Person: Thibert, Ronald
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Thibert
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Thibert, Ronald
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Publication Validation of a smartphone-based EEG among people with epilepsy: A prospective study(Springer Nature, 2017) McKenzie, Erica D.; Lim, Andrew S. P.; Leung, Edward C. W.; Cole, Andrew; Lam, Alice; Eloyan, Ani; Nirola, Damber K.; Tshering, Lhab; Thibert, Ronald; Garcia, Rodrigo; Bui, Esther; Deki, Sonam; Lee, Liesly; Clark, Sarah J.; Cohen, Joseph M.; Mantia, Jo; Brizzi, Kate; Sorets, Tali R.; Wahlster, Sarah; Borzello, Mia; Stopczynski, Arkadiusz; Cash, Sydney; Mateen, FarrahOur objective was to assess the ability of a smartphone-based electroencephalography (EEG) application, the Smartphone Brain Scanner-2 (SBS2), to detect epileptiform abnormalities compared to standard clinical EEG. The SBS2 system consists of an Android tablet wirelessly connected to a 14-electrode EasyCap headset (cost ~300USD). SBS2 and standard EEG were performed in people with suspected epilepsy in Bhutan (2014-2015), and recordings were interpreted by neurologists. Among 205 participants (54% female, median age 24 years), epileptiform discharges were detected on 14% of SBS2 and 25% of standard EEGs. The SBS2 had 39.2% sensitivity (95% confidence interval (CI) 25.8%, 53.9%) and 94.8% specificity (95% CI 90.0%, 97.7%) for epileptiform discharges with positive and negative predictive values of 0.71 (95% CI 0.51, 0.87) and 0.82 (95% CI 0.76, 0.89) respectively. 31% of focal and 82% of generalized abnormalities were identified on SBS2 recordings. Cohen’s kappa (κ) for the SBS2 EEG and standard EEG for the epileptiform versus non-epileptiform outcome was κ=0.40 (95% CI 0.25, 0.55). No safety or tolerability concerns were reported. Despite limitations in sensitivity, the SBS2 may become a viable supportive test for the capture of epileptiform abnormalities, and extend EEG access to new, especially resource-limited, populations at a reduced cost.Publication A Quantitative Electrophysiological Biomarker of Duplication 15q11.2-q13.1 Syndrome(Public Library of Science, 2016) Frohlich, Joel; Senturk, Damla; Saravanapandian, Vidya; Golshani, Peyman; Reiter, Lawrence T.; Sankar, Raman; Thibert, Ronald; DiStefano, Charlotte; Huberty, Scott; Cook, Edwin H.; Jeste, Shafali S.Background: Duplications of 15q11.2-q13.1 (Dup15q syndrome) are highly penetrant for autism spectrum disorder (ASD). A distinct electrophysiological (EEG) pattern characterized by excessive activity in the beta band has been noted in clinical reports. We asked whether EEG power in the beta band, as well as in other frequency bands, distinguished children with Dup15q syndrome from those with non-syndromic ASD and then examined the clinical correlates of this electrophysiological biomarker in Dup15q syndrome. Methods: In the first study, we recorded spontaneous EEG from children with Dup15q syndrome (n = 11), age-and-IQ-matched children with ASD (n = 10) and age-matched typically developing (TD) children (n = 9) and computed relative power in 6 frequency bands for 9 regions of interest (ROIs). Group comparisons were made using a repeated measures analysis of variance. In the second study, we recorded spontaneous EEG from a larger cohort of individuals with Dup15q syndrome (n = 27) across two sites and examined age, epilepsy, and duplication type as predictors of beta power using simple linear regressions. Results: In the first study, spontaneous beta1 (12–20 Hz) and beta2 (20–30 Hz) power were significantly higher in Dup15q syndrome compared with both comparison groups, while delta (1–4 Hz) was significantly lower than both comparison groups. Effect sizes in all three frequency bands were large (|d| > 1). In the second study, we found that beta2 power was significantly related to epilepsy diagnosis in Dup15q syndrome. Conclusions: Here, we have identified an electrophysiological biomarker of Dup15q syndrome that may facilitate clinical stratification, treatment monitoring, and