Biology subjects

Kay, K. N.

Publications and source records attributed to Kay, K. N..

9 recordsLinked to original sources

bioRxiv2025

Mnemonic maps of visual space in human prefrontal cortex

Neural theories of how the prefrontal cortex (PFC) supports working memory rely on evidence from decades of pioneering macaque research. In some respects, efforts to translate these animal models of working memory in human PFC using neuroimaging have largely failed. One possible explanation, before concluding key non-homologies between the species, is that previous neuroimaging studies used resolutions too coarse to be sensitive to intermixed distributions of neurons tuned to memorized features. To resolve this concern, we scanned human PFC at 900 micron resolution using 7T fMRI. We found that population activity in retinotopically-organized superior precentral sulcus-rather than the predicted midlateral PFC-persists during memory, encodes fine-grained information about memorized items, predicts behavioral errors in memory, and forms a stable subspace with a topological organization yoked to visual space. These results have important implications for both functional homologies between the species and theories of how the PFC supports working memory.

neuroscience↗

bioRxiv2025

Single pulse electrical stimulation in white matter modulates iEEG visual responses in human early visual cortex

Electrical stimulation is increasingly used to modulate brain networks for clinical purposes. The basic unit of neurostimulation, a single electrical pulse, can travel through white matter to influence connected neuronal populations. However, the mechanisms by which it influences connected populations is not well understood: stimulation may excite, inhibit, or add noise to neuronal population activity. In this study, we investigated how single pulses modulate the neuronal processing of images in a well-controlled visual paradigm. In two human subjects implanted with iEEG electrodes for clinical purposes, single pulses were delivered to electrodes in white matter tracts connected to measurement electrodes in visual cortex. Images appeared on-screen at 0, 100, or 200 ms after each pulse. Using finite impulse response modeling, we decomposed the broadband and evoked potential responses into separate components induced by electrical stimulation and by visual processing. Single pulses induced transient broadband increases followed by suppression, but they did not modulate the visual broadband responses (i.e., stimulation response was additive to visual response). In contrast, single pulses elicited prominent brain stimulation evoked potentials and they modulated the visual evoked potentials. Specifically, visual evoked potentials were larger when stimulation occurred closer to visual onset. This indicates that a single electrical pulse can increase the strength or synchrony of visual inputs. Overall, these findings suggest that the effects of electrical stimulation in the visual system are two-fold: stimulation induces additive effects on broadband power, possibly by adding noise, and it interacts with synchronous visual inputs to amplify them.

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bioRxiv2025

Hierarchical Processing of Natural Scenes in the Human Pulvinar

The hierarchical organization of the ventral visual cortex has been the focus of theories and computational models characterizing high-level visual processing and object recognition, often overlooking potential contributions of subcortical structures. The pulvinar, through its extensive reciprocal connections with ventral visual cortex, is well-positioned to play a prominent role in high-level visual recognition processes. Here, we investigated whether the pulvinar plays such a role using a high-resolution 7T fMRI dataset of responses to tens of thousands of natural scenes. Encoding models testing the representation of different stimulus features revealed a pulvinar region selective for bodies and faces presented within the contralateral visual hemifield. Model-free analyses demonstrated that this region is predominantly co-active with body- and face-selective cortical areas during natural scene viewing. This functional specificity was embedded within a broader gradient of cortical correlations across the pulvinar mirroring the hierarchical organization of ventral visual cortex. These findings challenge cortico-centric models of object vision and implicate a role of the pulvinar in high-level vision. More broadly, these results demonstrate that principles of cortical organization, including functional clustering and hierarchical organization, also manifest in subcortex, and highlight the value of using naturalistic stimuli to probe visual function.

neuroscience↗

bioRxiv2025

Cortical substrates of perceptual confusion between pitch and timbre

Pitch and timbre, two fundamental perceptual attributes of sound, are critical for both speech and music. Although governed by different acoustic parameters, pitch and brightness (one of the primary dimensions of timbre) can be perceptually confused. Here we identify potential neural substrates of this well-known illusion by independently manipulating pitch and brightness while measuring fMRI responses. First, we show that the preferred pitch of individual voxels within auditory cortex decreases systematically as stimulus brightness increases, and vice versa, consistent with predictions from perceptual confusion. Second, we demonstrate that pitch and brightness share a common tuning gradient across auditory cortex, implying a shared trajectory of cortical activation for changes in each dimension. The pattern of results at both local and global cortical levels are consistent with the principle of efficient coding, with the brain exploiting correlations between pitch and brightness in natural sounds via a shared neural code.

