Biology subjects

Kay, K.

Publications and source records attributed to Kay, K..

At least 19 recordsLinked to original sources

bioRxiv2026

A cognitive representation in primary visual cortex modulated by vision

Primary visual cortex (V1) is a critical substrate for mammalian vision. Traditionally, visual inputs are thought to be the main drivers of V1 activity, with internal signals playing a modulatory role. Here we show that this relationship is inverted for a large fraction of V1 neurons. In rats completing a navigation task in darkness, these neurons encoded progress along physically distinct paths with a shared turn structure. Under illumination, visual stimuli gain-modulated this path-invariant activity rather than replacing it with stimulus-driven responses. Path-invariant V1 neurons were also preferentially coordinated with hippocampal ensembles during sharp-wave ripples, linking them to a brain-wide network involved in learning. These findings establish that an internal model of the world can serve as a primary driver of activity in sensory cortex.

neuroscience↗

bioRxiv2026

Transitive reasoning as linear classification

Transitive inference (TI) is the ability to reason about transitive relationships in an ordered set of items (e.g., if A>B and B>C, then A>C). TI is widely held to depend on a linear representation of the serial (rank) order of those items. By what computational mechanism is such an ordering constructed during learning, and how is it used to make choices that obey transitivity? Here we take a minimalist approach, applying least-squares estimation (LSE) to a serial learning task commonly used to test TI in humans and animals. In this formulation, LSE computes a linear classifier that maps task conditions onto behavioral outcomes. This algorithm makes no explicit assumptions about transitivity or serial order, yet it reproduces key empirical features of TI; namely, the ability to generalize beyond the training set, and a symbolic distance effect (SDE) in performance accuracy. Applying the classifier to individual items produces an internally ordered representation of rank from which both generalization and the SDE naturally emerge. The approach also yields a decision mechanism, in the form of a differencing operation, for selecting the correct item from any pair. These findings reframe TI as a linear classification problem, challenging conventional assumptions about the cognitive mechanisms required for transitive reasoning.

neuroscience↗

bioRxiv2026

Naturalistic behavior and self-generated neural activity predictive of self-correction.

Changing ones mind -- revising a past decision independently of external cues -- can lead to better outcomes, but its neural basis remains poorly understood. We therefore developed a challenging spatial task for rats where a conflict between innate foraging biases and task rules leads to abundant, spontaneous, and characteristically corrective changes-of-mind (COMs). Neural recordings in the hippocampus, a brain region implicated in counterfactual thinking, revealed two distinct stages wherein more local representations gave way to representations of distant alternatives after animals had begun to reverse course. These representations predicted their eventual choice, and often serially encoded trial start and end locations. Our novel task paradigm reveals distinct representational phases engaged during self-correction and uncovers a rich repertoire of hippocampal spatial representations tied to behavior.

neuroscience↗

bioRxiv2026

Emergent representations of graphical structure in mechanistic neural models of causal judgment

Humans have a remarkable ability to judge causal relationships from a limited number of un-reliable observations. Past work on causal cognition has largely focused on normative accounts of human behavior, leaving unknown how biologically plausible neural systems could learn causal relationships from observations and update their representations of causal structure with additional evidence. Here, we leverage task-optimized recurrent neural networks to discover candidate implementation-level neural mechanisms of causal judgment. We propose a novel cognitive task in which a subject observes stochastic samples from an unknown causal structure (e.g. among variables A, B, and C with unknown causal relationships), and must judge whether a specific causal relationship is present given a query (e.g. "does A cause C?"). We found that, after training, recurrent neural networks perform the task with high accuracy, adopt strategies that incorporate the behavior of non-queried variables to form their judgments, and, despite being trained only on pairwise queries ("does A cause B?", "does C cause A?", etc), form implicit beliefs about the complete graphical structure underlying the observations. Lastly, we use dynamical systems analysis to identify a set of low-level neural mechanisms that implement causal judgment and representation of causal graphical structure. Together, these findings lay the groundwork for a "bottom-up" approach to causal cognition, providing a potential basis for subsequent experimental study in the brain.

