Altered auditory feature discrimination in a rat model of Fragile X Syndrome

Abstract

Atypical sensory processing, particularly in the auditory domain, is one of the most common and quality-of-life affecting symptoms seen in autism spectrum disorders (ASD). Fragile X Syndrome (FXS) is the leading inherited cause of ASD and a majority of FXS individuals present with auditory processing alterations. While auditory hypersensitivity is a common phenotype observed in FXS and Fmr1 KO rodent models, it is important to consider other auditory coding impairments that could contribute to sound processing difficulties and disrupted language comprehension in FXS. We have shown previously that a Fmr1 knockout (KO) rat model of FXS exhibits heightened sound sensitivity that coincided with abnormal perceptual integration of sound bandwidth, indicative of altered spectral processing. Frequency discrimination is a fundamental aspect of sound encoding that is important for a range of auditory processes, such as source segregation and speech comprehension, and disrupted frequency coding could thus contribute to a range of auditory issues in FXS and ASD. Here we explicitly characterized spectral processing deficits in male Fmr1 KO rats using an operant conditioning tone discrimination assay and in vivo electrophysiology recordings from the auditory cortex and inferior colliculus. We found that Fmr1 KO rats exhibited poorer frequency resolution, which corresponded with neuronal hyperactivity and broader frequency tuning in auditory cortical but not collicular neurons. Using an experimentally informed population model, we show that these cortical physiological differences can recapitulate the observed behavior discrimination deficits, with decoder performance being tightly linked to differences in cortical tuning width and signal-to-noise ratios. These findings suggest that cortical hyperexcitability may account for a range of auditory behavioral phenotypes in FXS, providing a potential locus for development of novel biomarkers and treatment strategies that could extend to other forms of ASD.

Keywords: auditory cortex; autism; fragile x syndrome; frequency discrimination; hyperexcitability; inferior colliculus; sensory processing; tuning.

Figure 1.. Comparable hearing thresholds and tone detection behavior in WT and Fmr1^KO rats.

Schematic of Go/No-Go operant tone detection task. Wildtype and Fmr1 KO rats were trained to report the detection of any tone burst (HIT), with failure to do so resulting in a MISS. On 30% of trials no sound was presented (Catch trials). Responding on catch trials resulted in a false alarm (FA), while refraining from responding resulted in a correct rejection (CR). (B) Average tone detection performance across animals for all tone frequencies (4,8,16, and 32 kHz). Detection performance was comparable between wildtype (black) and Fmr1 KO (red) animals. (C) Behavioral detection thresholds for each tone frequency using a criterion of d’ = 1.5. No genotype difference was observed at any test frequency. (D) Schematic of auditory brainstem response (ABR) recording setup. (E) Representative ABR waveforms from a wildtype (back) and Fmr1 KO (red) rat. ABR threshold was defined as the lowest intensity that evoked a discernable ABR waveform (black and red arrows) (F) No difference in ABR thresholds between wildtype (black) and Fmr1 KO (red) rats was observed at any sound frequency tested. ns = not significant.

Figure 2.. Tone discrimination behavior is impaired in Fmr1 KO rats.

(A) Schematic of Go/No-Go operant discrimination task. In this task, rats were trained to report the detection of a single Go-tone frequency (4,8,16, or 32 kHz) and inhibit their response to a No-Go tone either 1 octave above or below the Go tone. (B) Hit and false alarm (FA) rate over training sessions for wildtype (black) and Fmr1 KO (red) rats demonstrating that both genotypes can learn and perform octave discrimination at comparable levels. (C) Fine-frequency discrimination task where No-Go tone frequency was varied in 1/12 octave steps from Go tone. Top: Schematic showing the addition of multiple octave steps between the Go and No-Go tones (1/12 of an octave per step). Bottom: FA rate as a function of No-Go tone frequency in wildtype and Fmr1 KO rats. (D) FA rates grouped by 1/3 octaves from Go showing a significant reduction in KO performance only in the optimally difficult middle band, while performance is comparable at the most difficult and easy bands. *p < 0.05, ***p < 0.001, ns = not significant.

Figure 3.. Altered cortical response properties in Fmr1 KO rats.

(A) Schematic of recording set-up. Simultaneous recordings with multichannel electrodes were made from across the tonotopic axis of contralateral ACx and IC of wildtype and Fmr1 KO rats. (B-C) Rate level functions showing relationship between firing rate and sound intensity at CF for each multi-unit cluster in the (B) IC and (C) ACx of wildtype (black) and Fmr1 KO (red) rats. (D-E) Interpolated response minimum (Min) and maximum (Max) from (D) IC and (E) ACx response functions. (F-G) Interpolated response threshold (Thresh) and gain (Gain) from (F) IC and (G) ACx response functions. *p < 0.05, **p < 0.01, ns = not significant.

Figure 4.. Tuning properties are unaltered in the IC of Fmr1^KO rats.

(A) Example tuning curves recorded from the IC of wildtype (left) and Fmr1 KO (right) rats. Each cell represents 30 trials for a given frequency-intensity combination. (B) Distributions of characteristic frequency (CF, left) and minimum threshold (right) for multi-unit clusters from the IC of WT (black) and Fmr1 KO (red) rats. (C) Q-value measure of tuning precision at 10, 20, 30, and 40 dB above response threshold. (D) Neural discriminability of sound frequency as assessed by changes in spike train dissimilarity (Δ Spike-Distance) in response to CF and neighboring tone frequencies.

Figure 5.. Broadened cortical tuning properties in Fmr1^KO rats.

(A) Example tuning curves recorded from the ACx of wildtype (left) and Fmr1 KO (right) rats. Each cell represents 30 trials for a given intensity frequency combination. (B) Distributions of characteristic frequency (CF, left) and minimum threshold(right) for multi-unit clusters from the ACx of WT (black) and Fmr1 KO (red) rats. (C) Q-value measure of tuning precision at 10, 20, 30, and 40 dB above response threshold. Lower Q-values in the ACx of Fmr1 KO rats are indicative of broader tuning. (D) Neural discriminability of sound frequency as assessed by changes in spike train dissimilarity (Δ Spike-Distance) in response to CF and neighboring tone frequencies. Decreased Spike-Distance in the ACx of Fmr1 KO rats is indicative of poorer neural discriminability. **p < 0.01, ***p < 0.0001, ns = not significant.

Figure 6.. Modeling Fmr1 KO discrimination deficits using a population decoder.

(A) Schematic of Bayesian decoding from a simulated network of tonotopically organized neurons whose tuning parameters are derived from the population data recorded from the ACx or IC of either WT or Fmr1 KO rats. (B-C) Error rates from model readout layer as a function of octave distance using tuning parameters from the (B) ACx or (C) IC of WT (black) or Fmr1 KO (red) rats. (D) Decoder performance for No-Go tones 1/3–2/3 octave from Go tone as a function of model parameters in ACx. Error rate was determined for models using all WT (WT_full) or KO (KO_full) parameters, as well as for each unique combination of individual KO parameters for spontaneous firing rates (spont), peak sound-evoked firing rate (peak), and tuning width (width). (E) Schematic summarizing results.