Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder

BackgroundRecent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain’s wiring diagram, i.e., the connectome.MethodsWe acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60–90 % of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures.ResultsIn subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD.ConclusionsOur findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.

[1]  Alan C. Evans,et al.  Network efficiency in autism spectrum disorder and its relation to brain overgrowth , 2013, Front. Hum. Neurosci..

[2]  Ruth A. Carper,et al.  Autism and Abnormal Development of Brain Connectivity , 2004, The Journal of Neuroscience.

[3]  B. Aouizerate,et al.  Neuronal Correlates of Obsessions in the Caudate Nucleus , 2008, Biological Psychiatry.

[4]  S. Baron-Cohen,et al.  The "Reading the Mind in the Eyes" Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism. , 2001, Journal of child psychology and psychiatry, and allied disciplines.

[5]  Nashaat N. Boutros,et al.  The Brain and Behavior: An Introduction to Behavioral Neuroanatomy , 2005 .

[6]  Alan Connelly,et al.  Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.

[7]  Chun-Hung Yeh,et al.  Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data , 2008, NeuroImage.

[8]  Mara Cercignani,et al.  Twenty‐five pitfalls in the analysis of diffusion MRI data , 2010, NMR in biomedicine.

[9]  A. Dale,et al.  Age‐Related Changes in Prefrontal White Matter Measured by Diffusion Tensor Imaging , 2005, Annals of the New York Academy of Sciences.

[10]  J. Piven,et al.  Repetitive and self-injurious behaviors: associations with caudate volume in autism and fragile X syndrome , 2013, Journal of Neurodevelopmental Disorders.

[11]  Jung-Lung Hsu,et al.  Gender differences and age-related white matter changes of the human brain: A diffusion tensor imaging study , 2008, NeuroImage.

[12]  M. Sams,et al.  Constrained spherical deconvolution-based tractography and tract-based spatial statistics show abnormal microstructural organization in Asperger syndrome , 2015, Molecular Autism.

[13]  Mikko Sams,et al.  Increased Coherence of White Matter Fiber Tract Organization in Adults with Asperger Syndrome: A Diffusion Tensor Imaging Study , 2013, Autism research : official journal of the International Society for Autism Research.

[14]  Sam Wass,et al.  Distortions and disconnections: Disrupted brain connectivity in autism , 2011, Brain and Cognition.

[15]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[16]  J. Mazziotta,et al.  Local cerebral glucose metabolic rates in obsessive-compulsive disorder. A comparison with rates in unipolar depression and in normal controls. , 1987, Archives of general psychiatry.

[17]  Simon Baron-Cohen,et al.  The systemizing quotient: an investigation of adults with Asperger syndrome or high-functioning autism, and normal sex differences. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[18]  S. Folstein,et al.  Genetics of austim: complex aetiology for a heterogeneous disorder , 2001, Nature Reviews Genetics.

[19]  Klaus H. Maier-Hein,et al.  Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis , 2014, International Journal of Computer Assisted Radiology and Surgery.

[20]  Jesse A. Brown,et al.  Altered functional and structural brain network organization in autism☆ , 2012, NeuroImage: Clinical.

[21]  E. Fombonne.  Epidemiology of Pervasive Developmental Disorders , 2009, Pediatric Research.

[22]  A. Leemans,et al.  Hemispheric lateralization of topological organization in structural brain networks , 2014, Human brain mapping.

[23]  K. Kaski,et al.  Intensity and coherence of motifs in weighted complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[25]  Katherine A. Johnson,et al.  White Matter and Visuospatial Processing in Autism: A Constrained Spherical Deconvolution Tractography Study , 2013, Autism research : official journal of the International Society for Autism Research.

[26]  H. M. AMI.  The Royal Canadian Institute , 1914, Nature.

[27]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[28]  Olaf Sporns,et al.  Weight-conserving characterization of complex functional brain networks , 2011, NeuroImage.

[29]  A. Connelly,et al.  Determination of the appropriate b value and number of gradient directions for high‐angular‐resolution diffusion‐weighted imaging , 2013, NMR in biomedicine.

