Biology subjects

Toyama, A.

Publications and source records attributed to Toyama, A..

4 recordsLinked to original sources

bioRxiv2026

Vision-language representations link food perception to subjective valuation

Subjective values of foods guide dietary decisions. Yet, how the brain computes such values from visual input remains unclear. Here, we show that visual food valuation relies on higher-level conceptual representations captured by a multimodal vision-language deep neural network model. Using food images and human ratings (N = 194 and 200), we found that a vision-language model predicted subjective value more accurately than vision-only models. Representational analyses demonstrated that, compared with the vision-only model, the vision-language model preferentially encoded higher-level food attributes, including perceived healthiness, while encoding less image-level visual information. Multivariate analyses of fMRI data (N = 31) showed that embedding features from both models corresponded to activity patterns along the visual pathways. Critically, vision-language features showed greater correspondence than vision-only features in the fusiform gyrus, a downstream region of the ventral visual pathway. These findings suggest that higher-level conceptual representations link visual food processing to subjective valuation.

neuroscience↗

bioRxiv2026

A densely sampled fMRI dataset for investigating food valuation

Subjective valuation of food rewards guides our dietary choices and is fundamental to human health and well-being. Extensive literature in human functional magnetic resonance imaging (fMRI) studies has consistently shown that a network of reward-processing brain regions, including the ventromedial prefrontal cortex (vmPFC) and ventral striatum, encodes the subjective values of food rewards. However, the representational geometry of value signals and the mechanisms by which they are constructed in the brain remain poorly understood. This is partly because most fMRI studies on food valuation rely on small stimulus sets, yielding datasets too shallow for advanced analyses such as multi-voxel pattern analysis, and deep neural network modeling. Here, we present a densely sampled fMRI dataset wherein 31 participants provided subjective value ratings for over 500 food images across three separate days. We validate the dataset by replicating the well-established findings regarding the neural encoding of subjective value in the vmPFC and ventral striatum. We anticipate this resource will facilitate diverse studies on neural food valuation using advanced analytical methods.

neuroscience↗

bioRxiv2024

Quantification of Cyclin-CDK dissociation constants in living cells using fluorescence cross-correlation spectroscopy with green and near-infrared fluorescent proteins.

The cell cycle is a highly coordinated process governed by cyclin-bound cyclin-dependent kinases (CDKs). While the interaction between cyclin and CDK are well-documented, the dissociation constants (Kd) between specific cyclin-CDK pairs within living cells remain poorly understood. Fluorescence cross-correlation spectroscopy (FCCS) enables the quantification of the Kd, but challenges remain in selecting an optimal pair of fluorescent molecules for FCCS in a living cell. In this study, we demonstrate that mNeonGreen and phycocyanobilin-bound miRFP670 represent a suitable pair for FCCS in living cells from the viewpoint of high photostability and low bleed-through. This fluorescent protein pair enables us to measure the Kd values of the cyclin-dependent kinase Cdc2 and B-type cyclin Cdc13 in fission yeast cells. Moreover, we conducted a comprehensive analysis of the Kd values for 36 cyclin-CDK complexes, formed by 9 distinct cyclins and 4 CDKs, in mammalian cells, including unconventional cyclin-CDK pairs. These findings provide insights into the redundancy of cyclin-CDK binding in cell cycle progression, with potential implications for understanding cell cycle regulation in both fission yeast and higher eukaryotes.

cell biology↗

bioRxiv2022

Cardiac cycle affects the asymmetric value updating in instrumental reward learning

This study aimed to investigate whether instrumental reward learning is affected by the cardiac cycle. To this end, we examined the effects of the cardiac cycle (systole or diastole) on the computational processes underlying the participants choices in the instrumental learning task. In the instrumental learning task, participants were required to select one of two discriminative stimuli (neutral visual stimuli) and immediately receive reward/punishment feedback depending on the probability assigned to the chosen stimuli. To manipulate the cardiac cycle, the presentation of discriminative stimuli was timed to coincide with either cardiac systole or diastole. We fitted the participants choices in the task with reinforcement learning (RL) models and estimated parameters involving instrumental learning (i.e., learning rate and inverse temperature) separately in the systole and diastole trials. Model-based analysis revealed that the learning rate for positive prediction errors was higher than that for negative prediction errors in the systole trials; however, learning rates did not differ between positive and negative prediction errors in the diastole trials. These results demonstrate that the natural fluctuation of cardiac afferent signals can affect asymmetric value updating in instrumental reward learning.

neuroscience↗