measurement of target engagement for future clinical trials. Future work will investigate the genetic and neural underpinnings of this electrophysiological signature as well as the functional consequences of excessive beta oscillations in Dup15q syndrome.Publication Delta rhythmicity is a reliable EEG biomarker in Angelman syndrome: a parallel mouse and human analysis(BioMed Central, 2017) Sidorov, Michael S.; Deck, Gina M.; Dolatshahi, Marjan; Thibert, Ronald; Bird, Lynne M.; Chu, Catherine; Philpot, Benjamin D.Background: Clinicians have qualitatively described rhythmic delta activity as a prominent EEG abnormality in individuals with Angelman syndrome, but this phenotype has yet to be rigorously quantified in the clinical population or validated in a preclinical model. Here, we sought to quantitatively measure delta rhythmicity and evaluate its fidelity as a biomarker. Methods: We quantified delta oscillations in mouse and human using parallel spectral analysis methods and measured regional, state-specific, and developmental changes in delta rhythms in a patient population. Results: Delta power was broadly increased and more dynamic in both the Angelman syndrome mouse model, relative to wild-type littermates, and in children with Angelman syndrome, relative to age-matched neurotypical controls. Enhanced delta oscillations in children with Angelman syndrome were present during wakefulness and sleep, were generalized across the neocortex, and were more pronounced at earlier ages. Conclusions: Delta rhythmicity phenotypes can serve as reliable biomarkers for Angelman syndrome in both preclinical and clinical settings. Electronic supplementary material The online version of this article (doi:10.1186/s11689-017-9195-8) contains supplementary material, which is available to authorized users.Publication Understanding Relationships Between Autism, Intelligence, and Epilepsy: A Cross-Disorder Approach(Wiley-Blackwell, 2012) van Eeghen, Agnies M; Pulsifer, Margaret; Merker, Vanessa; Neumeyer, Ann; van Eeghen, Elmer E; Thibert, Ronald; Cole, Andrew; Leigh, Fawn; Plotkin, Scott; Thiele, ElizabethAim As relationships between autistic traits, epilepsy, and cognitive functioning remain poorly understood, these associations were explored in the biologically related disorders tuberous sclerosis complex (TSC), neurofibromatosis type 1 (NF1), and epilepsy. Method The Social Responsiveness Scale (SRS), a quantitative measure of autistic traits, was distributed to caregivers or companions of patients with TSC, NF1, and childhood-onset epilepsy of unknown cause (EUC), and these results were compared with SRS data from individuals with idiopathic autism spectrum disorders (ASDs) and their unaffected siblings. Scores and trait profiles of autistic features were compared with cognitive outcomes, epilepsy variables, and genotype. Results A total of 180 SRS questionnaires were filled out in the TSC, NF1, and EUC outpatient clinics at the Massachusetts General Hospital (90 females, 90 males; mean age 21y, range 4–63y), and SRS data from 210 patients with ASD recruited from an autism research collaboration (167 males, 43 females; mean age 9y range 4–22y) and 130 unaffected siblings were available. Regression models showed a significant association between SRS scores and intelligence outcomes (p<0.001) and various seizure variables (p<0.02), but not with a specific underlying disorder or genotype. The level of autistic features was strongly associated with intelligence outcomes in patients with TSC and epilepsy (p<0.01); in patients with NF1 these relationships were weaker (p=0.25). For all study groups, autistic trait subdomains covaried with neurocognitive comorbidity, with endophenotypes similar to that of idiopathic autism. Interpretation Our data show that in TSC and childhood-onset epilepsy, the severity and phenotype of autistic features are inextricably linked with intelligence and epilepsy outcomes. Such relationships were weaker for individuals with NF1. Findings suggest that ASDs are not specific for these conditions.