[30]  C. Arango,et al.  The neurobiology of autism spectrum disorders , 2014, European Psychiatry.

[31]  Wilfried Philips,et al.  Isotropic non-white matter partial volume effects in constrained spherical deconvolution , 2014, Front. Neuroinform..

[32]  S. Baron-Cohen,et al.  The Autism-Spectrum Quotient (AQ): Evidence from Asperger Syndrome/High-Functioning Autism, Malesand Females, Scientists and Mathematicians , 2001, Journal of autism and developmental disorders.

[33]  Jan Sijbers,et al.  Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution , 2011, Human brain mapping.

[34]  Mara Cercignani,et al.  Strategies for Patient–Control Comparison of Diffusion MR Data , 2010 .

[35]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[36]  Wilfried Philips,et al.  Informed constrained spherical deconvolution (iCSD) , 2015, Medical Image Anal..

[37]  A. Connelly,et al.  White matter fiber tractography: why we need to move beyond DTI. , 2013, Journal of neurosurgery.

[38]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[39]  Zhong Xue,et al.  Network‐based analysis reveals stronger local diffusion‐based connectivity and different correlations with oral language skills in brains of children with high functioning autism spectrum disorders , 2014, Human brain mapping.

[40]  Alexander Leemans,et al.  The B‐matrix must be rotated when correcting for subject motion in DTI data , 2009, Magnetic resonance in medicine.

[41]  Jan Sijbers,et al.  ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data , 2009 .

[42]  Alexander Leemans,et al.  Reliability of the corticospinal tract and arcuate fasciculus reconstructed with DTI-based tractography: implications for clinical practice , 2012, European Radiology.

[43]  L. Nummenmaa,et al.  The brains of high functioning autistic individuals do not synchronize with those of others☆ , 2013, NeuroImage: Clinical.

[44]  Klaus H. Maier-Hein,et al.  Methodological considerations on tract-based spatial statistics (TBSS) , 2014, NeuroImage.

[45]  Nash N. Boutros,et al.  The Brain and Behavior: Frontmatter , 2010 .

[46]  Agatha D. Lee,et al.  Genetics of Brain Fiber Architecture and Intellectual Performance , 2009, The Journal of Neuroscience.

[47]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[48]  G Gerig,et al.  Network inefficiencies in autism spectrum disorder at 24 months , 2014, Translational Psychiatry.

[49]  Derek K. Jones,et al.  Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging , 2013, Human brain mapping.

[50]  Marieke Langen,et al.  Caudate Nucleus Is Enlarged in High-Functioning Medication-Naive Subjects with Autism , 2007, Biological Psychiatry.

[51]  Mikko Kivelä,et al.  Generalizations of the clustering coefficient to weighted complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[52]  Marcel Adam Just,et al.  Inter-Regional Brain Communication and Its Disturbance in Autism , 2011, Front. Syst. Neurosci..

[53]  J. J. Ryan,et al.  Scoring Reliability on the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) , 2003, Assessment.

[54]  S. Baron-Cohen,et al.  The Empathy Quotient: An Investigation of Adults with Asperger Syndrome or High Functioning Autism, and Normal Sex Differences , 2004, Journal of autism and developmental disorders.

[55]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[56]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[57]  G. Fagiolo.  Clustering in complex directed networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[58]  Jessica A. Grahn,et al.  The cognitive functions of the caudate nucleus , 2008, Progress in Neurobiology.

[59]  T Varilo,et al.  Molecular genetics of the Finnish disease heritage. , 1999, Human molecular genetics.

[60]  Katherine A. Johnson,et al.  Abnormal functional connectivity during visuospatial processing is associated with disrupted organisation of white matter in autism , 2013, Front. Hum. Neurosci..

[61]  Do P. M. Tromp,et al.  Diffusion Tensor Imaging in Autism Spectrum Disorder: A Review , 2012, Autism research : official journal of the International Society for Autism Research.

[62]  Shiva Kintali,et al.  Betweenness Centrality : Algorithms and Lower Bounds , 2008, ArXiv.

[63]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[64]  Alan Connelly,et al.  Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution , 2004, NeuroImage.