Biology subjects

Chikazoe, J.

Publications and source records attributed to Chikazoe, J..

11 recordsLinked to original sources

bioRxiv2026

Multi-modal Ensemble Approach for Decoding Player Intentions in Table Tennis

This study aims to predict human intentions during intense sports activities, specifically in table tennis. Using a publicly available Real World Table Tennis dataset containing simultaneous EEG and video recordings, we developed a series of subject-specific classifiers for nine players (7 males and 2 females; age range 18-30), based on pose features and EEG signals. The pose-based classifier used a stochastic gradient descent model with logistic loss, whereas the EEG-based classifier employed a modified convolutional neural network architecture (EEGNet). Both classifiers successfully predicted left-right attack intentions from the time windows preceding racket-ball impact, with optimal decoding occurring at -100 ms for pose features and -500 ms for EEG signals. Although EEG-based decoding achieved comparable performance to pose-based decoding, a multi-modal ensemble further improved prediction significantly, reaching a mean macro F1 score of 0.563 (bootstrapped 95% CI: 0.523-0.603), corresponding to gains of +0.03 over pose-only and +0.02 over EEG-only classifiers. Because each classifier is trained independently, the ensemble can be feasibly extended to incorporate additional modalities in the future. These results suggest potential relevance of this framework as neurofeedback tools for sports training and neural prosthetic systems in future work.

neuroscience↗

bioRxiv2026

Cross Domain Consistency of Aesthetic Preference-driven Social Behavior

Aesthetic preference is a primary driver of social behavior in the digital era, yet the extent to which these preferences remain consistent across disparate domains remains poorly understood. We hypothesize that aesthetic judgment is governed by a domain-invariant latent structure, such that individuals who exhibit similar preferences in one category will demonstrate comparable alignment in seemingly unrelated domains. To test this, we recruited 37 participants to evaluate stimuli across three distinct aesthetic domains: art, faces (male and female), and scenes. We developed a novel computational framework that reformulates cross-domain preference as a user-based collaborative filtering problem, encoding individual profiles through inter-subject similarity matrices. Our model successfully predicted participant responses in a target domain based on their similarity to the cohort in a separate source domain. These results demonstrate robust cross-domain consistency, suggesting that aesthetic evaluation is mediated by an abstract, domain-general mechanism rather than being purely stimulus-dependent. We propose that this consistency is rooted in a shared neurophysiological pathway, likely involving the orbitofrontal cortex (OFC) and the Default Mode Network (DMN), and discuss how these findings provide a foundation for more sophisticated, cross-modal recommendation systems and the study of individual social identity.

neuroscience↗

bioRxiv2024

Enhancing Prediction of Human Traits and Behaviors through Ensemble Learning of Traditional and Novel Resting-State fMRI Connectivity Analyses

Recent efforts in cognitive neuroscience have focused on leveraging resting-state functional connectivity (RSFC) data from fMRI to predict human traits and behaviors more accurately. Traditional methods typically analyze RSFC by correlating averaged time-series data between regions of interest (ROIs) or networks, a process that may overlook critical spatial signal patterns. To address this limitation, we introduced a novel linear regression technique that estimates RSFC by predicting spatial brain activity patterns in a target ROI from those in a seed ROI. We applied both traditional and our novel RSFC estimation methods to a large-scale dataset from the Human Connectome Project, analyzing resting-state fMRI data to predict sex, age, personality traits, and psychological task performance. Additionally, we developed an ensemble learner that integrates these methods using a weighted average approach to enhance prediction accuracy. Our findings revealed that hierarchical clustering of RSFC patterns using our novel method displays distinct whole-brain grouping patterns compared to the traditional approach. Importantly, the ensemble model outperformed the traditional RSFC method in predicting human traits and behaviors. Notably, the predictions from the traditional and novel methods showed relatively low similarity, indicating that our novel approach captures unique and previously undetected information about human traits and behaviors through fine-grained local spatial patterns of neural activation. These results highlight the potential of combining traditional and innovative RSFC analysis techniques to enrich our understanding of the neural basis of human traits and behaviors.

neuroscience↗

bioRxiv2024

Healthy dietary choices involve prefrontal mechanisms associated with long-term reward maximization but not cognitive control

