Biology subjects

Jimura, K.

Publications and source records attributed to Jimura, K..

15 recordsLinked to original sources

bioRxiv2026

Human parietal retrieval states prioritize an absent temporal reference to reconstruct event order

How the brain judges when something happened is thought to involve reconstruction--deriving temporal judgments from information recovered from memory rather than simply reading out a fixed temporal code. Testing this has been difficult because conventional tasks present both compared items at retrieval, so reinstatement could reflect cue processing as much as memory recovery. Here we dissociated what a judgment requires from what its cue supplies: during fMRI scanning, participants (n = 55) judged a targets temporal position relative to a milestone that was never shown at retrieval. We found that retrieval patterns in the precuneus and angular gyrus resembled the absent milestone more than the target, a bias strongest in the precuneus. Moreover, trial-by-trial similarity to the encoded path linking milestone to target--rather than to either alone--predicted correct judgments independently of retrieval activation. Human parietal retrieval is thus configured more by what a judgment requires than by what the cue supplies, providing content-specific representational evidence for reconstructive temporal-order memory.

neuroscience↗

bioRxiv2026

Beyond Temporary Scaffolding: Persistent Hippocampal Engagement and Brain-Wide Network Reorganization in Remote Memory Consolidation

The standard model of memory consolidation posits a temporary role for the hippocampus. We challenge this view by combining a novel face-name associative memory task with cross-modal decoding of fMRI data. We demonstrate that the hippocampus remains persistently engaged in representing both recent and remote memories, with distinct subregional contributions: recent memories were represented in the right posterior hippocampus, whereas remote memories additionally recruited the left anterior hippocampus. This sustained hippocampal involvement occurred alongside a large-scale network reorganization. Remote memory retrieval was accompanied by increased global connectivity among semantic regions, yet the hippocampus itself did not emerge as a major hub. Instead, network hubs shifted from the medial prefrontal cortex (mPFC) for recent memories to the mPFC and bilateral inferior parietal lobule for remote memories. These findings reveal a dynamic process in which the hippocampus preserves long-lasting representational functions while the broader memory network undergoes extensive reorganization.

neuroscience↗

bioRxiv2026

Hippocampal-midcingulate connectivity is associated with representational integration during cognitive map updating

Cognitive maps organize experience into structured internal models that support goal-directed behavior. Although map formation has been extensively studied in the human hippocampus, the neural mechanisms supporting flexible updating of established maps remain unclear. We used fMRI to track the formation and updating of a spatial cognitive map across three days. Participants learned object-location associations and later updated them by remapping objects among fixed locations. Representational similarity analysis revealed two concurrent signatures in the right hippocampus: prior relational structure remained differentiated, whereas updated associations became integrated. Map updating engaged a frontoparietal network and modulated hippocampal-cortical connectivity. Critically, hippocampal-midcingulate connectivity was associated with the integration of updated associations, but not with the differentiation of prior relational structure. These results suggest that cognitive map updating involves coordinated hippocampal-cortical interactions that incorporate new information while preserving prior relational structure, reflecting a balance of stability and plasticity in internal models.

neuroscience↗

bioRxiv2026

System identification and surrogate data analyses imply approximate Gaussianity and non-stationarity of resting-brain dynamics

Compared with model-based and phenomenological descriptions of the spatiotemporal dynamics of resting-brain activity, statistical characterizations of resting-state fMRI (rs-fMRI) data remain relatively underexplored. Some sophisticated analysis techniques, such as Mapper-based topological data analysis (TDA) and innovation-driven coactivation pattern analysis (iCAP), can distinguish real data from phase-randomized (PR) surrogates, suggesting that rs-fMRI data are not as simple as stationary Gaussian processes. However, the exact statistical properties that distinguish real rs-fMRI data from PR surrogates have not yet been determined. In this study, we conducted system identification analysis and surrogate data analysis to specify key statistical properties that allow TDA and iCAP to discriminate real rs-fMRI data from PR surrogates. We first analyzed rs-fMRI data concatenated across scans using autoregressive (AR) modeling and found that the scan-concatenated rs-fMRI data were weakly non-Gaussian. However, non-Gaussianity alone was insufficient to reproduce realistic TDA and iCAP results because of non-stationarity across scans. AR modeling of single-scan data revealed that rs-fMRI data were statistically indistinguishable from a Gaussian distribution within a single scan, although TDA and iCAP results still differed between the real data and PR surrogates. A new surrogate dataset designed to preserve non-stationarity successfully reproduced realistic TDA and iCAP results, suggesting that TDA and iCAP likely capture the non-stationarity of rs-fMRI data to distinguish it from PR surrogates. Together, these results indicate approximate Gaussianity and non-stationarity in rs-fMRI data, providing a data-driven and statistical characterization of resting-state brain activity that can serve as a quantitative reference for whole brain simulations and generative models.

