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OBJECTIVE: Neurological soft signs (NSS) are core features of psychiatric disorders with significant neurodevelopmental origin. However, it is unclear whether NSS correlates are associated with neuropathological processes underlying the disease or if they are confounded by medication. Given that NSS are also present in healthy persons (HP), investigating HP could reveal NSS correlates, which are not biased by disease-specific processes or drug treatment. Therefore, we used a combination of diffusion MRI analysis tools to provide a framework of specific white matter (WM) microstructure variations underlying NSS in HP. METHOD: NSS of 59 HP were examined on the Heidelberg Scale and related to diffusion associated metrics. Using tract-based spatial statistics (TBSS), we studied WM variations in fractional anisotropy (FA) as well as radial (RD), axial (AD), and mean diffusivity (MD). Using graph analytics (clustering coefficient-CC, local betweenness centrality -BC), we then explored DTI-derived structural network variations in regions identified by previous MRI studies on NSS. RESULTS: NSS scores were negatively associated with RD, AD and MD in corpus callosum, brainstem and cerebellum (P < 0.05, corr.). NSS scores were negatively associated with CC and BC of the pallidum, the superior parietal gyrus, the precentral sulcus, the insula, and the cingulate gyrus (P < 0.05, uncorr.). CONCLUSION: The present study supports the notion that WM microstructure variations in subcortical and cortical sensorimotor regions contribute to NSS expression in young HP. Hum Brain Mapp 38:3552-3565, 2017. © 2017 Wiley Periodicals, Inc.
RESUMEN
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.