Taste and health are critical factors to be considered when choosing foods. Prioritizing healthiness over tastiness requires self-control. It has also been suggested that self-control is guided by cognitive control. We then hypothesized that neural mechanisms underlying healthy food choice are associated with both self-control and cognitive control. Human participants performed a food choice task and a working memory (WM) task during functional MRI scanning. Their degree of self-control was assessed behaviorally by the value discount of delayed monetary rewards in intertemporal choice. Prioritizing healthiness in food choice was associated with greater activity in the superior, dorsolateral, and medial prefrontal cortices. Importantly, the prefrontal activity was greater in individuals with smaller delay discounting (i.e., high self-control) who preferred a delayed larger reward to an immediate smaller reward in intertemporal choice. On the other hand, WM activity did not show a correlation with delay discounting or food choice activity, which was further supported by supplementary results that analyzed data from the Human Connectome Project. Our results suggest that the prefrontal cortex plays a critical role in healthy food choice, which requires self-control, but not cognitive control, for maximization of reward attainments in a remote future.

neuroscience↗

bioRxiv2023

Cognitive Dynamics Estimation: A whole-brain spatial regression paradigm for extracting the temporal dynamics of cognitive processes

Functional MRI (fMRI) has been instrumental in understanding how cognitive processes are spatially mapped in the brain, yielding insights about brain regions and functions. However, in case the orthogonality of behavioral or stimulus timing is not guaranteed, the estimated brain maps fail to dissociate each cognitive process, and the resultant maps become unstable. Also, the brain mapping exercise can not provide temporal information on the cognitive process. Here we propose a qualitatively different approach to fMRI analysis, named Cognitive Dynamics Estimation (CDE), that estimates how multiple cognitive processes change over time even when behavior or stimulus logs are unavailable. This method transposes the conventional brain mapping; the brain activity pattern at each time point is subject to regression analysis with data-driven maps of cognitive processes as regressors, resulting in the time series of cognitive processes. The estimated time series captured the fluctuation of intensity and timing of cognitive processes on a trial-by-trial basis, which conventional analysis could not capture. Notably, the estimated time series predicted participants cognitive ability to perform each psychological task. As an addition to our fMRI analytic toolkit, these results suggest the potential for CDE to elucidate underexplored cognitive phenomena, especially in the temporal domain. HighlightsWe propose a novel fMRI analysis that are equivalently effective to the brain mapping approach The temporal dynamics of multiple cognitive processes were captured on a trial-by-trial basis. This analysis can be applied data-driven even when the researchers do not have any hypothesis about what kind of cognitive processes are recruited in the task. This analysis can be applied even when the cognitive processes are strongly correlated in the temporal domain. The estimated temporal dynamics well predicted individuals cognitive ability required for each task.

neuroscience↗

bioRxiv2022

Subjectivity of time perception alters choice preference for future rewards through fronto-striatal value signal dynamics

Our preference for a reward depends on the time of delay for its delivery. Here we show that changes in the external environment can manipulate the perceived duration of time, which alters the formation of the choice preference through value signals in the cortical and subcortical brain regions. Humans anticipated a real liquid reward delayed by tens of seconds, during which colors of visually presented panels gradually changed. The color-change delay was perceived as shorter than a control color-constant delay. Interestingly, participants with greater perceptual bias of the delay showed stronger preference for rewards with the color-change delay. The ventrolateral prefrontal cortex (vlPFC) and ventral striatum (VS) showed dynamic neural signatures of value components that were modulated by subjectively perceived duration. Crucially, these effects were specifically observed while a future reward was anticipated, and moreover, the vlPFC activity was weaker in participants with greater bias in the duration perception. These results demonstrate that subjective time experience leads to biased choice preference of delayed rewards, which is regulated by dynamic value signals of future rewards and accurate perception of the external world in the vlPFC-VS systems.

neuroscience↗

bioRxiv2021

Perceptual uncertainty alternates top-down and bottom-up fronto-temporal network signaling during response inhibition

Response inhibition is a primary executive control function that allows the withholding of inappropriate responses, and requires appropriate perception of the external environment to achieve a behavioral goal. It remains unclear, however, how response inhibition is achieved when goal-relevant information involves perceptual uncertainty. Twenty-six human participants of both sexes performed a go/no-go task where visually presented random-dot motion stimuli involved perceptual uncertainties. The right inferior frontal cortex (rIFC) was involved in response inhibition, and the middle temporal (MT) region showed greater activity when dot motions involved less uncertainty. A neocortical temporal region in the superior temporal sulcus (STS) specifically showed greater activity during response inhibition in more perceptually certain trials. In this STS region, activity was greater when response inhibition was successful than when it failed. Directional effective connectivity analysis revealed that in more coherent trials, the MT and STS regions showed enhanced connectivity to the rIFC, whereas in less coherent trials, the signal direction was reversed. These results suggest that a reversible fronto-temporal functional network guides response inhibition under perceptual uncertainty, and in this network, perceptual information in the MT is converted to control information in the rIFC via STS, enabling achievement of response inhibition. Significance statementResponse inhibition refers to withholding inappropriate behavior and is an important for achieving goals. Often, however, decision must be made based on limited environmental evidence. We showed that successful response inhibition is guided by a neocortical temporal region that plays a hub role in converting perceived information coded in a posterior temporal region to control information coded in the prefrontal cortex. Interestingly, when a perceived stimulus becomes more uncertain, the prefrontal cortex supplements stimulus encoding in the temporal regions. Our results highlight fronto-temporal mechanisms of response inhibition in which conversion of stimulus-control information is regulated based on the uncertainty of environmental evidence.

