Altered Temporospatial Variability of Dynamic Amplitude of Low-Frequency Fluctuation in Children with Autism Spectrum Disorder.

Researchers used brain scans to study how brain activity changes over time in 105 children with autism compared to 102 children without autism. They found that children with autism had different patterns of brain activity variation in several brain networks that control attention, movement, and social thinking. Most importantly, changes in the 'default mode network' (active during rest) could predict how severe a child's autism symptoms were, suggesting these brain patterns might help understand autism better.

This neuroimaging study analyzed resting-state fMRI data from 105 children with autism spectrum disorder and 102 typically developing controls to examine dynamic brain activity patterns. Using advanced analysis techniques, researchers found altered temporal and spatial variability of brain activity across multiple networks in children with ASD. Key findings included enhanced temporal variability in visual, somatomotor, and salience networks, but reduced variability in the dorsal attention network. Spatial variability was decreased in visual, salience, and limbic networks but increased in attention and default mode networks.

Combined temporal-spatial variability was elevated in visual, somatomotor, and default mode networks. Importantly, alterations in the default mode network could predict autism symptom severity, suggesting these brain activity patterns may serve as potential biomarkers.

Findings suggest that dynamic brain activity patterns could serve as potential biomarkers for autism assessment and monitoring. The ability to predict symptom severity from default mode network patterns may inform future diagnostic and intervention approaches, though clinical validation is needed.

Study relies on cross-sectional neuroimaging data without longitudinal follow-up. The clinical significance of these brain activity differences remains unclear. Sample characteristics and analysis methodology details are not fully specified in the abstract, limiting interpretation of findings.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with altered brain activity. However, little is known about the integrated temporospatial variation of dynamic spontaneous brain activity in ASD. In the present study, resting-state functional magnetic resonance imaging data were analyzed for 105 ASD and 102 demographically-matched typically developmental controls (TC) children obtained from the Autism Brain Imaging Data Exchange database. Using the sliding-window approach, temporal, spatial, and temporospatial variability of dynamic amplitude of low-frequency fluctuation (tvALFF, svALFF, and tsvALFF) were calculated for each participant.

Group-comparisons were further performed at global, network, and brain region levels to quantify differences between ASD and TC groups. The relationship between temporospatial dynamic amplitude of low-frequency fluctuation variation alterations and clinical symptoms of ASD was finally explored by a support vector regression model. Relative to TC, we found enhanced tvALFF in visual network (Vis), somatomotor network (SMT), and salience/ventral attention network (SVA) of ASD, and weakened tvALFF in dorsal attention network (DAN) of ASD. Besides, ASD showed decreased svALFF in Vis, SVA, and limbic network (Limbic), and increased svALFF in DAN and default mode network (DMN).

Elevated tsvALFF was found in the Vis, SMT, and DMN of ASD. More importantly, the altered tsvALFF from the DMN can predict the symptom severity of ASD. These findings demonstrate altered temporospatial dynamics of the spontaneous brain activity in ASD and provide novel insights into the neural mechanism underlying ASD.