Building maps from maps in primary visual cortex

Early postnatal development of the primary visual areas 17 and 18 of the cat cerebral cortex: An SMI-32 study

Using anti-neurofilament H non-phosphorylated antibodies (SMI-32) as markers for the neuronal maturation level and Y channel responsible for motion processing, we investigated early postnatal development of the primary visual areas 17 and 18 in cats aged 0, 10, 14, and 34 days and in adults. Two analyzed parameters of SMI-32-immunolabeling were used: the total proportion of SMI-32-labeling and the density of labeled neurons. (i) The developmental time course of the total proportion of SMI-32-labeling shows the general increase in the accumulation of heavy-chain neurofilaments. This parameter showed a different time course for cortical layer development; the maximal increment in the total labeling in layer V occurred between the second and fifth postnatal weeks and in layers II-III and VI after the fifth postnatal week. In addition, the delay in accumulation of SMI-32-labeling was shown in layer V of the area 17 periphery representation during the first two postnatal weeks. (ii) The density of SMI-32-labeled neurons decreased in all layers of area 18, but was increased, decreased, or had a transient peak in layers II-III, V, and VI of area 17, respectively. The transient peak is in good correspondence with some transient neurochemical features previously revealed for different classes of cortical and thalamic neurons and reflects the time course of the early development of the thalamocortical circuitry. Some similarities between the time courses for the development of SMI-32-labeling in areas 17/18 and in A- and C-laminae of the LGNd allow us to propose heterochronous postnatal development of two Y sub-channels.

High-throughput sequencing of single neuron projections reveals spatial organization in the olfactory cortex

In most sensory modalities, neuronal connectivity reflects behaviorally relevant stimulus features, such as spatial location, orientation, and sound frequency. By contrast, the prevailing view in the olfactory cortex, based on the reconstruction of dozens of neurons, is that connectivity is random. Here, we used high-throughput sequencing-based neuroanatomical techniques to analyze the projections of 5,309 mouse olfactory bulb and 30,433 piriform cortex output neurons at single-cell resolution. Surprisingly, statistical analysis of this much larger dataset revealed that the olfactory cortex connectivity is spatially structured. Single olfactory bulb neurons targeting a particular location along the anterior-posterior axis of piriform cortex also project to matched, functionally distinct, extra-piriform targets. Moreover, single neurons from the targeted piriform locus also project to the same matched extra-piriform targets, forming triadic circuit motifs. Thus, as in other sensory modalities, olfactory information is routed at early stages of processing to functionally diverse targets in a coordinated manner.

Projection of Orthogonal Tiling from the Retina to the Visual Cortex

In higher mammals, the primary visual cortex (V1) is organized into diverse tuning maps of visual features. The topography of these maps intersects orthogonally, but it remains unclear how such a systematic relationship can develop. Here, we show that the orthogonal organization already exists in retinal ganglion cell (RGC) mosaics, providing a blueprint of the organization in V1. From analysis of the RGC mosaics data in monkeys and cats, we find that the ON-OFF RGC distance and ON-OFF angle of neighboring RGCs are organized into a topographic tiling across mosaics, analogous to the orthogonal intersection of cortical tuning maps. Our model simulation shows that the ON-OFF distance and angle in RGC mosaics correspondingly initiate ocular dominance/spatial frequency tuning and orientation tuning, resulting in the orthogonal intersection of cortical tuning maps. These findings suggest that the regularly structured ON-OFF patterns mirrored from the retina initiate the uniform representation of combinations of map features over the visual space.

Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes

BACKGROUND

Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context.

METHODS

We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels.

RESULTS

We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample.

CONCLUSIONS

Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research.

Early Emergence of Solid Shape Coding in Natural and Deep Network Vision

Area V4 is the first object-specific processing stage in the ventral visual pathway, just as area MT is the first motion-specific processing stage in the dorsal pathway. For almost 50 years, coding of object shape in V4 has been studied and conceived in terms of flat pattern processing, given its early position in the transformation of 2D visual images. Here, however, in awake monkey recording experiments, we found that roughly half of V4 neurons are more tuned and responsive to solid, 3D shape-in-depth, as conveyed by shading, specularity, reflection, refraction, or disparity cues in images. Using 2-photon functional microscopy, we found that flat- and solid-preferring neurons were segregated into separate modules across the surface of area V4. These findings should impact early shape-processing theories and models, which have focused on 2D pattern processing. In fact, our analyses of early object processing in AlexNet, a standard visual deep network, revealed a similar distribution of sensitivities to flat and solid shape in layer 3. Early processing of solid shape, in parallel with flat shape, could represent a computational advantage discovered by both primate brain evolution and deep-network training.

