Electroencephalography signals in a female Fragile X Syndrome mouse model

Fragile X syndrome (FXS) is the leading monogenic cause of Autism. No broadly effective support option currently exists for FXS, and drug development has suffered many failures in clinical trials based on promising preclinical findings. Thus, effective translational biomarkers of treatment outcomes are needed. Recently, electroencephalography (EEG) has been proposed as a translational biomarker in FXS. Recent years have seen an exciting emergence of novel EEG signal analyses from FXS patients. However, there is a notable gap in corresponding analyses conducted on animal models of the disorder. Being X-linked, FXS is more prevalent in males than females, and there exist significant phenotype differences between males and females with FXS. Recent studies involving male FXS participants and rodent models have identified an increase in absolute gamma EEG power, while alpha power is found to be either decreased or unchanged. However, there is not enough research on female FXS patients or models. In addition, studying EEG activity in both young and adult FXS patients or rodent models is crucial for better understanding of the disorder's effects on brain development. Therefore, using the well-established fmr1 knockout (KO) mouse model of FXS, we aim to compare EEG signal between female wild-type (WT) and female model mice at both juvenile and adult ages.

Frontal-parietal differential EEG was recorded using a stand-alone Open-Source Electrophysiology Recording system for Rodents (OSERR). EEG activity was recorded in three different conditions: a) in the subject's home cage, and in the arenas for b) light-dark test and c) open field test. Absolute and relative EEG power as well as peak alpha frequency, theta-beta ratio, phase-amplitude and amplitude-amplitude coupling, and EEG signal complexity were computed for each condition. Analyses of absolute and relative power, particularly gamma power, were a priori confirmatory based on established findings in human and rodent FXS literature. All additional EEG features (peak alpha frequency, theta-beta ratio, cross-frequency coupling, and signal complexity) were treated as exploratory.

In our study, we found EEG signals were stable across different recording conditions. Our results indicate that absolute alpha, beta, gamma and total EEG power is increased in the female model compared to WT controls, and the difference is more pronounced at the adult age. Alongside, relative theta power is decreased in the model. Additionally, phase-amplitude and amplitude-amplitude coupling are altered in the model. Furthermore, peak alpha frequency is increased, and theta-beta ratio is decreased in the model. Lastly, no change in EEG signal complexity is found.

Consistent with most findings from FXS patients and rodent models, our results demonstrated an increase in gamma power in fmr1KO female mice, reinforcing gamma power as a robust and reliable EEG phenotype across FXS models. Additionally, theta-gamma cross frequency amplitude coupling is inversely coupled in female FXS model, which is similar to what has been reported in FXS patients. Overall, our findings reveal that some, but not all EEG biomarkers observed in FXS patients are replicated in the female FXS model. For example, amplitude-amplitude coupling exhibited a similar trend between the fmr1KO mouse models and FXS patients, supporting its potential as a translational EEG biomarker. In contrast, other measures such as peak alpha frequency, theta-beta ratio, and brain signal complexity showed notable discrepancies between the mouse models and human data. Additionally, when compared to previously reported EEG changes in male FXS mouse models, our results highlight the presence of a potential sex-based difference in EEG phenotypes at both juvenile and adult stages of fmr1KO mouse models. Together, our study indicates that certain EEG parameters may be more translatable between rodent models and FXS patients than others and underscore the importance of considering sex and developmental stage as a critical factor when using EEG as a biomarker in FXS research.