neuroscience↗

bioRxiv2026

Intracortical vessel density mapping at mesoscale reveals regionally specific angioarchitecture in the human brain

The metabolic demands of the human brain are met by a complex vascular architecture, yet our characterization of this network remains incomplete. While we have mapped the macroscopic vessels on the brains surface and the microscopic capillaries within small tissue samples, the mesoscopic scale consisting of the penetrating vessels that plunge through cortex remains an anatomical terra incognita. Mapping the interface between the macroscopic and microscopic scales is essential to understanding the critical vascular supply that sustains brain health. Here, we leveraged the BigBrain dataset and developed custom detection and tracing algorithms to reveal a whole-cortex record of the mesoscopic vascular network. We find that vascular density is not uniform across the cortex, but is a heterogeneous landscape that shows clear relationships to traditional areal boundaries. Our results constitute a reference for human mesoscopic angioarchitecture and demonstrates the power of repurposing high-resolution histological at-lases. Ultimately, this work lays the groundwork for validating recently developed in vivo MRI techniques for imaging the human cerebrovascular system at mesoscale.

neuroscience↗

bioRxiv2026

Objective Quality Assessment for Precision Functional MRI Data

Precision functional mapping (PFM) enables individual-level characterization of brain network organization but requires substantially more and higher-quality fMRI data than is standard. Despite its growing use, objective criteria for data sufficiency and quality needed to ensure interpretable and replicable individual-level results remain unclear. Here, we introduce the Network Similarity Index (NSI), an objective measure of the extent to which functional connectivity (FC) patterns in an individual dataset express the large-scale network structure required for PFM. NSI captures the integrity of low-spatial-frequency, coherent network organization and denoising fidelity, and aligns closely with blinded expert assessments of PFM usability. NSI also accounts for variability in the rate at which FC becomes reliable across individuals. Here, we provide an open-source framework for NSI-based data quality evaluation and models for linking NSI values with expert-judged PFM suitability. This framework can also inform expected returns from additional data collection, enabling principled decisions about data sufficiency and replication in precision fMRI research.

neuroscience↗

bioRxiv2025

Attention Improves Population Codes by Warping Neural Manifolds in Human Visual Cortex

Decades of research on visual attention have revealed its numerous effects on neural responses. Two competing models have been proposed for how these effects lead to improved population representations: one highlights changes in neural tuning, while the other points towards changes in trial-by-trial noise correlations. Here, we develop a neural population manifold framework that interprets changes in neural responses as geometric transformations in high-dimensional neural space, allowing us to disentangle and quantify the effects of tuning changes and correlation changes induced by attention. Applying this framework to extensive measurements of cortical responses during different attentional tasks, we find that tuning changes are the primary driver of improved population representations. In contrast, correlation changes, though present, have minimal--or even detrimental--effects to information content due to its strong interactions with other changes (e.g., tuning, variability). Our results support the "tuning change" model of visual attention and demonstrate a general framework for adjudicating how different aspects of neural coding affect information processing.

neuroscience↗

bioRxiv2025

Representational Geometries of Perception and Working Memory: A Pilot Study

This study examined the representational geometry of visual working memory using human 7T fMRI. Observers viewed a facescene blended sample, attended to either the face or scene, and judged whether the attended aspect matched a subsequent test image. We found that the coding dimension distinguishing faces and scenes rotated between encoding and maintenance phases, in both visual and association areas. In these regions, exclusive combinations (i.e., encoded face and maintained scene vs. encoded scene and maintained face) were linearly decodable, indicating encoding and maintenance phases represent visual information using distinct subspaces. Such high-dimensional, flexible geometry can, in principle, protect maintained visual information from incoming input. At the same time, robust crossdecoding was observed across phases, reflecting the stability and generalizability of the represented contents. The balance between representational flexibility and stability varied along the cortical hierarchy: early sensory regions emphasized flexibility, whereas transmodal regions showed greater stability.

