Biology subjects

Asan, L.

Publications and source records attributed to Asan, L..

6 recordsLinked to original sources

bioRxiv2026

Withdrawal latency in a human hot plate analog tracks inter-individual differences in pain sensitivity

Pain research relies mainly on self-report in humans and nocifensive behavior in animals. This asymmetry limits cross-species translation and neglects movement as integral to human pain responses. We therefore developed a human analog of the rodent hot plate test and tested whether temporal and kinematic features of nocifensive withdrawal can capture inter-individual differences in pain sensitivity. Ninety-one healthy participants rested their forearm on a thermode that rapidly heated to and held a fixed noxious temperature (46 {degrees}C or 48 {degrees}C) and were instructed to withdraw once the percept reached their pain threshold or tolerance. From wrist accelerometry, we extracted withdrawal latency and peak acceleration, an index of withdrawal vigor, and related both to pain intensity ratings from a separate, temperature-matched task and to calibrated pain sensitivity. Participants withdrew earlier and with higher peak acceleration at 48 {degrees}C than at 46 {degrees}C. Crucially, withdrawal latency tracked inter-individual differences in pain sensitivity: Participants who withdrew earlier reported higher pain in the separate task (48 {degrees}C: r = -.47 and -.50) and showed lower pain thresholds and higher suprathreshold sensitivity during calibration. In contrast, withdrawal vigor showed no such relationship, despite high reliability. Withdrawal latency can thus serve as a behavioral readout of inter-individual differences in pain sensitivity, whereas vigor might reflect intra-individual motor scaling with stimulus intensity. By separating the decision to withdraw from its motor execution, this paradigm offers a behavioral complement to traditional self-report and provides a translational link between human and preclinical pain assays.

neuroscience↗

bioRxiv2026

The pursuit of motivational goals reduces pain through an opioidergic mechanism

Pain promotes protective behavior but can interfere with other biologically important goals. Survival may require overcoming pain to obtain rewards, secure resources or escape danger, yet evidence for pain modulation by competing demands and endogenous modulatory systems during goal pursuit is lacking. We developed a paradigm in which participants chose whether to pursue monetary rewards despite painful heat stimulation during fMRI, under placebo or opioid receptor blockade with naloxone. Actively pursuing motivational goals during painful stimulation reduced perceived pain and increased fMRI signal in pain-modulatory cortical regions, including multiple subregions of the rostral anterior cingulate cortex (rACC) and dorsolateral prefrontal cortex, alongside enhanced rACC-periaqueductal gray coupling, consistent with recruitment of the descending pain modulatory system. Behavioral and neural effects were attenuated by naloxone, supporting a mediating role for endogenous opioids. These findings provide convergent evidence that active goal pursuit engages opioidergic pain modulatory mechanisms to reduce pain in humans.

neuroscience↗

bioRxiv2023

Loss of secondary motor cortex neurons in chronic neuropathic pain

Chronic neuropathic pain is associated with structural plasticity of the brain on different spatial scales, yet, little is known on the mesoscopic scale of tissue composition. Here, we determined the cellular composition of cortical areas and structural variability of entire neurons during the development of chronic neuropathic pain using longitudinal in vivo two-photon microscopy and behavioral assessment. When monitoring cell type composition in response to spared-nerve injury in 84 cortical volumes containing {bsim}25000 cells each, we found neuronal loss in the secondary motor cortex region M2 immediately adjacent to the cingulate cortex already one week after surgery. Loss of mostly interneurons was also evident when monitoring individual M2 neurons over time. This neuronal loss was preceded by decreased spine density and loss of distal dendritic branches. In conclusion, our work delineates M2 as a novel site and neuronal loss as a so far underappreciated mechanism underlying chronic neuropathic pain states.

neuroscience↗

bioRxiv2022

Comprehensive monitoring of tissue composition using in vivo imaging of cell nuclei and deep learning