neuroscience↗

bioRxiv2021

Anticipatory dynamics in the human brain guide foraging for primary rewards

Deciding whether to wait for a future reward is crucial for acquiring rewards in an uncertain world and involves anticipating future reward attainment. While seeking for a reward in natural environments, behavioral agents constantly face a trade-off between staying in their current environment or leaving it. It remains unclear, however, how humans make continuous decisions in such situations. Here we show that anticipatory brain activity in the anterior prefrontal cortex (aPFC) and hippocampus underpins continuous stay-leave decision making. Human participants awaited for real liquid rewards available after tens of seconds, and continuous decision was tracked by monitoring dynamic patterns of brain activity. Participants stopped waiting more frequently and sooner after they experienced longer delays and received smaller rewards. When dynamic activity reflecting the anticipation of a future reward was enhanced in the aPFC, participants remained in their current environment, but when this activity diminished, they left the environment for a new one. The anticipatory activity in the aPFC and hippocampus was associated with distinct decision strategies; aPFC activity was enhanced in participants adopting a leave strategy, whereas those remaining stationary showed enhanced activity in the hippocampus. Our results suggest that fronto-hippocampal anticipatory dynamics underlie continuous decision making while anticipating a future reward.

neuroscience↗

bioRxiv2021

On co-activation pattern analysis and non- stationarity of resting brain activity

The non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the dynamic characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, the statistical nature of the dynamics captured by these techniques needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity. HighlightsO_LICAP was conducted for real fMRI data and a stationary null model. C_LIO_LIThe results of CAP analysis were similar for the real and simulated data. C_LIO_LISimilar results for real and simulated data in both ROI- and voxel-based analyses. C_LIO_LIResults of CAP analysis may not reflect non-stationarity of resting brain activity. C_LI

neuroscience↗

bioRxiv2021

Vision-to-value transformations in artificial network and human brains

Humans and now computers can derive subjective valuations from sensory events although such transformation process is essentially unknown. In this study, we elucidated unknown neural mechanisms by comparing convolutional neural networks (CNNs) to their corresponding representations in humans. Specifically, we optimized CNNs to predict aesthetic valuations of paintings and examined the relationship between the CNN representations and brain activity via multivoxel pattern analysis. Primary visual cortex and higher association cortex activities were similar to computations in shallow CNN layers and deeper layers, respectively. The vision-to-value transformation is hence proved to be a hierarchical process which is consistent with the principal gradient that connects unimodal to transmodal brain regions (i.e. default mode network). The activity of the frontal and parietal cortices was approximated by goal-driven CNN. Consequently, representations of the hidden layers of CNNs can be understood and visualized by their correspondence with brain activity-facilitating parallels between artificial intelligence and neuroscience.

neuroscience↗

bioRxiv2020

Flexible decision under ambiguous cues reverses neural signaling from occipitotemporal to prefrontal cortex

Adaptation to changing environments involves the appropriate extraction of environmental information to achieve a behavioral goal. It remains unclear how behavioral flexibility is guided under situations where the relevant behavior is ambiguous. Using functional brain mapping of machine-learning decoders and directional functional connectivity, we show that brain-wide reversible neural signaling underpins task encoding and behavioral flexibility in ambiguously changing environments. When relevant behavior is cued ambiguously during behavioral shifting, neural coding is attenuated in distributed cortical regions, but top-down signals from the prefrontal cortex complement the coding. When behavioral shifting is cued more explicitly, modality-specialized occipitotemporal regions implement distinct neural coding about relevant behavior, and bottom-up signals from the occipitotemporal region to the prefrontal cortex supplement the behavioral shift. These results suggest that our adaptation to an ever-changing world is orchestrated by the alternation of top-down and bottom-up signaling in the fronto-occipitotemporal circuit depending on the availability of environmental information.

neuroscience↗