Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis
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Diagnosis of autism spectrum disorders (ASD) is difficult, as symptoms vary greatly and are difficult to quantify objectively. Recent work has focused on the assessment of non-invasive diffusion tensor imaging-based biomarkers that reflect the microstructural characteristics of neuronal pathways in the brain. While tractography-based approaches typically analyze specific structures of interest, a graph-based large-scale network analysis of the connectome can yield comprehensive measures of larger-scale architectural patterns in the brain. Commonly applied global network indices, however, do not provide any specificity with respect to functional areas or anatomical structures. Aim of this work was to assess the concept of network centrality as a tool to perform locally specific analysis without disregarding the global network architecture and compare it to other popular network indices.
We create connectome networks from fiber tractographies and parcellations of the human brain and compute global network indices as well as local indices for Wernicke’s Area, Broca’s Area and the Motor Cortex. Our approach was evaluated on 18 children suffering from ASD and 18 typically developed controls using magnetic resonance imaging-based cortical parcellations in combination with diffusion tensor imaging tractography.
We show that the network centrality of Wernicke’s area is significantly (p \(<\) 0.001) reduced in ASD, while the motor cortex, which was used as a control region, did not show significant alterations. This could reflect the reduced capacity for comprehension of language in ASD.
The betweenness centrality could potentially be an important metric in the development of future diagnostic tools in the clinical context of ASD diagnosis. Our results further demonstrate the applicability of large-scale network analysis tools in the domain of region-specific analysis with a potential application in many different psychological disorders.
KeywordsConnectomics Network analysis Diffusion imaging Autism spectrum disorder Open-source
- 2.Baio J (2012) Prevalence of autism spectrum disorders autism and developmental disabilities monitoring network, 14 sites, united states, 2008. Department of Health and Human Services. Centers for Disease Control and Prevention, Morbidity and Mortality Weekly ReportGoogle Scholar
- 5.Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T, Andrews-Hanna JR, Sperling RA, Johnson KA (2009) Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 29(6):1860–1873PubMedCentralPubMedCrossRefGoogle Scholar
- 8.Cauda F, Costa T, Palermo S, D’Agata F, Diano M, Bianco F, Duca S, Keller R (2013) Concordance of white matter and gray matter abnormalities in autism spectrum disorders: a voxel-based meta-analysis study. Hum Brain Mapp. doi:10.1002/hbm.22313
- 9.Constantino JN, Davis SA, Todd RD, Schindler MK, Gross MM, Brophy SL, Metzger LM, Shoushtari CS, Splinter R, Reich W (2003) Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised. J Autism Dev Disord 33(4):427–433PubMedCrossRefGoogle Scholar
- 11.Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3):968–980PubMedCrossRefGoogle Scholar
- 12.DeWitt I, Rauschecker JP (2012) Phoneme and word recognition in the auditory ventral stream. Proc Natl Acad Sci USA 109(8):E505–E514Google Scholar
- 13.Ecker C, Marquand A, Mouro-Miranda J, Johnston P, Daly EM, Brammer MJ, Maltezos S, Murphy CM, Robertson D, Williams SC, Murphy DGM (2010) Describing the brain in autism in five dimensions: magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. J Neurosci 30(32):10,612–10,623CrossRefGoogle Scholar
- 16.Ford A, Triplett W, Sudhyadhom A, Gullett JM, McGregor K, FitzGerald D, Mareci T, White K, Crosson B (2013) Brocas area and its striatal and thalamic connections: A diffusion-mri tractography study. Front Neuroanat 7:8. doi:10.3389/fnana.2013.00008
- 19.Fritzsche KH, Neher PF, Reicht I, van Bruggen T, Goch C, Reisert M, Nolden M, Zelzer S, Meinzer HP, Stieltjes B (2012) Mitk diffusion imaging. Methods Inf Med 51(5):441–448Google Scholar
- 20.Goch C, Stieltjes B, Henze R, Hering J, Meinzer HP, Fritzsche K (2013) Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis. In: Meinzer HP, Deserno TM, Handels H, Tolxdorff T (eds) Bildverarbeitung für die Medizin 2013, Informatik aktuell. Springer, Berlin, pp 51–56CrossRefGoogle Scholar
- 31.Joseph RM, Fricker Z, Fenoglio A, Lindgren KA, Knaus TA, Tager-Flusberg H (2013) Structural asymmetries of language-related gray and white matter and their relationship to language function in young children with ASD. Brain Imaging Behav 1–13.Google Scholar
- 35.Lewis WW, Sahin M, Scherrer B, Peters JM, Suarez RO, Vogel-Farley VK, Jeste SS, Gregas MC, Prabhu SP, Nelson CA, Warfield SK (2012) Impaired language pathways in tuberous sclerosis complex patients with autism spectrum disorders. Cereb Cortex 23(7):1526–1532Google Scholar
- 36.Li H, Xue Z, Ellmore TM, Frye RE, Wong ST (2012) 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. Hum Brain Mapp 35(2):396–413Google Scholar
- 37.Li Y, Liu Y, Li J, Qin W, Li K, Yu C, Jiang T (2009) Brain anatomical network and intelligence. PLoS Comput Biol 5(5): e1000395Google Scholar
- 41.Mills BD, Lai J, Brown TT, Erhart M, Halgren E, Reilly J, Dale A, Appelbaum M, Moses P (2013) White matter microstructure correlates of narrative production in typically developing children and children with high functioning autism. Neuropsychologia 51(10):1933–1941 Google Scholar
- 42.Mueller S, Keeser D, Samson AC, Kirsch V, Blautzik J, Grothe M, Erat O, Hegenloh M, Coates U, Reiser MF, Hennig-Fast K, Meindl T (2013) Convergent findings of altered functional and structural brain connectivity in individuals with high functioning autism: a multimodal mri study. PLoS ONE 8(6):e67,329Google Scholar
- 43.Nebel MB, Joel SE, Muschelli J, Barber AD, Caffo BS, Pekar JJ, Mostofsky SH (2012) Disruption of functional organization within the primary motor cortex in children with autism. Hum Brain Mapp 35(2):567–580Google Scholar
- 44.Neher PF, Stieltjes B, Reisert M, Reicht I, Meinzer HP, Fritzsche KH (2012) MITK Global Tractography. In SPIE medical imaging 2012: image processingGoogle Scholar
- 48.Raven JC, Court JH, Raven J (1995) Coloured progressive matrices. Psychologist Press, OxfordGoogle Scholar
- 50.Roine U, Roine T, Salmi J, Nieminen-Von Wendt T, Leppämäki S, Rintahaka P, Tani P, Leemans A, Sams M (2013) Increased coherence of white matter fiber tract organization in adults with Asperger’s syndrome: a diffusion tensor imaging study. Autism Res 6(6):642–650Google Scholar
- 55.Travers BG, Adluru N, Ennis C, Tromp DPM, Destiche D, Doran S, Bigler ED, Lange N, Lainhart JE, Alexander AL (2012) Diffusion tensor imaging in autism spectrum disorder: a review. Autism Res 5(5):289–313Google Scholar