Alexander Michael Puckett

Using a magnet and a mind, we revealed the brain’s somatotopic organization without touching the body. Fingertip representations were first mapped using vibrotactile stimulation at 7T, then again by directing attention across the fingers under constant vibration. Finally, attention was directed to the fingertips and facial regions using only auditory cues, without any external tactile stimulation. Prior training in a sensory-restricted float tank subjectively sharpened attentional precision and bodily awareness. Remarkably, somatotopic maps emerged in the absence of external tactile stimulation – showing that structured attention alone can reveal the brain’s somatotopic organization.

Topographic organization, whereby neighboring cortical locations encode neighboring features in sensory or cognitive space, is a fundamental principle of brain function. Existing approaches for obtaining individual-specific topographic maps either require resource-intensive functional neuroimaging or, when relying on population atlases, lack precision for individual-level inference. Here, we introduce deepRetinotopy toolbox, a deep learning-based application for predicting the functional topographic organization of human visual cortex from cortical anatomy alone. DeepRetinotopy toolbox produces accurate retinotopic maps across diverse experimental conditions, imaging sites, and scanner types. We demonstrate how predicted maps can be utilized to automatically generate individual-specific visual area boundaries, overcoming common biases in manual annotations. Finally, we applied our method to 11,060 anatomical scans, which allowed us to quantify age-related changes in the functional organization of visual cortex predictable from anatomy alone, underscoring the method’s broad utility for scalable, anatomy-based functional brain mapping.

In animal models, the primary somatosensory cortex (S1) exhibits columnar organisation, where vertically arranged neurons share functional properties. In humans, however, the thinness and folding of S1 have limited non-invasive investigations of such columnar structures. In this study, we aimed to identify columns in human S1 by delivering alternating bursts of 3 Hz and 30 Hz fingertip vibration while acquiring functional MRI time series at 7 Tesla. Using cortical surface modelling, we identified functional patterns in S1 that showed higher reliability, stronger differential responses, and greater statistical sensitivity than those observed in a frontal cortex control region ( p = .001–.012 for reliability; p < .001 for differential signal; p = .004–.011 for sensitivity). Laminar analyses revealed depth-consistent frequency preferences in approximately 20–45% of S1 nodes, a pattern compatible with vertically organised functional structure. Although the relative signal difference between 3 Hz and 30 Hz was small (0.14% signal change), frequency tuning was reliably observed. Taken together, these findings reveal functional patterns in human S1 consistent with aspects of columnar-like organisation, providing non-invasive evidence of fine-scale functional architecture. Teaser: fMRI reveals highly reliable but modestly selective responses in human S1, consistent with column-like functional organisation.

Topographic organization, whereby neighboring cortical locations encode neighboring features in sensory or cognitive space, is a fundamental principle of brain function. Existing approaches for obtaining individual-specific topographic maps either require resource-intensive functional neuroimaging or, when relying on population atlases, lack precision for individual-level inference. Here, we introduce deepRetinotopy toolbox , a deep learning-based application for predicting the functional topographic organization of human visual cortex from cortical anatomy alone. DeepRetinotopy toolbox produces accurate retinotopic maps across diverse experimental conditions, imaging sites, and scanner types. We demonstrate how predicted maps can be utilized to automatically generate individual-specific visual area boundaries, overcoming common biases in manual annotations. Finally, we applied our method to 11,060 anatomical scans, which allowed us to quantify age-related changes in the functional organization of visual cortex predictable from anatomy alone, underscoring the method’s broad utility for scalable, anatomy-based functional brain mapping.

The visual cortex encodes the visual field through numerous bilaterally paired, topologically organized two-dimensional maps along the cortical surface. Although these representations exhibit a largely consistent organization across individuals, substantial interindividual variability exists in both the structure and functional organization of the visual cortex. To better characterize this variability, researchers have increasingly turned to computational approaches, alongside empirical methodologies and encoding models, to predict individual-level retinotopic organization. As these advances continue to shape the study of retinotopic organization, it is crucial to revisit the methodologies used to generate and model these maps. In this review, we examine empirical and theoretical work aimed at reconstructing and modeling visual field maps in the human visual cortex. Specifically, we discuss how empirical retinotopic mapping has facilitated the development of theoretical and computational models of retinotopy and, in turn, how these models have enhanced our understanding of the retinotopic organization of the human visual cortex. Finally, we outline a non-exhaustive set of future directions for leveraging models of retinotopy to further investigate structure-function relationships, interindividual variability, and more.

