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Altered Development and Multifaceted Band-Specific Abnormalities of Resting State Networks in Autism

      Abstract

      Background

      Extensive evidence indicates that cortical connectivity patterns are abnormal in autism spectrum disorders (ASD), showing both overconnectivity and underconnectivity. Since, however, studies to date have focused on either spatial or spectral dimensions, but not both simultaneously, much remains unknown about the nature of these abnormalities. In particular, it remains unknown whether abnormal connectivity patterns in ASD are driven by specific frequency bands, by spatial network properties, or by some combination of these factors.

      Methods

      Magnetoencephalography recordings (15 ASD, 15 control subjects) mapped back onto cortical space were used to study resting state networks in ASD with both spatial and spectral specificity. The data were quantified using graph theoretic metrics.

      Results

      The two major factors that drove the nature of connectivity abnormalities in ASD were the mediating frequency band and whether the network included frontal nodes. These factors determined whether clustering and integration were increased or decreased in cortical resting state networks in ASD. These measures also correlated with abnormalities in the developmental trajectory of resting state networks in ASD. Lastly, these measures correlated with ASD severity in some frequency bands and spatially specific subnetworks.

      Conclusions

      Our findings suggest that network abnormalities in ASD are widespread, are more likely in subnetworks that include the frontal lobe, and can be opposite in nature depending on the frequency band. These findings thus elucidate seemingly contradictory prior findings of both overconnectivity and underconnectivity in ASD.

