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Journal Article•DOI•

Decreased Interhemispheric Functional Connectivity in Autism

Jeffrey S. Anderson1, T. Jason Druzgal, Alyson L. Froehlich2, Molly B. DuBray2, Nicholas Lange3, Nicholas Lange4, Andrew L. Alexander5, Tracy J. Abildskov6, Jared A. Nielsen2, Annahir N. Cariello2, Jason R. Cooperrider2, Erin D. Bigler1, Erin D. Bigler6, Janet E. Lainhart1, Janet E. Lainhart2 •
Allen Institute for Brain Science1, University of Utah2, Harvard University3, McLean Hospital4, University of Wisconsin-Madison5, Brigham Young University6
01 May 2011-Cerebral Cortex (Oxford University Press)-Vol. 21, Iss: 5, pp 1134-1146
TL;DR: The findings suggest that long-range connectivity abnormalities in autism are spatially heterogeneous and that transcallosal connectivity is decreased most in regions with functions associated with behavioral abnormalities in Autism.
Abstract: The cortical underconnectivity theory asserts that reduced long-range functional connectivity might contribute to a neural mechanism for autism. We examined resting-state blood oxygen level-dependent interhemispheric correlation in 53 males with high-functioning autism and 39 typically developing males from late childhood through early adulthood. By constructing spatial maps of correlation between homologous voxels in each hemisphere, we found significantly reduced interhemispheric correlation specific to regions with functional relevance to autism: sensorimotor cortex, anterior insula, fusiform gyrus, superior temporal gyrus, and superior parietal lobule. Observed interhemispheric connectivity differences were better explained by diagnosis of autism than by potentially confounding neuropsychological metrics of language, IQ, or handedness. Although both corpus callosal volume and gray matter interhemispheric connectivity were significantly reduced in autism, no direct relationship was observed between them, suggesting that structural and functional metrics measure different aspects of interhemispheric connectivity. In the control but not the autism sample, there was decreasing interhemispheric correlation with subject age. Greater differences in interhemispheric correlation were seen for more lateral regions in the brain. These findings suggest that long-range connectivity abnormalities in autism are spatially heterogeneous and that transcallosal connectivity is decreased most in regions with functions associated with behavioral abnormalities in autism. Autism subjects continue to show developmental differences in interhemispheric connectivity into early adulthood.

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Citations
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Journal Article•DOI•
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.

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Chao-Gan Yan1, Xin-Di Wang2, Xi-Nian Zuo1, Yu-Feng Zang3•
Chinese Academy of Sciences1, McGovern Institute for Brain Research2, Hangzhou Normal University3
01 Jul 2016-Neuroinformatics
TL;DR: The newly developed toolbox, DPABI, which was evolved from REST and DPARSF is introduced, designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies.
Abstract: Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.

2,179 citations

Cites background or methods from "Decreased Interhemispheric Function..."

  • ...The following R-fMRI-based indices of intrinsic brain function were examined: 1) ALFF (Zang et al., 2007) / fALFF (Zou et al., 2008); 2) ReHo (Zang et al., 2004); 3) VMHC (Anderson et al., 2011; Zuo et al., 2010b); 4) seed-based correlation analysis of the posterior cingulate cortex (PCC: 0, −53, 26; 10mm diameter sphere) (Satterthwaite et al., 2012; Van Dijk et al., 2012; Yan et al., 2013a); and 5) network degree centrality (Buckner et al., 2009; Zuo et al., 2012)....

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  • ...The DPARSF analysis pipeline automated and overcame time-consuming manual procedures in preprocessing R-fMRI data and generating R-fMRI derivatives, e.g., functional connectivity (Biswal et al., 1995), regional homogeneity (ReHo) (Zang et al., 2004), amplitude of low frequency fluctuations (ALFF) (Zang et al., 2007), fractional ALFF (fALFF) (Zou et al., 2008), voxel-mirrored homotopic connectivity (VMHC) (Zuo et al., 2010b) and degree centrality (Buckner et al., 2009)....

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  • ...…indices of intrinsic brain function were examined: 1) ALFF (Zang et al., 2007) / fALFF (Zou et al., 2008); 2) ReHo (Zang et al., 2004); 3) VMHC (Anderson et al., 2011; Zuo et al., 2010b); 4) seed-based correlation analysis of the posterior cingulate cortex (PCC: 0, −53, 26; 10mm diameter…...

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  • ...2004); 3) VMHC (Anderson et al. 2011; Zuo et al. 2010b); 4) seedbased correlation analysis of the posterior cingulate cortex (PCC: 0, −53, 26; 10 mm diameter sphere) (Satterthwaite et al....

