S. Anderson
- Department of Radiology, University of Utah, Salt Lake City, Utah
- Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, Utah
| | | | - Erin D. Bigler
- Department of Psychology, Brigham Young University, Provo, Utah
- Neuroscience Center, Brigham Young University, Provo, Utah
| | - Nicholas Lange
- Department of Psychiatry, Harvard School of Medicine, Boston, Massachusetts
- Neurostatistics Laboratory, McLean Hospital, Belmont, Massachusetts
| | - Janet E. Lainhart
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Andrew L. Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
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Koolschijn PCMP, Geurts HM, van der Leij AR, Scholte HS. Are Autistic Traits in the General Population Related to Global and Regional Brain Differences? J Autism Dev Disord 2016; 45:2779-91. [PMID: 25847757 PMCID: PMC4553146 DOI: 10.1007/s10803-015-2441-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
There is accumulating evidence that autistic-related traits in the general population lie on a continuum, with autism spectrum disorders representing the extreme end of this distribution. Here, we tested the hypothesis of a possible relationship between autistic traits and brain morphometry in the general population. Participants completed the short autism-spectrum quotient-questionnaire (AQ); T1-anatomical and DWI-scans were acquired. Associations between autistic traits and gray matter, and white matter microstructural-integrity were performed on the exploration-group (N = 204; 105 males, M-age = 22.85), and validated in the validation-group (N = 304; 155 males, M-age = 22.82). No significant associations were found between AQ-scores and brain morphometry in the exploration-group, or after pooling the data. This questions the assumption that autistic traits and their morphological associations do lie on a continuum in the general population.
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41
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Subirà M, Cano M, de Wit SJ, Alonso P, Cardoner N, Hoexter MQ, Kwon JS, Nakamae T, Lochner C, Sato JR, Jung WH, Narumoto J, Stein DJ, Pujol J, Mataix-Cols D, Veltman DJ, Menchón JM, van den Heuvel OA, Soriano-Mas C. Structural covariance of neostriatal and limbic regions in patients with obsessive-compulsive disorder. J Psychiatry Neurosci 2016; 41:115-23. [PMID: 26505142 PMCID: PMC4764480 DOI: 10.1503/jpn.150012] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Abstract
BACKGROUND Frontostriatal and frontoamygdalar connectivity alterations in patients with obsessive-compulsive disorder (OCD) have been typically described in functional neuroimaging studies. However, structural covariance, or volumetric correlations across distant brain regions, also provides network-level information. Altered structural covariance has been described in patients with different psychiatric disorders, including OCD, but to our knowledge, alterations within frontostriatal and frontoamygdalar circuits have not been explored. METHODS We performed a mega-analysis pooling structural MRI scans from the Obsessive-compulsive Brain Imaging Consortium and assessed whole-brain voxel-wise structural covariance of 4 striatal regions (dorsal and ventral caudate nucleus, and dorsal-caudal and ventral-rostral putamen) and 2 amygdalar nuclei (basolateral and centromedial-superficial). Images were preprocessed with the standard pipeline of voxel-based morphometry studies using Statistical Parametric Mapping software. RESULTS Our analyses involved 329 patients with OCD and 316 healthy controls. Patients showed increased structural covariance between the left ventral-rostral putamen and the left inferior frontal gyrus/frontal operculum region. This finding had a significant interaction with age; the association held only in the subgroup of older participants. Patients with OCD also showed increased structural covariance between the right centromedial-superficial amygdala and the ventromedial prefrontal cortex. LIMITATIONS This was a cross-sectional study. Because this is a multisite data set analysis, participant recruitment and image acquisition were performed in different centres. Most patients were taking medication, and treatment protocols differed across centres. CONCLUSION Our results provide evidence for structural network-level alterations in patients with OCD involving 2 frontosubcortical circuits of relevance for the disorder and indicate that structural covariance contributes to fully characterizing brain alterations in patients with psychiatric disorders.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Carles Soriano-Mas
- Correspondence to: C. Soriano-Mas, Bellvitge Biomedical Research Institute-IDIBELL, Psychiatry Department, Bellvitge University Hospital, Feixa Llarga s/n 08907, Hospitalet de Llobregat, Barcelona, Spain;
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42