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bioRxiv2022

GLMsingle: a toolbox for improving single-trial fMRI response estimates

Advances in modern artificial intelligence (AI) have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to tens of thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle, a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Furthermore, these improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. Specifically, we demonstrate that GLMsingle: (i) improves the decorrelation of response estimates between trials that are nearby in time; (ii) enhances representational similarity between subjects both within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets that sample brain activity across many experimental conditions.

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bioRxiv2021

Variability of the Surface Area of the V1, V2, and V3 Maps in a Large Sample of Human Observers

How variable is the functionally-defined structure of early visual areas in human cortex and how much variability is shared between twins? Here we quantify individual differences in the best understood functionally-defined regions of cortex: V1, V2, V3. The Human Connectome Project 7T Retinotopy Dataset includes retinotopic measurements from 181 subjects, including many twins. We trained four "anatomists" to manually define V1-V3 using retinotopic features. These definitions were more accurate than automated anatomical templates and showed that surface areas for these maps varied more than three-fold across individuals. This three-fold variation was little changed when normalizing visual area size by the surface area of the entire cerebral cortex. In addition to varying in size, we find that visual areas vary in how they sample the visual field. Specifically, the cortical magnification function differed substantially among individuals, with the relative amount of cortex devoted to central vision varying by more than a factor of 2. To complement the variability analysis, we examined the similarity of visual area size and structure across twins. Whereas the twin sample sizes are too small to make precise heritability estimates (50 monozygotic pairs, 34 dizygotic pairs), they nonetheless reveal high correlations, consistent with strong effects of the combination of shared genes and environment on visual area size. Collectively, these results provide the most comprehensive account of individual variability in visual area structure to date, and provide a robust population benchmark against which new individuals and developmental and clinical populations can be compared. Significance StatementAreas V1, V2, and V3 are among the best studied functionally-defined regions in human cortex. Using the largest retinotopy dataset to date, we characterized the variability of these regions across individuals and the similarity between twin pairs. We find that the size of visual areas varies dramatically (up to 3.5x) across healthy young adults, far more than the variability of the cerebral cortex size as a whole. Much of this variability appears to arise from inherited factors, as we find very high correlations in visual area size between monozygotic twin-pairs, and lower but still substantial correlations between dizygotic twin pairs. These results provide the most comprehensive assessment of how functionally defined visual cortex varies across the population to date.

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bioRxiv2020

Dissociation of tonotopy and pitch in human auditory cortex

Frequency-to-place mapping, or tonotopy, is a fundamental organizing principle from the earliest stages of auditory processing in the cochlea to subcortical and cortical regions. Although cortical maps are referred to as tonotopic, previous studies employed sounds that covary in spectral content and higher-level perceptual features such as pitch, making it unclear whether these maps are inherited from cochlear organization and are indeed tonotopic, or instead reflect transformations based on higher-level features. We used high-resolution fMRI to measure BOLD responses in 10 participants as they listened to pure tones that varied in frequency or complex tones that independently varied in either spectral content or fundamental frequency (pitch). We show that auditory cortical gradients are in fact a mixture of maps organized both by spectral content and pitch. Consistent with hierarchical organization, primary regions were tuned predominantly to spectral content, whereas higher-level pitch tuning was observed bilaterally in surrounding non-primary regions.

