Quantitative EEG Analysis in Angelman Syndrome: Candidate Method for Assessing Therapeutics

2020 ◽  
pp. 155005942097309
Author(s):  
Luis A. Martinez ◽  
Heather A. Born ◽  
Sarah Harris ◽  
Angelique Regnier-Golanov ◽  
Joseph C. Grieco ◽  
...  

The goal of these studies was to use quantitative (q)EEG techniques on data from children with Angelman syndrome (AS) using spectral power analysis, and to evaluate this as a potential biomarker and quantitative method to evaluate therapeutics. Although characteristic patterns are evident in visual inspection, using qEEG techniques has the potential to provide quantitative evidence of treatment efficacy. We first assessed spectral power from baseline EEG recordings collected from children with AS compared to age-matched neurotypical controls, which corroborated the previously reported finding of increased total power driven by elevated delta power in children with AS. We then retrospectively analyzed data collected during a clinical trial evaluating the safety and tolerability of minocycline (3 mg/kg/d) to compare pretreatment recordings from children with AS (4-12 years of age) to EEG activity at the end of treatment and following washout for EEG spectral power and epileptiform events. At baseline and during minocycline treatment, the AS subjects demonstrated increased delta power; however, following washout from minocycline treatment the AS subjects had significantly reduced EEG spectral power and epileptiform activity. Our findings support the use of qEEG analysis in evaluating AS and suggest that this technique may be useful to evaluate therapeutic efficacy in AS. Normalizing EEG power in AS therefore may become an important metric in screening therapeutics to gauge overall efficacy. As therapeutics transition from preclinical to clinical studies, it is vital to establish outcome measures that can quantitatively evaluate putative treatments for AS and neurological disorders with distinctive EEG patterns.

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A310-A310
Author(s):  
Nishitha Hosamane ◽  
Yuval Levin ◽  
Taylor McNair ◽  
Michael Sidorov

Abstract Introduction Angelman syndrome (AS) is a neurodevelopmental disorder resulting from decreased expression of the maternal copy of the imprinted UBE3A gene on chromosome 15. This disorder is characterized by intellectual disability, impaired speech and motor skills, and sleep abnormalities but currently lacks any treatment. However, mouse models have shown that un-silencing the dormant paternal copy of UBE3A has been an effective mechanism to restore the functionality of the UBE3A protein, thus clinical trials using this approach are on the near horizon. Developing biomarkers is essential for assessing responses to treatment when clinical trials begin, and quantitative EEG analysis has shown great promise as a biomarker for AS. Methods Here, we sought to define EEG biomarkers directly linked to sleep impairments seen in up to 90% of individuals with AS (Trickett). We analyzed nine overnight sleep studies from patients with AS with age and sex matched Down syndrome and neurotypical controls. We specifically examined low-frequency delta rhythms and sleep spindles during NREM sleep. Results We confirmed that low- delta rhythms are increased during overnight sleep in AS, and that this biomarker appears more reliable than possible changes in sleep spindles. Conclusion Our results suggest that quantitative measurement of delta rhythms during sleep can be used as a potential biomarker for treatments in Angelman syndrome clinical trials. Support (if any):


2021 ◽  
Vol 11 (3) ◽  
pp. 330
Author(s):  
Dalton J. Edwards ◽  
Logan T. Trujillo

Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4–7 Hz, alpha: 8–13 Hz, low beta: 14–20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.


Author(s):  
Jacopo Lanzone ◽  
Lorenzo Ricci ◽  
Mario Tombini ◽  
Marilisa Boscarino ◽  
Oriano Mecarelli ◽  
...  

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A264-A264
Author(s):  
J Yoon ◽  
E Lee ◽  
S Lee ◽  
K Jung ◽  
S Park ◽  
...  

2013 ◽  
Vol 71 (12) ◽  
pp. 937-942 ◽  
Author(s):  
Aline Souza Marques da Silva Braga ◽  
Bruno Della Ripa Rodrigues Assis ◽  
Jamil Thiago Rosa Ribeiro ◽  
Patricia Maria Sales Polla ◽  
Breno Jose Hulle Pereira ◽  
...  

Objective To investigate the use of quantitative EEG (qEEG) in patients with acute encephalopathies (AEs) and EEG background abnormalities. Method Patients were divided into favorable outcome (group A, 43 patients) and an unfavorable outcome (group B, 5 patients). EEGLAB software was used for the qEEG analysis. A graphic of the spectral power from all channels was generated for each participant. Statistical comparisons between the groups were performed. Results In group A, spectral analysis revealed spectral peaks (theta and alpha frequency bands) in 84% (38/45) of the patients. In group B, a spectral peak in the delta frequency range was detected in one patient. The remainder of the patients in both groups did not present spectral peaks. Statistical analysis showed lower frequencies recorded from the posterior electrodes in group B patients. Conclusion qEEG may be useful in the evaluations of patients with AEs by assisting with the prognostic determination.


2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Janne Kananen ◽  
Heta Helakari ◽  
Vesa Korhonen ◽  
Niko Huotari ◽  
Matti Järvelä ◽  
...  

Abstract Resting-state functional MRI has shown potential for detecting changes in cerebral blood oxygen level-dependent signal in patients with epilepsy, even in the absence of epileptiform activity. Furthermore, it has been suggested that coefficient of variation mapping of fast functional MRI signal may provide a powerful tool for the identification of intrinsic brain pulsations in neurological diseases such as dementia, stroke and epilepsy. In this study, we used fast functional MRI sequence (magnetic resonance encephalography) to acquire ten whole-brain images per second. We used the functional MRI data to compare physiological brain pulsations between healthy controls (n = 102) and patients with epilepsy (n = 33) and furthermore to drug-naive seizure patients (n = 9). Analyses were performed by calculating coefficient of variation and spectral power in full band and filtered sub-bands. Brain pulsations in the respiratory-related frequency sub-band (0.11–0.51 Hz) were significantly (P < 0.05) increased in patients with epilepsy, with an increase in both signal variance and power. At the individual level, over 80% of medicated and drug-naive seizure patients exhibited areas of abnormal brain signal power that correlated well with the known clinical diagnosis, while none of the controls showed signs of abnormality with the same threshold. The differences were most apparent in the basal brain structures, respiratory centres of brain stem, midbrain and temporal lobes. Notably, full-band, very low frequency (0.01–0.1 Hz) and cardiovascular (0.8–1.76 Hz) brain pulses showed no differences between groups. This study extends and confirms our previous results of abnormal fast functional MRI signal variance in epilepsy patients. Only respiratory-related brain pulsations were clearly increased with no changes in either physiological cardiorespiratory rates or head motion between the subjects. The regional alterations in brain pulsations suggest that mechanisms driving the cerebrospinal fluid homeostasis may be altered in epilepsy. Magnetic resonance encephalography has both increased sensitivity and high specificity for detecting the increased brain pulsations, particularly in times when other tools for locating epileptogenic areas remain inconclusive.


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