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Pagani M, Bifone A, Gozzi A. Structural covariance networks in the mouse brain. Neuroimage 2016; 129:55-63. [PMID: 26802512 DOI: 10.1016/j.neuroimage.2016.01.025] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 12/03/2015] [Accepted: 01/11/2016] [Indexed: 11/24/2022] Open
Abstract
The presence of networks of correlation between regional gray matter volume as measured across subjects in a group of individuals has been consistently described in several human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain mapping modalities like functional and diffusion-weighted imaging, the approach can provide precious insights into the mutual influence of trophic and plastic processes in health and pathological states. To investigate whether analogous scMRI networks are present in lower mammal species amenable to genetic and experimental manipulation such as the laboratory mouse, we employed high resolution morphoanatomical MRI in a large cohort of genetically-homogeneous wild-type mice (C57Bl6/J) and mapped scMRI networks using a seed-based approach. We show that the mouse brain exhibits robust homotopic scMRI networks in both primary and associative cortices, a finding corroborated by independent component analyses of cortical volumes. Subcortical structures also showed highly symmetric inter-hemispheric correlations, with evidence of distributed antero-posterior networks in diencephalic regions of the thalamus and hypothalamus. Hierarchical cluster analysis revealed six identifiable clusters of cortical and sub-cortical regions corresponding to previously described neuroanatomical systems. Our work documents the presence of homotopic cortical and subcortical scMRI networks in the mouse brain, thus supporting the use of this species to investigate the elusive biological and neuroanatomical underpinnings of scMRI network development and its derangement in neuropathological states. The identification of scMRI networks in genetically homogeneous inbred mice is consistent with the emerging view of a key role of environmental factors in shaping these correlational networks.
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Affiliation(s)
- Marco Pagani
- Istituto Italiano di Tecnologia Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Trento 38068, Italy; Center for Mind and Brain Sciences, University of Trento, Rovereto, Trento 38068, Italy
| | - Angelo Bifone
- Istituto Italiano di Tecnologia Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Trento 38068, Italy
| | - Alessandro Gozzi
- Istituto Italiano di Tecnologia Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Trento 38068, Italy.
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43
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Minkova L, Eickhoff SB, Abdulkadir A, Kaller CP, Peter J, Scheller E, Lahr J, Roos RA, Durr A, Leavitt BR, Tabrizi SJ, Klöppel S. Large-scale brain network abnormalities in Huntington's disease revealed by structural covariance. Hum Brain Mapp 2016; 37:67-80. [PMID: 26453902 PMCID: PMC6867397 DOI: 10.1002/hbm.23014] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Revised: 08/10/2015] [Accepted: 09/24/2015] [Indexed: 01/05/2023] Open
Abstract
Huntington's disease (HD) is a progressive neurodegenerative disorder that can be diagnosed with certainty decades before symptom onset. Studies using structural MRI have identified grey matter (GM) loss predominantly in the striatum, but also involving various cortical areas. So far, voxel-based morphometric studies have examined each brain region in isolation and are thus unable to assess the changes in the interrelation of brain regions. Here, we examined the structural covariance in GM volumes in pre-specified motor, working memory, cognitive flexibility, and social-affective networks in 99 patients with manifest HD (mHD), 106 presymptomatic gene mutation carriers (pre-HD), and 108 healthy controls (HC). After correction for global differences in brain volume, we found that increased GM volume in one region was associated with increased GM volume in another. When statistically comparing the groups, no differences between HC and pre-HD were observed, but increased positive correlations were evident for mHD, relative to pre-HD and HC. These findings could be explained by a HD-related neuronal loss heterogeneously affecting the examined network at the pre-HD stage, which starts to dominate structural covariance globally at the manifest stage. Follow-up analyses identified structural connections between frontoparietal motor regions to be linearly modified by disease burden score (DBS). Moderator effects of disease load burden became significant at a DBS level typically associated with the onset of unequivocal HD motor signs. Together with existing findings from functional connectivity analyses, our data indicates a critical role of these frontoparietal regions for the onset of HD motor signs.