Comprehensive analysis of tissue composition has so far been limited to ex-vivo approaches. Here, we introduce NuCLear (Nucleus-instructed tissue composition using deep learning), an approach combining in vivo two-photon imaging of histone 2B-eGFP-labeled cell nuclei with subsequent deep learning-based identification of cell types from structural features of the respective cell nuclei. Using NuCLear, we were able to classify almost all cells per imaging volume in the secondary motor cortex of the mouse brain (0.25 mm3 containing [~]25000 cells) and to identify their position in 3D space in a non-invasive manner using only a single label throughout multiple imaging sessions. Twelve weeks after baseline, cell numbers did not change yet astrocytic nuclei significantly decreased in size. NuCLear opens a window to study changes in relative abundance and location of different cell types in the brains of individual mice over extended time periods, enabling comprehensive studies of changes in cellular composition in physiological and pathophysiological conditions.

neuroscience↗

bioRxiv2022

Gray matter cortical thickness predicts individual pain sensitivity: a multi-center machine learning approach

Pain sensitivity is known to considerably vary across individuals. While the variability in pain has been linked to structural neural correlates, it is still unclear how well these findings replicate in independent data and whether they are powerful enough to provide reliable pain sensitivity predictions on the individual level. In this study, we constructed a predictive model of pain sensitivity utilising structural MRI-based cortical thickness data from a multi-center dataset (3 centers, 131 healthy participants). Cross-validated estimates revealed a statistically significant and clinically relevant predictive performance (Pearsons r = 0.36, p < 0.0005). The predictions were found to be specific to pain sensitivity and not biased towards potential confounding effects (e.g., anxiety, stress, depression, center-effects). Analysis of model coefficients suggests that the most robust cortical thickness predictors of pain sensitivity are the right rostral anterior cingulate gyrus, left parahippocampal gyrus and left temporal pole. Cortical thickness in these regions was negatively correlated to pain sensitivity. Our results can be considered as a proof-of-concept for the capacity of brain morphology to predict pain sensitivity, paving the way towards future multimodal brain-based biomarkers of pain. HighlightsO_LIWe present a robust, brain structure-based multi-center predictive model for pain sensitivity. C_LIO_LIOur model based on gray matter cortical thickness explains 13% of the variance in pain sensitivity and generalizes to out-of-center data. C_LIO_LIThe predictions are specific to pain sensitivity and not driven by potential confounders such as stress, depression, anxiety, and center-effects. C_LIO_LIThe most important predictors were rostral anterior cingulate cortex, parahippocampal gyrus and temporal pole, all negatively correlated with pain sensitivity. C_LI

neuroscience↗

bioRxiv2019

Grey matter volume changes and corresponding cellular metrics identified in a longitudinal in vivo imaging approach

Magnetic resonance imaging (MRI) of the brain combined with voxel-based morphometry (VBM) has revealed structural changes of grey and white matter in a range of neurological and psychiatric disorders. However, the cellular basis of volume changes observed with VBM has remained unclear. We devised an approach to systematically correlate changes in grey matter volume (GMV) with cellular composition. Mice were alternately examined with structural MRI and two-photon in vivo microscopy at three time points, taking advantage of age-dependent changes in brain structure. We chose to image fluorescently labelled cell nuclei, because these can be readily imaged in large tissue volumes and allow inferences on several structural parameters: (1) the physical volume as determined from a subset of nuclei used to generate a geometrically defined space, (2) the number of cells, (3) the nearest neighbour distance measured between all nuclei as an indicator of cell clustering, and (4) the volume of the cell nuclei. Using this approach, we found that physical volume did not significantly correlate with GMV change, whereas mean nuclear volume was inversely correlated. When focusing on layers within the imaging volume, positive correlations of GMV were found with cell number near the cortical surface and nearest neighbour distance in deeper layers. Thus, the novel approach introduced here provided new insights into the factors underlying grey matter volume changes.

neuroscience↗