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Journal Article•DOI•
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

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A Di Martino1, C-G Yan2, Qingyang Li3, E Denio1, Francisco X. Castellanos1, Francisco X. Castellanos2, Kaat Alaerts4, Kaat Alaerts1, Jeffrey S. Anderson5, Michal Assaf6, Susan Y. Bookheimer7, Mirella Dapretto7, Ben Deen6, Ben Deen8, Sonja Delmonte9, Ilan Dinstein10, Ilan Dinstein11, Birgit Ertl-Wagner12, Damien A. Fair13, Louise Gallagher9, Daniel P. Kennedy14, Daniel P. Kennedy15, Christopher L. Keown16, Christian Keysers17, Christian Keysers18, Janet E. Lainhart19, Catherine Lord20, Beatriz Luna21, Vinod Menon22, Nancy J. Minshew21, Christopher S. Monk23, Sophia Mueller12, R-A Müller16, Mary Beth Nebel24, Joel T. Nigg13, Kirsten O'Hearn21, Kevin A. Pelphrey6, Scott Peltier23, Jeffrey D. Rudie7, Stefan Sunaert4, Mark Thioux17, Mark Thioux18, J M Tyszka14, Lucina Q. Uddin22, Judith Verhoeven4, Nicole Wenderoth4, Jillian Lee Wiggins23, Stewart H. Mostofsky24, Stewart H. Mostofsky25, Michael P. Milham3, Michael P. Milham2 •
New York University1, Nathan Kline Institute for Psychiatric Research2, MIND Institute3, Katholieke Universiteit Leuven4, University of Utah5, Yale University6, University of California, Los Angeles7, Massachusetts Institute of Technology8, Trinity College, Dublin9, Ben-Gurion University of the Negev10, Carnegie Mellon University11, Ludwig Maximilian University of Munich12, Oregon Health & Science University13, California Institute of Technology14, Indiana University15, San Diego State University16, University of Groningen17, Netherlands Institute for Neuroscience18, University of Wisconsin-Madison19, Cornell University20, University of Pittsburgh21, Stanford University22, University of Michigan23, Kennedy Krieger Institute24, Johns Hopkins University25
01 Jun 2014-Molecular Psychiatry
TL;DR: W Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity.
Abstract: Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

1,939 citations

Journal Article•DOI•
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics

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Chao-Gan Yan1, Brian Cheung2, Clare Kelly3, Stanley J. Colcombe1, R. Cameron Craddock4, R. Cameron Craddock2, Adriana Di Martino3, Qingyang Li2, Xi-Nian Zuo5, F. Xavier Castellanos1, F. Xavier Castellanos3, Michael P. Milham2, Michael P. Milham1 •
Nathan Kline Institute for Psychiatric Research1, MIND Institute2, New York University3, Virginia Tech4, Chinese Academy of Sciences5
01 Aug 2013-NeuroImage
TL;DR: A comprehensive voxel-based examination of the impact of motion on the BOLD signal suggests that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact.

1,300 citations

Cites background from "Decreased Interhemispheric Function..."

  • ...…1995), ALFF/fALFF (Zang et al., 2007; Zou et al., 2008; Zuo et al., 2010a), regional homogeneity (ReHo, Zang et al., 2004; Zuo et al., 2013), voxel-mirrored homotopic connectivity (VMHC, Anderson et al., 2011; Zuo et al., 2010c) and degree centrality (DC, Buckner et al., 2009; Zuo et al., 2012)....

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  • ...…to exhibit highly similar head motion and may disproportionately and spuriously increase iFC between symmetric brain regions, and possibly contribute to the well-documented ubiquity of homotopic connectivity (Anderson et al., 2011; Salvador et al., 2005; Stark et al., 2008; Zuo et al., 2010c)....

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  • ...Homotopic areas appear to exhibit highly similar head motion and may disproportionately and spuriously increase iFC between symmetric brain regions, and possibly contribute to the well-documented ubiquity of homotopic connectivity (Anderson et al., 2011; Salvador et al., 2005; Stark et al., 2008; Zuo et al., 2010c)....

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Journal Article•DOI•
Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression