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Affiliation(s)
- Lora Minkova
- Department of Psychiatry and PsychotherapyUniversity Medical Center FreiburgFreiburgGermany
- Freiburg Brain Imaging CenterUniversity Medical Center FreiburgFreiburgGermany
- Department of PsychologyLaboratory for Biological and Personality Psychology, University of FreiburgFreiburgGermany
| | - Simon B. Eickhoff
- Department of Clinical Neuroscience and Medical PsychiatryHeinrich‐Heine UniversityDüsseldorfGermany
- Research Center Jülich, Institute of Neuroscience and Medicine (INM‐1), Department of Psychiatry, Psychotherapy and Psychosomatics, University HospitalJülichGermany
| | - Ahmed Abdulkadir
- Freiburg Brain Imaging CenterUniversity Medical Center FreiburgFreiburgGermany
- Department of Computer ScienceUniversity of FreiburgFreiburgGermany
| | - Christoph P. Kaller
- Freiburg Brain Imaging CenterUniversity Medical Center FreiburgFreiburgGermany
- Department of NeurologyUniversity Medical Center FreiburgFreiburgGermany
- BrainLinks‐BrainTools Cluster of Excellence, University of FreiburgFreiburgGermany
| | - Jessica Peter
- Department of Psychiatry and PsychotherapyUniversity Medical Center FreiburgFreiburgGermany
- Freiburg Brain Imaging CenterUniversity Medical Center FreiburgFreiburgGermany
| | - Elisa Scheller
- Department of Psychiatry and PsychotherapyUniversity Medical Center FreiburgFreiburgGermany
- Freiburg Brain Imaging CenterUniversity Medical Center FreiburgFreiburgGermany
| | - Jacob Lahr
- Department of Psychiatry and PsychotherapyUniversity Medical Center FreiburgFreiburgGermany
- Freiburg Brain Imaging CenterUniversity Medical Center FreiburgFreiburgGermany
| | - Raymund A. Roos
- Department of NeurologyLeiden University Medical CentreLeidenNetherlands
| | - Alexandra Durr
- Department of Genetics and CytogeneticsPitié‐ Salpêtrière University HospitalParisFrance
| | - Blair R. Leavitt
- Department of Medical GeneticsCentre for Molecular Medicine and Therapeutics, University of British ColumbiaVancouverCanada
| | - Sarah J. Tabrizi
- Department of Neurodegenerative DiseaseUniversity College London, Institute of NeurologyLondonUnited Kingdom
| | - Stefan Klöppel
- Department of Psychiatry and PsychotherapyUniversity Medical Center FreiburgFreiburgGermany
- Freiburg Brain Imaging CenterUniversity Medical Center FreiburgFreiburgGermany
- Department of NeurologyUniversity Medical Center FreiburgFreiburgGermany
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44
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Hafkemeijer A, Möller C, Dopper EGP, Jiskoot LC, van den Berg-Huysmans AA, van Swieten JC, van der Flier WM, Vrenken H, Pijnenburg YAL, Barkhof F, Scheltens P, van der Grond J, Rombouts SARB. Differences in structural covariance brain networks between behavioral variant frontotemporal dementia and Alzheimer's disease. Hum Brain Mapp 2015; 37:978-88. [PMID: 26660857 DOI: 10.1002/hbm.23081] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Accepted: 11/30/2015] [Indexed: 12/24/2022] Open
Abstract
Disease-specific patterns of gray matter atrophy in Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) overlap with distinct structural covariance networks (SCNs) in cognitively healthy controls. This suggests that both types of dementia target specific structural networks. Here, we study SCNs in AD and bvFTD. We used structural magnetic resonance imaging data of 31 AD patients, 24 bvFTD patients, and 30 controls from two centers specialized in dementia. Ten SCNs were defined based on structural covariance of gray matter density using independent component analysis. We studied group differences in SCNs using F-tests, with Bonferroni corrected t-tests, adjusted for age, gender, and study center. Associations with cognitive performance were studied using linear regression analyses. Cross-sectional group differences were found in three SCNs (all P < 0.0025). In bvFTD, we observed decreased anterior cingulate network integrity compared with AD and controls. Patients with AD showed decreased precuneal network integrity compared with bvFTD and controls, and decreased hippocampal network and anterior cingulate network integrity compared with controls. In AD, we found an association between precuneal network integrity and global cognitive performance (P = 0.0043). Our findings show that AD and bvFTD target different SCNs. The comparison of both types of dementia showed decreased precuneal (i.e., default mode) network integrity in AD and decreased anterior cingulate (i.e., salience) network integrity in bvFTD. This confirms the hypothesis that AD and bvFTD have distinct anatomical networks of degeneration and shows that structural covariance gives valuable insights in the understanding of network pathology in dementia.