      Keywords

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      References

        • Wass S.
        Distortions and disconnections: Disrupted brain connectivity in autism.
        Brain Cogn. 2011; 75: 18-28
        • Müller R.
        • Shih P.
        • Keehn B.
        • Deyoe J.R.
        • Leyden K.M.
        • Shukla D.K.
        Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders.
        Cereb Cortex. 2011; 21: 2233-2243
        • Vissers M.E.
        • Cohen M.X.
        • Geurts H.M.
        Brain connectivity and high functioning autism: A promising path of research that needs refined models, methodological convergence, and stronger behavioral links.
        Neurosci Biobehav Rev. 2012; 36: 604-625
        • Greicius M.D.
        • Menon V.
        Default-mode activity during a passive sensory task: Uncoupled from deactivation but impacting activation.
        J Cogn Neurosci. 2004; 16: 1484-1492
        • Kim H.
        Dissociating the roles of the default-mode, dorsal, and ventral networks in episodic memory retrieval.
        Neuroimage. 2010; 50: 1648-1657
        • Kim H.
        • Daselaar S.M.
        • Cabeza R.
        Overlapping brain activity between episodic memory encoding and retrieval: Roles of the task-positive and task-negative networks.
        Neuroimage. 2010; 49: 1045-1054
        • Saxe R.
        • Kanwisher N.
        People thinking about thinking people: The role of the temporo-parietal junction in “theory of mind.”.
        Neuroimage. 2003; 19: 1835-1842
        • Spreng R.N.
        • Grady C.L.
        Patterns of brain activity supporting autobiographical memory, prospection, and theory of mind, and their relationship to the default mode network.
        J Cogn Neurosci. 2010; 22: 1112-1123
        • Cherkassky V.L.
        • Kana R.K.
        • Keller T.A.
        • Just M.A.
        Functional connectivity in a baseline resting-state network in autism.
        Neuroreport. 2006; 17: 1687-1690
        • Kennedy D.P.
        • Courchesne E.
        Functional abnormalities of the default network during self- and other-reflection in autism.
        Soc Cogn Affect Neurosci. 2008; 3: 177-190
        • Weng S.J.
        • Wiggins J.L.
        • Peltier S.J.
        • Carrasco M.
        • Risi S.
        • Lord C.
        • Monk C.S.
        Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders.
        Brain Res. 2010; 1313: 202-214
        • Monk C.S.
        • Peltier S.J.
        • Wiggins J.L.
        • Weng S.
        • Carrasco M.
        • Risi S.
        • Lord C.
        Abnormalities of intrinsic functional connectivity in autism spectrum disorders.
        Neuroimage. 2009; 47: 764-772
        • Ebisch S.J.
        • Gallese V.
        • Willems R.M.
        • Mantini D.
        • Groen W.B.
        • Romani G.L.
        • et al.
        Altered intrinsic functional connectivity of anterior and posterior insula regions in high-functioning participants with autism spectrum disorder.
        Hum Brain Mapp. 2011; 32: 1013-1028
        • Tyszka J.M.
        • Kennedy D.P.
        • Paul L.K.
        • Adolphs R.
        Largely typical patterns of resting-state functional connectivity in high-functioning adults with autism.
        Cereb Cortex. 2014; 24: 1894-1905
        • Wiggins J.L.
        • Peltier S.J.
        • Ashinoff S.
        • Weng S.J.
        • Carrasco M.
        • Welsh R.C.
        • et al.
        Using a self-organizing map algorithm to detect age-related changes in functional connectivity during rest in autism spectrum disorders.
        Brain Res. 2011; 1380: 187-197
        • Washington S.D.
        • Gordon E.M.
        • Brar J.
        • Warburton S.
        • Sawyer A.T.
        • Wolfe A.
        • et al.
        Dysmaturation of the default mode network in autism.
        Hum Brain Mapp. 2014; 35: 1284-1296
        • Van Dijk K.R.
        • Sabuncu M.R.
        • Buckner R.L.
        The influence of head motion on intrinsic functional connectivity MRI.
        Neuroimage. 2012; 59: 431-438
        • Jones T.B.
        • Bandettini P.A.
        • Kenworthy L.
        • Case L.K.
        • Milleville S.C.
        • Martin A.
        • Birn R.M.
        Sources of group differences in functional connectivity: An investigation applied to autism spectrum disorder.
        Neuroimage. 2010; 49: 401-414
        • Nair A.
        • Keown C.L.
        • Datko M.
        • Shih P.
        • Keehn B.
        • Müller R.
