The Choice-wide Behavioral Association Study: Data-driven Identification of Interpretable Behavioral Components

Overview

Journal bioRxiv

Date 2024 Mar 11

PMID 38464037

Authors

David B Kastner

Greer Williams

Cristofer Holobetz

Joseph P Romano

Peter Dayan

Affiliations

Soon will be listed here.

Abstract

Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method-the choice-wide behavioral association study: CBAS-that systematically identifies such behavioral features. CBAS uses a powerful, resampling-based, method of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.

References

Meister M . Learning, fast and slow. Curr Opin Neurobiol. 2022; 75:102555. PMC: 9509687. DOI: 10.1016/j.conb.2022.102555. View

Geuther B, Chen M, Galante R, Han O, Lian J, George J . High-throughput visual assessment of sleep stages in mice using machine learning. Sleep. 2021; 45(2). PMC: 8842275. DOI: 10.1093/sleep/zsab260. View

Akhund-Zade J, Ho S, OLeary C, de Bivort B . The effect of environmental enrichment on behavioral variability depends on genotype, behavior, and type of enrichment. J Exp Biol. 2019; 222(Pt 19). DOI: 10.1242/jeb.202234. View

Bialek W . On the dimensionality of behavior. Proc Natl Acad Sci U S A. 2022; 119(18):e2021860119. PMC: 9170048. DOI: 10.1073/pnas.2021860119. View

Pereira T, Aldarondo D, Willmore L, Kislin M, Wang S, Murthy M . Fast animal pose estimation using deep neural networks. Nat Methods. 2018; 16(1):117-125. PMC: 6899221. DOI: 10.1038/s41592-018-0234-5. View

Feher da Silva C, Hare T . Humans primarily use model-based inference in the two-stage task. Nat Hum Behav. 2020; 4(10):1053-1066. DOI: 10.1038/s41562-020-0905-y. View

Singer A, Carr M, Karlsson M, Frank L . Hippocampal SWR activity predicts correct decisions during the initial learning of an alternation task. Neuron. 2013; 77(6):1163-73. PMC: 3751175. DOI: 10.1016/j.neuron.2013.01.027. View

Sheppard K, Gardin J, Sabnis G, Peer A, Darrell M, Deats S . Stride-level analysis of mouse open field behavior using deep-learning-based pose estimation. Cell Rep. 2022; 38(2):110231. PMC: 8796662. DOI: 10.1016/j.celrep.2021.110231. View

Akam T, Rodrigues-Vaz I, Marcelo I, Zhang X, Pereira M, Oliveira R . The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection. Neuron. 2020; 109(1):149-163.e7. PMC: 7837117. DOI: 10.1016/j.neuron.2020.10.013. View

10.

Geuther B, Peer A, He H, Sabnis G, Philip V, Kumar V . Action detection using a neural network elucidates the genetics of mouse grooming behavior. Elife. 2021; 10. PMC: 8043749. DOI: 10.7554/eLife.63207. View

11.

Dunn T, Marshall J, Severson K, Aldarondo D, Hildebrand D, Chettih S . Geometric deep learning enables 3D kinematic profiling across species and environments. Nat Methods. 2021; 18(5):564-573. PMC: 8530226. DOI: 10.1038/s41592-021-01106-6. View

12.

Wiltschko A, Johnson M, Iurilli G, Peterson R, Katon J, Pashkovski S . Mapping Sub-Second Structure in Mouse Behavior. Neuron. 2015; 88(6):1121-1135. PMC: 4708087. DOI: 10.1016/j.neuron.2015.11.031. View

13.

Dezfouli A, Griffiths K, Ramos F, Dayan P, Balleine B . Models that learn how humans learn: The case of decision-making and its disorders. PLoS Comput Biol. 2019; 15(6):e1006903. PMC: 6588260. DOI: 10.1371/journal.pcbi.1006903. View

14.

Ioannidis J . Why most published research findings are false. PLoS Med. 2005; 2(8):e124. PMC: 1182327. DOI: 10.1371/journal.pmed.0020124. View

15.

Weinreb C, Pearl J, Lin S, Osman M, Zhang L, Annapragada S . Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. Nat Methods. 2024; 21(7):1329-1339. PMC: 11245396. DOI: 10.1038/s41592-024-02318-2. View

16.

Krakauer J, Ghazanfar A, Gomez-Marin A, MacIver M, Poeppel D . Neuroscience Needs Behavior: Correcting a Reductionist Bias. Neuron. 2017; 93(3):480-490. DOI: 10.1016/j.neuron.2016.12.041. View

17.

Kumar P, Dayan P, Wolfers T . From Complexity to Precision-Charting Decision-Making Through Normative Modeling. JAMA Psychiatry. 2023; 81(2):117-118. DOI: 10.1001/jamapsychiatry.2023.4611. View

18.

Werkhoven Z, Bravin A, Skutt-Kakaria K, Reimers P, Pallares L, Ayroles J . The structure of behavioral variation within a genotype. Elife. 2021; 10. PMC: 8526060. DOI: 10.7554/eLife.64988. View

19.

Border R, Johnson E, Evans L, Smolen A, Berley N, Sullivan P . No Support for Historical Candidate Gene or Candidate Gene-by-Interaction Hypotheses for Major Depression Across Multiple Large Samples. Am J Psychiatry. 2019; 176(5):376-387. PMC: 6548317. DOI: 10.1176/appi.ajp.2018.18070881. View

20.

Fernandez-Ruiz A, Oliva A, de Oliveira E, Rocha-Almeida F, Tingley D, Buzsaki G . Long-duration hippocampal sharp wave ripples improve memory. Science. 2019; 364(6445):1082-1086. PMC: 6693581. DOI: 10.1126/science.aax0758. View