Most prior fMRI studies of intrinsic brain activity have used either spatial independent component analysis (sICA) or seed-based correlation approaches to define functional brain networks.
ARTIFICIAL ACADEMY 2 LAG FIX WINDOWS 10 SERIES
Although there is large consensus that altered brain circuitry underlies the atypical behavioral manifestations observed in ASD, the precise nature of these alterations continues to be debated.Ĭonventional analyses model resting-state fMRI time series as a combination of network processes that evolve over time. The authors take these findings as evidence of reduced network integration and differentiation across several brain networks in ASD. All of these previous studies used conventional measures of functional connectivity, including independent component analysis, seed-based functional connectivity, and whole-brain functional connectivity matrix computation. Another recent report using the Autism Brain Imaging Data Exchange (ABIDE) dataset provides evidence for globally reduced network cohesion and density and increased dispersion of networks in ASD compared with typically developing participants. More recent work with larger samples exploring wider age ranges, however, provides evidence for both hypoconnectivity and hyperconnectivity in ASD. Specifically, in the young adult age range of interest in the current study, there have been reports of null findings when comparing within- and between-network functional connectivity in clinical and neurotypical (NT) groups. The recent proliferation of resting state fMRI investigations in ASD has provided mixed evidence for impaired communication among brain network in the disorder. Multiple studies have sought to characterize disordered brain function using resting-state functional magnetic resonance imaging (rs-fMRI).
ASD has long been associated with impaired communication among brain networks. Atypical patterns were concentrated in salience, executive, visual, and default-mode network areas of the brain that have previously been implicated in the pathophysiology of the disorder.Īutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by repetitive behaviors, restricted interests, and impaired social interaction and communication skills. We found that ASD individuals had altered information flow patterns across brain regions. Using an ultra-fast neuroimaging procedure, we investigated communication across brain regions in adults with ASD compared with neurotypical (NT) individuals. Lay SummaryĪutism spectrum disorder (ASD) is characterized by atypical neurodevelopment. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc. Taken together, these results suggest that altered lag patterns indicating atypical spread of activity between large-scale functional brain networks may contribute to the ASD phenotype. 92.3% and 84.6% of the significant RSN pairs revealed shorter mean and median temporal lags in ASD versus NT, respectively. Alterations in lag patterns were concentrated in salience, executive, visual, and default-mode networks, supporting earlier findings of impaired brain connectivity in these regions in ASD. DLA analyses indicated that 10.8% of the 120 RSN pairs had statistically significant ( P-value <0.003) dynamic lag pattern differences that survived correction with surrogate data thresholding.
The dynamic lag pattern variation between each RSN pair was investigated using DLA, which measures time lag variation between each RSN pair combination and statistically defines how these lag patterns are altered between ASD and NT groups. Sixteen resting-state networks (RSNs) with the highest spatial correlation between NT individuals ( n = 20) and individuals with ASD ( n = 20) were analyzed. The use of 3D magnetic resonance encephalography data with repetition time = 100 msec enables highly accurate analysis of the spread of activity between brain networks. This study investigated whole-brain dynamic lag pattern variations between neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) by applying a novel technique called dynamic lag analysis (DLA).