Dataset Information

Dynamics of neuronal oscillations underlying nociceptive response in the mouse primary somatosensory cortex.

ABSTRACT: Pain is caused by tissue injury, inflammatory disease, pathogen invasion, or neuropathy. The perception of pain is attributed to the neuronal activity in the brain. However, the dynamics of neuronal activity underlying pain perception are not fully known. Herein, we examined theta-oscillation dynamics of local field potentials in the primary somatosensory cortex of a mouse model of formalin-induced pain, which usually shows a bimodal behavioral response interposed between pain-free periods. We found that formalin injection exerted a reversible shift in the theta-peak frequency toward a slower frequency. This shift was observed during nociceptive phases but not during the pain-free period and was inversely correlated with instantaneous pain intensity. Furthermore, instantaneous oscillatory analysis indicated that the probability of slow theta oscillations increased during nociceptive phases with an association of augmented slow theta power. Finally, cross-frequency coupling between theta and gamma oscillations indicated that the coupling peak frequency of theta oscillations was also shifted toward slower oscillations without affecting coupling strength or gamma power. Together, these results suggest that the dynamic changes in theta oscillations in the mouse primary somatosensory cortex represent the ongoing status of pain sensation.

SUBMITTER: Iwamoto S

PROVIDER: S-EPMC7813887 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Dynamics of neuronal oscillations underlying nociceptive response in the mouse primary somatosensory cortex.

Iwamoto Shosuke S Tamura Makoto M Sasaki Atsushi A Nawano Masao M

Scientific reports 20210118 1

Pain is caused by tissue injury, inflammatory disease, pathogen invasion, or neuropathy. The perception of pain is attributed to the neuronal activity in the brain. However, the dynamics of neuronal activity underlying pain perception are not fully known. Herein, we examined theta-oscillation dynamics of local field potentials in the primary somatosensory cortex of a mouse model of formalin-induced pain, which usually shows a bimodal behavioral response interposed between pain-free periods. We f ...[more]

PMID: 33462296

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Project description:Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

Dataset Information

Dynamics of neuronal oscillations underlying nociceptive response in the mouse primary somatosensory cortex.

Publications

Dynamics of neuronal oscillations underlying nociceptive response in the mouse primary somatosensory cortex.

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