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bioRxiv2019

TDM: a temporal decomposition method for removing venous effects from task-based fMRI

Most functional magnetic resonance imaging (fMRI) is conducted with gradient-echo pulse sequences. Although this yields high sensitivity to blood oxygenation level dependent (BOLD) signals, gradient-echo acquisitions are heavily influenced by venous effects which limit the ultimate spatial resolution and spatial accuracy of fMRI. While alternative acquisition methods such as spin-echo can be used to mitigate venous effects, these methods lead to serious reductions in signal-to-noise ratio and spatial coverage, and are difficult to implement without leakage of undesirable non-spin-echo effects into the data. Moreover, analysis heuristics such as masking veins or sampling inner cortical depths using high-resolution fMRI may be helpful, but sacrifice information from many parts of the brain. Here, we describe a new analysis method that is compatible with conventional gradient-echo acquisition and provides venous-free response estimates throughout the entire imaged volume. The method involves fitting a low-dimensional manifold characterizing variation in response timecourses observed in a given dataset, and then using identified early and late timecourses as basis functions for decomposing responses into components related to the microvasculature (capillaries and small venules) and the macrovasculature (veins), respectively. We show that this Temporal Decomposition through Manifold Fitting (TDM) method is robust, consistently deriving meaningful timecourses in individual fMRI scan sessions. Moreover, we show that by removing late components, TDM substantially reduces the superficial cortical depth bias present in gradient-echo BOLD responses and eliminates artifacts in cortical activity maps. TDM is general: it can be applied to any task-based fMRI experiment, can be used with standard- or high-resolution fMRI acquisitions, and can even be used to remove residual venous effects from specialized acquisition methods like spin-echo. We suggest that TDM is a powerful method that improves the spatial accuracy of fMRI and provides insight into the origins of the BOLD signal.

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bioRxiv2017

Broadband spectral responses in visual cortex revealed by a new MEG denoising algorithm

Currently, non-invasive methods for studying the human brain do not reliably measure spike-rate-dependent signals, independent of other responses such as hemodynamic coupling (fMRI) and subthreshold neuronal synchrony (oscillations and event-related potentials). In contrast, invasive methods - animal microelectrode recordings and human electrocorticography (ECoG) - have recently measured broadband power elevation in field potentials (~50-200Hz) as a proxy for the locally averaged spike rates. Here, we sought to detect and quantify stimulus-related broadband responses using magnetoencephalography (MEG) in individual subjects. Because extracranial measurements like MEG have multiple global noise sources and a relatively low signal-to-noise ratio, we developed an automated denoising technique, adapted from (Kay et al., 2013), that helps reveal the broadband signal of interest. Subjects viewed 12-Hz contrast-reversing patterns in the left, right, or bilateral visual field. Sensor time series were separated into an evoked component (12-Hz amplitude) and a broadband component (60-150Hz, excluding stimulus harmonics). In all subjects, denoised broadband responses were reliably measured in sensors over occipital cortex. The spatial pattern of the broadband measure depended on the stimulus, with greater broadband power in sensors contralateral to the stimulus. Because we obtain reliable broadband estimates with relatively short experiments (~20 minutes), with a sufficient signal-to-noise-ratio to distinguish responses to different stimuli, we conclude that MEG broadband signals, denoised with our method, offer a practical, non-invasive means for characterizing spike-rate-dependent neural activity for a wide range of scientific questions about human brain function.\n\nSignificance StatementNeuronal activity causes perturbations in nearby electrical fields. These perturbations can be measured non-invasively in the living human brain using EEG and MEG. These techniques have emphasized two kinds of measurements: oscillations and event-related responses. A third type of signal, a stimulus-related increase in power spanning a wide range of frequencies ( broadband), is routinely measured in invasive recordings, but not with MEG and EEG. This broadband response is of great interest because unlike oscillations and event-related responses, it is correlated with neuronal spike rates. Here we report quantitative, spatially specific measurements of broadband fields in individual human subjects using MEG. These results demonstrate that a spike-rate-dependent measure of brain activity can be obtained non-invasively from the living human brain.

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