Abstract
The brainstem dorsal column nuclei (DCN) are essential to inform the brain of tactile and proprioceptive events experienced by the body. However, little is known about how ascending somatosensory information is represented in the DCN. Our objective was to investigate the usefulness of high-frequency (HF) and low-frequency (LF) DCN signal features (SFs) in predicting the nerve from which signals were evoked. We also aimed to explore the robustness of DCN SFs and map their relative information content across the brainstem surface. DCN surface potentials were recorded from urethane-anesthetized Wistar rats during sural and peroneal nerve electrical stimulation. Five salient SFs were extracted from each recording electrode of a seven-electrode array. We used a machine learning approach to quantify and rank information content contained within DCN surface-potential signals following peripheral nerve activation. Machine-learning of SF and electrode position combinations was quantified to determine a hierarchy of information importance for resolving the peripheral origin of nerve activation. A supervised back-propagation artificial neural network (ANN) could predict the nerve from which a response was evoked with up to 96.8 ± 0.8% accuracy. Guided by feature-learnability, we maintained high prediction accuracy after reducing ANN algorithm inputs from 35 (5 SFs from 7 electrodes) to 6 (4 SFs from one electrode and 2 SFs from a second electrode). When the number of input features were reduced, the best performing input combinations included HF and LF features. Feature-learnability also revealed that signals recorded from the same midline electrode can be accurately classified when evoked from bilateral nerve pairs, suggesting DCN surface activity asymmetry. Here we demonstrate a novel method for mapping the information content of signal patterns across the DCN surface and show that DCN SFs are robust across a population. Finally, we also show that the DCN is functionally asymmetrically organized, which challenges our current understanding of somatotopic symmetry across the midline at sub-cortical levels.
Highlights
The brainstem dorsal column nuclei (DCN) are one of the first processing centers for ascending somatosensory information, preceding conscious perception in the somatosensory cortex
We found that the mean distance from the brainstem midline to the left electrodes was 479 ± 101 μm, and 453 ± 101 μm to the right electrodes, indicating that on average there was a small shift to the left of approximately 13 μm
We were able to maintain a high level of classification accuracy for determining peripheral nerve types and locations from signal feature (SF) acquired from as little as two electrodes, and we provide further evidence that DCN SFs evoked from bilateral nerve pairs are asymmetrically represented across the DCN surface (Loutit et al, 2017)
Summary
The brainstem DCN are one of the first processing centers for ascending somatosensory information, preceding conscious perception in the somatosensory cortex. There is still little known about how afferent signals are processed in this region. We previously characterized electrical signals acquired from the DCN surface in response to peripheral nerve stimulation demonstrating a range of highly characteristic SFs (Loutit et al, 2017). These SFs are significantly different when evoked from peripheral nerves with compositions of afferents that innervate different peripheral structures – either primarily cutaneous (sural nerve), or a mixture of cutaneous and deep structures (peroneal nerve) – and can, reveal information about the type of sensory input arising from the periphery. The reproducibility and conservation of these SFs within and across different animals suggests that they might be indicative of physiologically relevant neural processes (Loutit et al, 2017)
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