AUTHOR=Yamazaki Shuhei J. , Ohara Kazuya , Ito Kentaro , Kokubun Nobuo , Kitanishi Takuma , Takaichi Daisuke , Yamada Yasufumi , Ikejiri Yosuke , Hiramatsu Fumie , Fujita Kosuke , Tanimoto Yuki , Yamazoe-Umemoto Akiko , Hashimoto Koichi , Sato Katsufumi , Yoda Ken , Takahashi Akinori , Ishikawa Yuki , Kamikouchi Azusa , Hiryu Shizuko , Maekawa Takuya , Kimura Koutarou D. TITLE=STEFTR: A Hybrid Versatile Method for State Estimation and Feature Extraction From the Trajectory of Animal Behavior JOURNAL=Frontiers in Neuroscience VOLUME=13 YEAR=2019 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.00626 DOI=10.3389/fnins.2019.00626 ISSN=1662-453X ABSTRACT=

Animal behavior is the final and integrated output of brain activity. Thus, recording and analyzing behavior is critical to understand the underlying brain function. While recording animal behavior has become easier than ever with the development of compact and inexpensive devices, detailed behavioral data analysis requires sufficient prior knowledge and/or high content data such as video images of animal postures, which makes it difficult for most of the animal behavioral data to be efficiently analyzed. Here, we report a versatile method using a hybrid supervised/unsupervised machine learning approach for behavioral state estimation and feature extraction (STEFTR) only from low-content animal trajectory data. To demonstrate the effectiveness of the proposed method, we analyzed trajectory data of worms, fruit flies, rats, and bats in the laboratories, and penguins and flying seabirds in the wild, which were recorded with various methods and span a wide range of spatiotemporal scales—from mm to 1,000 km in space and from sub-seconds to days in time. We successfully estimated several states during behavior and comprehensively extracted characteristic features from a behavioral state and/or a specific experimental condition. Physiological and genetic experiments in worms revealed that the extracted behavioral features reflected specific neural or gene activities. Thus, our method provides a versatile and unbiased way to extract behavioral features from simple trajectory data to understand brain function.