@inproceedings{5b37adb4b8524f8aaf9fdf80bf3266d2,
title = "Spatio-Temporal Analysis of LTP-like Neuroplasticity in Pigs",
abstract = "In our laboratory, we have recently established a large animal model of LTP-like pain and extracted cortical features as objective measurements of nociception. We have previ-ously reported an increase in the S1 cortical activity for both local-field potentials (LFP) and spike activity up to 90 min after induction of high-frequency stimulation. Our analysis so far has been based on averaging signals obtained from an intracortical array, thus losing any spatial information. The aim of this work was therefore to investigate spatio-temporal neural changes. In-tracortical EEG recordings from pigs (n=7) were acquired using a 16-channel microelectrode array (MEA) placed in S1. To as-sess the cortical response, electrical stimulation was delivered to the ulnar nerve. Each experiment was divided into four blocks (T0-T3). The intervention group (n=5) received LTP between T0 and T1. We extracted the N1-P1 amplitude as a feature in the LFP signal range and the area under the curve (AUC) of the PSTH response as a feature to represent the spike signals. We found that LTP induced spatio-temporal changes in both the LFP and spike activity in the T2 and T3 phases, which is in line with our previous results [1]. However, in the present work, we additionally observed that the location of the maximal activity moved spatially between T0 and T2 (3/5 animals for LFP activity, 4/5 animals for spike activity). Also, we observed a cortical suppression in the T3 phase associ-ated with long-term depression. A more detailed understanding of the cortical response and plasticity to nociception may poten-tially be a more suitable platform to investigate the efficacy of novel drugs to treat pain.",
keywords = "Dyrefors{\o}g, Feature, LTP Model, Mikroelektrode, Neuroplasticitet, Smerte, LFP, LTP, pig, spatio-temporal analysis, spike",
author = "M.B. Danyar and H.F. Clark and N.A. Atchuthan and L.K. Daugbjerg and A.K. Andersen and T.A.M. Janjua and W. Jensen",
year = "2023",
month = may,
day = "19",
doi = "10.1109/NER52421.2023.10123814",
language = "English",
isbn = "978-1-6654-6293-8",
series = "International IEEE/EMBS Conference on Neural Engineering, NER",
publisher = "IEEE",
booktitle = "11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 - Proceedings",
address = "United States",
}