Research Article
Machine Learning Identifies Negative Emotions Encoded in the Cerebellum
@INPROCEEDINGS{10.1007/978-3-031-86203-8_28, author={Yitong Zhang and Chenxuan Wu and Beiyang Lin and Yongyi Dou and Lizhi Cao and Hao Wei and Tianyi Yan}, title={Machine Learning Identifies Negative Emotions Encoded in the Cerebellum}, proceedings={Wireless and Satellite Systems. 14th EAI International Conference, WiSATS 2024, Harbin, China, August 23--25, 2024, Proceedings, Part II}, proceedings_a={WISATS PART 2}, year={2025}, month={3}, keywords={machine learning local field potential (LFP) power spectra}, doi={10.1007/978-3-031-86203-8_28} }- Yitong Zhang
Chenxuan Wu
Beiyang Lin
Yongyi Dou
Lizhi Cao
Hao Wei
Tianyi Yan
Year: 2025
Machine Learning Identifies Negative Emotions Encoded in the Cerebellum
WISATS PART 2
Springer
DOI: 10.1007/978-3-031-86203-8_28
Abstract
This study investigates the cerebellum’s role in processing stress-induced negative emotions by analyzing neurophysiological signals from the deep cerebellar nuclei (DCN) in mice. We compared signals during chronic restraint stress and the tail suspension test, using implanted microwire electrodes to collect data from various DCN subnuclei, with a focus on the dentate and interstitial nuclei. Seventeen machine learning classifiers were employed to identify emotion encoding within low-frequency (0.5-49Hz) local field potentials (LFPs) in the cerebellar dentate nucleus. Notably, Medium Gaussian SVM, Medium Neural Network, Wide Neural Network, and Bilayered Neural Network demonstrated high accuracy in classifying emotional states via the cerebellar dentate nucleus, interstitial nucleus, and DCN. Our work proposes four classifiers suitable for distinguishing cerebellar negative emotion valence and provides evidence for the cerebellum’s role in emotion encoding from a machine learning perspective. This research offers new insights into the neural circuitry mechanisms of depression and theoretical support for developing novel neuromodulation paradigms to treat depression.
