Sub-bundle based analysis reveals the role of human optic radiation in visual working memory.

White matter (WM) functional activity has been reliably detected through functional magnetic resonance imaging (fMRI). Previous studies have primarily examined WM bundles as unified entities, thereby obscuring the functional heterogeneity inherent within these bundles. Here, for the first time, we investigate the function of sub-bundles of a prototypical visual WM tract-the optic radiation (OR). We use the 7T retinotopy dataset from the Human Connectome Project (HCP) to reconstruct OR and further subdivide the OR into sub-bundles based on the fiber's termination in the primary visual cortex (V1). The population receptive field (pRF) model is then applied to evaluate the retinotopic properties of these sub-bundles, and the consistency of the pRF properties of sub-bundles with those of V1 subfields is evaluated. Furthermore, we utilize the HCP working memory dataset to evaluate the activations of the foveal and peripheral OR sub-bundles, along with LGN and V1 subfields, during 0-back and 2-back tasks. We then evaluate differences in 2bk-0bk contrast between foveal and peripheral sub-bundles (or subfields), and further examine potential relationships between 2bk-0bk contrast and 2-back task d-prime. The results show that the pRF properties of OR sub-bundles exhibit standard retinotopic properties and are typically similar to the properties of V1 subfields. Notably, activations during the 2-back task consistently surpass those under the 0-back task across foveal and peripheral OR sub-bundles, as well as LGN and V1 subfields. The foveal V1 displays significantly higher 2bk-0bk contrast than peripheral V1. The 2-back task d-prime shows strong correlations with 2bk-0bk contrast for foveal and peripheral OR fibers. These findings demonstrate that the blood oxygen level-dependent (BOLD) signals of OR sub-bundles encode high-fidelity visual information, underscoring the feasibility of assessing WM functional activity at the sub-bundle level. Additionally, the study highlights the role of OR in the top-down processes of visual working memory beyond the bottom-up processes for visual information transmission. Conclusively, this study innovatively proposes a novel paradigm for analyzing WM fiber tracts at the individual sub-bundle level and expands understanding of OR function.

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.

State-dependent complexity of the local field potential in the primary visual cortex.

The local field potential (LFP) is as a measure of the combined activity of neurons within a region of brain tissue. While biophysical modeling schemes for LFP in cortical circuits are well established, there is a paramount lack of understanding regarding the LFP properties along the states assumed in cortical circuits over long periods. Here we use a symbolic information approach to determine the statistical complexity based on Jensen disequilibrium measure and Shannon entropy of LFP data recorded from the primary visual cortex (V1) of urethane-anesthetized rats and freely moving mice. Using these information quantifiers, we find consistent relations between LFP recordings and measures of cortical states at the neuronal level. More specifically, we show that LFP's statistical complexity is sensitive to cortical state (characterized by spiking variability), as well as to cortical layer. In addition, we apply these quantifiers to characterize behavioral states of freely moving mice, where we find indirect relations between such states and spiking variability.

Running into differences.

Body movement does not significantly increase neuronal activity in the primary visual cortex of marmosets, in contrast to the effects observed in mice.

Longitudinal stability of individual brain plasticity patterns in blindness.

The primary visual cortex (V1) in blindness is engaged in a wide spectrum of tasks and sensory modalities, including audition, touch, language, and memory. This widespread involvement raises questions regarding the constancy of its role and whether it might exhibit flexibility in its function over time, connecting to diverse network functions specific to task demands. This would suggest that reorganized V1 assumes a role like multiple-demand system regions. Alternatively, varying patterns of plasticity in blind V1 may be attributed to individual factors, with different blind individuals recruiting V1 preferentially for different functions. In support of this, we recently showed that V1 functional connectivity (FC) varies greatly across blind individuals. But do these represent stable individual patterns of plasticity, or are they driven more by instantaneous changes, like a multiple-demand system now inhabiting V1? Here, we tested whether individual FC patterns from the V1 of blind individuals are stable over time. We show that over two years, FC from the V1 is unique and highly stable in a small sample of repeatedly sampled congenitally blind individuals. Further, using multivoxel pattern analysis, we demonstrate that the unique reorganization patterns of these individuals allow decoding of participant identity. Together with recent evidence for substantial individual differences in V1 connectivity, this indicates that there may be a consistent role for V1 in blindness, which may differ for each individual. Further, it suggests that the variability in visual reorganization in blindness across individuals could be used to seek stable neuromarkers for sight rehabilitation and assistive approaches.

