Imaging data for Representations of the intrinsic value of information in mouse orbitofrontal cortex

These data were used in the analysis and production of the paper "Representations of the intrinsic value of information in mouse orbitofrontal cortex."Animals are motivated to seek information that does not influence reward outcomes, suggesting that information has intrinsic value. We developed an odor-based information seeking task that revealed that mice choose to receive information even though it does not alter the reward outcome. Moreover, mice were willing to pay for information by sacrificing water reward, suggesting that information is of intrinsic value to a mouse. We used a microendoscope to reveal neural activity in orbitofrontal cortex (OFC) while mice learned the information seeking task. We observed the emergence of distinct representations of odors predictive of information and odors predictive of water reward. A latent variable model recapitulated these different representations in the low-dimensional dynamics of OFC neuronal population activity. These data suggest that mice have evolved separate pathways to represent the intrinsic value of information and the extrinsic value of water reward. Thus, the desire to acquire knowledge is observed in mice, and the value of this information is represented in the OFC. The mouse now provides a facile experimental system to study the representation of the value of information, a higher cognitive variable.The code used for data collection and analyses in the paper is available at https://github.com/jjbussell/Bussell2026InfoValue.The dataset here contains the preprocessed and motion corrected single-photon calcium imaging files, one file per behavioral training session. Mice were injected with GCaMP6f virus (AAV1.CaMK2a.GCaMP6f.WPRE. bGHpA, 3.35x10^12 vG/mL, Inscopix) into lateral orbitofrontal cortex at coordinates medial-lateral: 1.0; anterior-posterior: 2.4; dorsal-ventral: 2.45mm relative to Bregma. Imaging data were acquired using Inscopix nVista3.0 systems during training on the information seeking task (and variants) described in Bussell et al. Raw video data were spatially downsampled at 4x and saved as Tiff files using the Inscopix Python API. We used the NormCorre rigid motion algorithm to both remove movement artifacts from imaging videos and to align the field of view across sessions to facilitate cell registration. Details are available in the paper Methods section.Filenames begin with the mouseID, recording session date, and time, and then indicate that they have been preprocessed and motion corrected and aligned to a single reference session per animal. Filenames without a prefix belong to the "main" dataset of the primary information seeking task.Those prefixed by "Learning" comprise data surrounding the introduction of information and probabilistic reward and were used to observe the emergence of information-predicting neural activity.Those prefixed by "Delay" were used in delay manipulation and control experiments to observe changes in the representation of predicted information with shorter periods of uncertainty (task reward delay).Those prefixed by "WaterValue" comprise the dataset from the task with 4 center port odors used to compare representations of predicted information and water reward value.Complete inventories of imaging sessions and their use in different analyses are available in the Supplementary Data published online with the paper.This work was supported by the Simons Foundation (J.J.B., L.F.A, R.P.B., C.D.M., K.R), the Howard Hughes Medical Institute (R.A.), NIH Grants MH106435, R01MH110594, R01MH116937 (to Ilya Monosov) and RF1 DA056403 (R.P.B., C.D.M., K.R.), an NSF CAREER Award (2427124, K.R.), the James S. McDonnell Foundation (220020466, K.R.), the McKnight Endowment Fund (K.R.), the Gatsby Charitable Foundation (L.F.A.), and the CIFAR Azrieli Global Scholar Program (R.P.B., C.D.M., K.R.). This work has been made possible in part by a gift from the Chan Zuckerberg Initiative Foundation to establish the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University.