Dysregulation of temporal dynamics of synchronous neural activity in adolescents on autism spectrum

Abstract

Autism spectrum disorder is increasingly understood to be based on atypical signal transfer among multiple interconnected networks in the brain. Relative temporal patterns of neural activity have been shown to underlie both the altered neurophysiology and the altered behaviors in a variety of neurogenic disorders. We assessed brain network dynamics variability in Autism Spectrum Disorders (ASD) using measures of synchronization (phase-locking) strength, and timing of synchronization and desynchronization of neural activity (desynchronization ratio) across frequency bands of resting state EEG. Our analysis indicated that fronto-parietal synchronization is higher in ASD, but with more short periods of desynchronization. It also indicates that the relationship between the properties of neural synchronization and behavior is different in ASD and typically developing populations. Recent theoretical studies suggest that neural networks with high desynchronization ratio have increased sensitivity to inputs. Our results point to the potential significance of this phenomenon to autistic brain. This sensitivity may disrupt production of an appropriate neural and behavioral responses to external stimuli. Cognitive processes dependent on integration of activity from multiple networks may be, as a result, particularly vulnerable to disruption. Lay SummaryParts of the brain can work together by synchronizing activity of the neurons. We recorded electrical activity of the brain in adolescents with autism spectrum disorder, and then compared the recording to that of their peers without the diagnosis. We found that in participants with autism, there were a lot of very short time periods of non-synchronized activity between frontal and parietal parts of the brain. Mathematical models show that the brain system with this kind of activity is very sensitive to external events. IntroductionHuman behavior in health and disease is undergirded by temporal synchronization of distributed networks, which allows for information processing (e.g., Buzsaki, 2006;Harris & Gordon, 2015). The difficulty in understanding of the temporal regulation of brain networks is partially due to the disparity of temporal scales for different types of neural data (e.g. between millisecond scale of brain-activity-driven EEG and second-to-minute scale of blood-flow-based fMRI).Experimental data from different modalities, such as EEG, MEG, and fMRI, indicate that better understanding of temporal dynamics of brain activity may help elucidate mechanisms of brain network organization in ASD population. During rest, ASD participants show increased coherence (or, static overconnectivity) in long-range brain networks (Buckley et al., 2015). However, the increase in coherence coincides with longer dwell times in a globally-disconnected state (Rashid et al., 2018), and increased variability in connectivity over time (Mash et al., 2019). Analyses of EEG and MEG recordings, which provide higher temporal resolution, indicate longrange functional underconnectivity (O'Reilly ...

Noise Effect on the Temporal Patterns of Neural Synchrony

“…The observed phenomena are interesting to consider in the context of the experimental results on the temporal patterning of neural synchrony in the brain. Multiple experiments show that moderately synchronized brain activity at rest has a very specific temporal pattern: most of the desynchronizations are very short, lasting for just one cycle of oscillation (Park et al, 2010;Ahn and Rubchinsky, 2013;Ahn et al, 2014;Malaia et al, 2020; see also Introduction). This study suggests that noise may be one of the factors promoting this regime of desynchronization dynamics.…”

“…These techniques revealed that neural synchrony in the brain shows a very specific patterning: it is interrupted by potentially numerous but very short desynchronization episodes. This was observed in different species (rodents, humans), different brain signals (spikes, LFPs, EEG), different brain areas (cortex, hippocampus, basal ganglia), and different brain states (healthy and diseased), see (Park et al, 2010;Ahn and Rubchinsky, 2013;Ahn et al, 2014;Ratnadurai-Giridharan et al, 2016;Malaia et al, 2020) for the different experiments. The distribution of desynchronization durations is altered under different conditions.…”

“…The distribution of desynchronization durations is altered under different conditions. It was found to be related to the severity of symptoms of Parkinson's disease, addiction, and autism (Ahn et al, 2014;Ahn et al, 2018;Malaia et al, 2020, Dos Santos Lima et al, 2020. However, the mode of this distribution is always one (see studies mentioned above).…”

Spike-Timing Dependent Plasticity Effect on the Temporal Patterning of Neural Synchronization

“…This phenomenon was observed in the synchrony between local field potentials (LFPs) and spikes in different parts of the basal ganglia and EEG in Parkinson's disease (Park et al, 2010;Ratnadurai-Giridharan et al, 2016;Ahn et al, 2018), in synchronization between LFPs recorded in the prefrontal cortex and hippocampus of normal and amphetamine-sensitized mice (Ahn et al, 2014), in EEG of healthy human subjects (Ahn and Rubchinsky, 2013), and in EEG in autism spectrum disorders (Malaria et al, 2020). The differences in the temporal patterning are correlated with certain behavioral features but the prevalence of short desynchronizations persisted nevertheless (Ahn et al, 2014(Ahn et al, , 2018Malaia et al, 2020). Therefore, short desynchronizations may be functionally important and the properties and mechanisms of desynchronization durations merit exploration.…”

LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder

“…According to Liu [5], the frequency of autistic children's brain waves shows a decrease in both their functional complexity and synchrony with the brain development. Studies carried out in [6][7][8] observed that there were noticeable differences found between the right and the central side of brain areas. Previous studies have shown that there is a relationship between autistic and non-autistic brain EEG signals [9][10][11][12][13].…”