Biology subjects

Nickel, M. M.

Publications and source records attributed to Nickel, M. M..

6 recordsLinked to original sources

bioRxiv2025

EEG Reveals Robust Within-Person but Unstable Between-Person Neural Encoding of Pain

The perception of pain varies both within and between individuals, even when sensory input remains constant. Understanding how the brain encodes these intra- and interindividual variations is essential for elucidating the neural mechanisms of pain in health and disease. Yet, previous findings have been inconsistent, and their robustness, in terms of both repeatability and replicability, has remained unclear. Here, we used electroencephalography (EEG) in 161 healthy participants to re-investigate the neural correlates of intra- and inter-individual variations in the perception of brief painful stimuli, independent of stimulus intensity. Using Bayesian multivariate multi-model regression, we related pain ratings to evoked (N1, N2, P2) and induced oscillatory responses (alpha, beta, gamma). To assess the robustness of our findings, the experiment was repeated after four weeks in the same participants and replicated in an independent cohort (n = 111). This design allowed us to examine within-person variability at both short (moment-to-moment) and long (day-to-day) timescales. Inter-individual differences in pain were primarily associated with the P2 response, an effect that was repeatable in the same but not replicable in the independent cohort. In contrast, intra-individual variations were explained by a multicomponent EEG pattern that was both repeatable across time and replicable across cohorts. These findings demonstrate that intra- and inter-individual variability in pain is differentially encoded in the human brain and reveal greater robustness of within-person brain-behavior associations. EEG markers may therefore be more suitable for tracking longitudinal changes in pain within individuals than for comparing pain sensitivity across individuals.

neuroscience↗

bioRxiv2025

Expectation effects on repetition suppression in nociception

Repetition suppression, the reduced neural response upon repeated presentation of a stimulus, can be explained by models focussing on bottom-up (i.e. adaptation) or top-down (i.e. expectation) mechanisms. Predictive coding models fall into the latter category and propose that repetitions are expected and therefore elicit smaller prediction error responses. While studies in the visual and auditory domain provide some support for such models, in nociception evidence remains inconclusive, despite the substantial influence expectations exert on pain perception. To assess expectation effects on repetition suppression in nociception, we developed a paradigm in which healthy volunteers received brief CO2 laser stimuli, while we acquired electroencephalographic (EEG) and peripheral physiological data. Importantly, laser stimuli could be either repeated after one second or not be repeated, with the probability of repetitions manipulated in a block-wise fashion, such that repetitions were either expected or unexpected. We observed repetition suppression in laser-evoked potentials as well as laser-induced gamma band oscillations, but not in laser-induced desynchronisations in the alpha and beta band. Critically, neither these EEG responses, nor the peripheral physiological data showed significant differences between the expectation conditions, with Bayesian analyses mostly providing evidence for an absence of effects. This indicates that repetition suppression to brief nociceptive laser stimuli is not driven by top-down factors, but rather mediated by other adaptation processes. While this does not preclude an influence of predictive coding models in nociception, it suggests that when the nervous system receives highly precise input, its responses are less susceptible to influence from expectations.

neuroscience↗

bioRxiv2024

EEG-based analysis of intrinsic brain network function in chronic pain: Insights from a comprehensive multi-data set study

Chronic pain is associated with alterations in brain function. A better understanding of these alterations might help to develop new approaches for the diagnosis, prediction, and treatment of chronic pain. Here, we analyzed associations between chronic pain and alterations of intrinsic brain network function using resting-state electroencephalography. We included data from 537 people with chronic pain obtained from various research groups worldwide. We found strong evidence for associations between pain intensity and intrinsic brain network connectivity, but the replicability of these associations in independent data was mostly low. However, a mega-analysis revealed associations of chronic pain with salience-somatomotor network connectivity at theta frequencies and with more complex patterns of intrinsic brain network connectivity. These findings provide novel insights into brain network function in chronic pain. Moreover, they highlight the need for collaborative multi-center studies, which can be guided by the present approach to promote replicability and consistency of findings.

neuroscience↗

bioRxiv2023

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher- dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets, the LEMON dataset, containing 213 EEG recordings of healthy participants, and the TDBRAIN dataset, containing 1274 EEG recordings, mainly from patients with a psychiatric condition. Additionally, we performed an exploratory analysis of the LEMON dataset that could inspire biomarkers of healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.

neuroscience↗

bioRxiv2022

Local brain oscillations and inter-regional connectivity differentially serve sensory and expectation effects on pain

Pain emerges from the integration of sensory information about threats and contextual information such as an individuals expectations. However, how sensory and contextual effects on pain are served by the brain is not fully understood so far. To address this question, we applied brief painful stimuli to 40 healthy human participants and independently varied stimulus intensity and expectations. Concurrently, we recorded electroencephalography. We assessed local oscillatory brain activity and inter-regional functional connectivity in a network of six brain regions playing key roles in the processing of pain. We found that sensory information predominantly influenced local brain oscillations. In contrast, expectations exclusively influenced inter-regional connectivity. Specifically, expectations altered connectivity at alpha (8-12 Hz) frequencies from prefrontal to somatosensory cortex. Moreover, discrepancies between sensory information and expectations, i.e., prediction errors, influenced connectivity at gamma (60-100 Hz) frequencies. These findings reveal how fundamentally different brain mechanisms serve sensory and contextual effects on pain. TeaserSensory and expectation effects on pain are implemented by fundamentally different brain mechanisms.

neuroscience↗

bioRxiv2020

Modulating Brain Rhythms of Pain using Transcranial Alternating Current Stimulation (tACS)? A Sham-controlled Study in Healthy Human Participants

Pain protects the body. However, pain can also occur for longer periods without serving protective functions. Such chronic pain conditions are difficult to treat. Thus, a better understanding of the underlying neural mechanisms and new approaches for the treatment of pain are urgently needed. Here, we investigated a causal role of oscillatory brain activity for pain and explored the potential of transcranial alternating current stimulation (tACS) as a new treatment approach for pain. To this end, we investigated whether tACS can modulate pain and pain-related autonomic activity in 29 healthy human participants using a tonic heat pain paradigm as an experimental model of chronic pain. In 6 recording sessions, participants received tACS over prefrontal or somatosensory cortices at alpha or gamma frequencies or sham tACS. During tACS, pain ratings and autonomic responses were collected. TACS did not modulate pain intensity, the stability of pain ratings or the translation of the noxious stimulus into pain. Likewise, tACS did not change autonomic responses. Bayesian statistics further indicated a lack of tACS effects in most conditions. The only exception was alpha tACS over somatosensory cortex where evidence for tACS effects was inconclusive. Taken together, the present study did not find significant tACS effects on tonic experimental pain in healthy human participants. However, considering the conceptual plausibility of using tACS to modulate pain and the urgent need for novel pain treatments, further tACS studies are warranted. Based on the present findings, such studies might apply refined stimulation protocols targeting alpha oscillations in somatosensory cortices.

neuroscience↗