Background Diffusion tensor imaging (DTI) enables measurements and visualization of the microstructure of neural fiber tracts. The existing literature on autism spectrum disorders (ASDs) and DTI is heterogenous both regarding methodology and results. Purpose To compare brain white matter of high-functioning individuals with ASDs and controls. Material and Methods Tract-based spatial statistics (TBSS), a voxel-based approach to DTI, was used to compare 27 subjects with ASDs (mean age 14.7 years, range 11.4–17.6 years, 20 boys, 7 girls) and 26 control subjects (mean age 14.5 years, range 11.7–17.3 years, 17 boys, 9 girls). Mean fractional anisotropy (FA) image (skeleton) was created and each subject's aligned FA data were then projected onto this skeleton. Voxelwise cross-subject statistics on the skeletonized FA data, mean diffusivity (MD), and measures of diffusion direction were calculated. Importantly, the data were corrected across the whole image instead of using ROI-based methods. Results The ASD group showed significantly greater FA ( P < 0.05, corrected) in the area containing clusters of optic radiation and the right inferior fronto-occipital fasciculus (iFOF). In the same area, λ 3 (representing transverse diffusion) was significantly reduced in the ASD group. No age-related changes were found. Conclusion The results suggest that the reduced transverse diffusion within the iFOF is related to abnormal information flow between the insular salience processing areas and occipital visual areas.
In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.
Abstract Introduction There has been a growing effort to characterize the time‐varying functional connectivity of resting state (RS) fMRI brain networks (RSNs). Although voxel‐wise connectivity studies have examined different sliding window lengths, nonsequential volume‐wise approaches have been less common. Methods Inspired by earlier co‐activation pattern (CAP) studies, we applied hierarchical clustering (HC) to classify the image volumes of the RS‐fMRI data on 28 adolescents with autism spectrum disorder (ASD) and their 27 typically developing (TD) controls. We compared the distribution of the ASD and TD groups' volumes in CAPs as well as their voxel‐wise means. For simplification purposes, we conducted a group independent component analysis to extract 14 major RSNs. The RSNs' average z ‐scores enabled us to meaningfully regroup the RSNs and estimate the percentage of voxels within each RSN for which there was a significant group difference. These results were jointly interpreted to find global group‐specific patterns. Results We found similar brain state proportions in 58 CAPs (clustering interval from 2 to 30). However, in many CAPs, the voxel‐wise means differed significantly within a matrix of 14 RSNs. The rest‐activated default mode‐positive and default mode‐negative brain state properties vary considerably in both groups over time. This division was seen clearly when the volumes were partitioned into two CAPs and then further examined along the HC dendrogram of the diversifying brain CAPs. The ASD group network activations followed a more heterogeneous distribution and some networks maintained higher baselines; throughout the brain deactivation state, the ASD participants had reduced deactivation in 12/14 networks. During default mode‐negative CAPs, the ASD group showed simultaneous visual network and either dorsal attention or default mode network overactivation. Conclusion Nonsequential volume gathering into CAPs and the comparison of voxel‐wise signal changes provide a complementary perspective to connectivity and an alternative to sliding window analysis.
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