Development of ocular dominance columns across rodents and other species: revisiting the concept of critical period plasticity.

The existence of cortical columns, regarded as computational units underlying both lower and higher-order information processing, has long been associated with highly evolved brains, and previous studies suggested their absence in rodents. However, recent discoveries have unveiled the presence of ocular dominance columns (ODCs) in the primary visual cortex (V1) of Long-Evans rats. These domains exhibit continuity from layer 2 through layer 6, confirming their identity as genuine ODCs. Notably, ODCs are also observed in Brown Norway rats, a strain closely related to wild rats, suggesting the physiological relevance of ODCs in natural survival contexts, although they are lacking in albino rats. This discovery has enabled researchers to explore the development and plasticity of cortical columns using a multidisciplinary approach, leveraging studies involving hundreds of individuals-an endeavor challenging in carnivore and primate species. Notably, developmental trajectories differ depending on the aspect under examination: while the distribution of geniculo-cortical afferent terminals indicates matured ODCs even before eye-opening, consistent with prevailing theories in carnivore/primate studies, examination of cortical neuron spiking activities reveals immature ODCs until postnatal day 35, suggesting delayed maturation of functional synapses which is dependent on visual experience. This developmental gap might be recognized as 'critical period' for ocular dominance plasticity in previous studies. In this article, I summarize cross-species differences in ODCs and geniculo-cortical network, followed by a discussion on the development, plasticity, and evolutionary significance of rat ODCs. I discuss classical and recent studies on critical period plasticity in the venue where critical period plasticity might be a component of experience-dependent development. Consequently, this series of studies prompts a paradigm shift in our understanding of species conservation of cortical columns and the nature of plasticity during the classical critical period.

Dissonant music engages early visual processing.

The neuroscientific examination of music processing in audio-visual contexts offers a valuable framework to assess how auditory information influences the emotional encoding of visual information. Using fMRI during naturalistic film viewing, we investigated the neural mechanisms underlying the effect of music on valence inferences during mental state attribution. Thirty-eight participants watched the same short-film accompanied by systematically controlled consonant or dissonant music. Subjects were instructed to think about the main character's intentions. The results revealed that increasing levels of dissonance led to more negatively valenced inferences, displaying the profound emotional impact of musical dissonance. Crucially, at the neuroscientific level and despite music being the sole manipulation, dissonance evoked the response of the primary visual cortex (V1). Functional/effective connectivity analysis showed a stronger coupling between the auditory ventral stream (AVS) and V1 in response to tonal dissonance and demonstrated the modulation of early visual processing via top-down feedback inputs from the AVS to V1. These V1 signal changes indicate the influence of high-level contextual representations associated with tonal dissonance on early visual cortices, serving to facilitate the emotional interpretation of visual information. Our results highlight the significance of employing systematically controlled music, which can isolate emotional valence from the arousal dimension, to elucidate the brain's sound-to-meaning interface and its distributive crossmodal effects on early visual encoding during naturalistic film viewing.

Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex.

Time courses of neural responses underlie real-time sensory processing and perception. How these temporal dynamics change may be fundamental to how sensory systems adapt to different perceptual demands. By simultaneously recording from hundreds of neurons in mouse primary visual cortex, we examined neural population responses to visual stimuli at sub-second timescales, during different behavioural states. We discovered that during active behavioural states characterised by locomotion, single-neurons shift from transient to sustained response modes, facilitating rapid emergence of visual stimulus tuning. Differences in single-neuron response dynamics were associated with changes in temporal dynamics of neural correlations, including faster stabilisation of stimulus-evoked changes in the structure of correlations during locomotion. Using Factor Analysis, we examined temporal dynamics of latent population responses and discovered that trajectories of population activity make more direct transitions between baseline and stimulus-encoding neural states during locomotion. This could be partly explained by dampening of oscillatory dynamics present during stationary behavioural states. Functionally, changes in temporal response dynamics collectively enabled faster, more stable and more efficient encoding of new visual information during locomotion. These findings reveal a principle of how sensory systems adapt to perceptual demands, where flexible neural population dynamics govern the speed and stability of sensory encoding.