Binocular global motion perception is improved by dichoptic segregation when stimuli have high contrast and high speed

The brain combines information from the two eyes during vision. This combination is obligatory to a remarkable extent: In random-dot kinematograms (RDKs), randomly moving noise dots were similarly effective at preventing observers from seeing the motion of coherently moving signals dots, independent of whether the signal and noise were presented to the same eye or segregated to different eyes. However, motion detectors have varied binocularity: Neurons in visual brain area V1 that encode high contrast, high speed stimuli may be less completely binocular than neurons that encode low contrast, low speed stimuli. Also, neurons in MT often have unbalanced inputs from the two eyes. We predicted that for high contrast, high speed stimuli only, there would be a benefit to segregating the signal and noise of the RDK into different eyes. We found this benefit, both when performance was measured by percent coherence thresholds and when it was measured by luminance contrast ratio (signal-dot-contrast to noise-dot-contrast) thresholds. Thus, for high contrast, high speed stimuli, binocular fusion of local motion is not complete before the extraction of global motion. We also replicated a cross-over interaction: At high speed, global motion extraction was generally more efficient when dot contrast was high, but at low speed it was more efficient when dot contrast was low. We provide a schematic model of binocular global motion perception, to show how the contrast-speed interaction can be predicted from neurophysiology and why it should be exaggerated for segregated viewing. Our data bore out these predictions. We conclude that different neural populations limit performance during binocular global motion perception, depending on stimulus contrast and speed.

Origins of Functional Organization in the Visual Cortex

How are the complex maps for orientation selectivity (OS) created in the primary visual cortex (V1)? Rodents and rabbits have a random distribution of OS preferences across V1 while in cats, ferrets, and all primates cells with similar OS preferences cluster together into relatively wide cortical columns. Given other clear similarities in the organization of the visual pathways, why is it that maps coding OS preferences are so radically different? Prominent models have been created of cortical OS mapping that incorporate Hebbian plasticity, intracortical interactions, and the properties of growing axons. However, these models suggest that the maps arise primarily through intracortical interactions. Here we focus on several other features of the visual system and brain that may influence V1 structure. These are: eye divergence, the total number of cells in V1, the thalamocortical networks, the topography of the retina and phylogeny. We outline the evidence for and against these factors contributing to map formation. One promising theory is that the central-to-peripheral ratio (CP ratio) of retinal cell density can be used to predict whether or not a species has pinwheel maps. Animals with high CP ratios (>7) have orientation columns while those with low CP ratios (<4) have random OS maps. The CP ratio is related to the total number of cells in cortex, which also appears to be a reasonable contributing factor. However, while these factors correlate with map structure to some extent, there is a gray area where certain species do not fit elegantly into the theory. A problem with the existing literature is that OS maps have been investigated in only a small number of mammals, from a small fraction of the mammalian phylogenetic tree. We suggest four species (agouti, fruit bat, sheep, and wallaby) that have a range of interesting characteristics, which sit at intermediate locations between primates and rodents, that make them good targets for filling in the missing gaps in the literature. We make predictions about the map structures of these species based on the organization of their brains and visual systems and, in doing so, set possible paths for future research.

The hyperbolic model for edge and texture detection in the primary visual cortex

The modeling of neural fields in the visual cortex involves geometrical structures which describe in mathematical formalism the functional architecture of this cortical area. The case of contour detection and orientation tuning has been extensively studied and has become a paradigm for the mathematical analysis of image processing by the brain. Ten years ago an attempt was made to extend these models by replacing orientation (an angle) with a second-order tensor built from the gradient of the image intensity, and it was named the structure tensor. This assumption does not follow from biological observations (experimental evidence is still lacking) but from the idea that the effectiveness of texture processing with the structure tensor in computer vision may well be exploited by the brain itself. The drawback is that in this case the geometry is not Euclidean but hyperbolic instead, which complicates the analysis substantially. The purpose of this review is to present the methodology that was developed in a series of papers to investigate this quite unusual problem, specifically from the point of view of tuning and pattern formation. These methods, which rely on bifurcation theory with symmetry in the hyperbolic context, might be of interest for the modeling of other features such as color vision or other brain functions.

