Biology subjects

Silver, B.

Publications and source records attributed to Silver, B..

3 recordsLinked to original sources

bioRxiv2024

KLHDC7B, a novel gene associated with age-related hearing loss in humans, is required for the maintenance of hearing in mice.

BackgroundAge-related hearing loss (ARHL) is the most common sensory loss in older adults, but the underlying pathological mechanisms remain unclear. Recent genome wide association studies (GWAS) have linked variation in a large number of genes with increased risk of ARHL for the first time. Amongst the strongest of these associations is variation in KLHDC7B, a gene of unknown function and one not previously linked to hearing. MethodsTo confirm whether KLHDC7B plays a role in hearing we investigated auditory function in two independent knockout mutant mouse models of Klhdc7b: Klhdc7bIMPC-/-and Klhdc7bRegn{Delta}/{Delta} on the C57BL/6N background and the B6.CAST-Cdh23753A>G background respectively. The B6.CAST-Cdh23753A>G background was backcrossed to correct a known age-related hearing loss (ahl) mutation in cadherin 23 present in both the C57BL/6N and C57BL/6J strains. ResultsWe showed that Klhdc7b is expressed exclusively in inner and outer sensory hair cells within the cochlea of mice at the RNA and protein level. Homozygous mutants for both knockout mouse models display a similar early-onset, progressive and severe hearing loss. Histological characterization of the two mouse models suggests that hair cells develop normally and are present in neonates. However, after the onset of hearing there is a progressive loss of outer hair cells in a gradient from base to apex of the cochlea consistent with the hearing deficit in the mice and the pattern of hearing loss in ARHL. Inner hair cells remain intact up to the latest age examined (~8 weeks). ConclusionsOur data suggests KLHDC7B is required for maintenance of auditory function rather than in its development, supporting the novel association with ARHL in humans detected in recent GWAS. To our knowledge, this is the first validation in mouse of an ARHL association in humans detected in a GWAS. Our work also provides two distinct mouse models to further investigate the role of KLHDC7B in auditory function and for use in the development of therapeutic tools to prevent or treat ARHL.

genetics↗

bioRxiv2019

Characteristics of respiratory measures in young adults scanned at rest, including systematic changes and \"missed\" respiratory events

Breathing rate and depth influence the concentration of carbon dioxide in the blood, altering cerebral blood flow and thus functional magnetic resonance imaging (fMRI) signals. Such respiratory fluctuations can have substantial influence in studies of fMRI signal covariance in subjects at rest, the so-called \"resting state functional connectivity\" technique. If respiration is monitored during fMRI scanning, it is typically done using a belt about the subjects abdomen to record abdominal circumference. Several measures have been derived from these belt records, including the windowed envelope of the waveform (ENV), the windowed variance in the waveform (respiration variation, RV), and a measure of the amplitude of each breath divided by the cycle time of the breath (respiration volume per time, RVT). Any attempt to gauge respiratory contributions to fMRI signals requires a respiratory measure, but little is known about how these measures compare to each other, or how they perform beyond the small studies in which they were initially proposed. In this paper, we examine the properties of these measures in hundreds of healthy young adults scanned for an hour each at rest, a subset of the Human Connectome Project chosen for having high-quality physiological records. We find: 1) ENV, RV, and RVT are all similar, though ENV and RV are more similar to each other than to RVT; 2) respiratory events like deep breaths exhibit characteristic fMRI signal changes, head motions, and image quality abnormalities time-locked to deep breaths evident in the belt traces; 3) all measures can \"miss\" respiratory events evident in the belt traces; 4) RVT \"misses\" deep breaths (i.e., yawns and sighs) more than ENV or RV; 5) all respiratory measures change systematically over the course of a 14.4-minute scan, decreasing in mean value. We discuss the implication of these findings for the literature, and ways to move forward in modeling respiratory influences on fMRI scans.\n\nHighlights- Examines 3 respiratory measures in resting state fMRI scans of healthy young adults\n- All respiratory measures \"miss\" respiratory events, some more than others\n- Respiration volume per time (RVT) frequently \"misses\" deep breaths\n- All respiratory measures decrease systematically over 14.4 minute scans\n- Systematic decreases are due to decreased breathing depth and rate

neuroscience↗

bioRxiv2019

Distinctions among real and apparent respiratory motions in human fMRI data

Head motion estimates in functional magnetic resonance imaging (fMRI) scans appear qualitatively different with sub-second image sampling rates compared to the multi-second sampling rates common in the past. Whereas formerly the head appeared still for much of a scan with brief excursions from baseline, the head now appears to be in constant motion, and motion estimates often seem to divulge little information about what is happening in a scan. This constant motion has been attributed to respiratory oscillations that do not alias at faster sampling rates, and investigators are divided on the extent to which such motion is \"real\" motion or only \"apparent\" pseudomotion. Some investigators have abandoned the use of motion estimates entirely due to these considerations. Here we investigate the properties of motion in several fMRI datasets sampled at rates between 720-1160 ms, and describe 5 distinct kinds of respiratory motion: 1) constant real respiratory motion in the form of head nodding most evident in vertical position and pitch, which can be very large; 2) constant pseudomotion at the same respiratory rate as real motion, occurring only in the phase encode direction; 3) punctate real motions occurring at times of very deep breaths; 4) a low-frequency pseudomotion in only the phase encode direction following very deep breaths; 5) slow modulation of vertical and anterior-posterior head position by the respiratory envelope. We reformulate motion estimates in light of these considerations and obtain good concordance between motion estimates, physiologic records, image quality measures, and events evident in the fMRI signals.\n\nHighlights- Examines several fast-TR datasets with sampling rates of 720-1160 ms\n- Identifies 7 kinds of motion in fMRI scans, 5 of them related to respiration\n- Identifies 2 forms of pseudomotion occurring only in phase encode direction\n- Pseudomotion is a function of soft tissue mass, not lung volume\n- Reformulates motion estimates to draw out particular kinds of motion

neuroscience↗