Band-specific atypical functional connectivity pattern in childhood autism spectrum disorder

Takahashi, Tetsuya; Yamanishi, Tomonori; Nobukawa, Sou; Kasakawa, Shinya; Yoshimura, Yuko; Hiraishi, Hirotoshi; Hasegawa, Chiaki; Ikeda, Takashi; Hirosawa, Tetsu; Munesue, Toshio; Higashida, Haruhiro; Minabe, Yoshio; Kikuchi, Mitsuru

doi:10.1016/j.clinph.2017.05.010

Cited by 30 publications

(42 citation statements)

References 71 publications

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“…reported hypo‐ and hyper‐connectivity as being partially attributable to the age of the participants examined and hypothesized that in children with ASD, there is an abnormal developmental pattern that shifts from intrinsic hyper‐connectivity to hypo‐connectivity during the pubertal period. In a previous study using graph theory, enhanced and reduced small‐worldness, representing the efficiency of information processing, were observed in ASD children (aged 40–70 months) …”

Section: Studies Of Auditory Steady‐state Responsementioning

confidence: 89%

Auditory steady‐state response at 20 Hz and 40 Hz in young typically developing children and children with autism spectrum disorder

Kudoh

Ikeda

Takahashi

et al. 2020

Psychiatry Clin Neurosci

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Aim The early detection of autistic tendencies in children is essential for providing proper care and education. The auditory steady‐state response (ASSR) provides a passive, non‐invasive technique for assessing neural synchrony at specific response frequencies in many mental disorders, including autism spectrum disorder (ASD), but few studies have investigated its use in young children. This study investigated the ASSR at 20 Hz and 40 Hz in typically developing (TD) children and children with ASD aged 5–7 years. Methods The participants were 23 children with ASD and 32 TD children aged 5–7 years. Using a custom‐made magnetoencephalography device, we measured ASSR at 20 Hz and 40 Hz, compared the results between groups, and evaluated the association with intellectual function as measured by Kaufmann Assessment Battery for Children. Results Responses to 20 Hz and 40 Hz were clearly detected in both groups with no significant difference identified. Consistent with previous findings, right dominance of the 40‐Hz ASSR was observed in both groups. In the TD children, the right‐side 40‐Hz ASSR was correlated with age. The Kaufmann Assessment Battery for Children score was correlated with the left‐side 40‐Hz ASSR in both groups. Conclusion Right‐dominant ASSR was successfully detected in young TD children and children with ASD. No difference in ASSR was observed between the children with ASD and the TD children, although the right‐side 40‐Hz ASSR increased with age only in the TD children. Left‐side 40‐Hz ASSR was associated with intelligence score in both groups.

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Section: Studies Of Auditory Steady‐state Responsementioning

confidence: 89%

Auditory steady‐state response at 20 Hz and 40 Hz in young typically developing children and children with autism spectrum disorder

Kudoh

Ikeda

Takahashi

et al. 2020

Psychiatry Clin Neurosci

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“…Furthermore, using a short epoch length cannot capture behaviors with slow frequency components. To balance them, for PLI analysis the continuous 50 s (10,000 data points) was divided into 10 epochs of 5 s (61,62).…”

Section: Eeg Recordingsmentioning

confidence: 99%

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease

et al. 2020

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Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among widespread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-andregional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods

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“… 44–47 It is noteworthy that participants with autism spectrum disorders having epilepsy 44–46 or showing IEDs in their EEG 47 were excluded from analyses in those studies. Among the remaining three studies, 48–50 however, two reports described non-significant difference in C (in terms of coherence 48 or PLI-based 49 graphs) between children with and without autism spectrum disorders. 48 , 49 One report described higher C (in terms of weighted-PLI-based graphs) in adolescents with autism spectrum disorders compared to age-matched healthy controls.…”

