Probing a neural unreliability account of auditory sensory processing atypicalities in Rett Syndrome

Abstract

Background: In the search for objective tools to quantify neural function in Rett Syndrome (RTT), which are crucial in the evaluation of therapeutic efficacy in clinical trials, recordings of sensory-perceptual functioning using event-related potential (ERP) approaches have emerged as potentially powerful tools. Considerable work points to highly anomalous auditory evoked potentials (AEPs) in RTT. However, an assumption of the typical signal-averaging method used to derive these measures is "stationarity" of the underlying responses - i.e. neural responses to each input are highly stereotyped. An alternate possibility is that responses to repeated stimuli are highly variable in RTT. If so, this will significantly impact the validity of assumptions about underlying neural dysfunction, and likely lead to overestimation of underlying neuropathology. To assess this possibility, analyses at the single-trial level assessing signal-to-noise ratios (SNR), inter-trial variability (ITV) and inter-trial phase coherence (ITPC) are necessary.

Methods: AEPs were recorded to simple 100Hz tones from 18 RTT and 27 age-matched controls (Ages: 6-22 years). We applied standard AEP averaging, as well as measures of neuronal reliability at the single-trial level (i.e. SNR, ITV, ITPC). To separate signal-carrying components from non-neural noise sources, we also applied a denoising source separation (DSS) algorithm and then repeated the reliability measures.

Results: Substantially increased ITV, lower SNRs, and reduced ITPC were observed in auditory responses of RTT participants, supporting a "neural unreliability" account. Application of the DSS technique made it clear that non-neural noise sources contribute to overestimation of the extent of processing deficits in RTT. Post-DSS, ITV measures were substantially reduced, so much so that pre-DSS ITV differences between RTT and TD populations were no longer detected. In the case of SNR and ITPC, DSS substantially improved these estimates in the RTT population, but robust differences between RTT and TD were still fully evident.

Conclusions: To accurately represent the degree of neural dysfunction in RTT using the ERP technique, a consideration of response reliability at the single-trial level is highly advised. Non-neural sources of noise lead to overestimation of the degree of pathological processing in RTT, and denoising source separation techniques during signal processing substantially ameliorate this issue.

Keywords: AEP; Auditory Evoked Potential; Auditory discrimination; Duration ERP; EEG, event-related potential; ERP; Females, Rett Syndrome Severity Scale (RSSS) Rett Syndrome Severity Scale (RSSS), Denoising Source Separation (DSS), signal-noise ratio (SNR), inter-trial variability (ITV), Inter-Trial Phase Coherence (ITPC); High-density electrical mapping; MECP2, Neurodevelopmental disorder; X-linked mutation.

Figure 1.. Standard Mean AEP (1 second epochs) for TDs (left) and RTT (right) over fronto-central scalp (averaged over electrodes FC3, FCz, FC4).

Panels (A-C) shows colored traces representing an average of all trials in response to standard tones for each participant and their grand average AEP (green for TD and red for RTT trace with black traces – standard deviation) for all SOA conditions. TDs produced classic AEP waveforms while the RTT group exhibited atypical responses with reduced AEP amplitude across SOAs. A clear initial peak (P1) within the time period from 50 to 100 – blue shaded panels was present for all SOAs in both groups. Distribution of mean standard amplitude and quartiles are plotted at the far right in panels (A-C) for TD (green) and RTT (red) during the period of initial peak (from 50 – 100 ms) across SOAs. Significant difference between the groups is marked by asterisk (for the 450 (p=0.05), 900ms (p=0.80) and 1800ms (p=0.04) SOAs). Panel (D) shows change in AEP morphology as a function of SOA seen in the control and RTT group. Panel (E) illustrates the locations of the averaged fronto-cental scalp electors that yielded the AEPs.

Figure 2.. ERP waveforms in TD and RTT, for the SOA conditions.

Representative Individual Participant AEP: average trials from four typically developing control participants (green shades) and four individuals with RTT (red shades) over fronto-central electrode (FC3), in 4 age ranges (6–7, 8–9, 10–12 and 14–16 years old). Gray and pink bars represent the two time windows for analysis: P1 (50–100) and N2 (200–300), respectively.

Figure 3.. ERP waveforms in TD and RTT, pre-and post DSS.

A. participant waveforms in response to standard tones overlaid (top panels) and averaged (bottom panels) across individuals, shown for each Group and for each SOA condition, pre- and post denoise source separation (DSS). B. Group averaged ERP signals (mean ± SEM in shaded lines) compared N2 between TD and RTT, for each SOA condition. Lower panels show cluster permutation statistics between groups (thick red line indicates significant temporal regions, p < 0.05). C. Same as B, however a comparison is presented between averaged ERPs for all SOA conditions in each of the groups.

Figure 4:. AEP values at N1 (200–300ms) across groups and SOA conditions.

A. AEP values for each group and condition, pre- and post-DSS

Figure 6:. Inter-Trial phase coherence (ITPC) for each group and condition pre- and post-DSS.

A. time-frequency ITPC plots show a reduction of coherence values in the RTT group across conditions, following stimulus onset. The average ITPC across frequencies is overlaid in white, on top of each plot. B. Raincloud plots of ITPC peak values derived from each participants (as seen in white in 4A), for each condition, pre- and post DSS. C. Same ITPC values shown in 4B, but now plotted within group for each condition, and compared between pre- and post DSS.