Biology subjects

Goltermann, O.

Publications and source records attributed to Goltermann, O..

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↗

bioRxiv2026

Opposing BOLD signals and oxygen metabolism largely arise from statistical uncertainty in metabolic estimates

Recent work by Epp et al. (2025) reported widespread voxel-wise sign discordance between task-evoked blood-oxygenation-level-dependent (BOLD) responses and estimated changes in cerebral metabolic rate of oxygen ({Delta}CMRO2), raising important questions about the interpretability of BOLD functional magnetic resonance imaging. Reanalysing the dataset, we found that {Delta}CMRO2 estimates showed substantial voxel-wise variability across participants, consistent with the noise sensitivity of model-based metabolic estimates. When this variability was taken into account, 77.2% of voxels could not be robustly classified, as {Delta}CMRO2 effects lacked sufficient statistical support to determine concordance or discordance. Where classification was possible, positive BOLD responses were predominantly concordant with metabolism, whereas discordance was considerably higher for negative BOLD responses. These findings suggest that the observed BOLD-metabolism discordance reported previously largely reflects statistical uncertainty in CMRO2 estimates rather than widespread physiological sign reversal.

neuroscience↗

bioRxiv2024

The utility of explainable A.I. for MRI analysis: Relating model predictions to neuroimaging features of the aging brain

IntroductionDeep learning models highly accurately predict brain-age from MRI but their explanatory capacity is limited. Explainable A.I. (XAI) methods can identify relevant voxels contributing to model estimates, yet, they do not reveal which biological features these voxels represent. In this study, we closed this gap by relating voxel-based contributions to brain-age estimates, extracted with XAI, to human-interpretable structural features of the aging brain. MethodsTo this end, we associated participant-level XAI-based relevance maps extracted from two ensembles of 3D-convolutional neural networks (3D-CNN) that were trained on T1-weighted and fluid attenuated inversion recovery images of 2016 participants (age range 18-82 years), respectively, with regional cortical and subcortical gray matter volume and thickness, perivascular spaces (PVS) and water diffusion-based fractional anisotropy of main white matter tracts. ResultsWe found that all neuroimaging markers of brain aging, except for PVS, were highly correlated with the XAI-based relevance maps. Overall, the strongest correlation was found between ventricular volume and relevance (r = 0.69), and by feature, temporal-parietal cortical thickness and volume, cerebellar gray matter volume and frontal-occipital white matter tracts showed the strongest correlations with XAI-based relevance. ConclusionOur ensembles of 3D-CNNs took into account a plethora of known aging processes in the brain to perform age prediction. Some age-associated features like PVS were not consistently considered by the models, and the cerebellum was more important than expected. Taken together, we highlight the ability of end-to-end deep learning models combined with XAI to reveal biologically relevant, multi-feature relationships in the brain.

neuroscience↗

bioRxiv2023

Neuroimaging genomics as a window into the evolution of human sulcal organization

Primate brain evolution has involved prominent expansions of the cerebral cortex, with largest effects observed in the human lineage. Such expansions were accompanied by fine-grained anatomical alterations, including increased cortical folding. However, the molecular bases of evolutionary alterations in human sulcal organisation are not yet well understood. Here, we integrated data from recently completed large-scale neuroimaging genetic analyses with annotations of the human genome relevant to various periods and events in our evolutionary history. These analyses identified single-nucleotide polymorphism (SNP) heritability enrichments in foetal brain human-gained enhancer elements for a number of sulcal structures, including the central sulcus, which is implicated in human hand dexterity. We zeroed-in on a genomic region which harbours DNA variants associated with left central sulcus shape, a human-gained enhancer element, and genetic loci involved in neurogenesis including ZIC4, to illustrate the value of this approach for probing the complex factors contributing to human sulcal evolution.

neuroscience↗

bioRxiv2023

Improving fMRI-based prediction of individual pain ratings accounting for interindividual variance

We challenge the pessimistic conclusion of a recently published paper by Hoeppli et al that fMRI-BOLD measures are useless in predicting interindividual differences in pain perception. By conducting a re-analysis of publicly available data of their study, we propose an alternative analysis approach that addresses the issue of interindividual variance differences in BOLD readouts, aiming to enhance the predictive power of fMRI-based measures. Instead of using absolute values of both, pain ratings and BOLD measures, we make use of robust intraindividual differences between the two experimental conditions in their study. Our findings demonstrate a statistically significant positive linear relationship between the neurologic pain signature (NPS) score, a multivariate pain classifier based on BOLD fMRI, and individual differences in perceived pain ratings. This relationship is driven by individuals that report pain sensitivity to both experimental conditions and can clearly distinguish between the two. Our results provide evidence for the potential of fMRI-BOLD measures in predicting interindividual differences in pain perception and allow for a more optimistic conclusion regarding the ongoing debate whether fMRI can be used as an objective measure for pain perception. ARISING FROMHoeppli, M.E., Nahman-Averbuch, H., Hinkle, W.A. et al. Dissociation between individual differences in self-reported pain intensity and underlying fMRI brain activation. Nat Commun 13, 3569 (2022). https://doi.org/10.1038/s41467-022-31039-3

neuroscience↗