Section: Discussionmentioning

confidence: 95%

“…Among the remaining three studies, 48–50 however, two reports described non-significant difference in C (in terms of coherence 48 or PLI-based 49 graphs) between children with and without autism spectrum disorders. 48 , 49 One report described higher C (in terms of weighted-PLI-based graphs) in adolescents with autism spectrum disorders compared to age-matched healthy controls. 50 In contrast to the studies described above, 44–47 participants with autism spectrum disorders having co-occurring epilepsy 48 , 49 or recordings containing IEDs 50 were not explicitly excluded from those three studies.…”

Section: Discussionmentioning

confidence: 95%

“…It is noteworthy that one study including children with autism spectrum disorder, but not explicitly excluding children with autism spectrum disorder having epilepsy, found no significant difference in either C or L in an autism spectrum disorder group 48 compared to healthy controls. Similarly, after Takahashi et al 49 analysed children with autism spectrum disorder, but without excluding children who had epilepsy, they reported that neither the difference in C nor in L was significant. Here again, although direct comparison of these results is difficult, the existing literature suggests that brain networks of autism spectrum disorder show lower C and increased L over widely various frequencies in a broad age range, but they might depend on the presence of epilepsy.…”

Section: Introductionmentioning

confidence: 99%

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Epileptiform discharges relate to altered functional brain networks in autism spectrum disorders

Hirosawa

Soma

et al. 2021

Brain Communications

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Many individuals with autism spectrum disorders have comorbid epilepsy. Even in the absence of observable seizures, interictal epileptiform discharges are common in individuals with autism spectrum disorders. However, how these interictal epileptiform discharges are related to autistic symptomatology remains unclear. This study used magnetoencephalography to investigate the relation between interictal epileptiform discharges and altered functional brain networks in children with autism spectrum disorders. Instead of particularly addressing individual brain regions, we specifically examine network properties. For this case-control study, we analysed 70 children with autism spectrum disorders (52 boys, 18 girls, 38–92 months old) and 19 typically developing children (16 boys, 3 girls, 48–88 months old). After assessing the participants’ social reciprocity using the Social Responsiveness Scale, we constructed graphs of functional brain networks from frequency band separated task-free magnetoencephalography recordings. Nodes corresponded to Desikan–Killiany atlas-based 68 brain regions. Edges corresponded to phase lag index values between pairs of brain regions. To elucidate the effects of the existence of interictal epileptiform discharges on graph metrics, we matched each of three pairs from three groups (typically developing children, children with autism spectrum disorders who had interictal epileptiform discharges and those who did not) in terms of age and sex. We used a coarsened exact matching algorithm and applied adjusted regression analysis. We also investigated the relation between social reciprocity and the graph metric. Results show that, in children with autism spectrum disorders, the average clustering coefficient in the theta band was significantly higher in children who had interictal epileptiform discharges. Moreover, children with autism spectrum disorders who had no interictal epileptiform discharges had a significantly lower average clustering coefficient in the theta band than typically developing children had. However, the difference between typically developing children and children with autism spectrum disorder who had interictal epileptiform discharges was not significant. Furthermore, the higher average clustering coefficient in the theta band corresponded to severe autistic symptoms in children with autism spectrum disorder who had interictal epileptiform discharges. However, the association was not significant in children with autism spectrum disorders who had no interictal epileptiform discharge. In conclusion, results demonstrate that alteration of functional brain networks in children with autism spectrum disorders depends on the existence of interictal epileptiform discharges. Interictal epileptiform discharges might ‘normalize’ the deviation of altered brain networks in autism spectrum disorders, increasing the clustering coefficient. However, when the effect exceeds tolerance, it actually exacerbates autistic symptoms.

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Band-specific atypical functional connectivity pattern in childhood autism spectrum disorder

Cited by 30 publications

References 71 publications

Auditory steady‐state response at 20 Hz and 40 Hz in young typically developing children and children with autism spectrum disorder

Auditory steady‐state response at 20 Hz and 40 Hz in young typically developing children and children with autism spectrum disorder

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease

Epileptiform discharges relate to altered functional brain networks in autism spectrum disorders

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