Biology subjects

Hewitt, D.

Publications and source records attributed to Hewitt, D..

5 recordsLinked to original sources

bioRxiv2025

Tonic pain revalues associative memories of phasic pain

Tonic pain is proposed to adapt protective behaviours during recovery from injury. A key untested prediction of this homeostatic model is that it appropriately reshapes internal representations of phasic pain. We investigated whether lateralised tonic pain modulates phasic pain-predictive cues on that side. Using a virtual-reality Pavlovian revaluation paradigm, we assessed physiological and neural conditioned responses with EEG in theta, alpha, and beta frequency bands. Pain-predictive cues elicited neural enhanced alpha and beta suppression and increased pupil diameter during conditioning acquisition. Critically, tonic pain revalued phasic conditioned responses during extinction, with reduced midfrontal theta synchronisation when the laterality of tonic pain was congruent with predicted phasic pain. Greater tonic pain unpleasantness also enhanced posterior beta suppression for congruent cues. These findings provide evidence for an internal representation of cue-pain associations that is topographically modulated by tonic pain, suggesting that tonic pain actively reconfigures pain predictions, enabling anticipatory protective behaviours.

neuroscience↗

bioRxiv2025

Phasic and tonic pain serve distinct functions during adaptive behaviour

Pain drives self-protective behaviour, and evolutionary theories suggest it acts over different timescales to serve distinct functions. Whilst phasic pain provides a teaching signal to drive avoidance of new injury, tonic pain is argued to support recuperative behaviour, for instance by reducing motivational vigour. We test this hypothesis in an immersive virtual reality EEG foraging task where subjects harvested fruit in a forest: some fruit elicited brief phasic pain to the grasping hand, and this reduced choice probability. Simultaneously, tonic pressure pain to the contralateral upper arm was associated with reduced action velocities. This could be explained by a free-operant computational framework that formalises and quantifies the function of tonic and phasic pain in terms of motivational vigour and decision value, and model parameters correlated with physiological and neural responses. Overall, the results show how tonic and phasic pain subserve distinct objective motivational functions that support harm minimisation during ongoing adaptive behaviour.

neuroscience↗

bioRxiv2025

Physical confinement regulates transition in nematode motility

How do worms navigate their complex natural surroundings? Undulatory microswimmers such as nematodes typically inhabit environments such as soil, vegetable matter, and host tissues. While the natural habitats of nematodes are often three-dimensional granular niches with spatiotemporally varying visco-elasto-plastic material properties that impose physical constraints on their motion, current knowledge about nematode motility patterns broadly comes from investigating model organisms such as Caenorhabditis elegans either inside liquid cultures or the surface of soft agar pads. How nematodes move through 3D granular niches across different degrees of physical confinement remains poorly understood due to a lack of optically transparent 3D granular matrices. We bridge this gap by engineering an optically transparent granular matrix to directly visualise and quantitatively analyse nematode motion. Importantly, nematodes can freely move through this matrix by generating a minimal yield stress; once the nematode moves away, the matrix self-heals to ensure the material properties remain invariant. Using these platforms, we observe that the propulsive speed of nematodes shows a non-monotonic relation with the yield stress of their microenvironment. This non-monotonicity emerges as nematodes optimize for efficient navigation at higher yield stress, wherein, their forward propulsive speed matches the wave speed along their body. This regulation of locomotory behaviour is purely dictated by the physical interaction of the nematode with its environment without involving soft-touch sensory neurons. Remarkably, predictions from a slender body theory of undulatory motion exactly capture the scaling behaviour for both efficiency and mode of motion as obtained from the experimental data. Finally, in a phase space described by non-dimensional propulsive efficiency and a non-dimensional time scale of motility, we capture a gait transition from poorly efficient thrashing under low confinement to more efficient crawling under high confinement. Thus, our work establishes a new regulatory paradigm describing how distinct modes of undulatory motion emerge under different degrees of physical confinement.

biophysics↗

bioRxiv2025

Is cultural context the crucial touch? Neurophysiological and self-reported responses to affective touch in women in South Africa and the United Kingdom.

Affective touch, involving touch-sensitive C-tactile (CT-) afferent nerve fibres, is integral to human development and wellbeing. Despite presumed cultural differences, affective touch research typically includes Western, minority-world contexts, with findings extrapolated cross-culturally. We report the first cross-cultural study to experimentally investigate subjective and neurophysiological correlates of affective touch in women in South Africa (SA) and the United Kingdom (UK) using (1) subjective touch ratings, and (2) cortical oscillations for slow CT-optimal (vs. faster non-CT-optimal) touch on two body regions (arm, palm). Cultural context modulated affective touch experiences: SA (vs. UK) participants rated touch as more positive and less intense, with enhanced differentiation in sensorimotor beta band oscillations, especially during palm touch. UK participants differentiated between stroking speeds, with opposite directions of effects at arm and palm for frontal theta oscillations. Results highlight the importance of cultural context in subjective experience and neural processing of affective touch.

neuroscience↗

bioRxiv2023

External Validation of Machine Learning and EEG for Continuous Pain Intensity Prediction in Healthy Individuals

Previous research has predicted subjective pain intensity from electroencephalographic (EEG) data using machine learning (ML) models. However, there is a paucity of externally validated ML models for pain assessment, particularly for continuous pain prediction (e.g., decoding pain ratings on a 101-point scale). We aimed to conduct the first external validation paradigm for ML regression models for pain intensity prediction from EEG data. Ninety-one subjects were recruited across three samples. Sample one (n = 40) was used for model development, sample two (n = 51) was used as a cross-subject external validation set, whilst sample three (n = 25) was used as a within-subjects temporal external validation set. Pneumatic pressure stimuli were delivered to the left-hand index fingernail bed at 10 graded intensity levels. Single-trial time-frequency features of peri-stimulus EEG were used to train a Random Forest (RF) model and long short-term memory (LSTM) network to predict pain intensity responses. Results demonstrated that both the RF model and LSTM network predicted pain intensity significantly more accurately than a random prediction model, with the mean absolute error (MAE) of the RF (best performing model) at 19.59, 21.29, and 18.90 for internal validation, cross-subject external validation, and within-subject external validation, respectively. However, neither model was able to predict pain intensity better than a baseline dummy model, which predicted the mean behavioural rating of the training set and did not have access to neural data. Moreover, in a replication of our recent work, we developed a RF model for the classification of low and high-pain trials, which demonstrated internal and external validation accuracies up to 64% and 58%, respectively. Taken together, our results suggest that using ML and EEG to predict continuous pain ratings is not currently feasible. However, classification models demonstrate some potential, consistently outperforming chance across validation samples. Further improvements such as composite measures are required to elevate ML performance to a clinically meaningful level.

neuroscience↗