Motor-evoked potentials after focal electrical stimulation predict drug-induced convulsion potentials in rats

Kuga, Keisuke; Ozaki, Harushige; Fujiki, Minoru

doi:10.1080/15376516.2022.2101038

Cited by 2 publications

(2 citation statements)

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“…In addition, in vivo procedures encompassing the measurement of liquid markers and electrophysiological and imaging modalities have been performed for animal seizure potential assessments. However, their invasive nature and intricate implementation often hinder their practicality (Engel and Pitkänen, 2020;Kuga et al, 2023). From the perspective of animal welfare and shortening drug development time, less intricate evaluation methods are required to reasonably integrate testing into the non-clinical development stage of drugs.…”

Section: Introductionmentioning

confidence: 99%

Novel predictive approaches for drug-induced convulsions in non-human primates using machine learning and heart rate variability analysis

Kuga,

Shiotani,

Hori

et al. 2024

J. Toxicol. Sci.

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Drug-induced convulsions are a major challenge to drug development because of the lack of reliable biomarkers. Using machine learning, our previous research indicated the potential use of an index derived from heart rate variability (HRV) analysis in non-human primates as a biomarker for convulsions induced by GABA A receptor antagonists. The present study aimed to explore the application of this methodology to other convulsants and evaluate its specificity by testing non-convulsants that affect the autonomic nervous system. Telemetry-implanted males were administered various convulsants (4-aminopyridine, bupropion, kainic acid, and ranolazine) at different doses. Electrocardiogram data gathered during the pre-dose period were employed as training data, and the convulsive potential was evaluated using HRV and multivariate statistical process control. Our findings show that the Q-statistic-derived convulsive index for 4-aminopyridine increased at doses lower than that of the convulsive dose. Increases were also observed for kainic acid and ranolazine at convulsive doses, whereas bupropion did not change the index up to the highest dose (1/3 of the convulsive dose). When the same analysis was applied to nonconvulsants (atropine, atenolol, and clonidine), an increase in the index was noted. Thus, the index elevation appeared to correlate with or even predict alterations in autonomic nerve activity indices, implying that this method might be regarded as a sensitive index to fluctuations within the autonomic nervous system. Despite potential false positives, this methodology offers valuable insights into predicting druginduced convulsions when the pharmacological profile is used to carefully choose a compound.

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Section: Introductionmentioning

confidence: 99%

Novel predictive approaches for drug-induced convulsions in non-human primates using machine learning and heart rate variability analysis

Kuga,

Shiotani,

Hori

et al. 2024

J. Toxicol. Sci.

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“…Whilst this indicates that brain activity changes during movement preparation are associated with incentive-induced (reward/punishment) improvements in motor performance, the origins of such CSE changes remain unclear. Namely, changes in CSE reflect the excitability of cortical, subcortical, as well as spinal structures 12 , which can further reflect an increase in glutamatergic (GLUTergic) and/or a decrease in gammaaminobutyric acid (GABA)ergic activity 13,14 . Importantly, rewards and punishments are increasingly recognised as potential enhancers of rehabilitation following physical and brain insults [15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

The Neurochemical Mechanisms Underlying the Enhancing Effects of Rewards and Punishments on Motor Performance

Hamel

Pearson

Sifi

et al. 2023

Preprint

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Monetary rewards and punishments enhance motor performance and are associated with corticospinal excitability (CSE) increases within the motor cortex (M1) during movement preparation. However, such CSE changes have unclear origins; they could stem from increased glutamatergic (GLUTergic) facilitation and/or decreased type A gamma-aminobutyric acid (GABAA)-mediated inhibition within M1. To investigate this, paired-pulse transcranial magnetic stimulation was used to assess GLUTergic facilitation and GABAA inhibition within M1 whilst participants prepared to execute 4-element finger-press sequences. Behaviourally, rewards and punishments enhanced both reaction and movement times. Neurochemically, regardless of rewards or punishments, a digit-specific increase in GLUTergic facilitation and digit-unspecific decrease in GABAA inhibition occurred during preparation as movement onset approached. In parallel, both rewards and punishments non-specifically increased GLUTergic facilitation, but only rewards non-specifically decreased GABAA inhibition during preparation. This suggests that, to enhance performance, rewards both increase GLUTergic facilitation and decrease GABAA inhibition whilst punishments selectively increase GLUTergic facilitation. A control experiment revealed that such changes were not observed post-movement as participants processed reward and punishment feedback, indicating they were selective to movement preparation. Collectively, these results map the neurochemical changes in M1 by which incentives enhance motor performance.

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Motor-evoked potentials after focal electrical stimulation predict drug-induced convulsion potentials in rats

Cited by 2 publications

References 23 publications

Novel predictive approaches for drug-induced convulsions in non-human primates using machine learning and heart rate variability analysis

Novel predictive approaches for drug-induced convulsions in non-human primates using machine learning and heart rate variability analysis

The Neurochemical Mechanisms Underlying the Enhancing Effects of Rewards and Punishments on Motor Performance

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