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Affiliation(s)
- Anne Hafkemeijer
- Department of Methodology and Statistics, Institute of Psychology, Leiden University, 2300 RB, Leiden, the Netherlands.,Department of Radiology, Leiden University Medical Center, Postzone C2-S, 2300 RC, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2300 RC, Leiden, the Netherlands
| | - Christiane Möller
- Alzheimer Center & Department of Neurology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Center, Postzone C2-S, 2300 RC, Leiden, the Netherlands.,Alzheimer Center & Department of Neurology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands.,Alzheimer Center & Department of Neurology, Erasmus Medical Center, 3000 CA, Rotterdam, the Netherlands
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Center, Postzone C2-S, 2300 RC, Leiden, the Netherlands.,Alzheimer Center & Department of Neurology, Erasmus Medical Center, 3000 CA, Rotterdam, the Netherlands.,Department of Neuropsychology, Erasmus Medical Center, 3000 CA, Rotterdam, the Netherlands
| | | | - John C van Swieten
- Alzheimer Center & Department of Neurology, Erasmus Medical Center, 3000 CA, Rotterdam, the Netherlands.,Department of Clinical Genetics, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center & Department of Neurology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands.,Department of Physics and Medical Technology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center & Department of Neurology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center & Department of Neurology, VU University Medical Center, 1007 MB, Amsterdam, the Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Postzone C2-S, 2300 RC, Leiden, the Netherlands
| | - Serge A R B Rombouts
- Department of Methodology and Statistics, Institute of Psychology, Leiden University, 2300 RB, Leiden, the Netherlands.,Department of Radiology, Leiden University Medical Center, Postzone C2-S, 2300 RC, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2300 RC, Leiden, the Netherlands
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45
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Eisenberg IW, Wallace GL, Kenworthy L, Gotts SJ, Martin A. Insistence on sameness relates to increased covariance of gray matter structure in autism spectrum disorder. Mol Autism 2015; 6:54. [PMID: 26435832 PMCID: PMC4591718 DOI: 10.1186/s13229-015-0047-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 09/23/2015] [Indexed: 12/16/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is characterized by atypical development of cortical and subcortical gray matter volume. Subcortical structural changes have been associated with restricted and repetitive behavior (RRB), a core component of ASD. Behavioral studies have identified insistence on sameness (IS) as a separable RRB dimension prominent in high-functioning ASD, though no simple brain-behavior relationship has emerged. Structural covariance, a measure of morphological coupling among brain regions using magnetic resonance imaging (MRI), has proven an informative measure of anatomical relationships in typical development and neurodevelopmental disorders. In this study, we use this measure to characterize the relationship between brain structure and IS. Methods We quantified the structural covariance of cortical and subcortical gray matter volume in 55 individuals with high-functioning ASD using 3T MRI. We then related these structural metrics to individual IS scores, as assessed by the Repetitive Behavior Scale-Revised (RBS-R). Results We found that increased coupling among subcortical regions and between subcortical and cortical regions related to greater IS symptom severity. Most pronounced, the striatum and amygdala participated in a plurality of identified relationships, indicating a central role for these structures in IS symptomatology. These structural associations were specific to IS and did not relate to any of the other RRB subcomponents measured by the RBS-R. Conclusions This study indicates that behavioral dimensions in ASD can relate to the coordination of development across multiple brain regions, which might be otherwise obscured using typical brain-behavior correlations. It also expands the structures traditionally related to RRB in ASD and provides neuroanatomical evidence supportive of IS as a separate RRB dimension. Trial registration ClinicalTrials.gov NCT01031407 Electronic supplementary material The online version of this article (doi:10.1186/s13229-015-0047-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ian W Eisenberg
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD USA
| | - Gregory L Wallace
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD USA ; Department of Speech and Hearing Sciences, The George Washington University, Washington, DC USA
| | - Lauren Kenworthy
- Center for Autism Spectrum Disorders, Children's National Medical Center, Rockville, MD USA
| | - Stephen J Gotts
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD USA
| | - Alex Martin
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD USA