The mechanics of correlated variability in segregated cortical excitatory subnetworks.

Understanding the genesis of shared trial-to-trial variability in neuronal population activity within the sensory cortex is critical to uncovering the biological basis of information processing in the brain. Shared variability is often a reflection of the structure of cortical connectivity since it likely arises, in part, from local circuit inputs. A series of experiments from segregated networks of (excitatory) pyramidal neurons in the mouse primary visual cortex challenge this view. Specifically, the across-network correlations were found to be larger than predicted given the known weak cross-network connectivity. We aim to uncover the circuit mechanisms responsible for these enhanced correlations through biologically motivated cortical circuit models. Our central finding is that coupling each excitatory subpopulation with a specific inhibitory subpopulation provides the most robust network-intrinsic solution in shaping these enhanced correlations. This result argues for the existence of excitatory-inhibitory functional assemblies in early sensory areas which mirror not just response properties but also connectivity between pyramidal cells. Furthermore, our findings provide theoretical support for recent experimental observations showing that cortical inhibition forms structural and functional subnetworks with excitatory cells, in contrast to the classical view that inhibition is a nonspecific blanket suppression of local excitation.

Rapid effects of tryptamine psychedelics on perceptual distortions and early visual cortical population receptive fields.

N, N-dimethyltryptamine (DMT) is a psychedelic tryptamine acting on 5-HT2A serotonin receptors, which is associated with intense visual hallucinatory phenomena and perceptual changes such as distortions in visual space. The neural underpinnings of these effects remain unknown. We hypothesised that changes in population receptive field (pRF) properties in the primary visual cortex (V1) might underlie visual perceptual experience. We tested this hypothesis using magnetic resonance imaging (MRI) in a within-subject design. We used a technique called pRF mapping, which measures neural population visual response properties and retinotopic maps in early visual areas. We show that in the presence of visual effects, as documented by the Hallucinogen Rating Scale (HRS), the mean pRF sizes in V1 significantly increase in the peripheral visual field for active condition (inhaled DMT) compared to the control. Eye and head movement differences were absent across conditions. This evidence for short-term effects of DMT in pRF may explain perceptual distortions induced by psychedelics such as field blurring, tunnel vision (peripheral vision becoming blurred while central vision remains sharp) and the enlargement of nearby visual space, particularly at the visual locations surrounding the fovea. Our findings are also consistent with a mechanistic framework whereby gain control of ongoing and evoked activity in the visual cortex is controlled by activation of 5-HT2A receptors.

Sex-specific alterations in visual properties induced by single prolonged stress model.

Patients with post-traumatic stress disorder (PTSD) exhibit sex differences in symptomology, with women more likely to report higher rates of intrusive and avoidance symptoms than men, underscoring the need for sex-informed approaches to research and treatment. Our study delved into the sex-specific aspects of stress-induced visual impairments using the single prolonged stress (SPS) model, a partially validated rodent model for PTSD. Male SPS mice exhibit heightened optimal spatial frequency (SF) of primary visual cortex (V1) neurons, while female counterparts exhibit decreased optimal temporal frequency (TF) of V1 neurons. This phenomenon persisted until the 29th day after SPS modeling, and it may be the physiological basis for the observed increase in visual acuity in male SPS mice in visual water task. Furthermore, our study found that corticotropin-releasing factor receptor 1 regulated optimal TF and optimal SF of V1 in mice, but did not exhibit sex differences. These findings indicated that severe stress induces sex-specific effects on visual function.

The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations.

