A Pig White Matter Atlas and Common Connectivity Space Provide a Roadmap for the Introduction of a New Animal Model in Translational Neuroscience

Ra, Benn; Mars, RB; Xu, T.; Rodríguez‐Esparragoza, Luis; Montesinos, Paula; Manzano-Patrón, José P; López-Martín, Gonzalo J.; Fuster, Valentín; Sánchez‐González, Javier; Duff, Eugene P.; Ibáñez, Borja

doi:10.21203/rs.3.rs-105759/v1

Cited by 5 publications

(2 citation statements)

References 58 publications

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“…Finally, detailed comparative anatomy of human and swine brain is lacking, and the electrodes placement may not have captured the activity of the primary visual cortex. Yet, published anatomical studies support the positioning adopted in the present study [ 23 , 24 ].…”

Section: Discussionsupporting

confidence: 83%

Spatio-temporal electroencephalographic power distribution in experimental pigs receiving propofol

Mirra,

Hight,

Spadavecchia

et al. 2024

PLoS ONE

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Introduction When assessing the spatio-temporal distribution of electroencephalographic (EEG) activity, characteristic patterns have been identified for several anesthetic drugs in humans. A shift in EEG power from the occipital to the prefrontal regions has been widely observed during anesthesia induction. This has been called “anteriorization” and has been correlated with loss of consciousness in humans. The spatio-temporal distribution of EEG spectral power in pigs and its modulation by anesthetics have not been described previously. The aim of the present study was to analyze EEG power across an anterior-posterior axis in pigs receiving increasing doses of propofol to 1) characterize the region of highest EEG power during wakefulness, 2) depict its spatio-temporal modification during propofol infusion, and 3) determine the region demonstrating the most significant modulations across different doses administered. Materials and methods Six pigs with a body weight of 33.3 ± 3.6 kg and aged 11.3 ± 0.5 weeks were included in a prospective experimental study. Electroencephalographic activity was collected at the occipital, parietal and prefrontal regions at increasing doses of propofol (starting at 10 mg kg-1 h-1 and increasing it by 10 mg kg-1 h-1 every 15 minutes). The EEG power was assessed using a generalized linear mixed model in which propofol doses and regions were treated as fixed effects, whereas pig was used as a random effect. Pairwise comparisons of marginal linear predictions were used to assess the change in power when the specific propofol dose (or region) was considered. Results During both wakefulness and propofol infusion, the highest EEG power was located in the prefrontal region (p<0.001). The EEG power, both total and for each frequency band, mostly followed the same pattern, increasing from awake until propofol 20 mg kg-1 h-1 and then decreasing at propofol 30 mg kg-1 h-1. The region showing the strongest differences in EEG power across propofol doses was the prefrontal. Conclusion In juvenile pigs receiving increasing doses of propofol, the prefrontal region showed the highest EEG power both during wakefulness and propofol administration and was the area in which the largest frequency-band specific variations were observed across different anesthetic doses. The assessment of the spectral EEG activity at this region could be favorable to distinguish DoA levels in pigs.

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Section: Discussionsupporting

confidence: 83%

Spatio-temporal electroencephalographic power distribution in experimental pigs receiving propofol

Mirra,

Hight,

Spadavecchia

et al. 2024

PLoS ONE

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“…To demonstrate the feasibility of applying our U-Net tool in other primates, and other mammals, we included chimpanzee data ( N =1) from a repository of previously collected scans (the National Chimpanzee Brain Resource; https://www.chimpanzeebrain.org ), the common marmoset template data ( N =1) from the Riken marmoset atlas ( https://brainatlas.brain.riken.jp/marmoset_html ), a common marmoset dataset ( N =5) from the coauthor SS, and a pig dataset ( N =5) ( Benn et al, 2020 ). We directly applied the U-Net 12+7 model to the chimpanzee data ( Fig.…”

Section: Resultsmentioning

confidence: 99%

U-net model for brain extraction: Trained on humans for transfer to non-human primates

Wang

Cho

et al. 2021

NeuroImage

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Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction .

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Affinity of structural white matter tracts between infant and adult pig

Sun,

Ahmed,

Dubrof

et al. 2024

Journal of Neuroscience Methods

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A Pig White Matter Atlas and Common Connectivity Space Provide a Roadmap for the Introduction of a New Animal Model in Translational Neuroscience

Cited by 5 publications

References 58 publications

Spatio-temporal electroencephalographic power distribution in experimental pigs receiving propofol

Spatio-temporal electroencephalographic power distribution in experimental pigs receiving propofol

U-net model for brain extraction: Trained on humans for transfer to non-human primates

Affinity of structural white matter tracts between infant and adult pig

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