neuroscience↗

bioRxiv2025

A transformation from vision to imagery in the human brain

Extensive work has shown that the visual cortex is reactivated during mental imagery, and that models trained on visual data can predict imagery activity and decode imagined stimuli. These findings may explain why imagery can feel and function like vision, but give little insight into how the brain activity patterns that encode seen and mental images differ. While one popular theory ("weak vision") suggests that imagery differs from vision only in strength, recent work points to more complex differences. To clarify the relationship between visual and imagery activity in the human brain, we introduce the concept of an imagery transformation--a mapping from visual to imagery activity patterns evoked by the same stimulus. Importantly, this approach can describe a variety of possible scenarios, from simple rescaling to the selective removal or reorientation of activity dimensions. Using two 7T fMRI datasets, we estimated imagery transformations across different visual areas and found they accurately predict imagery activity. We show that imagery transformations are indeed more complex than simple weakening: in early visual cortex, they halve the number of active dimensions and reorient them, such that reconstructions of visual activity in terms of imagery dimensions explain only 25-50% of the variance. Nonetheless, these reoriented dimensions still coarsely encode the features encoded by the principal dimensions of visual activity. These findings help to explain the "same but different" relationship between mental imagery and vision: imagery activity patterns are transformations of visual activity patterns that approximate them, but encode fewer features and occupy a distinct subspace within the overall space of brain activations.

neuroscience↗

bioRxiv2025

Developmental prosopagnosics have normal spatial integration in posterior ventral face-selective regions

Population receptive field (pRF) mapping is an influential neuroimaging technique used to estimate the region of visual space modulating the response of a neuronal population. While pRF mapping has advanced our understanding of visual cortical organization, evidence linking variation in pRF properties to behavioral performance remains limited. One of the most compelling pRF-to-behavior relationships has emerged from research into developmental prosopagnosia (DP). Individuals with DP have severe deficits in facial identity recognition sometimes linked to diminished holistic processing of faces. This perceptual deficit could be explained at the neural level by abnormally small pRFs in face-selective regions that restrict spatial integration of the face information. This hypothesis is supported by data from a small group of DPs but needs to be rigorously evaluated in a larger sample. Here, we measured pRF properties in 20 individuals with DP and 20 controls using a stimulus designed to robustly activate both low- and high-level visual areas. Consistent with previous studies, DPs exhibited weaker face selectivity in core ventral face-selective areas. Crucially, however, across the visual processing hierarchy - from early visual cortex, to intermediate visual areas, and face-selective areas - DPs and controls exhibited remarkable similarity in pRF properties, including pRF size, visual field coverage, and the distribution of pRF centers. Using a larger sample and the latest methods for mapping and modeling pRFs, these results challenge theories attributing DP to reduced spatial integration in face-selective regions. This underscores the need to explore alternative neural mechanisms of DP and to re-evaluate links between pRF properties and behavior more broadly.

neuroscience↗

bioRxiv2025

A two-dimensional space of linguistic representations shared across individuals

Our ability to extract meaning from linguistic inputs and package ideas into word sequences is supported by a network of left-hemisphere frontal and temporal brain areas. Despite extensive research, previous attempts to discover differences among these language areas have not revealed clear dissociations or spatial organization. All areas respond similarly during controlled linguistic experiments as well as during naturalistic language comprehension. To search for finer-grained organizational principles of language processing, we applied data-driven decomposition methods to ultra-high-field (7T) fMRI responses from eight participants listening to 200 linguistically diverse sentences. Using a cross-validation procedure that identifies shared structure across individuals, we find that two components successfully generalize across participants, together accounting for about 32% of the explainable variance in brain responses to sentences. The first component corresponds to processing difficulty, and the second--to meaning abstractness; we formally support this interpretation through targeted behavioral experiments and information-theoretic measures. Furthermore, we find that the two components are systematically organized within frontal and temporal language areas, with the meaning-abstractness component more prominent in the temporal regions. These findings reveal an interpretable, low-dimensional, spatially structured representational basis for language processing, and advance our understanding of linguistic representations at a detailed, fine-scale organizational level.

