Biology subjects

Hutchinson, J. B.

Publications and source records attributed to Hutchinson, J. B..

9 recordsLinked to original sources

bioRxiv2025

Hippocampal-guided reconstruction of an event's prior temporal context

Leading theories of episodic memory argue that when events from the past are remembered, temporally-adjacent events are also reinstated ( temporal context reinstatement). However, direct evidence for temporal context reinstatement is surprisingly limited. Here, we tested for temporal context reinstatement in a human fMRI continuous recognition memory experiment in which natural scene images were repeatedly encountered. For the original encounter with each scene, we defined its temporal context as the visual content of temporally-adjacent scenes. Using voxelwise encoding models, we tested whether fMRI activity patterns evoked when a scene was re-encountered carried information about the original encounters temporal context. Indeed, we found robust temporal context reinstatement within high-level visual cortex (lateral occipitotemporal cortex; LOTC), despite the fact that reinstated content was entirely incidental to task demands. However, temporal context reinstatement only occurred when stimuli were successfully recognized, indicating that reinstatement was behaviorally-relevant, even if incidental. Finally, the strength of temporal context reinstatement in LOTC was predicted by the similarity of hippocampal activity patterns across encounters, demonstrating distinct, but complimentary, roles for the hippocampus and neocortex in reinstating temporal context information.

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bioRxiv2024

Temporal asymmetry of neural representations predicts memory decisions

A stimulus can be familiar for multiple reasons. It might have been recently encountered, or is similar to recent experience, or is similar to typical experience. Understanding how the brain translates these sources of similarity into memory decisions is a fundamental, but challenging goal. Here, using fMRI, we computed neural similarity between a current stimulus and events from different temporal windows in the past and future (from seconds to days). We show that trial-by-trial memory decisions (is this stimulus old?) were predicted by the difference in similarity to past vs. future events (temporal asymmetry). This relationship was (i) evident in lateral parietal and occipitotemporal cortices, (ii) strongest when considering events from the recent past (minutes ago), and (iii) most pronounced when veridical (true) memories were weak. These findings suggest a new perspective in which the brain supports memory decisions by comparing what actually occurred to what is likely to occur.

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bioRxiv2024

Benefits of spaced learning are predicted by re-encoding of past experience in ventromedial prefrontal cortex

More than a century of research shows that spaced learning improves long-term memory. Yet, there remains debate concerning why. A major limitation to resolving theoretical debates is the lack of evidence for how neural representations change as a function of spacing. Here, leveraging a massive-scale 7T human fMRI dataset, we tracked neural representations and behavioral expressions of memory as participants viewed thousands of natural scene images that repeated at lags ranging from seconds to many months. We show that spaced learning increases the similarity of human ventromedial prefrontal cortex representations across stimulus encounters and, critically, these increases parallel and predict the behavioral benefits of spacing. Additionally, we show that these spacing benefits critically depend on remembering and, in turn, re-encoding past experience. Collectively, our findings provide fundamental insight into how spaced learning influences neural representations and why spacing is beneficial.

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bioRxiv2022

Adding meaning to memories: How parietal cortex combines semantic content with episodic experience

Neuroimaging studies of human memory have consistently found that univariate responses in parietal cortex track episodic experience with stimuli (whether stimuli are old or new). More recently, pattern-based fMRI studies have shown that parietal cortex also carries information about the semantic content of remembered experiences. However, it is not well understood how memory-based and content-based signals are integrated within parietal cortex. Here, we used voxel-wise encoding models and a recognition memory task to predict the fMRI activity patterns evoked by complex natural scene images based on (a) the episodic history and (b) the semantic content of each image. Models were generated and compared across distinct subregions of parietal cortex and for occipitotemporal cortex. We show that parietal and occipitotemporal regions each encode memory and content information, but they differ in how they combine this information. Among parietal subregions, angular gyrus was characterized by robust and overlapping effects of memory and content. Moreover, subject-specific semantic tuning functions revealed that successful recognition shifted the amplitude of tuning functions in angular gyrus but did not change the selectivity of tuning. In other words, effects of memory and content were additive in angular gyrus. This pattern of data contrasted with occipitotemporal cortex where memory and content effects were interactive: memory effects were preferentially expressed by voxels tuned to the content of a remembered image. Collectively, these findings provide unique insight into how parietal cortex combines information about episodic memory and semantic content.