        Impact of methodological variables on functional connectivity findings in autism spectrum disorders.
        Hum Brain Mapp. 2014; 35: 4035-4048
        • Uhlhaas P.
        • Singer W.
        Neural synchrony in brain disorders: Relevance for cognitive dysfunctions and pathophysiology.
        Neuron. 2006; 52: 155-168
        • Uhlhaas P.J.
        • Roux F.
        • Rodriguez E.
        • Rotarska-Jagiela A.
        • Singer W.
        Neural synchrony and the development of cortical networks.
        Trends Cogn Sci. 2010; 14: 72-80
        • Uhlhaas P.J.
        • Singer W.
        Abnormal neural oscillations and synchrony in schizophrenia.
        Nat Rev Neurosci. 2010; 11: 100-113
        • Murias M.
        • Webb S.J.
        • Greenson J.
        • Dawson G.
        Resting state cortical connectivity reflected in EEG coherence in individuals with autism.
        Biol Psychiatry. 2007; 62: 270-273
        • Barttfeld P.
        • Wicker B.
        • Cukier S.
        • Navarta S.
        • Lew S.
        • Sigman M.
        A big-world network in ASD: Dynamical connectivity analysis reflects a deficit in long-range connections and an excess of short-range connections.
        Neuropsychologia. 2011; 49: 254-263
        • Pollonini L.
        • Patidar U.
        • Situ N.
        • Rezaie R.
        • Papanicolaou A.C.
        • Zouridakis G.
        Functional connectivity networks in the autistic and healthy brain assessed using Granger causality.
        Conf Proc IEEE Eng Med Biol Soc. 2010; 2010: 1730-1733
        • Tsiaras V.
        • Simos P.G.
        • Rezaie R.
        • Sheth B.R.
        • Garyfallidis E.
        • Castillo E.M.
        • Papanicolaou A.C.
        Extracting biomarkers of autism from MEG resting-state functional connectivity networks.
        Comput Biol Med. 2011; 41: 1166-1177
        • Ghanbari Y.
        • Bloy L.
        • Edgar J.C.
        • Blaskey L.
        • Verma R.
        • Roberts T.P.L.
        Joint analysis of band-specific functional connectivity and signal complexity in autism [published online ahead of print August 21].
        J Autism Dev Disord. 2013;
        • Peters J.M.
        • Taquet M.
        • Vega C.
        • Jeste S.S.
        • Fernandez I.S.
        • Tan J.
        • et al.
        Brain functional networks in syndromic and non-syndromic autism: A graph theoretical study of EEG connectivity.
        BMC Med. 2013; 11: 54
        • Kenet T.
        • Orekhova E.V.
        • Bharadwaj H.
        • Shetty N.R.
        • Israeli E.
        • Lee A.K.
        • et al.
        Disconnectivity of the cortical ocular motor control network in autism spectrum disorders.
        Neuroimage. 2012; 61: 1226-1234
        • Khan S.
        • Gramfort A.
        • Shetty N.R.
        • Kitzbichler M.G.
        • Ganesan S.
        • Moran J.M.
        • et al.
        Local and long-range functional connectivity is reduced in concert in autism spectrum disorders.
        Proc Natl Acad Sci U S A. 2013; 110: 3107-3112
        • Erdös P.
        • Rényi A.
        On the strength of connectedness of a random graph.
        Acta Mathematica Hungarica. 1961; 12: 261-267
        • Diestel R.
        Graph Theory, 4th ed, Graduate Texts in Mathematics.
        vol. 173. Springer-Verlag, Heidelberg, Germany2000
        • Palva J.M.
        • Monto S.
        • Kulashekhar S.
        • Palva S.
        Neuronal synchrony reveals working memory networks and predicts individual memory capacity.
        Proc Natl Acad Sci U S A. 2010; 107: 7580-7585
        • Taulu S.
        • Kajola M.
        • Simola J.
        Suppression of interference and artifacts by the Signal Space Separation Method.
        Brain Topogr. 2004; 16: 269-275
        • Taulu S.
        • Simola J.
        Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements.
        Phys Med Biol. 2006; 51: 1759-1768
        • Nolte G.
        • Hamalainen M.S.
        Partial signal space projection for artefact removal in MEG measurements: A theoretical analysis.
        Phys Med Biol. 2001; 46: 2873-2887
        • Fischl B.
        FreeSurfer.
        Neuroimage. 2012; 62: 774-781
        • Lin F.H.
        • Wald L.L.
        • Ahlfors S.P.
        • Hämäläinen M.S.
        • Kwong K.K.
        • Belliveau J.W.
        Dynamic magnetic resonance inverse imaging of human brain function.
        Magn Reson Med. 2006; 56: 787-802
        • Hipp J.F.
        • Hawellek D.J.
