What is the role of the hippocampus and parahippocampal gyrus in the persistence of tinnitus?

The hippocampus and parahippocampal gyrus have been implicated as part of a tinnitus network by a number of studies. These structures are usually considered in the context of a "limbic system," a concept typically invoked to explain the emotional response to tinnitus. Despite this common framing, it is not apparent from current literature that this is necessarily the main functional role of these structures in persistent tinnitus. Here, we highlight a different role that encompasses their most commonly implicated functional position within the brain-that is, as a memory system. We consider tinnitus as an auditory object that is held in memory, which may be made persistent by associated activity from the hippocampus and parahippocampal gyrus. Evidence from animal and human studies implicating these structures in tinnitus is reviewed and used as an anchor for this hypothesis. We highlight the potential for the hippocampus/parahippocampal gyrus to facilitate maintenance of the memory of the tinnitus percept via communication with auditory cortex, rather than (or in addition to) mediating emotional responses to this percept.

Comparison of distortion correction preprocessing pipelines for DTI in the upper limb.

PURPOSE: DTI characterizes tissue microstructure and provides proxy measures of nerve health. Echo-planar imaging is a popular method of acquiring DTI but is susceptible to various artifacts (e.g., susceptibility, motion, and eddy currents), which may be ameliorated via preprocessing. There are many pipelines available but limited data comparing their performance, which provides the rationale for this study. METHODS: DTI was acquired from the upper limb of heathy volunteers at 3T in blip-up and blip-down directions. Data were independently corrected using (i) FSL's TOPUP & eddy, (ii) FSL's TOPUP, (iii) DSI Studio, and (iv) TORTOISE. DTI metrics were extracted from the median, radial, and ulnar nerves and compared (between pipelines) using mixed-effects linear regression. The geometric similarity of corrected b = 0 images and the slice matched T1-weighted (T1w) images were computed using the Sörenson-Dice coefficient. RESULTS: Without preprocessing, the similarity coefficient of the blip-up and blip-down datasets to the T1w was 0·80 and 0·79, respectively. Preprocessing improved the geometric similarity by 1% with no difference between pipelines. Compared to TOPUP & eddy, DSI Studio and TORTOISE generated 2% and 6% lower estimates of fractional anisotropy, and 6% and 13% higher estimates of radial diffusivity, respectively. Estimates of anisotropy from TOPUP & eddy versus TOPUP were not different but TOPUP reduced radial diffusivity by 3%. The agreement of DTI metrics between pipelines was poor. CONCLUSIONS: Preprocessing DTI from the upper limb improves geometric similarity but the choice of the pipeline introduces clinically important variability in diffusion parameter estimates from peripheral nerves.

The contribution of short-term memory for sound features to speech-in-noise perception and cognition.

Speech-in-noise (SIN) perception is a fundamental ability that declines with aging, as does general cognition. We assess whether auditory cognitive ability, in particular short-term memory for sound features, contributes to both. We examined how auditory memory for fundamental sound features, the carrier frequency and amplitude modulation rate of modulated white noise, contributes to SIN perception. We assessed SIN in 153 healthy participants with varying degrees of hearing loss using measures that require single-digit perception (the Digits-in-Noise, DIN) and sentence perception (Speech-in-Babble, SIB). Independent variables were auditory memory and a range of other factors including the Pure Tone Audiogram (PTA), a measure of dichotic pitch-in-noise perception (Huggins pitch), and demographic variables including age and sex. Multiple linear regression models were compared using Bayesian Model Comparison. The best predictor model for DIN included PTA and Huggins pitch (r2 = 0.32, p < 0.001), whereas the model for SIB included the addition of auditory memory for sound features (r2 = 0.24, p < 0.001). Further analysis demonstrated that auditory memory also explained a significant portion of the variance (28 %) in scores for a screening cognitive test for dementia. Auditory memory for non-speech sounds may therefore provide an important predictor of both SIN and cognitive ability.