High-amplitude fast activity in EEG: An early diagnostic marker in children with beta-propeller protein-associated neurodegeneration (BPAN)

Kidokoro, Hiroyuki; Yamamoto, Hiroyuki; Motobayashi, Mitsuo; Miyamoto, Yusaku; Nakata, T.; Takano, Kyoko; Shiba, Naoko; Okai, Yu; Tanaka, Masaharu; Sakaguchi, Yoko; Maki, Yuki; Kawaguchi, Masahiro; Suzuki, Takeshi; Muramatsu, Kazuhiro; Natsume, Jun

doi:10.1016/j.clinph.2020.06.006

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…The relationship between different forms of hyperexcitability and the development of abnormal neuronal rhythms is not yet fully understood, and the generation of excessive rhythmic activity in different frequency bands, with or without epileptic discharges, may support the diagnosis of genetic syndromes ( Alfei et al, 2014 ). For example, high-amplitude rhythmic slow-wave activity has long been recognized as a feature of Angelman Syndrome; increased fast rhythms, beta and gamma waves, have been extensively reported in Fragile X Syndrome ( Goodspeed et al, 2023 ), and high amplitude fast activity is often seen in patients with Beta-propeller Protein-Associated Neurodegeneration (BPAN), ( Kidokoro et al, 2020 ), and Lissencephaly Syndrome ( Alfei, et al, 2014 ). These observations suggest that increased rhythmic alpha-beta activity may be an EEG pattern reflecting undetectable neuroradiological malformations or dysfunctional circuitry ( Alfei et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Developmental outcome of electroencephalographic findings in SYNGAP1 encephalopathy

Ribeiro-Constante,

Tristán-Noguero,

Martínez Calvo

et al. 2024

Front. Cell Dev. Biol.

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SYNGAP1 haploinsufficiency results in a developmental and epileptic encephalopathy (DEE) causing generalized epilepsies accompanied by a spectrum of neurodevelopmental symptoms. Concerning interictal epileptiform discharges (IEDs) in electroencephalograms (EEG), potential biomarkers have been postulated, including changes in background activity, fixation-off sensitivity (FOS) or eye closure sensitivity (ECS). In this study we clinically evaluate a new cohort of 36 SYNGAP1-DEE individuals. Standardized questionnaires were employed to collect clinical, electroencephalographic and genetic data. We investigated electroencephalographic findings, focusing on the cortical distribution of interictal abnormalities and their changes with age. Among the 36 SYNGAP1-DEE cases 18 presented variants in the SYNGAP1 gene that had never been previously reported. The mean age of diagnosis was 8 years and 8 months, ranging from 2 to 17 years, with 55.9% being male. All subjects had global neurodevelopmental/language delay and behavioral abnormalities; 83.3% had moderate to profound intellectual disability (ID), 91.7% displayed autistic traits, 73% experienced sleep disorders and 86.1% suffered from epileptic seizures, mainly eyelid myoclonia with absences (55.3%). A total of 63 VEEGs were revised, observing a worsening of certain EEG findings with increasing age. A disorganized background was observed in all age ranges, yet this was more common among older cases. The main IEDs were bilateral synchronous and asynchronous posterior discharges, accounting for ≥50% in all age ranges. Generalized alterations with maximum amplitude in the anterior region showed as the second most frequent IED (≥15% in all age ranges) and were also more common with increasing age. Finally, diffuse fast activity was much more prevalent in cases with 6 years or older. To the best of our knowledge, this is the first study to analyze EEG features across different age groups, revealing an increase in interictal abnormalities over infancy and adolescence. Our findings suggest that SYNGAP1 haploinsufficiency has complex effects in human brain development, some of which might unravel at different developmental stages. Furthermore, they highlight the potential of baseline EEG to identify candidate biomarkers and the importance of natural history studies to develop specialized therapies and clinical trials.

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Section: Discussionmentioning

confidence: 99%

Developmental outcome of electroencephalographic findings in SYNGAP1 encephalopathy

Ribeiro-Constante,

Tristán-Noguero,

Martínez Calvo

et al. 2024

Front. Cell Dev. Biol.

