Fallon, N. | Search bioRxiv

bioRxiv2025

White matter reorganization of motor and affective-motivational networks in pain-indifferent carriers of the R221W mutation

Congenital insensitivity to pain (CIP) due to the R221W mutation on the nerve growth factor gene results in reduced peripheral C-nociceptor density and behavioural indifference to painful stimuli. While functional neuroimaging has revealed altered cortical and sub-cortical pain processing in R221W carriers, structural white matter changes remain unexplored and may suggest an anatomical basis of symptoms. Heterozygous R221W carriers (n = 11) and age-, sex-, education-matched controls (n = 11) diffusion tensor imaging data were compared using fixel-based analysis, and complimentary edge and node analyses using graph theory, and network-based statistics. Whole-brain and region of interest (ROI) fixel-based analyses revealed significantly reduced fibre density and fibre-bundle cross-section in brainstem motor tracts of R221W carriers, encompassing the corticospinal pathways, corona radiata, external capsule, cerebellar peduncles, and pontine crossing (p < 0.05). Graph theory analysis of pain-processing ROIs demonstrated reduced local efficiency in right anterior cingulate cortex (ACC) and altered betweenness centrality in bilateral insula and left ACC of R221W carriers. Despite R221W carriers showing higher node degrees in the somatosensory cortex and ACC, these connections had reduced efficiency and integration with cortical network regions. Network-based statistics identified a possible compensatory subnetwork with stronger connectivity from right thalamus to left ACC and left insula in R221W carriers (p < 0.019). These findings suggest that congenitally reduced peripheral nociception could lead to abnormalities in the thalamocortical and motor efferent pathway, but not sensory afferent pathways. The combination of reduced brainstem motor tract integrity and altered cortical network efficiency, alongside potentially compensatory thalamo-cortical connectivity, could support a model of R221W CIP as motor under-reactivity rather than sensory insensitivity.

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↗