Advanced Circuit and Cellular Imaging Methods in Nonhuman Primates

Novel genetically encoded tools and advanced microscopy methods have revolutionized neural circuit analyses in insects and rodents over the last two decades. Whereas numerous technical hurdles originally barred these methodologies from success in nonhuman primates (NHPs), current research has started to overcome those barriers. In some cases, methodological advances developed with NHPs have even surpassed their precursors. One such advance includes new ultra-large imaging windows on NHP cortex, which are larger than the entire rodent brain and allow analysis unprecedented ultra-large-scale circuits. NHP imaging chambers now remain patent for periods longer than a mouse's lifespan, allowing for long-term all-optical interrogation of identified circuits and neurons over timeframes that are relevant to human cognitive development. Here we present some recent imaging advances brought forth by research teams using macaques and marmosets. These include technical developments in optogenetics; voltage-, calcium- and glutamate-sensitive dye imaging; two-photon and wide-field optical imaging; viral delivery; and genetic expression of indicators and light-activated proteins that result in the visualization of tens of thousands of identified cortical neurons in NHPs. We describe a subset of the many recent advances in circuit and cellular imaging tools in NHPs focusing here primarily on the research presented during the corresponding mini-symposium at the 2019 Society for Neuroscience annual meeting.

Thalamocortical Circuits and Functional Architecture

The thalamocortical pathway is the main route of communication between the eye and the cerebral cortex. During embryonic development, thalamocortical afferents travel to L4 and are sorted by receptive field position, eye of origin, and contrast polarity (i.e., preference for light or dark stimuli). In primates and carnivores, this sorting involves numerous afferents, most of which sample a limited region of the binocular field. Devoting abundant thalamocortical resources to process a limited visual field has a clear advantage: It allows many stimulus combinations to be sampled at each spatial location. Moreover, the sampling efficiency can be further enhanced by organizing the afferents in a cortical grid for eye input and contrast polarity. We argue that thalamocortical interactions within this eye-polarity grid can be used to represent multiple stimulus combinations found in nature and to build an accurate cortical map for multidimensional stimulus space.

Efficient Receptive Field Tiling in Primate V1

The primary visual cortex (V1) encodes a diverse set of visual features, including orientation, ocular dominance (OD), and spatial frequency (SF), whose joint organization must be precisely structured to optimize coverage within the retinotopic map. Prior experiments have only identified efficient coverage based on orthogonal maps. Here we used two-photon calcium imaging to reveal an alternative arrangement for OD and SF maps in macaque V1; their gradients run parallel but with unique spatial periods, whereby low-SF regions coincide with monocular regions. Next we mapped receptive fields and found surprisingly precise micro-retinotopy that yields a smaller point-image and requires more efficient inter-map geometry, thus underscoring the significance of map relationships. While smooth retinotopy is constraining, studies suggest that it improves both wiring economy and the V1 population code read downstream. Altogether, these data indicate that connectivity within V1 is finely tuned and precise at the level of individual neurons.

Spatial clustering of tuning in mouse primary visual cortex

The primary visual cortex of higher mammals is organized into two-dimensional maps, where the preference of cells for stimulus parameters is arranged regularly on the cortical surface. In contrast, the preference of neurons in the rodent appears to be arranged randomly, in what is termed a salt-and-pepper map. Here we revisited the spatial organization of receptive fields in mouse primary visual cortex by measuring the tuning of pyramidal neurons in the joint orientation and spatial frequency domain. We found that the similarity of tuning decreases as a function of cortical distance, revealing a weak but statistically significant spatial clustering. Clustering was also observed across different cortical depths, consistent with a columnar organization. Thus, the mouse visual cortex is not strictly a salt-and-pepper map. At least on a local scale, it resembles a degraded version of the organization seen in higher mammals, hinting at a possible common origin.

The preference of cells in mouse primary visual cortex are thought to be randomly distributed in a salt-and-pepper map, in contrast to the smooth cortical maps observed in higher mammals. Here the authors show that excitatory cells in mouse primary visual cortex are spatially clustered, resembling a degraded version of the organization seen in higher mammals.

Cortical Maps

In this article, we review functional organization in sensory cortical regions—how the cortex represents the world. We consider four interrelated aspects of cortical organization: (1) the set of receptive fields of individual cortical sensory neurons, (2) how lateral interaction between cortical neurons reflects the similarity of their receptive fields, (3) the spatial distribution of receptive-field properties across the horizontal extent of the cortical tissue, and (4) how the spatial distributions of different receptive-field properties interact with one another. We show how these data are generally well explained by the theory of input-driven self-organization, with a family of computational models of cortical maps offering a parsimonious account for a wide range of map-related phenomena. We then discuss important challenges to this explanation, with respect to the maps present at birth, maps present under activity blockade, the limits of adult plasticity, and the lack of some maps in rodents. Because there is not at present another credible general theory for cortical map development, we conclude by proposing key experiments to help uncover other mechanisms that might also be operating during map development.

