Effect of a small change in auricle projection on sound localization

Pinnae assist in sound localization, and changes in auricle shape, position, or projection can theoretically alter the perceived position of a sound. The minimal displacement required to affect perceived sound location is undefined. This study quantified the error in horizontal sound localization when auricle projection is slightly decreased. The study was conducted at two sites by different experimenters, using different (though similar) systems, over a year apart. There were 21 normal-hearing participants: 11 at the University of Virginia (UVA) and 10 at James Madison University (JMU). Both UVA and JMU protocols involved a normal listening condition and a second condition with a headband that slightly altered pinnae projection by pushing the helixes medially against the temporal bones. Participants identified the location of a short, moderate-intensity noise burst from one of 8 speakers distributed in a horizontal array. Root mean squared error was calculated from tests of 48 trials. Localization errors in the UVA data were greater with the headband than without (t10=2.6; p=.023; Cohen’s d=.8 or ‘large’ effect size). The experiment was repeated at JMU and results replicated; localization errors were greater with than without the headband (t9=2.4; p=.034; Cohen’s d=.8). There was no effect of testing order and no consistent direction of error in either protocol. None of six anatomical measurements of pinnae correlated with the decrease in azimuth accuracy. Combined data from both experiments show a highly significant effect of the slightly medialized helix (t20=3.6, p=.002; d=.9). These data indicate the minimum pinna change required to alter sound localization is at least as small as the 15 mm average movement of the helix or 29 degree reduction in auricle projection. These data on psychoacoustic effect of altering auricle projection may be of relevance after otoplastic operations

Plasma Metabolite Signature Classifies Male LRRK2 Parkinson’s Disease Patients

Parkinson’s disease (PD) is a progressive neurodegenerative disease, causing loss of motor and nonmotor function. Diagnosis is based on clinical symptoms that do not develop until late in the disease progression, at which point the majority of the patients’ dopaminergic neurons are already destroyed. While many PD cases are idiopathic, hereditable genetic risks have been identified, including mutations in the gene for LRRK2, a multidomain kinase with roles in autophagy, mitochondrial function, transcription, molecular structural integrity, the endo-lysosomal system, and the immune response. A definitive PD diagnosis can only be made post-mortem, and no noninvasive or blood-based disease biomarkers are currently available. Alterations in metabolites have been identified in PD patients, suggesting that metabolomics may hold promise for PD diagnostic tools. In this study, we sought to identify metabolic markers of PD in plasma. Using a 1H-13C heteronuclear single quantum coherence spectroscopy (HSQC) NMR spectroscopy metabolomics platform coupled with machine learning (ML), we measured plasma metabolites from approximately age/sex-matched PD patients with G2019S LRRK2 mutations and non-PD controls. Based on the differential level of known and unknown metabolites, we were able to build a ML model and develop a Biomarker of Response (BoR) score, which classified male LRRK2 PD patients with 79.7% accuracy, 81.3% sensitivity, and 78.6% specificity. The high accuracy of the BoR score suggests that the metabolomics/ML workflow described here could be further utilized in the development of a confirmatory diagnostic for PD in larger patient cohorts. A diagnostic assay for PD will aid clinicians and their patients to quickly move toward a definitive diagnosis, and ultimately empower future clinical trials and treatment options