The overall purpose of this project is to advance understanding of the neurophysiological features of Rett syndrome (RTT), MECP2 Duplication (MECP2 Dup) and RTT-related disorders (CDKL5, FOXG1) to gain insight into disease pathogenesis, with an emphasis on identifying biomarkers of disease evolution and severity. This specific study is intertwined to the core study Natural History of Rett Syndrome and Related Disorders (RTT5211), which characterizes range of clinical involvement and genotype-phenotype correlations and will provide phenotypical data for determining the clinical relevance of the neurophysiologic parameters; study subjects here are co- and primarily enrolled in RTT5211. The proposed studies will serve as basis of future translational investigations, including further refinement of biomarkers, development of outcome measures, and clinical trials per se.
Individuals with RTT, MECP2 Dup and RTT-related disorders have significant abnormalities on a number of neurophysiological measures such as EEG and Evoked Potentials (EP). Studies in representative animal models reproduce many of these abnormalities. Little is known about the relationship between these neurophysiological findings to disease evolution, severity and specific clinical features. Therefore, it is considered likely that detailed understanding of such neurophysiological features would provide additional insight into disease pathogenesis and will lead to biomarkers of disease state and severity of different features. Consequently, specialized neurophysiological assessments will be acquired, without sedation or any other type of pharmacological manipulation, on a subset of 170 subjects: 60 RTT, 18 MECP2 Dup, 32 RTT-related disorders, and 60 age-matched typically developing controls (30 females, 30 males). Primary evaluations will include auditory ERP (AEP) and visual ERP (VEP), as well as secondary analyses of specific rhythms/band activities obtained during the ERP acquisitions (gamma band changes and frontal alpha band asymmetry). Individuals will be recruited across the spectra of ages and severity. The main goal of the project is to identify potential biomarkers that can become measures for intervention and other translational studies and, at the same time, provide insight into abnormal synaptic activity and pathogenesis of RTT, MECP2 Dup, and RTT-related disorders. Therefore, the proposed assessments will be performed in all three groups of subjects enrolled in this consortium (RTT5211): RTT, MECP2 Dup, and RTT-related disorders. Findings in each set of disorders will be linked to the objectives of the the longitudinal clinical and neurobehavioral data (RTT5211) as well as to biological factors and genotyping that may be linked to clinical severity (RTT5213). The neurophysiological parameters for RTT, MECP2 Dup, and RTT-related disorders will not only be correlated with each other but also to disease staging, overall clinical severity scores and through exploratory analyses with specific clinical features; these will be repeated up to 3 times (i.e., annual \[every 10-14 month\] evaluations, in the context of visits for the RTT5211 protocol) during the course of study. For this purpose, linear regression and linear mixed models will be used. Preliminary and published data indicate that RTT and MECP2 Dup have distinct patterns of cortical processing on AEP, VEP demonstrates disorder and age/disease-stage dependent changes. Phenotypic severity may be related to specific ERP parameters, as some modest effects of (severity) category of mutations were observed. In addition, the secondary analyses of specific EEG rhythms/band activities will expand our preliminary studies demonstrating alpha band asymmetry as a marker of an anxiety-like response in RTT.
Study Type
OBSERVATIONAL
Enrollment
185
Specifically, through up to three standardized sessions (i.e., annual \[every 10-14 months\]), we will assess AEP and VEP. ERP recordings will also provide data for specific rhythms/band (gamma and alpha) pattern analyses as secondary measures as well as technical control data, which will help to exclude those with co-current seizures.
University of Colorado Denver
Denver, Colorado, United States
Boston Children's Hospital
Boston, Massachusetts, United States
Cincinnati Children's Hospital
Cincinnati, Ohio, United States
Children's Hospital of Philadelphia
Philadelphia, Pennsylvania, United States
Vanderbilt University
Nashville, Tennessee, United States
Auditory Event-related potentials
EEG will be filtered between 0.5 and 400Hz. The EEG will be segmented around each stimulus presentation. 200msec prior to 1000msec post each stimulus will be collected and averaged for each trial for each electrode. The electrodes with highest averaged N1 waveforms, predicted to be posterior temporal (T5/P3/T3) electrodes, will be used for subsequent analysis. The averaged waveforms will be analyzed for latency to N1 and P1 peak frm which the auditory event related potentials will be the main parameter for statistical analysis.
Time frame: 3 years
Visual Event-related potentials
VEP analysis will be similar to the AEP analysis. EEG will be prepared using the same methodology but using occipital electrodes with Oz as the primary electrode of analysis. The EEG will be averaged from 200msec prior to 1000ms post stimulus. The N1, P1, and N2 components will be identified and will be averaged and the latency and amplitude of the peaks quantified. P1 latency and N1-P1 time will be the primary end point of the study. The latency will be used for the statistical parameter.
Time frame: 3 years
EEG
For frequency based analysis, 10-20 ten-second epochs of noise free EEG without clear eye blinks during wakefulness and eyes open; 10 ten-second epochs of wakefulness and eyes closed (assessed by video); and 10-20 ten-second epochs of EEG during each stage of sleep will be analyzed. A prescreen of EEG using a template matching algorithm (EEGlab) can be used to reduce amount of data to be reviewed. For theta and gamma band activity, the EEG will be band passed filtered between 2-10 and 25-70Hz, respectively, and a FFT performed on the filtered data. Spike location, frequency, and activity (change with sleep, eye closure, stimulation) will be calculated.
Time frame: 3 years
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