Perceptual brain regions represent the world using 2D maps. In the visual cortex in particular, the 2D cortical surface represents the 2D visual field topologically. That is, adjacent neurons in the visual brain represent adjacent locations on the retina, resembling a miniature map of the outside world. Here, we review empirical and theoretical work to access and model these miniature maps in human visual cortex. We aim to present a balanced view of the foundational work of the past and the current state-of-the-art capabilities. Specifically, we discuss how empirical retinotopic mapping enabled the development of theoretical and computational models of retinotopy and, subsequently, how these models have been used to better understand the retinotopic organization of the human visual cortex. Finally, we discuss a non-exhaustive list of potential future directions to further our understanding of these visual field maps, including their structure-functional relationships, individual variability, and more.

Visual field maps in human early extrastriate areas (V2 and V3) are traditionally thought to form mirror-image representations which surround the primary visual cortex (V1). According to this scheme, V2 and V3 form nearly symmetrical halves with respect to the calcarine sulcus, with the dorsal halves representing lower contralateral quadrants, and the ventral halves representing upper contralateral quadrants. This arrangement is considered to be consistent across individuals, and thus predictable with reasonable accuracy using templates. However, data that deviate from this expected pattern have been observed, but mainly treated as artifactual. Here, we systematically investigate individual variability in the visual field maps of human early visual cortex using the 7T Human Connectome Project (HCP) retinotopy dataset. Our results demonstrate substantial and principled inter-individual variability. Visual field representation in the dorsal portions of V2 and V3 was more variable than in their ventral counterparts, including substantial departures from the expected mirror-symmetrical patterns. In addition, left hemisphere retinotopic maps were more variable than those in the right hemisphere. Surprisingly, only one-third of individuals had maps that conformed to the expected pattern in the left hemisphere. Visual field sign analysis further revealed that in many individuals the area conventionally identified as dorsal V3 shows a discontinuity in the mirror-image representation of the retina, associated with a Y-shaped lower vertical representation. Our findings challenge the current view that inter-individual variability in early extrastriate cortex is negligible, and that the dorsal portions of V2 and V3 are roughly mirror images of their ventral counterparts.

Visual field maps in human early extrastriate areas (V2 and V3) are traditionally thought to form mirror-image representations which surround the primary visual cortex (V1). According to this scheme, V2 and V3 form nearly symmetrical halves with respect to the calcarine sulcus, with the dorsal halves representing lower contralateral quadrants, and the ventral halves representing upper contralateral quadrants. This arrangement is considered to be consistent across individuals, and thus predictable with reasonable accuracy using templates. However, data that deviate from this expected pattern have been observed, but mainly treated as artifactual. Here we systematically investigate individual variability in the visual field maps of human early visual cortex using the 7T Human Connectome Project (HCP) retinotopy dataset. Our results demonstrate substantial and principled inter-individual variability. Visual field representation in the dorsal portions of V2 and V3 was more variable than in their ventral counterparts, including substantial departures from the expected mirror-symmetrical patterns. In addition, left hemisphere retinotopic maps were more variable than those in the right hemisphere. Surprisingly, only one-third of individuals had maps that conformed to the expected pattern in the left hemisphere. Visual field sign analysis further revealed that in many individuals the area conventionally identified as dorsal V3 shows a discontinuity in the mirror-image representation of the retina, associated with a Y-shaped lower vertical representation. Our findings challenge the current view that inter-individual variability in early extrastriate cortex is negligible, and that the dorsal portions of V2 and V3 are roughly mirror images of their ventral counterparts.

Functional magnetic resonance imaging (fMRI) is a powerful tool for imaging somatosensory cortex, providing a means to non-invasively measure cortical activity in awake and behaving humans. Notably, this technique has permitted the homunculus – a hallmark of primary somatosensory cortex (S1) organization – to be examined with unprecedented detail. With the development of high-resolution fMRI (mostly at ultra-high field, 7 Tesla), it is now possible to investigate the finer topographic details of the sensory homunculus in almost any individual. Moreover, fMRI can be used to investigate other various bottom-up response properties as well as more top-down perceptual and cognitive processes (e.g., attention and prediction) across a wide range of experimental conditions. This chapter mainly focuses on tactile experiments, outlining a number of experimental paradigms and analysis techniques; practical and participant-specific difficulties are noted. Although we focus on fMRI for imaging primary somatosensory cortex, this technique can also be used to image cortical activity in other areas involved in somatosensory processing such as secondary somatosensory cortex (S2), insular cortex, or the cerebellum.