        • Corbetta M.
        • Siegel M.
        • Engel A.K.
        Large-scale cortical correlation structure of spontaneous oscillatory activity.
        Nat Neurosci. 2012; 15: 884-890
        • Schoffelen J.M.
        • Gross J.
        Source connectivity analysis with MEG and EEG.
        Hum Brain Mapp. 2009; 30: 1857-1865
        • Palva S.
        • Palva J.M.
        Discovering oscillatory interaction networks with M/EEG: Challenges and breakthroughs.
        Trends Cogn Sci. 2012; 16: 219-230
        • Watts D.J.
        • Strogatz S.H.
        Collective dynamics of "small-world" networks.
        Nature. 1998; 393: 440-442
        • Sporns O.
        Graph theory methods for the analysis of neural connectivity patterns.
        in: Kötter R. Neuroscience Databases. A Practical Guide. Klüwer, Boston2002: 171-186
        • Ginestet C.E.
        • Nichols T.E.
        • Bullmore E.T.
        • Simmons A.
        Brain Network Analysis: Separating cost from topology using cost-integration.
        PLoS One. 2011; 6: e21570
        • Ginestet C.E.
        • Fournel A.P.
        • Simmons A.
        Statistical network analysis for functional MRI: Summary networks and group comparisons.
        Front Comput Neurosci. 2014; 8: 51
        • Hamalainen M.
        • Sarvas J.
        Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data.
        IEEE Trans Biomed Eng. 1989; 36: 165-171
        • Hämäläinen M.
        • Hari R.
        • Ilmoniemi R.J.
        • Knuutila J.
        • Lounasmaa O.V.
        Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain.
        Rev Mod Phys. 1993; 65: 413-497
        • Dale A.M.
        • Sereno M.I.
        Improved localizadon of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach.
        J Cogn Neurosci. 1993; 5: 162-176
        • Fischl B.
        • Sereno M.I.
        • Tootell R.B.
        • Dale A.M.
        High-resolution intersubject averaging and a coordinate system for the cortical surface.
        Hum Brain Mapp. 1999; 8: 272-284
        • Dale A.M.
        • Liu A.K.
        • Fischl B.R.
        • Buckner R.L.
        • Belliveau J.W.
        • Lewine J.D.
        • Halgren E.
        Dynamic statistical parametric mapping: Combining fMRI and MEG for high-resolution imaging of cortical activity.
        Neuron. 2000; 26: 55-67
        • Cornew L.
        • Roberts T.
        • Blaskey L.
        • Edgar J.
        Resting-state oscillatory activity in autism spectrum disorders.
        J Autism Dev Disord. 2012; 42: 1884-1894
        • Brown C.
        • Gruber T.
        • Boucher J.
        • Rippon G.
        • Brock J.
        Gamma abnormalities during perception of illusory figures in autism.
        Cortex. 2005; 41: 364-376
        • Braeutigam S.
        • Swithenby S.J.
        • Bailey A.J.
        Contextual integration the unusual way: A magnetoencephalographic study of responses to semantic violation in individuals with autism spectrum disorders.
        Eur J Neurosci. 2008; 27: 1026-1036
        • Orekhova E.V.
        • Stroganova T.A.
        • Nygren G.
        • Tsetlin M.M.
        • Posikera I.N.
        • Gillberg C.
        • Elam M.
        Excess of high frequency electroencephalogram oscillations in boys with autism.
        Biol Psychiatry. 2007; 62: 1022-1029
        • Wilson T.W.
        • Rojas D.C.
        • Reite M.L.
        • Teale P.D.
        • Rogers S.J.
        Children and adolescents with autism exhibit reduced MEG steady-state gamma responses.
        Biol Psychiatry. 2007; 62: 192-197
        • Velazquez J.P.
        • Barcelo F.
        • Hung Y.
        • Leshchenko Y.
        • Nenadovic V.
        • Belkas J.
        • et al.
        Decreased brain coordinated activity in autism spectrum disorders during executive tasks: Reduced long-range synchronization in the fronto-parietal networks.
        Int J Psychophysiol. 2009; 73: 341-349
        • Sheikhani A.
        • Behnam H.
        • Mohammadi M.R.
        • Noroozian M.
        • Mohammadi M.
        Detection of abnormalities for diagnosing of children with autism disorders using of quantitative electroencephalography analysis.
        J Med Syst. 2012; 36: 957-963
        • Buschman T.J.
        • Miller E.K.
        Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices.
        Science. 2007; 315: 1860-1862
        • Miller E.K.
        • Buschman T.J.
        Cortical circuits for the control of attention.
        Curr Opin Neurobiol. 2013; 23: 216-222
