Biology subjects

Garcia, B. D.

Publications and source records attributed to Garcia, B. D..

4 recordsLinked to original sources

bioRxiv2023

A retinotopic code structures the interaction between perception and memory systems

Conventional views of brain organization suggest that the cortical apex processes internally-oriented information using an abstract, amodal neural code. Yet, recent reports have described the presence of retinotopic coding at the cortical apex, including the default mode network. What is the functional role of retinotopic coding atop the cortical hierarchy? Here, we report that retinotopic coding structures interactions between internally-oriented (mnemonic) and externally-oriented (perceptual) brain areas. Using fMRI, we observed robust, inverted (negative) retinotopic coding in category-selective memory areas at the cortical apex, which is functionally linked to the classic (positive) retinotopic coding in category-selective perceptual areas in high-level visual cortex. Specifically, these functionally-linked retinotopic populations in mnemonic and perceptual areas exhibit spatially-specific opponent responses during both bottom-up perception and top-down recall, suggesting that these areas are interlocked in a mutually-inhibitory dynamic. Together, these results show that retinotopic coding structures interactions between perceptual and mnemonic neural systems, thereby scaffolding their dynamic interaction.

neuroscience↗

bioRxiv2022

Scene perception and visuospatial memory converge at the anterior edge of visually-responsive cortex

To fluidly engage with the world, our brains must simultaneously represent both the scene in front of us and our memory of the immediate surrounding environment (i.e., local visuospatial context). How does the brains functional architecture enable sensory and mnemonic representations to closely interface, while also avoiding sensory-mnemonic interference? Here, we asked this question using first-person, head-mounted virtual reality (VR) and fMRI. Using VR, human participants of both sexes learned a set of immersive, real-world visuospatial environments in which we systematically manipulated the extent of visuospatial context associated with a scene image in memory across three learning conditions, spanning from a single field-of-view to a city street. We used individualized, within-subject fMRI to determine which brain areas support memory of the visuospatial context associated with a scene during recall (Exp. 1) and recognition (Exp. 2). Across the whole brain, activity in three patches of cortex was modulated by the amount of known visuospatial context, each located immediately anterior to one of the three scene perception areas of high-level visual cortex. Individual subject analyses revealed that these anterior patches corresponded to three functionally-defined place memory areas, which selectively respond when visually recalling personally familiar places. In addition to showing activity levels that were modulated by the amount of visuospatial context, multivariate analyses showed that these anterior areas represented the identity of the specific environment being recalled. Together, these results suggest a convergence zone for scene perception and memory of the local visuospatial context at the anterior edge of high-level visual cortex. Significance statementAs we move through the world, the visual scene around us is integrated with our memory of the wider visuospatial context. Here, we sought to understand how the functional architecture of the brain enables coexisting representations of the current visual scene and memory of the surrounding environment. Using a combination of immersive virtual reality and fMRI, we show that memory of visuospatial context outside the current field-of-view is represented in a distinct set of brain areas immediately anterior and adjacent to the perceptually-oriented scene-selective areas of high-level visual cortex. This functional architecture would allow efficient interaction between immediately adjacent mnemonic and perceptual areas, while also minimizing mnemonic-perceptual interference.

neuroscience↗

bioRxiv2022

Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI

fMRI is an indispensable tool for neuroscience investigation, but this technique is limited by multiple sources of physiological and measurement noise. These noise sources are particularly problematic for analysis techniques that require high signal-to-noise ratio for stable model fitting, such as voxel-wise modeling. Multi-echo data acquisition in combination with echo-time dependent ICA denoising (ME-ICA) represents one promising strategy to mitigate physiological and hardware-related noise sources as well as motion-related artifacts. However, most studies employing ME-ICA to date are resting-state fMRI studies, and therefore we have a limited understanding of the impact of ME-ICA on task or model-based fMRI paradigms. Here, we addressed this knowledge gap by comparing data quality and model fitting performance on data acquired during a visual population receptive field (pRF) mapping paradigm (N=13 participants) after using one of three preprocessing procedures: ME-ICA, optimally combined multi-echo data without ICA-denoising, and typical single echo processing. As expected, multi-echo fMRI improved temporal signal-to-noise compared to single echo fMRI, with ME-ICA amplifying the improvement compared to optimal combination alone. However, unexpectedly, this boost in temporal signal-to-noise did not directly translate to improved model fitting performance: compared to single echo acquisition, model fitting was only improved after ICA-denoising. Specifically, compared to single echo acquisition, ME-ICA resulted in improved variance explained by our pRF model throughout the visual system, including anterior regions of the temporal and parietal lobes where SNR is typically low, while optimal combination without ICA did not. ME-ICA also improved reliability of parameter estimates compared to single echo and optimally combined multi-echo data without ICA-denoising. Collectively, these results suggest that ME-ICA is effective for denoising task-based fMRI data for modeling analyses and maintains the integrity of the original data. Therefore, ME-ICA may be beneficial for complex fMRI experiments, including task fMRI studies, voxel-wise modeling, and naturalistic paradigms.

neuroscience↗

bioRxiv2021

Individual differences in visual search performance extend from artificial arrays to naturalistic environments.

Visual search is a universal human activity in naturalistic environments. Traditionally, visual search is investigated under tightly controlled conditions, where head-restricted participants locate a minimalistic target in a cluttered array presented on a computer screen. Do classic findings of visual search extend to naturalistic settings, where participants actively explore complex, real-world scenes? Here, we leverage advances in virtual reality (VR) technology to relate individual differences in classic visual search paradigms to naturalistic search behavior. In a naturalistic visual search task, participants looked for an object within their environment via a combination of head-turns and eye-movements using a head-mounted display. Then, in a classic visual search task, participants searched for a target within a simple array of colored letters using only eye-movements. We tested how set size, a property known to limit visual search within computer displays, predicts the efficiency of search behavior inside immersive, real-world scenes that vary in levels of visual clutter. We found that participants search performance was impacted by the level of visual clutter within real-world scenes. Critically, we also observed that individual differences in vi1-3sual search efficiency in classic search predicted efficiency in real-world search, but only when the comparison was limited to the forward-facing field of view for real-world search. These results demonstrate that set size is a reliable predictor of individual performance across computer-based and active, real-world visual search behavior.

neuroscience↗