Early visual areas are retinotopically organized in human and non-human primates. Population receptive field (pRF) size increases with eccentricity and from lower- to higher-level visual areas. Furthermore, the cortical magnification factor (CMF), a measure of how much cortical space is devoted to each degree of visual angle, is typically larger for foveal as opposed to peripheral regions of the visual field. Whether this fine-scale organization within and across visual areas depends on early visual experience has yet been unknown. Here, we employed 7T functional magnetic resonance imaging pRF mapping to assess the retinotopic organization of early visual regions (i.e., V1, V2, and V3) in eight sight recovery individuals with a history of congenital blindness until a maximum of 4 years of age. Compared with sighted controls, foveal pRF sizes in these individuals were larger, and pRF sizes did not show the typical increase with eccentricity and down the visual cortical processing stream (V1-V2-V3). Cortical magnification was overall diminished and decreased less from foveal to parafoveal visual field locations. Furthermore, cortical magnification correlated with visual acuity in sight recovery individuals. The results of this study suggest that early visual experience is essential for refining a presumably innate prototypical retinotopic organization in humans within and across visual areas, which seems to be crucial for acquiring full visual capabilities.

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 complex task or model-based fMRI paradigms. Here, we addressed this knowledge gap by comparing data quality and model fitting performance of data acquired during a visual population receptive field (pRF) mapping (N = 13 participants) experiment after applying 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 analyzes and maintains the integrity of the original data. Therefore, ME-ICA may be beneficial for complex fMRI experiments, including voxel-wise modeling and naturalistic paradigms.

Impairment of the geniculostriate pathway results in scotomas in the corresponding part of the visual field. Here, we present a case of patient IB with left eye microphthalmia and with lesions in most of the left geniculostriate pathway, including the Lateral Geniculate Nucleus (LGN). Despite the severe lesions, the patient has a very narrow scotoma in the peripheral part of the lower-right-hemifield only (beyond 15° of eccentricity) and complete visual field representation in the primary visual cortex. Population receptive field mapping (pRF) of the patient\'s visual field reveals orderly eccentricity maps together with contralateral activation in both hemispheres. With diffusion tractography, we revealed connections between superior colliculus (SC) and cortical structures in the hemisphere affected by the lesions, which could mediate the retinotopic reorganization at the cortical level. Our results indicate an astonishing case for the flexibility of the developing retinotopic maps where the contralateral thalamus receives fibers from both the nasal and temporal retinae.

Population receptive field (pRF) mapping is a popular tool in computational neuroimaging that allows for the investigation of receptive field properties, their topography and interrelations in health and disease. Furthermore, the possibility to invert population receptive fields provides a decoding model for constructing stimuli from observed cortical activation patterns. This has been suggested to pave the road towards pRF-based brain-computer interface (BCI) communication systems, which would be able to directly decode internally visualized letters from topographically organized brain activity. A major stumbling block for such an application is, however, that the pRF mapping procedure is computationally heavy and time consuming. To address this, we propose a novel and fast pRF mapping procedure that is suitable for real-time applications. The method is built upon hashed-Gaussian encoding of the stimulus, which tremendously reduces computational resources. After the stimulus is encoded, mapping can be performed using either ridge regression for fast offline analyses or gradient descent for real-time applications. We validate our model-agnostic approach in silico, as well as on empirical fMRI data obtained from 3T and 7T MRI scanners. Our approach is capable of estimating receptive fields and their parameters for millions of voxels in mere seconds. This method thus facilitates real-time applications of population receptive field mapping.

Topographic maps, a key principle of brain organization, emerge during development. It remains unclear, however, whether topographic maps can represent a new sensory experience learned in adulthood. MaMe, a congenitally blind individual, has been extensively trained in adulthood for perception of a 2D auditory-space (soundscape) where the y- and x-axes are represented by pitch and time, respectively. Using population receptive field mapping we found neural populations tuned topographically to pitch, not only in the auditory cortices but also in the parietal and occipito-temporal cortices. Topographic neural tuning to time was revealed in the parietal and occipito-temporal cortices. Some of these maps were found to represent both axes concurrently, enabling MaMe to represent unique locations in the soundscape space. This case study provides proof of concept for the existence of topographic maps tuned to the newly learned soundscape dimensions. These results suggest that topographic maps can be adapted or recycled in adulthood to represent novel sensory experiences.

Multivariate pattern analysis is a powerful technique; however, a significant theoretical limitation in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. This is exemplified by the continued controversy over the source of orientation decoding from fMRI responses in human V1. Recently Carlson (2014) identified a potential source of decodable information by modeling voxel responses based on the Hubel and Wiesel (1972) ice-cube model of visual cortex. The model revealed that activity associated with the edges of gratings covaries with orientation and could potentially be used to discriminate orientation. Here we empirically evaluate whether \"edge-related activity\" underlies orientation decoding from patterns of BOLD response in human V1. First, we systematically mapped classifier performance as a function of stimulus location using population receptive field modeling to isolate each voxel\'s overlap with a large annular grating stimulus. Orientation was decodable across the stimulus; however, peak decoding performance occurred for voxels with receptive fields closer to the fovea and overlapping with the inner edge. Critically, we did not observe the expected second peak in decoding performance at the outer stimulus edge as predicted by the edge account. Second, we evaluated whether voxels that contribute most to classifier performance have receptive fields that cluster in cortical regions corresponding to the retinotopic location of the stimulus edge. Instead, we find the distribution of highly weighted voxels to be approximately random, with a modest bias toward more foveal voxels. Our results demonstrate that edge-related activity is likely not necessary for orientation decoding.
A significant theoretical limitation of multivariate pattern analysis in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. For example, orientation can be decoded from BOLD activation patterns in human V1, even though orientation columns are at a finer spatial scale than 3T fMRI. Consequently, the source of decodable information remains controversial. Here we test the proposal that information related to the stimulus edges underlies orientation decoding. We map voxel population receptive fields in V1 and evaluate orientation decoding performance as a function of stimulus location in retinotopic cortex. We find orientation is decodable from voxels whose receptive fields do not overlap with the stimulus edges, suggesting edge-related activity does not substantially drive orientation decoding.