        • Buffalo E.A.
        • Fries P.
        • Landman R.
        • Buschman T.J.
        • Desimone R.
        Laminar differences in gamma and alpha coherence in the ventral stream.
        Proc Natl Acad Sci U S A. 2011; 108: 11262-11267
        • Buzsaki G.
        • Wang X.J.
        Mechanisms of gamma oscillations.
        Annu Rev Neurosci. 2012; 35: 203-225
        • Buschman T.J.
        • Denovellis E.L.
        • Diogo C.
        • Bullock D.
        • Miller E.K.
        Synchronous oscillatory neural ensembles for rules in the prefrontal cortex.
        Neuron. 2012; 76: 838-846
        • Dehaene S.
        • Kerszberg M.
        • Changeux J.P.
        A neuronal model of a global workspace in effortful cognitive tasks.
        Proc Natl Acad Sci U S A. 1998; 95: 14529-14534
        • Frith U.
        Autism: Explaining the Enigma.
        Blackwell, Oxford, UK1989
        • Brock J.
        • Brown C.C.
        • Boucher J.
        • Rippon G.
        The temporal binding deficit hypothesis of autism.
        Dev Psychopathol. 2002; 14: 209-224
        • Foss-Feig J.H.
        • Kwakye L.D.
        • Cascio C.J.
        • Burnette C.P.
        • Kadivar H.
        • Stone W.L.
        • Wallace M.T.
        An extended multisensory temporal binding window in autism spectrum disorders.
        Exp Brain Res. 2010; 203: 381-389
        • Kopell N.
        • Ermentrout G.B.
        • Whittington M.A.
        • Traub R.D.
        Gamma rhythms and beta rhythms have different synchronization properties.
        Proc Natl Acad Sci U S A. 2000; 97: 1867-1872
        • Kitzbichler M.G.
        • Henson R.N.A.
        • Smith M.L.
        • Nathan P.J.
        • Bullmore E.T.
        Cognitive effort drives workspace configuration of human brain functional networks.
        J Neurosci. 2011; 31: 8259-8270
        • Uddin L.Q.
        • Supekar K.
        • Menon V.
        Typical and atypical development of functional human brain networks: Insights from resting-state fMRI.
        Front Syst Neurosci. 2010; 4: 21
        • Sowell E.R.
        • Thompson P.M.
        • Holmes C.J.
        • Jernigan T.L.
        • Toga A.W.
        In vivo evidence for post-adolescent brain maturation in frontal and striatal regions.
        Nat Neurosci. 1999; 2: 859-861
        • Sowell E.R.
        • Delis D.
        • Stiles J.
        • Jernigan T.L.
        Improved memory functioning and frontal lobe maturation between childhood and adolescence: A structural MRI study.
        J Int Neuropsychol Soc. 2001; 7: 312-322
        • Mundy P.
        Annotation: The neural basis of social impairments in autism: The role of the dorsal medial-frontal cortex and anterior cingulate system.
        J Child Psychol Psychiatry. 2003; 44: 793-809
        • Courchesne E.
        • Redcay E.
        • Morgan J.T.
        • Kennedy D.P.
        Autism at the beginning: Microstructural and growth abnormalities underlying the cognitive and behavioral phenotype of autism.
        Dev Psychopathol. 2005; 17: 577-597
        • Lee P.S.
        • Yerys B.E.
        • Della Rosa A.
        • Foss-Feig J.
        • Barnes K.A.
        • James J.D.
        • et al.
        Functional connectivity of the inferior frontal cortex changes with age in children with autism spectrum disorders: A fcMRI study of response inhibition.
        Cereb Cortex. 2009; 19: 1787-1794
        • Koshino H.
        • Kana R.K.
        • Keller T.A.
        • Cherkassky V.L.
        • Minshew N.J.
        • Just M.A.
        fMRI investigation of working memory for faces in autism: Visual coding and underconnectivity with frontal areas.
        Cereb Cortex. 2008; 18: 289-300

      Linked Article

      • Connectivity in Context: Emphasizing Neurodevelopment in Autism Spectrum Disorder
        Biological PsychiatryVol. 77Issue 9
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          Convergent evidence from genetics, neuropathology, and functional neuroimaging implicates aberrant cortical connectivity in the pathogenesis of neurodevelopmental disorders, such as autism spectrum disorder (ASD). Chromosomal variants associated with ASD include genes that are critical for synaptic structure and function, leading to a hypothesis that ASD results from a failure to establish necessary cortical connections, particularly in brain regions critical for social cognition and language (1).
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