neuroscience↗

bioRxiv2025

Dissociating stimulus encoding and task demands in ECoG responses from human visual cortex

Brain responses to sensory stimuli depend on the specific task performed by the observer. Previously, fMRI measurements in ventral temporal cortex (VTC) showed that BOLD responses to visual input scale with task demands. This scaling of BOLD responses is thought to be driven by the engagement of cognitive functions required for the task. To better understand how these cognitive task demands influence neural activity in VTC, we measured electrocorticography (ECoG) responses to images of varying contrast in two human participants during either a fixation or image categorization task. ECoG high frequency broadband activity (>70 Hz) showed that local neuronal responses increased with image contrast as expected for sensory encoding, and were scaled by task demands approximately 200 ms after stimulus onset. In contrast, ECoG low frequency activity in the alpha/beta range (8-28 Hz) was insensitive to image contrast and showed larger decreases with increasing task demands. These results indicate that high frequencies in VTC reflect both the encoding of sensory inputs and task demands, similar to prior BOLD response measurements, whereas low frequency oscillations represent task demands, not sensory inputs per se. In line with the interpretation that low frequency oscillations reflect pulsed inhibition, we speculate that a power decrease in low frequency oscillations amplifies neural activity in VTC in support of visual task demands.

neuroscience↗

bioRxiv2025

In vivo reconstruction of Duvernoy's postmortem vasculature images

Non-invasive measurement of the human brains angioarchitecture is critical for understanding the basis of functional neuroimaging signals, diagnosing cerebrovascular diseases, and tracking neurodegeneration. Ultra-high-field magnetic resonance imaging (MRI) has achieved mesoscopic (< 0.5 mm) imaging of angioarchitecture, revealing fine vascular details that were previously inaccessible in vivo. However, current mesoscopic MRI methods for imaging angioarchitecture face two major limitations. First, acquisition times are prohibitively long, often exceeding 40 minutes, making integration into everyday clinical practice and research projects impractical. Second, even with data successfully acquired, conventional data visualization methods, such as 2D slice browsing and 3D vessel segmentation renders, are rudimentary and have limited effectiveness for navigating and interpreting the complex vascular network. In this paper, we present a fast whole-brain MRI protocol that provides robust images of the brains venous network at 0.35 mm resolution in under seven minutes. Additionally, we introduce novel data processing and visualization techniques that enable identification of specific vessel types and more informative navigation of the complex vascular network. We demonstrate that, with these advancements, we can reproduce, in vivo and without intravenous contrast application, the seminal postmortem vasculature images of Duvernoy and Vannson (1999). Furthermore, leveraging the ability for MRI to cover the entire brain, we achieve, for the first time, whole-brain intracortical mesoscopic vein maps in humans. Our acquisition and post-processing methods lay the groundwork for detailed examination of vascular organization across individuals, brain regions, and cortical layers. More generally, these methods make mesoscopic imaging of angioarchitecture viable for broad neuroscientific and clinical applications.

neuroscience↗

bioRxiv2025

Motion-corrected eye tracking (MoCET) improves gaze accuracy during visual fMRI experiments

Human eye movements are essential for understanding cognition, yet achieving high-precision eye tracking in fMRI remains challenging. Even slight head shifts from the initial calibration position can introduce drift in eye tracking data, leading to substantial gaze inaccuracies. To address this, we introduce Motion-Corrected Eye Tracking (MoCET), a novel approach that corrects drift using head motion parameters derived from the preprocessing of fMRI data. MoCET requires no additional hardware and can be applied retrospectively to existing datasets. We show that it outperforms traditional detrending methods with respect to accuracy of gaze estimation and offers higher spatial and temporal precision compared to MR-based eye tracking approaches. By overcoming a key limitation in integrating eye tracking with fMRI, MoCET facilitates investigations of naturalistic vision and cognition in fMRI research.