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46
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Balardin JB, Comfort WE, Daly E, Murphy C, Andrews D, Murphy DGM, Ecker C, Sato JR. Decreased centrality of cortical volume covariance networks in autism spectrum disorders. J Psychiatr Res 2015; 69:142-9. [PMID: 26343606 DOI: 10.1016/j.jpsychires.2015.08.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Revised: 08/03/2015] [Accepted: 08/06/2015] [Indexed: 01/01/2023]
Abstract
Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions characterized by atypical structural and functional brain connectivity. Complex network analysis has been mainly used to describe altered network-level organization for functional systems and white matter tracts in ASD. However, atypical functional and structural connectivity are likely to be also linked to abnormal development of the correlated structure of cortical gray matter. Such covariations of gray matter are particularly well suited to the investigation of the complex cortical pathology of ASD, which is not confined to isolated brain regions but instead acts at the systems level. In this study, we examined network centrality properties of gray matter networks in adults with ASD (n = 84) and neurotypical controls (n = 84) using graph theoretical analysis. We derived a structural covariance network for each group using interregional correlation matrices of cortical volumes extracted from a surface-based parcellation scheme containing 68 cortical regions. Differences between groups in closeness network centrality measures were evaluated using permutation testing. We identified several brain regions in the medial frontal, parietal and temporo-occipital cortices with reductions in closeness centrality in ASD compared to controls. We also found an association between an increased number of autistic traits and reduced centrality of visual nodes in neurotypicals. Our study shows that ASD are accompanied by atypical organization of structural covariance networks by means of a decreased centrality of regions relevant for social and sensorimotor processing. These findings provide further evidence for the altered network-level connectivity model of ASD.
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Affiliation(s)
- Joana Bisol Balardin
- Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo Andre, Brazil
| | - William Edgar Comfort
- Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo Andre, Brazil
| | - Eileen Daly
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Clodagh Murphy
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Derek Andrews
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Declan G M Murphy
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Christine Ecker
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - João Ricardo Sato
- Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo Andre, Brazil.
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47
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Green RR, Bigler ED, Froehlich A, Prigge MBD, Travers BG, Cariello AN, Anderson JS, Zielinski BA, Alexander A, Lange N, Lainhart JE. Beery VMI performance in autism spectrum disorder. Child Neuropsychol 2015; 22:795-817. [PMID: 26292997 DOI: 10.1080/09297049.2015.1056131] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Few studies have examined the visuomotor integration (VMI) abilities of individuals with autism spectrum disorder (ASD). An all-male sample consisting of 56 ASD participants (ages 3-23 years) and 36 typically developing (TD) participants (ages 4-26 years) completed the Beery-Buktenica Developmental Test of Visual-Motor Integration (Beery VMI) as part of a larger neuropsychological battery. Participants were also administered standardized measures of intellectual functioning and the Social Responsiveness Scale (SRS), which assesses autism and autism-like traits. The ASD group performed significantly lower on the Beery VMI and on all IQ measures compared to the TD group. VMI performance was significantly correlated with full scale IQ (FSIQ), performance IQ (PIQ), and verbal IQ (VIQ) in the TD group only. However, when FSIQ was taken into account, no significant Beery VMI differences between groups were observed. Only one TD participant scored 1.5 standard deviations (SDs) below the Beery VMI normative sample mean, in comparison to 21% of the ASD sample. As expected, the ASD group was rated as having significantly higher levels of social impairment on the SRS compared to the TD group across all major domains. However, level of functioning on the SRS was not associated with Berry VMI performance. These findings demonstrate that a substantial number of individuals with ASD experience difficulties compared to TD in performing VMI-related tasks, and that VMI is likely affected by general cognitive ability. The fact that lowered Beery VMI performance occurred only within a subset of individuals with ASD and did not correlate with SRS would indicate that visuomotor deficits are not a core feature of ASD, even though they present at a higher rate of impairment than observed in TD participants.