When stimulated, neural populations in the visual cortex exhibit fast rhythmic activity with frequencies in the gamma band (30-80 Hz). The gamma rhythm manifests as a broad resonance peak in the power-spectrum of recorded local field potentials, which exhibits various stimulus dependencies. In particular, in macaque primary visual cortex (V1), the gamma peak frequency increases with increasing stimulus contrast. Moreover, this contrast dependence is local: when contrast varies smoothly over visual space, the gamma peak frequency in each cortical column is controlled by the local contrast in that column's receptive field. No parsimonious mechanistic explanation for these contrast dependencies of V1 gamma oscillations has been proposed. The stabilized supralinear network (SSN) is a mechanistic model of cortical circuits that has accounted for a range of visual cortical response nonlinearities and contextual modulations, as well as their contrast dependence. Here, we begin by showing that a reduced SSN model without retinotopy robustly captures the contrast dependence of gamma peak frequency, and provides a mechanistic explanation for this effect based on the observed non-saturating and supralinear input-output function of V1 neurons. Given this result, the local dependence on contrast can trivially be captured in a retinotopic SSN which however lacks horizontal synaptic connections between its cortical columns. However, long-range horizontal connections in V1 are in fact strong, and underlie contextual modulation effects such as surround suppression. We thus explored whether a retinotopically organized SSN model of V1 with strong excitatory horizontal connections can exhibit both surround suppression and the local contrast dependence of gamma peak frequency. We found that retinotopic SSNs can account for both effects, but only when the horizontal excitatory projections are composed of two components with different patterns of spatial fall-off with distance: a short-range component that only targets the source column, combined with a long-range component that targets columns neighboring the source column. We thus make a specific qualitative prediction for the spatial structure of horizontal connections in macaque V1, consistent with the columnar structure of cortex.

Data-driven modelling of visual receptive fields: comparison between the generalized quadratic model and the nonlinear input model.

Objective: Neurons in primary visual cortex (V1) display a range of sensitivity in their response to translations of their preferred visual features within their receptive field: from high specificity to a precise position through to complete invariance. This visual feature selectivity and invariance is frequently modeled by applying a selection of linear spatial filters to the input image, that define the feature selectivity, followed by a nonlinear function that combines the filter outputs, that defines the invariance, to predict the neural response. We compare two such classes of model, that are both popular and parsimonious, the generalized quadratic model (GQM) and the nonlinear input model (NIM). These two classes of model differ primarily in that the NIM can accommodate a greater diversity in the form of nonlinearity that is applied to the outputs of the filters.Approach: We compare the two model types by applying them to data from multielectrode recordings from cat primary visual cortex in response to spatially white Gaussian noise After fitting both classes of model to a database of 342 single units (SUs), we analyze the qualitative and quantitative differences in the visual feature processing performed by the two models and their ability to predict neural response.Main results: We find that the NIM predicts response rates on a held-out data at least as well as the GQM for 95% of SUs. Superior performance occurs predominantly for those units with above average spike rates and is largely due to the NIMs ability to capture aspects of the model's nonlinear function cannot be captured with the GQM rather than differences in the visual features being processed by the two different models.Significance: These results can help guide model choice for data-driven receptive field modelling.

High-throughput volumetric mapping of synaptic transmission.

Volumetric imaging of synaptic transmission in vivo requires high spatial and high temporal resolution. Shaping the wavefront of two-photon fluorescence excitation light, we developed Bessel-droplet foci for high-contrast and high-resolution volumetric imaging of synapses. Applying our method to imaging glutamate release, we demonstrated high-throughput mapping of excitatory inputs at >1,000 synapses per volume and >500 dendritic spines per neuron in vivo and unveiled previously unseen features of functional synaptic organization in the mouse primary visual cortex.

Astrocyte coverage of excitatory synapses correlates to measures of synapse structure and function in ferret primary visual cortex.

