Abstract:BackgroundAutism spectrum disorders (ASD) are increasingly prevalent and have a significant impact on the lives of patients and their families. Currently, the diagnosis is determined by clinical judgment and no definitive physiological biomarker for ASD exists. Quantitative biomarkers obtainable from clinical neuroimaging data – such as the scalp electroencephalogram (EEG) - would provide an important aid to clinicians in the diagnosis of ASD. The interpretation of prior studies in this area has been limited b… Show more
“…The results on the functional connectivity in ASD are currently controversial [ 19 , 51 ]. The abnormal neural couplings change during the development of the nervous system.…”
Section: Discussionmentioning
confidence: 99%
“…Each method has its own advantage and disadvantage. Several studies reported that most linear and nonlinear association measurements work equally well on sensor and electrode brain data [ 51 , 55 , 56 ]. These findings make it difficult to reach consensus on the recommended metric in the brain graph field [ 52 , 57 ].…”
Section: Discussionmentioning
confidence: 99%
“…The results of association measurements without time delay and phase difference should be interpreted very carefully. Matlis et al [ 51 ] performed network inference by the cross correlation, and by the weighted phase lag index [ 62 ], i.e., a method which is less affected by electromagnetic field spread, to reproduce a part of the cross correlation results. In this study, authors improved the reliability of the cross correlation by alternative association measurement.…”
Autism spectrum disorder (ASD) is a developmental disorder that involves developmental delays. It has been hypothesized that aberrant neural connectivity in ASD may cause atypical brain network development. Brain graphs not only describe the differences in brain networks between clinical and control groups, but also provide information about network development within each group. In the present study, graph indices of brain networks were estimated in children with ASD and in typically developing (TD) children using magnetoencephalography performed while the children viewed a cartoon video. We examined brain graphs from a developmental point of view, and compared the networks between children with ASD and TD children. Network development patterns (NDPs) were assessed by examining the association between the graph indices and the raw scores on the achievement scale or the age of the children. The ASD and TD groups exhibited different NDPs at both network and nodal levels. In the left frontal areas, the nodal degree and efficiency of the ASD group were negatively correlated with the achievement scores. Reduced network connections were observed in the temporal and posterior areas of TD children. These results suggested that the atypical network developmental trajectory in children with ASD is associated with the development score rather than age.
“…The results on the functional connectivity in ASD are currently controversial [ 19 , 51 ]. The abnormal neural couplings change during the development of the nervous system.…”
Section: Discussionmentioning
confidence: 99%
“…Each method has its own advantage and disadvantage. Several studies reported that most linear and nonlinear association measurements work equally well on sensor and electrode brain data [ 51 , 55 , 56 ]. These findings make it difficult to reach consensus on the recommended metric in the brain graph field [ 52 , 57 ].…”
Section: Discussionmentioning
confidence: 99%
“…The results of association measurements without time delay and phase difference should be interpreted very carefully. Matlis et al [ 51 ] performed network inference by the cross correlation, and by the weighted phase lag index [ 62 ], i.e., a method which is less affected by electromagnetic field spread, to reproduce a part of the cross correlation results. In this study, authors improved the reliability of the cross correlation by alternative association measurement.…”
Autism spectrum disorder (ASD) is a developmental disorder that involves developmental delays. It has been hypothesized that aberrant neural connectivity in ASD may cause atypical brain network development. Brain graphs not only describe the differences in brain networks between clinical and control groups, but also provide information about network development within each group. In the present study, graph indices of brain networks were estimated in children with ASD and in typically developing (TD) children using magnetoencephalography performed while the children viewed a cartoon video. We examined brain graphs from a developmental point of view, and compared the networks between children with ASD and TD children. Network development patterns (NDPs) were assessed by examining the association between the graph indices and the raw scores on the achievement scale or the age of the children. The ASD and TD groups exhibited different NDPs at both network and nodal levels. In the left frontal areas, the nodal degree and efficiency of the ASD group were negatively correlated with the achievement scores. Reduced network connections were observed in the temporal and posterior areas of TD children. These results suggested that the atypical network developmental trajectory in children with ASD is associated with the development score rather than age.
“…Some estimates suggest that up to ~ 50–80% of individuals with AS meet diagnostic criteria for autism [ 18 ]; however, these estimates vary greatly due to the difficulties assessing autism with standardized clinical tests in AS individuals. Traditionally, individuals with autism were thought to have comparatively high coherence between nearby electrode pairs (local hyperconnectivity) and low coherence between long-distance signals (global hypoconnectivity) [ 9 – 13 ], but this view has been challenged and become more nuanced in recent years [ 14 – 17 , 25 ]. Thus, although specific connectivity patterns remain unclear, there is widespread consensus that EEG coherence is altered in autism.…”
BackgroundAngelman syndrome (AS) is a neurodevelopmental disorder characterized by intellectual disability, speech and motor impairments, epilepsy, abnormal sleep, and phenotypic overlap with autism. Individuals with AS display characteristic EEG patterns including high-amplitude rhythmic delta waves. Here, we sought to quantitatively explore EEG architecture in AS beyond known spectral power phenotypes. We were motivated by studies of functional connectivity and sleep spindles in autism to study these EEG readouts in children with AS.MethodsWe analyzed retrospective wake and sleep EEGs from children with AS (age 4–11) and age-matched neurotypical controls. We assessed long-range and short-range functional connectivity by measuring coherence across multiple frequencies during wake and sleep. We quantified sleep spindles using automated and manual approaches.ResultsDuring wakefulness, children with AS showed enhanced long-range EEG coherence across a wide range of frequencies. During sleep, children with AS showed increased long-range EEG coherence specifically in the gamma band. EEGs from children with AS contained fewer sleep spindles, and these spindles were shorter in duration than their neurotypical counterparts.ConclusionsWe demonstrate two quantitative readouts of dysregulated sleep composition in children with AS—gamma coherence and spindles—and describe how functional connectivity patterns may be disrupted during wakefulness. Quantitative EEG phenotypes have potential as biomarkers and readouts of target engagement for future clinical trials and provide clues into how neural circuits are dysregulated in children with AS.Electronic supplementary materialThe online version of this article (10.1186/s13229-018-0214-8) contains supplementary material, which is available to authorized users.
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