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“…So, BPAN without RTT‐like features may be more likely to have DEE than BPAN with RTT‐like features. Furthermore, Kidokoro et al in a recent study described that the electroencephalography of children with BPAN showed diffuse high‐amplitude fast activity (HAFA) continuously as early as 1 year of age (Kidokoro et al, 2020). While this analysis was not done in our case, we believe it may provide clues for diagnosis of BPAN in early childhood, and we are interested in examining in the future or not the patients with BPAN show HAFA present RTT‐like features.…”

Section: Discussion and Reviewmentioning

confidence: 99%

Beta‐propeller protein–associated neurodegeneration presenting Rett‐like features: A case report and literature review

Kano

Yamanaka

Muramatsu

et al. 2020

American J of Med Genetics Pt A

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Several patients with beta‐propeller protein–associated neurodegeneration (BPAN)/static encephalopathy with neurodegeneration in adulthood have been reported to present Rett syndrome (RTT)‐like features. This report presents an individual with BPAN showing clinical features of RTT. Psychomotor delay and epilepsy onset were noted at 1 year, and regression began at 4 years. Screening of the methyl‐CpG binding protein 2 (MECP2) did not show variants. At 22 years, basal ganglia iron deposits were found on magnetic resonance imaging (MRI), and the WD‐domain repeat 45 gene (WDR45) variant was identified. Review of the literature showed that BPAN with RTT‐like features is associated with more epileptic seizures and less deceleration of head growth, breathing irregularities, and cold extremities than classic RTT with MECP2 variants. These clinical presentations may provide clues for differentiating between these two disorders. However, both WDR45 and MECP2 should be screened in patients presenting a clinical picture of RTT without specific MRI findings of BPAN.

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“…Not only can it confirm the diagnosis (it can also clarify the type of epilepsy), but it can have a role in making therapeutic decisions (e.g., whether to stop treatment in patients without seizures) as well as prognostic significance (e.g., evaluating critically ill patients for possible epileptic status or development of encephalopathy) ( Trinka and Leitinger, 2022 ). Apart from mentioned diseases, this method is also widely used in the early diagnosis of dementia ( Al-Qazzaz et al, 2014 ), Mb Alzheimer’s ( Stam et al, 2023 ), brain tumors ( Ajinkya et al, 2021 ), sleep disorders ( Kaskie and Ferrarelli, 2019 ; Steiger and Pawlowski, 2019 ), as well as the most severe neurodegenerative diseases ( Kidokoro et al, 2020 ). Artificial intelligence methods show immense potential in detection of different medical conditions.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection

Pilcevic,

Djuric Jovicic,

Antonijevic

et al. 2023

Front. Physiol.

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Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including RNNs and artificial neural networks (ANNs), hold great promise, their effectiveness heavily relies on the initial values assigned to hyperparameters, which are crucial for their performance for concrete assignment. To tune RNN performance, the selection of hyperparameters is approached as a typical optimization problem, and metaheuristic algorithms are employed to further enhance the process. The modified hybrid sine cosine algorithm has been developed and used to further improve hyperparameter optimization. To facilitate testing, publicly available real-world EEG data is utilized. A dataset is constructed using captured data from healthy and archived data from patients confirmed to be affected by epilepsy, as well as data captured during an active seizure. Two experiments have been conducted using generated dataset. In the first experiment, models were tasked with the detection of anomalous EEG activity. The second experiment required models to segment normal, anomalous activity as well as detect occurrences of seizures from EEG data. Considering the modest sample size (one second of data, 158 data points) used for classification models demonstrated decent outcomes. Obtained outcomes are compared with those generated by other cutting-edge metaheuristics and rigid statistical validation, as well as results’ interpretation is performed.

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High-amplitude fast activity in EEG: An early diagnostic marker in children with beta-propeller protein-associated neurodegeneration (BPAN)

Cited by 6 publications

References 11 publications

Developmental outcome of electroencephalographic findings in SYNGAP1 encephalopathy

Developmental outcome of electroencephalographic findings in SYNGAP1 encephalopathy

Beta‐propeller protein–associated neurodegeneration presenting Rett‐like features: A case report and literature review

Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection

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