neuroscience↗

bioRxiv2024

Linear models replicate the energy landscape and dynamics of resting-state brain activity

The spatiotemporal dynamics of resting-state brain activity can be characterized by switching between multiple brain states, and numerous techniques have been developed to extract such dynamic features from resting-state functional magnetic resonance imaging (fMRI) data. However, many of these techniques are based on momentary temporal correlation and co-activation patterns and merely reflect stationary, linear features of the data, suggesting that the non-stationary dynamic features extracted by these techniques may be misinterpreted. To examine whether such misinterpretations occur when using techniques that are not based on momentary temporal correlation or co-activation patterns, we addressed Energy Landscape Analysis (ELA), a statistical physics-inspired method that was designed to extract multiple brain states and dynamics of resting-state fMRI data. We found that the ELA-derived features were almost identical for real data and surrogate data suggesting that the features derived from ELA were accounted for by stationary and linear properties of the real data rather than non-stationary or non-linear properties. To confirm that surrogate data were distinct from the real data, we replicated a previous finding that some topological properties of resting-state fMRI data differed between the real and surrogate data. Overall, the present finding that the energy landscape and activity dynamics of resting-state fMRI data were well captured by stationary and linear surrogate data supports the notion that linear models sufficiently describe the dynamics of resting-state brain activity.

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

Multi-modal deep neural decoding of visual object representation in humans

Perception and categorization of objects in a visual scene are essential to grasp the surrounding situation. However, it is unclear how neural activities in spatially distributed brain regions, especially in terms of temporal dynamics, represent visual objects. To address this issue, we explored the spatial and temporal organization of visual object representations using concurrent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), combined with neural decoding using deep neural networks (DNNs). Visualization of the fMRI DNN revealed that visual categorization (faces or non-face objects) occurred in brain-wide cortical regions, including the ventral temporal cortex. Interestingly, the EEG DNN valued the earlier phase of neural responses for categorization and the later phase of neural responses for sub-categorization. Combination of the two DNNs improved the classification performance for both categorization and sub-categorization. These deep learning-based results demonstrate a categorization principle in which visual objects are represented in a spatially organized and coarse-to-fine manner.

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↗

bioRxiv2022

An excitatory-inhibitory fronto-cerebellar loop resolves the Stroop effect

The Stroop effect is a well-known behavioral phenomenon in humans that refers to robust interference between language and color information. Although this effect has long been studied, it remains unclear when the interference occurs and how it is resolved in the brain. By manipulating the verbality of stimulus perception and response generation, here we show that the Stroop effect occurs during perception of color-word stimuli and is resolved by a cross-hemispheric, excitatory-inhibitory functional loop involving the lateral prefrontal cortex and cerebellum. Humans performed a Stroop task and a control task in which the stimulus did not contain verbal information, and made a response either vocally or manually. The resolution of Stroop interference involved the lateral prefrontal cortex in the left hemisphere and the cerebellum in the right hemisphere, independently of whether the response was made vocally or manually. In contrast, such cross-hemispheric lateralization was absent during the non-verbal control task. Moreover, the prefrontal cortex amplified cerebellar activity, whereas the cerebellum suppressed prefrontal activity, and these effects were enhanced during interference resolution. These results suggest that this fronto-cerebellar loop involving language and cognitive systems regulates goal-relevant information to resolve the interference occurring during simultaneous perception of a word and color.

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

Executive control by fronto-parietal activity explains counterintuitive decision behavior in complex value-based decision-making

In real life, humans make decisions by taking into account multiple independent factors, such as delay and probability. Cognitive psychology suggests that cognitive control mechanisms play a key role when facing such complex task conditions. However, in value-based decision-making, it still remains unclear to what extent cognitive control mechanisms become essential when the task condition is complex. In this study, we investigated decision-making behaviors and underlying neural mechanisms using a multifactor gambling task where participants simultaneously considered probability and delay. Decision-making behavior in the multifactor task was modulated by both probability and delay. The behavioral effect of probability was stronger than delay, consistent with previous studies. Furthermore, in a subset of conditions that recruited fronto-parietal activations, reaction times were paradoxically elongated despite lower probabilistic uncertainty. Notably, such a reaction time elongation did not occur in control tasks involving single factors. Meta-analysis of brain activations suggested an association between the paradoxical increase of reaction time and strategy switching. Together, these results suggest a novel aspect of complex value-based decision-makings that is strongly influenced by fronto-parietal cognitive control. Highlights* A value-based decision task with concurrent delay and probabilistic uncertainty * Stronger behavioral effect of probability than delay * Paradoxically long reaction time despite low probabilistic uncertainty * The task activated fronto-parietal cognitive control network * Reaction time elongation coincided with activation similar to strategy switching

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↗

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↗