neuroscience↗

bioRxiv2024

Variation in the geometry of concept manifolds acrosshuman visual cortex

Brain activity patterns in high-level visual cortex support accurate linear classification of visual concepts (e.g., objects or scenes). It has long been appreciated that the accuracy of linear classification in any brain area depends on the geometry of its concept manifolds--sets of brain activity patterns that encode images of a concept. However, it is unclear how the geometry of concept manifolds differs between regions of visual cortex that support accurate classification and those that dont, or how it differs between visual cortex and deep neural networks (DNNs). We estimated geometric properties of concept manifolds that, per a recent theory, directly determine the accuracy of simple "few-shot" linear classifiers. Using a large fMRI dataset, we show that variation in classification accuracy across human visual cortex is driven by a variation in a single geometric property: the distance between manifold centers ("geometric Signal"). In contrast, variation in classification accuracy across most DNN layers is driven by an increase in the effective number of manifold dimensions ("Dimensionality"). Despite this difference in the geometric properties that affect few-shot classification performance in the brain and DNNs, we find that Signal and Dimensionality are strongly, negatively correlated: when Signal increases across brain regions or DNN layers, Dimensionality decreases, and vice versa. We conclude that visual cortex and DNNs deploy different geometric strategies for accurate linear classification of concepts, even though both are subject to the same constraint.

neuroscience↗

bioRxiv2024

Vicarious Somatotopic Maps Tile Visual Cortex

Our sensory systems work together to generate a cohesive experience of the world around us. Watching others being touched activates brain areas representing our own sense of touch: the visual system recruits touch-related computations to simulate bodily consequences of visual inputs. One long-standing question is how the brain implements this interface between visual and somatosensory representations. To address this question, we developed a method to simultaneously map somatosensory body part tuning and visual field tuning throughout the brain. Applying this method on ongoing co-activations during rest resulted in detailed maps of the body-part tuning in the brains endogenous somatotopic network. During movie watching, somatotopic tuning explains responses throughout the entire dorsolateral visual system, revealing an array of somatotopic body maps that tile the cortical surface. The tuning of these maps aligned with those of visual maps, and predicted both preferences for visual field locations and the visual-category preferences for body parts. These results reveal a mode of brain organization in which aligned visual-somatosensory topographic maps connect visual and bodily reference frames. This cross-modal interface is ideally situated to translate raw sensory impressions into more abstract formats useful for action, social cognition, and semantic processing.

neuroscience↗

bioRxiv2024

γ-aminobutyric acid receptor B signaling drives glioblastoma in females in an immune-dependent manner

Sex differences in immune responses impact cancer outcomes and treatment response, including in glioblastoma (GBM). However, host factors underlying sex specific immune-cancer interactions are poorly understood. Here, we identify the neurotransmitter {gamma}-aminobutyric acid (GABA) as a driver of GBM-promoting immune response in females. We demonstrated that GABA receptor B (GABBR) signaling enhances L-Arginine metabolism and nitric oxide synthase 2 (NOS2) expression in female granulocytic myeloid-derived suppressor cells (gMDSCs). GABBR agonist and GABA analog promoted GBM growth in females in an immune-dependent manner, while GABBR inhibition reduces gMDSC NOS2 production and extends survival only in females. Furthermore, female GBM patients have enriched GABA transcriptional signatures compared to males, and the use of GABA analogs in GBM patients is associated with worse short-term outcomes only in females. Collectively, these results highlight that GABA modulates anti-tumor immune response in a sex-specific manner, supporting future assessment of GABA pathway inhibitors as part of immunotherapy approaches.

cancer biology↗

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.

neuroscience↗