Biology subjects

Luke, R.

Publications and source records attributed to Luke, R..

4 recordsLinked to original sources

bioRxiv2026

Cell vulnerability within the sublaterodorsal tegmental nucleus underlies REM sleep behaviour disorder in prodromal α-synucleinopathy

REM sleep behaviour disorder (RBD) is a prodromal manifestation of -synucleinopathies such as Parkinsons disease. Evidence suggests that degeneration of REM sleep regulating neurons in the sublaterodorsal tegmental nucleus (SLD) could give rise to RBD, yet the specific cellular populations involved and their contribution to synucleinopathy progression remain unclear. Here, we investigated the role of defined SLD cell types in RBD pathogenesis. Using viral vector and fibril-based models of -synucleinopathy, we show that -synuclein pathology in SLD neurons triggers RBD in mice. Notably, glutamatergic SLD neurons are selectively vulnerable to synucleinopathy and the loss of these cells correlates with RBD severity. Propagation of synucleinopathy from the SLD to midbrain and forebrain structures leads to the emergence of neurological deficits associated with Parkinsons disease. These findings establish that SLD neurons are critical substrates for RBD and provide insight into the cellular mechanisms at play in the early stages of synucleinopathies.

neuroscience↗

bioRxiv2021

Oxygenated hemoglobin signal provides greater predictive performance of experimental condition than de-oxygenated

Continuous-wave functional near-infrared spectroscopy (fNIRS) neuroimaging provides an estimate of relative changes in oxygenated and de-oxygenated hemoglobin content, from which regional neural activity is inferred. The relation between both signals is governed by neurovascular coupling mechanisms. However, the magnitude of concentration changes and the contribution of noise sources to each chromophore is unique. Subsequently, it is not apparent if either chromophore signal practically provides greater information about the underlying neural state and relation to an experimental condition. To assess this question objectively, we applied a machine-learning approach to four datasets and evaluated which hemoglobin signal best differentiated between experimental conditions. To further ensure the objective nature of the analysis, the algorithm utilized all samples from the epoched data rather than pre-selected features. Regardless of experimental task, brain region, or stimulus, the oxygenated hemoglobin signal was better able to differentiate between conditions than the de-oxygenated signal. Incorporating both signals into the analysis provided no additional improvement over oxygenated hemoglobin alone. These results indicate that oxyhemoglobin is the most informative fNIRS signal in relation to experimental condition.

neuroscience↗

bioRxiv2021

Characterization of Mayer wave oscillations in functional near-infrared spectroscopy using a physiologically informed model of the neural power spectra

OverviewO_ST_ABSSignificanceC_ST_ABSMayer waves are spontaneous oscillations in arterial blood pressure that can mask cortical hemodynamic responses associated with neural activity of interest. AimTo characterize the properties of oscillations in the fNIRS signal generated by Mayer waves in a large sample of fNIRS recordings. Further, we aim to determine the impact of short-channel correction for the attenuation of these unwanted signal components. ApproachMayer wave oscillation parameters were extracted from 310 fNIRS measurements using the Fitting Oscillations & One-Over-F (FOOOF) method to compute normative values. The effect of short-channel correction on Mayer wave oscillation power was quantified on 222 measurements. The practical benefit of the short-channel correction approach for reducing Mayer waves and improving response detection was also evaluated on a subgroup of 17 fNIRS measurements collected during a passive auditory speech detection experiment. ResultsMayer-wave oscillations had a mean frequency of 0.108 Hz, bandwidth of 0.04 Hz, and power of 3.5 M2/Hz. The distribution of oscillation signal power was positively skewed, with some measurements containing large Mayer waves. Short-channel correction significantly reduced the amplitude of these undesired signals; greater attenuation was observed for measurements containing larger Mayer-wave oscillations. ConclusionsA robust method for quantifying Mayer-wave oscillations in the fNIRS signal spectrum was presented and used to provide normative parameterization. Short-channel correction is recommended as an approach for attenuating Mayer waves, particularly in participants with large oscillations.

neuroscience↗

bioRxiv2020

Analysis methods for measuring fNIRS responses generated by a block-design paradigm

OverviewO_ST_ABSSignificanceC_ST_ABSfNIRS is an increasingly popular tool in auditory research, but the range of analysis procedures employed across studies complicates interpretation of data. AimTo assess the impact of different analysis procedures on the morphology, detection, and lateralization of auditory responses in fNIRS. Specifically, whether averaging or GLM-based analyses generate different experimental conclusions, when applied to a block-protocol design. The impact of parameter selection of GLMs on detecting auditory-evoked responses was also quantified. Approach17 listeners were exposed to three commonly employed auditory stimuli: noise, speech, and silence. A block design was employed, comprising sounds of 5-s duration, and 10-20 s silent intervals. ResultsBoth analysis procedures generated similar response morphologies and amplitude estimates, and both also indicated responses to speech to be significantly greater than to noise and silence. Neither approach indicated a significant effect of brain hemisphere on responses to speech. Methods to correct for systemic hemodynamic responses using short channels improved detection at the individual level. ConclusionsConsistent with theoretical considerations, simulations, and other experimental domains, GLM and averaging analyses generate the same group-level experimental conclusions. We release this dataset publicly for use in future development and optimization of algorithms.

neuroscience↗