Principles underlying sensory map topography in primary visual cortex

The primary visual cortex contains a detailed map of the visual scene, which is represented according to multiple stimulus dimensions including spatial location, ocular dominance and orientation. The maps for spatial location and ocular dominance originate from the spatial arrangement of thalamic axons in cortex. However, the origin of the other maps remains unclear. Here we demonstrate that the cortical maps for orientation, direction and retinal disparity are all strongly related to the organization for spatial location of light (ON) and dark (OFF) stimuli, an organization that we show is OFF-dominated, OFF-centric and runs orthogonal to ocular dominance columns. Because this ON/OFF organization originates from the clustering of ON and OFF thalamic afferents in visual cortex, we conclude that all main features of cortical topography, including orientation, direction and retinal disparity, follow a common organizing principle that arranges thalamic axons with similar retinotopy and ON/OFF polarity in neighboring cortical regions.

Modular Representation of Luminance Polarity in the Superficial Layers of Primary Visual Cortex

The spatial arrangement of luminance increments (ON) and decrements (OFF) falling on the retina provides a wealth of information used by central visual pathways to construct coherent representations of visual scenes. But how the polarity of luminance change is represented in the activity of cortical circuits remains unclear. Using wide-field epifluorescence and two-photon imaging we demonstrate a robust modular representation of luminance polarity (ON or OFF) in the superficial layers of ferret primary visual cortex. Polarity-specific domains are found with both uniform changes in luminance and single light/dark edges, and include neurons selective for orientation and direction of motion. The integration of orientation and polarity preference is evident in the selectivity and discrimination capabilities of most layer 2/3 neurons. We conclude that polarity selectivity is an integral feature of layer 2/3 neurons, ensuring that the distinction between light and dark stimuli is available for further processing in downstream extrastriate areas.

Monochromaticity of orientation maps in v1 implies minimum variance for hypercolumn size

In the primary visual cortex of many mammals, the processing of sensory information involves recognizing stimuli orientations. The repartition of preferred orientations of neurons in some areas is remarkable: a repetitive, non-periodic, layout. This repetitive pattern is understood to be fundamental for basic non-local aspects of vision, like the perception of contours, but important questions remain about its development and function. We focus here on Gaussian Random Fields, which provide a good description of the initial stage of orientation map development and, in spite of shortcomings we will recall, a computable framework for discussing general principles underlying the geometry of mature maps. We discuss the relationship between the notion of column spacing and the structure of correlation spectra; we prove formulas for the mean value and variance of column spacing, and we use numerical analysis of exact analytic formulae to study the variance. Referring to studies by Wolf, Geisel, Kaschube, Schnabel, and coworkers, we also show that spectral thinness is not an essential ingredient to obtain a pinwheel density of π, whereas it appears as a signature of Euclidean symmetry. The minimum variance property associated to thin spectra could be useful for information processing, provide optimal modularity for V1 hypercolumns, and be a first step toward a mathematical definition of hypercolumns. A measurement of this property in real maps is in principle possible, and comparison with the results in our paper could help establish the role of our minimum variance hypothesis in the development process.

What, if anything, are topological maps for?

What, if anything, is the functional significance of spatial patterning in cortical feature maps? We ask this question of four major theories of cortical map formation: self-organizing maps, wiring optimization, place coding, and reaction-diffusion. We argue that (i) self-organizing maps yield spatial patterning only as a by-product of efficient mechanisms for developing environmentally appropriate distributions of feature preferences, (ii) wiring optimization assumes rather than explains a map-like organization, (iii) place-coding mechanisms can at best explain only a subset of maps in functional terms, and (iv) reaction-diffusion models suggest two factors in the evolution of maps, the first based on efficient development of feature distributions, and the second based on generating feature-specific long-range recurrent cortical circuitry. None of these explanations for the existence of topological maps requires spatial patterning in maps to be useful. Thus despite these useful frameworks for understanding how maps form and how they are wired, the possibility that patterns are merely epiphenomena in the evolution of mammalian neocortex cannot be rejected. The article is intended as a nontechnical introduction to the assumptions and predictions of these four important classes of models, along with other possible functional explanations for maps.

Columnar organization of spatial phase in visual cortex

Images are processed in the primary visual cortex by neurons that encode different stimulus orientations and spatial phases. In primates and carnivores, neighboring cortical neurons share similar orientation preferences but spatial phases were thought to be randomly distributed. Here we reveal a columnar organization for spatial phase in cats that shares resemblances with the columnar organization for orientation. For both orientation and phase, the mean difference across vertically aligned neurons was less than 1/4 of a cycle. Cortical neurons showed three times more diversity in phase than orientation preference, however, the average phase of local neuronal populations was similar through the depth of layer 4. We conclude that columnar organization for visual space is not only defined by the spatial location of the stimulus but also by absolute phase. Taken together with previous studies, our results suggest that this phase-visuotopy is responsible for the emergence of orientation maps.