Quantification of Changes in Language-Related Brain Areas in Autism Spectrum Disorders Using Large-Scale Network Analysis

Goch, Caspar J.; Stieltjes, Bram; Henze, Romy; Hering, Jan; Meinzer, Hans Peter; Fritzsche, Klaus

doi:10.1007/978-3-642-36480-8_11

Cited by 6 publications

(10 citation statements)

References 10 publications

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“…The tractography outputs a tractogram of the full brain within the mask. While tractography-based approaches typically analyse 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 [Goch et al, 2014;Rubinov and Bullmore, 2013]. To construct a graph representation, the T1 image was used to create a parcellation of the brain using FreeSurfer and the atlas by Destrieux [Desikan et al, 2006;Fischl et al, 2004;Segonne et al, 2004].…”

Section: Large-scale Network Analysismentioning

confidence: 99%

“…DMRI is a multiparametric imaging technique that is sensitive to microstructural variations in the tissue and yields extremely valuable parameters in the context of subtle WM tissue alterations [Bach et al, 2014;Goch et al, 2014;Maier-Hein et al, 2014]. In the context of dMRI, the reconstruction of neuronal pathways in the brain by means of fiber tractography allows analyzing the brain structural network.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, we computed CC and BC for 38 nodes located in both hemispheres (Table I, Supporting Information Data) identified by previous MRI studies on NSS in HP [Thomann et al, 2015;Hirjak et al, 2016a]. CC is defined as a ratio of total number of edges among the neighbors of the node (region) to the total number of edges that can exist among the neighbors of the node per node [Goch et al, 2014]. BC of a node is defined on basis of the number of shortest paths from all vertices to all others that pass through that node [Freeman, 1977].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

White matter microstructure variations contribute to neurological soft signs in healthy adults

Hirjak

Thomann

Kubera

et al. 2017

Human Brain Mapping

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Section: Large-scale Network Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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White matter microstructure variations contribute to neurological soft signs in healthy adults

Hirjak

Thomann

Kubera

et al. 2017

Human Brain Mapping

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“…Connectome changes during development are characterized by features including increased cost‐efficient wiring, high levels of local clustering, formation of structural and functional modules, combined with an increase of short global communication pathways, which support the development of fundamental cognitive and cognitive‐motor functions (Collin & van den Heuvel, ). With respect to developmental disorders, only a few studies have demonstrated small‐world alterations and changes in large‐scale networks in ASD (Goch, Stieltjes, Henze, Hering, Poustka et al ., ; Rudie, Shehzad, Hernandez, Colich, Bookheimer et al ., ; Rudie, Brown, Beck‐Pancer, Hernandez, Dennis et al ., ; Valk, Di Martino, Milham & Bernhardt, ; Zhou, Yu & Duong, ). Moreover, our recent study in DCD (Debrabant et al ., ) has demonstrated weaker structural network segregation and integration in children with DCD.…”

Section: Introductionmentioning

confidence: 99%

Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism

Caeyenberghs

Taymans

Wilson

et al. 2016

Developmental Science

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Children with autism spectrum disorders (ASD) often exhibit motor clumsiness (Developmental Coordination Disorder, DCD), i.e. they struggle with everyday tasks that require motor coordination like dressing, self-care, and participating in sport and leisure activities. Previous studies in these neurodevelopmental disorders have demonstrated functional abnormalities and alterations of white matter microstructural integrity in specific brain regions. These findings suggest that the global organization of brain networks is affected in DCD and ASD and support the hypothesis of a 'dys-connectivity syndrome' from a network perspective. No studies have compared the structural covariance networks between ASD and DCD in order to look for the signature of DCD independent of comorbid autism. Here, we aimed to address the question of whether abnormal connectivity in DCD overlaps that seen in autism or comorbid DCD-autism. Using graph theoretical analysis, we investigated differences in global and regional topological properties of structural brain networks in 53 children: 8 ASD children with DCD (DCD+ASD), 15 ASD children without DCD (ASD), 11 with DCD only, and 19 typically developing (TD) children. We constructed separate structural correlation networks based on cortical thickness derived from Freesurfer. The children were assessed on the Movement-ABC and the Beery Test of Visual Motor Integration. Behavioral results demonstrated that the DCD group and DCD+ASD group scored on average poorer than the TD and ASD groups on various motor measures. Furthermore, although the brain networks of all groups exhibited small-world properties, the topological architecture of the networks was significantly altered in children with ASD compared with DCD and TD. ASD children showed increased normalized path length and higher values of clustering coefficient. Also, paralimbic regions exhibited nodal clustering coefficient alterations in singular disorders. These changes were disorder-specific, and included alterations in clustering coefficient in the isthmus of the right cingulate gyrus and the pars orbitalis of the right inferior frontal gyrus in ASD children, and DCD-related increases in the lateral orbitofrontal cortex. Children meeting criteria for both DCD and ASD exhibited topological changes that were more widespread from those seen in children with only DCD, i.e. children with DCD+ASD showed alterations of clustering coefficient in (para)limbic regions, primary areas, and association areas. The DCD+ASD group showed changes in clustering coefficient in the left association cortex relative to the ASD group. Finally, the DCD+ASD group shared ASD-specific abnormalities in the pars orbitalis of right inferior frontal gyrus, which was hypothesized to reflect atypical emotional-cognitive processing. Our results provide evidence that DCD and ASD are neurodevelopmental disorders with a low degree of overlap in abnormalities in connectivity. The co-occurrence of DCD+ASD was also associated with a distinct topological pattern, highlighting the...

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“…Research sparked by the Human Connectome project has been applied to Alzheimer disease and mild cognitive impairment (Reijmer et al, 2013), as well as autism spectrum disorders, in which Goch et al (2014) report that "the network centrality of Wernicke's area is significantly" reduced, suggesting that some brain disorders may be related to defective connectivity. Research sparked by the Human Connectome project has been applied to Alzheimer disease and mild cognitive impairment (Reijmer et al, 2013), as well as autism spectrum disorders, in which Goch et al (2014) report that "the network centrality of Wernicke's area is significantly" reduced, suggesting that some brain disorders may be related to defective connectivity.…”

Section: Applications To Diseasementioning

confidence: 99%

Future advances

Celesia

Hickok

2015

The Human Auditory System - Fundamental Organization and Clinical Disorders

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Quantification of Changes in Language-Related Brain Areas in Autism Spectrum Disorders Using Large-Scale Network Analysis

Cited by 6 publications

References 10 publications

White matter microstructure variations contribute to neurological soft signs in healthy adults

White matter microstructure variations contribute to neurological soft signs in healthy adults

Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism

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