Atypical EEG complexity in autism spectrum conditions: A multiscale entropy analysis

Catarino, Ana; Churches, Owen; Baron‐Cohen, Simon; Andrade, Alexandre; Ring, Howard

doi:10.1016/j.clinph.2011.05.004

Cited by 220 publications

(169 citation statements)

References 60 publications

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“…MSE estimates showed scale-dependent age differences (Figure 1). Development was accompanied by a general increase of entropy values at lower scales (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) ms; ANOVA at t = 8 ms, F 4,181 = 7.5, p < 0.001, Bonferroni corrected for comparisons at multiple time scales) and a general decrease at higher scales (68-72 ms; at t = 80 ms, F 4,181 = 5.64, p < 0.001). Pairwise comparisons (t-test, α = 5% Bonferroni corrected) revealed that, at lower scales (t = 8 ms), the two youngest age bins differed from each other and from all other age bins; at higher scales (t = 80 ms), all pairwise comparisons were significant aside those contrasting the two age bins within the youngest and oldest pairs.…”

Section: Resultsmentioning

confidence: 99%

Wide Range Multiscale Entropy Changes through Development

Polizzotto

Takahashi

Walker

et al. 2015

Entropy

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How variability in the brain's neurophysiologic signals evolves during development is important for a global, system-level understanding of brain maturation and its disturbance in neurodevelopmental disorders. In the current study, we use multiscale entropy (MSE), a measure that has been related to signal complexity, to investigate how this variability evolves during development across a broad range of temporal scales. We computed MSE, standard deviation (STD) and standard spectral analyses on resting EEG from 188 healthy individuals aged 8-22 years old. We found age-related increases in entropy at lower scales (<~20 ms) and decreases in entropy at higher scales (~60-80 ms). Decreases in the overall signal STD were anticorrelated with entropy, especially in the lower scales, where regression analyses showed substantial covariation of observed changes. Our findings document for the first time the scale dependency of developmental changes from childhood to early adulthood, challenging a parsimonious MSE-based account of brain maturation along a unidimensional, complexity measure. At the level of analysis permitted by electroencephalography (EEG), MSE could capture critical spatiotemporal variations in the role of noise in the brain. However, interpretations critically rely on defining how signal STD affects MSE properties.

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Section: Resultsmentioning

confidence: 99%

Wide Range Multiscale Entropy Changes through Development

Polizzotto

Takahashi

Walker

et al. 2015

Entropy

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“…The alteration in the development of normal brain connectivity may lead to a variety of developmental/neuropsychiatric disorders, such as ASD and attention deficient/hyperactivity disorders (ADHD) [40]. Studies comparing MSE between normally-developing children and children with neuropsychiatric disorders have revealed information with potential diagnostic, therapeutic, or prognostic value [9][10][11]18,19].…”

Section: Developmental and Neuropsychiatric Disordersmentioning

confidence: 99%

The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders

Chu

Chang

Shieh

et al. 2017

Entropy

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Electroencephalography (EEG) is frequently used in functional neurological assessment of children with neurological and neuropsychiatric disorders. Multiscale entropy (MSE) can reveal complexity in both short and long time scales and is more feasible in the analysis of EEG. Entropy-based estimation of EEG complexity is a powerful tool in investigating the underlying disturbances of neural networks of the brain. Most neurological and neuropsychiatric disorders in childhood affect the early stage of brain development. The analysis of EEG complexity may show the influences of different neurological and neuropsychiatric disorders on different regions of the brain during development. This article aims to give a brief summary of current concepts of MSE analysis in pediatric neurological and neuropsychiatric disorders. Studies utilizing MSE or its modifications for investigating neurological and neuropsychiatric disorders in children were reviewed. Abnormal EEG complexity was shown in a variety of childhood neurological and neuropsychiatric diseases, including autism, attention deficit/hyperactivity disorder, Tourette syndrome, and epilepsy in infancy and childhood. MSE has been shown to be a powerful method for analyzing the non-linear anomaly of EEG in childhood neurological diseases. Further studies are needed to show its clinical implications on diagnosis, treatment, and outcome prediction.

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“…Catarino et al found significantly decreased multiscale entropy in ASDdiagnosed participants compared to controls [33]. Bosl et al, found the same trend in multiscale entropy in infants who were not old enough to exhibit the defining characteristics of ASD.…”

Section: Introductionmentioning

confidence: 62%

“…Summing derived values over many scales, such as with the complexity index, includes some information from every scale. In biological systems, a lower level of chaotic dynamics is often associated with pathological conditions [58][59][60][61][62].…”

Section: Scalp Location Of Most Significant Featuresmentioning

confidence: 99%

Nonlinear EEG biomarker profiles for autism and absence epilepsy

Bosl

Loddenkemper

Nelson

2017

Neuropsychiatr Electrophysiol

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Background: Although autism and epilepsy are considered to be different disorders, epileptiform EEG activity is common in people with autism even when overt seizures are not present. The relatively high comorbidity between autism and all epilepsy syndromes suggests the possibility of common underlying neurophysiological mechanisms. Although many different epilepsies may be comorbid with autism, absence epilepsy is a generalized epilepsy syndrome with seizures that appear as staring spells, with no motor signs and no focal lesions, making it more difficult to diagnose. Application of nonlinear methods for EEG signal analysis may enable characterization of brain activity that can help to delineate neurophysiological commonalities and differences between autism and epilepsy. Multiscale entropy and recurrence quantitative analysis (RQA) were computed from EEG signals derived from children with autism or absence epilepsy and compared with the goal of finding significant and potentially clinically useful biomarkers neurophysiological differences between these two childhood disorders. Methods: Multiscale entropy and a multiscale version of RQA were computed from EEG data obtained from 92 children were collected in two different settings at Boston Children's Hospital. Short segments of alert resting state EEG were selected for analysis. A complexity index derived from entropy and RQA methods was computed from each of 19 standard EEG channels for all subjects using publicly available software. Statistical comparisons were made between the groups. Machine learning classifiers were also used to determine which derived features were most significantly different among the groups, and to determine classification specificity and sensitivity. Results: Significant differences were found between absence, autism, and control groups in a number of different scalp locations and the values of complexity index. Autism values appeared to be intermediate between epilepsy and control in many locations, and differences between controls and absence patients were more widely distributed across scalp locations. Classification algorithms were able to distinguish absence epilepsy and autism cases from controls with high (>95%) accuracy. Importantly, two independent control groups, although they were derived from different settings and with different equipment were statistically indistinguishable.

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Atypical EEG complexity in autism spectrum conditions: A multiscale entropy analysis

Cited by 220 publications

References 60 publications

Wide Range Multiscale Entropy Changes through Development

Wide Range Multiscale Entropy Changes through Development

The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders

Nonlinear EEG biomarker profiles for autism and absence epilepsy

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