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Ziad S. Saad1, Stephen J. Gotts, Kevin Murphy, Gang Chen, Hang Joon Jo, Alex Martin, Robert W. Cox •
National Institutes of Health1
10 May 2012-Brain connectivity
TL;DR: A problem that is still treated lightly despite its significant impact on RS-FMRI inferences is discussed; global signal regression (GSReg), the practice of projecting out signal averaged over the entire brain, can change resting-state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness.
Abstract: Resting-state functional magnetic resonance imaging (RS-FMRI) holds the promise of revealing brain functional connectivity without requiring specific tasks targeting particular brain systems. RS-FMRI is being used to find differences between populations even when a specific candidate target for traditional inferences is lacking. However, the problem with RS-FMRI is a lacking definition of what constitutes noise and signal. RS-FMRI is easy to acquire but is not easy to analyze or draw inferences from. In this commentary we discuss a problem that is still treated lightly despite its significant impact on RS-FMRI inferences; global signal regression (GSReg), the practice of projecting out signal averaged over the entire brain, can change resting-state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness. Although Murphy et al. in 2009 demonstrated that GSReg negatively biases correlations, the approach remains in wide use. We revisit this issue to argue the problem that GSReg is more than negative bias or the interpretability of negative correlations. Its usage can fundamentally alter interregional correlations within a group, or their differences between groups. We used an illustrative model to clearly convey our objections and derived equations formalizing our conclusions. We hope this creates a clear context in which counterarguments can be made. We conclude that GSReg should not be used when studying RS-FMRI because GSReg biases correlations differently in different regions depending on the underlying true interregional correlation structure. GSReg can alter local and long-range correlations, potentially spreading underlying group differences to regions that may never have had any. Conclusions also apply to substitutions of GSReg for denoising with decompositions of signals aggregated over the network's regions to the extent they cannot separate signals of interest from noise. We touch on the need for careful accounting of nuisance parameters when making group comparisons of correlation maps.

834 citations

Cites background from "Decreased Interhemispheric Function..."

  • ...Numerous studies since Murphy et al. (2009) have focused on whether or not anticorrelations are solely an artifact of GSReg with conclusions pointing in both directions (Anderson et al., 2011b; Fox et al., 2009; Chai et al., 2012; Chang and Glover, 2009a)....

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  • ..., 1995) and tantalizingly rich in detail (Anderson et al., 2011a; Bellec et al., 2010; Biswal et al., 2010; Gee et al., 2011; Honey et al., 2009; Jo et al., 2010; Kelly et al., 2010; Smith et al., 2009)....

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  • ...…Functional magnetic resonance imaging(RS-FMRI) data are spectacularly easy to collect (Biswal et al., 1995) and tantalizingly rich in detail (Anderson et al., 2011a; Bellec et al., 2010; Biswal et al., 2010; Gee et al., 2011; Honey et al., 2009; Jo et al., 2010; Kelly et al., 2010; Smith et…...

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Intellectual abilities in Alzheimer's disease patients: Contributions from the Wechsler Abbreviated Scale of Intelligence (WASI)

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Gabriela Peretti Wagner1, Tatiana Quarti Irigaray2, Clarissa Marceli Trentini1•
Universidade Federal do Rio Grande do Sul1, Pontifícia Universidade Católica do Rio Grande do Sul2
01 Jan 2010

737 citations

References
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Journal Article•DOI•
The assessment and analysis of handedness: The Edinburgh inventory

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R.C. Oldfield1•
University of Edinburgh1
01 Mar 1971-Neuropsychologia
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Journal Article•DOI•
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Nathalie Tzourio-Mazoyer1, B. Landeau1, D. Papathanassiou1, Fabrice Crivello1, Octave Etard1, Nicolas Delcroix1, Bernard Mazoyer2, Marc Joliot1 •
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13,678 citations

"Decreased Interhemispheric Function..." refers methods in this paper

  • ...The supratentorial brain was parcellated into 90 regions using the AAL brain atlas (Tzourio-Mazoyer et al. 2002; Maldjian et al. 2003)....

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Journal Article•DOI•
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

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Rahul S. Desikan1, Florent Ségonne2, Bruce Fischl2, Bruce Fischl3, Brian T. Quinn3, Bradford C. Dickerson3, Deborah Blacker3, Randy L. Buckner4, Randy L. Buckner3, Anders M. Dale5, R. Paul Maguire6, Bradley T. Hyman3, Marilyn S. Albert7, Ronald J. Killiany1 •
Boston University1, Massachusetts Institute of Technology2, Harvard University3, Howard Hughes Medical Institute4, University of California, San Diego5, Pfizer6, Johns Hopkins University School of Medicine7
01 Jul 2006-NeuroImage
TL;DR: An automated labeling system for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable and may be useful for both morphometric and functional studies of the cerebral cortex.

9,940 citations

"Decreased Interhemispheric Function..." refers methods in this paper

  • ...The cerebral cortex was parcellated into regions based on gyral and sulcal structure (Desikan et al. 2006)....

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Journal Article•DOI•
Cortical surface-based analysis. I. Segmentation and surface reconstruction

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Anders M. Dale1, Bruce Fischl1, Martin I. Sereno2•
Harvard University1, University of California, San Diego2
01 Feb 1999-NeuroImage
TL;DR: A set of automated procedures for obtaining accurate reconstructions of the cortical surface are described, which have been applied to data from more than 100 subjects, requiring little or no manual intervention.

9,599 citations

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