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Affiliation(s)
- Ryan R Green
- a Department of Psychology , Brigham Young University , Provo , UT , USA
| | - Erin D Bigler
- a Department of Psychology , Brigham Young University , Provo , UT , USA.,b Neuroscience Center , Brigham Young University , Provo , UT , USA.,c Department of Psychiatry , University of Utah , Salt Lake City , UT , USA
| | - Alyson Froehlich
- c Department of Psychiatry , University of Utah , Salt Lake City , UT , USA
| | - Molly B D Prigge
- c Department of Psychiatry , University of Utah , Salt Lake City , UT , USA
| | - Brittany G Travers
- d Waisman Laboratory for Brain Imaging and Behavior , University of Wisconsin , Madison , WI , USA
| | - Annahir N Cariello
- c Department of Psychiatry , University of Utah , Salt Lake City , UT , USA
| | - Jeffrey S Anderson
- e Department of Radiology , University of Utah , Salt Lake City , UT , USA
| | - Brandon A Zielinski
- f Department of Pediatrics and Neurology, School of Medicine , University of Utah , Salt Lake City , UT , USA
| | - Andrew Alexander
- d Waisman Laboratory for Brain Imaging and Behavior , University of Wisconsin , Madison , WI , USA.,g Department of Medical Physics , University of Wisconsin , Madison , WI , USA.,h Department of Psychiatry , University of Wisconsin , Madison , WI , USA
| | - Nicholas Lange
- i Departments of Psychiatry and Biostatistics , Harvard University , Boston , MA , USA.,j Neurostatistics Laboratory , McLean Hospital , Belmont , MA , USA
| | - Janet E Lainhart
- d Waisman Laboratory for Brain Imaging and Behavior , University of Wisconsin , Madison , WI , USA.,h Department of Psychiatry , University of Wisconsin , Madison , WI , USA
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48
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Cerliani L, Mennes M, Thomas RM, Di Martino A, Thioux M, Keysers C. Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder. JAMA Psychiatry 2015; 72:767-77. [PMID: 26061743 PMCID: PMC5008437 DOI: 10.1001/jamapsychiatry.2015.0101] [Citation(s) in RCA: 211] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
IMPORTANCE Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks. OBJECTIVES To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories. DESIGN, SETTING, AND PARTICIPANTS We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012. MAIN OUTCOMES AND MEASURES We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale. RESULTS Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t ≥ 3.13, P < .001 corrected). The strength of such connections was associated with the severity of autistic traits in the ASD group (all r ≥ 0.21, P < .0067 corrected). In addition, subcortico-cortical interaction decreased with age in the entire sample (all r ≤ -0.09, P < .012 corrected), although this association was significant only in typically developing participants (all r ≤ -0.13, P < .009 corrected). CONCLUSIONS AND RELEVANCE Our results showing ASD-related impairment in the interaction between primary sensory cortices and subcortical regions suggest that the sensory processes they subserve abnormally influence brain information processing in individuals with ASD. This might contribute to the occurrence of hyposensitivity or hypersensitivity and of difficulties in top-down regulation of behavior.