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bioRxiv2022

Perception and Memory Retrieval States are Reflected in Distributed Patterns of Background Functional Connectivity

The same visual input can serve as the target of perception or as a trigger for memory retrieval depending on whether cognitive processing is externally oriented (perception) or internally oriented (memory retrieval). While numerous human neuroimaging studies have characterized how visual stimuli are differentially processed during perception versus memory retrieval, perception and memory retrieval may also be associated with distinct neural states that are independent of stimulus-evoked neural activity. Here, we combined human fMRI with full correlation matrix analysis (FCMA) to reveal potential differences in "background" functional connectivity across perception and memory retrieval states. We found that perception and retrieval states could be discriminated with high accuracy based on patterns of connectivity across (1) the control network, (2) the default mode network (DMN), and (3) retrosplenial cortex (RSC). In particular, clusters in the control network increased connectivity with each other during the perception state, whereas clusters in the DMN were more strongly coupled during the retrieval state. Interestingly, RSC switched its coupling between networks as the cognitive state shifted from retrieval to perception. Finally, we show that background connectivity 1) was fully independent from stimulus-related variance in the signal and, further, 2) captured distinct aspects of cognitive states compared to traditional classification of stimulus-evoked responses. Together, our results reveal that perception and memory retrieval are associated with sustained cognitive states that manifest as distinct patterns of connectivity among large-scale brain networks.

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bioRxiv2022

Re-expression of CA1 and entorhinal activity patterns preserves temporal context memory at long timescales

Converging, cross-species evidence indicates that memory for time is supported by hippocampal area CA1 and entorhinal cortex. However, limited evidence characterizes how these regions preserve temporal memories over long timescales (e.g., months). At long timescales, memoranda may be encountered in multiple temporal contexts, potentially creating interference. Here, using 7T fMRI, we measured CA1 and entorhinal activity patterns as human participants viewed thousands of natural scene images distributed, and repeated, across many months. We show that memory for an images original temporal context was predicted by the degree to which CA1/entorhinal activity patterns from the first encounter with an image were re-expressed during re-encounters occurring minutes to months later. Critically, temporal memory signals were dissociable from predictors of recognition confidence, which were carried by distinct medial temporal lobe expressions. These findings suggest that CA1 and entorhinal cortex preserve temporal memories across long timescales by coding for and reinstating temporal context information.

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bioRxiv2021

Functional connectivity gradients as a common neural architecture for predictive processing in the human brain

Predictive processing is emerging as a common computational hypothesis to account for diverse psychological functions subserved by a brain, providing a systems-level framework for characterizing structure-function relationships of its distinct substructures. Here, we contribute to this framework by examining gradients of functional connectivity as a low dimensional spatial representation of functional variation in the brain and demonstrating their computational implications for predictive processing. Specifically, we investigated functional connectivity gradients in the cerebral cortex, the cerebellum, and the hippocampus using resting-state functional MRI data collected from large samples of healthy young adults. We then evaluated the degree to which these structures share common principles of functional organization by assessing the correspondence of their gradients. We show that the organizing principles of these structures primarily follow two functional gradients consistent with the existing hierarchical accounts of predictive processing: A model-error gradient that describes the flow of prediction and prediction error signals, and a model-precision gradient that differentiates regions involved in the representation and attentional modulation of such signals in the cerebral cortex. Using these gradients, we also demonstrated triangulation of functional connectivity involving distinct subregions of the three structures, which allows characterization of distinct ways in which these structures functionally interact with each other, possibly subserving unique and complementary aspects of predictive processing. These findings support the viability of computational hypotheses about the functional relationships between the cerebral cortex, the cerebellum, and the hippocampus that may be instrumental for understanding the brains dynamics within its large-scale predictive architecture.

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bioRxiv2021

A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. We present the Natural Scenes Dataset (NSD), in which high-resolution fMRI responses to tens of thousands of richly annotated natural scenes are measured while participants perform a continuous recognition task. To optimize data quality, we develop and apply novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we use NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality, and breadth, NSD opens new avenues of inquiry in cognitive and computational neuroscience.

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bioRxiv2020

A computational neural model for mapping degenerate neural architectures

Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy assumption in all three situations and demonstrating NTFAs utility in uncovering degeneracy. Lastly, we discussed the importance of testing degeneracy in fMRI and the implications of applying NTFA to do so.

neuroscience↗