Abstract
Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental disorders that are reportedly characterized by aberrant neural networks. Recently developed multiscale entropy analysis (MSE) can characterize the complexity inherent in electroencephalography (EEG) dynamics over multiple temporal scales in the dynamics of neural networks. We encountered an 18-year-old man with ASD whose refractory catatonic obsessive–compulsive symptoms were improved dramatically after electroconvulsive therapy (ECT). In this clinical case study, we strove to clarify the neurophysiological mechanism of ECT in ASD by assessing EEG complexity using MSE. Along with ECT, the frontocentral region showed decreased EEG complexity at higher temporal scales, whereas the occipital region expressed an increase at lower temporal scales. Furthermore, these changes were associated with clinical improvement associated with the elevation of brain-derived neurotrophic factor, which is a molecular hypothesis of ECT, playing key roles in ASD pathogenesis. Changes in EEG complexity in a region-specific and temporal scale-specific manner that we found might reflect atypical EEG dynamics in ASD. Although MSE is not a direct approach to measuring neural connectivity and the results are from only a single case, they might reflect specific aberrant neural network activity and the therapeutic neurophysiological mechanism of ECT in ASD.
Highlights
Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental disorders that are complicated by the co-existence of and symptomatic overlap with other psychiatric disorders (Leyfer et al, 2006)
Our study examined only a single case and it is limited by its lack of comparison with healthy controls, results described in these previous reports agree at least partially with our findings
This report describes a remarkable decrease of EEG complexity with electroconvulsive therapy (ECT) associated with clinical improvement in patients with depression, but this was apparent only at lower temporal scales (Okazaki et al, 2013)
Summary
Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental disorders that are complicated by the co-existence of and symptomatic overlap with other psychiatric disorders (Leyfer et al, 2006). Results of many earlier functional studies have supported the notion that aberrant neural networks lie at the heart of ASD and obsessive–compulsive disorders (OCD). The recent nonlinear approaches to characterizing complex temporal dynamics have provided new insights into EEG dynamical complexity in mental disorders including ASD (Takahashi, 2013). To investigate the variation in physiological signals across multiple temporal scales, Costa et al (2002) introduced multiscale entropy analysis (MSE), which is calculated based on SampEn, in recognition of the likelihood that the dynamical complexity of biological signals might operate across a range of temporal scales. As a consequence, characterizing the EEG complexity using MSE might add another dimension to already identified neural dynamics of ASD [see a review by Billeci et al (2013)]. This study examines changes in EEG complexity with ECT using MSE and its relation to clinical outcomes
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