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Affiliation(s)
- Leonardo Cerliani
- Department of Neuroscience, University of Groningen, The University Medical Center, Groningen, the Netherlands,Social Brain Laboratory, Netherlands Institute for Neuroscience, Amsterdam, the Netherlands
| | - Maarten Mennes
- Radboud University, Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, the Netherlands
| | - Rajat M. Thomas
- Social Brain Laboratory, Netherlands Institute for Neuroscience, Amsterdam, the Netherlands
| | - Adriana Di Martino
- Autism Spectrum Disorder Research and Clinical Program and Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at The Child Study Center, New York University Langone Medical Center, New York
| | - Marc Thioux
- Department of Neuroscience, University of Groningen, The University Medical Center, Groningen, the Netherlands,Social Brain Laboratory, Netherlands Institute for Neuroscience, Amsterdam, the Netherlands
| | - Christian Keysers
- Department of Neuroscience, University of Groningen, The University Medical Center, Groningen, the Netherlands,Social Brain Laboratory, Netherlands Institute for Neuroscience, Amsterdam, the Netherlands
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49
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Synergistic Effects of Age on Patterns of White and Gray Matter Volume across Childhood and Adolescence. eNeuro 2015; 2:eN-NWR-0003-15. [PMID: 26464999 PMCID: PMC4596017 DOI: 10.1523/eneuro.0003-15.2015] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2015] [Revised: 03/25/2015] [Accepted: 04/30/2015] [Indexed: 12/21/2022] Open
Abstract
The human brain develops with a nonlinear contraction of gray matter across late childhood and adolescence with a concomitant increase in white matter volume. Across the adult population, properties of cortical gray matter covary within networks that may represent organizational units for development and degeneration. Although gray matter covariance may be strongest within structurally connected networks, the relationship to volume changes in white matter remains poorly characterized. In the present study we examined age-related trends in white and gray matter volume using T1-weighted MR images from 360 human participants from the NIH MRI study of Normal Brain Development. Images were processed through a voxel-based morphometry pipeline. Linear effects of age on white and gray matter volume were modeled within four age bins, spanning 4-18 years, each including 90 participants (45 male). White and gray matter age-slope maps were separately entered into k-means clustering to identify regions with similar age-related variability across the four age bins. Four white matter clusters were identified, each with a dominant direction of underlying fibers: anterior-posterior, left-right, and two clusters with superior-inferior directions. Corresponding, spatially proximal, gray matter clusters encompassed largely cerebellar, fronto-insular, posterior, and sensorimotor regions, respectively. Pairs of gray and white matter clusters followed parallel slope trajectories, with white matter changes generally positive from 8 years onward (indicating volume increases) and gray matter negative (decreases). As developmental disorders likely target networks rather than individual regions, characterizing typical coordination of white and gray matter development can provide a normative benchmark for understanding atypical development.
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O'Hearn K, Velanova K, Lynn A, Wright C, Hallquist M, Minshew N, Luna B. Abnormalities in brain systems supporting individuation and enumeration in autism. Autism Res 2015; 9:82-96. [PMID: 26011184 DOI: 10.1002/aur.1498] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Revised: 03/02/2015] [Accepted: 04/06/2015] [Indexed: 11/12/2022]
Abstract
Previous work indicates that adults with autism display a decreased capacity when rapidly enumerating small sets of elements (i.e., subitizing), compared to typically developing (TD) individuals. This ability is crucial for fundamental visual functions such as object individuation and parallel processing. Thus, the deficit in autism suggests limits in these skills. To examine the neural basis of this limitation, adults with and without high functioning autism rapidly enumerated 1 to 8 randomly located squares during a neuroimaging study. Typically, adults are thought to use parallel visual processes to quantify up to three or four elements, and serial processes to enumerate more (5+) elements. We hypothesized that parietal lobe regions associated with counting would be recruited with smaller sets of elements in adults with autism, compared to TD adults. Consistent with this hypothesis, activation in parietal regions increased with smaller set sizes in adults with autism compared to TD adults. Increased activation for three elements was evident in several regions, including those thought to underlie subitizing. In addition, regions specific to the counting range in TD adults were often equally active for set sizes in the subitizing range in the adults with autism. Finally, significant deactivation was evident in TD adults, presumably reflecting relative suppression of regions specialized for competing processes, but was not apparent in adults with autism. These differences in brain function in adults with autism on a simple enumeration task suggest atypical brain organization and function that is likely to impact most visual tasks, especially those with multiple elements.
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Affiliation(s)
| | | | - Andrew Lynn
- Department of Psychiatry, University of Pittsburgh
| | | | | | - Nancy Minshew
- Department of Psychiatry, University of Pittsburgh.,Department of Neurology, University of Pittsburgh
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh
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