Most excitatory synapses in the mammalian brain are contacted or ensheathed by astrocyte processes, forming tripartite synapses. Astrocytes are thought to be critical regulators of the structural and functional dynamics of synapses. While the degree of synaptic coverage by astrocytes is known to vary across brain regions and animal species, the reason for and implications of this variability remains unknown. Further, how astrocyte coverage of synapses relates to in vivo functional properties of individual synapses has not been investigated. Here, we characterized astrocyte coverage of synapses of pyramidal neurons in the ferret visual cortex and, using correlative light and electron microscopy, examined their relationship to synaptic strength and sensory-evoked Ca2+ activity. Nearly, all synapses were contacted by astrocytes, and most were contacted along the axon-spine interface. Structurally, we found that the degree of synaptic astrocyte coverage directly scaled with synapse size and postsynaptic density complexity. Functionally, we found that the amount of astrocyte coverage scaled with how selectively a synapse responds to a particular visual stimulus and, at least for the largest synapses, scaled with the reliability of visual stimuli to evoke postsynaptic Ca2+ events. Our study shows astrocyte coverage is highly correlated with structural metrics of synaptic strength of excitatory synapses in the visual cortex and demonstrates a previously unknown relationship between astrocyte coverage and reliable sensory activation.

Neuronal representation of visual working memory content in the primate primary visual cortex.

The human ability to perceive vivid memories as if they "float" before our eyes, even in the absence of actual visual stimuli, captivates the imagination. To determine the neural substrates underlying visual memories, we investigated the neuronal representation of working memory content in the primary visual cortex of monkeys. Our study revealed that neurons exhibit unique responses to different memory contents, using firing patterns distinct from those observed during the perception of external visual stimuli. Moreover, this neuronal representation evolves with alterations in the recalled content and extends beyond the retinotopic areas typically reserved for processing external visual input. These discoveries shed light on the visual encoding of memories and indicate avenues for understanding the remarkable power of the mind's eye.

Biological direct-shortcut deep residual learning for sparse visual processing.

We show, based on the following three grounds, that the primary visual cortex (V1) is a biological direct-shortcut deep residual learning neural network (ResNet) for sparse visual processing: (1) We first highlight that Gabor-like sets of basis functions, which are similar to the receptive fields of simple cells in the primary visual cortex (V1), are excellent candidates for sparse representation of natural images; i.e., images from the natural world, affirming the brain to be optimized for this. (2) We then prove that the intra-layer synaptic weight matrices of this region can be reasonably first-order approximated by identity mappings, and are thus sparse themselves. (3) Finally, we point out that intra-layer weight matrices having identity mapping as their initial approximation, irrespective of this approximation being also a reasonable first-order one or not, resemble the building blocks of direct-shortcut digital ResNets, which completes the grounds. This biological ResNet interconnects the sparsity of the final representation of the image to that of its intra-layer weights. Further exploration of this ResNet, and understanding the joint effects of its architecture and learning rules, e.g. on its inductive bias, could lead to major advancements in the area of bio-inspired digital ResNets. One immediate line of research in this context, for instance, is to study the impact of forcing the direct-shortcuts to be good first-order approximations of each building block. For this, along with the ℓ 1 -minimization posed on the basis function coefficients the ResNet finally provides at its output, another parallel one could e.g. also be posed on the weights of its residual layers.

Convolutional neural network models applied to neuronal responses in macaque V1 reveal limited nonlinear processing.

Computational models of the primary visual cortex (V1) have suggested that V1 neurons behave like Gabor filters followed by simple nonlinearities. However, recent work employing convolutional neural network (CNN) models has suggested that V1 relies on far more nonlinear computations than previously thought. Specifically, unit responses in an intermediate layer of VGG-19 were found to best predict macaque V1 responses to thousands of natural and synthetic images. Here, we evaluated the hypothesis that the poor performance of lower layer units in VGG-19 might be attributable to their small receptive field size rather than to their lack of complexity per se. We compared VGG-19 with AlexNet, which has much larger receptive fields in its lower layers. Whereas the best-performing layer of VGG-19 occurred after seven nonlinear steps, the first convolutional layer of AlexNet best predicted V1 responses. Although the predictive accuracy of VGG-19 was somewhat better than that of standard AlexNet, we found that a modified version of AlexNet could match the performance of VGG-19 after only a few nonlinear computations. Control analyses revealed that decreasing the size of the input images caused the best-performing layer of VGG-19 to shift to a lower layer, consistent with the hypothesis that the relationship between image size and receptive field size can strongly affect model performance. We conducted additional analyses using a Gabor pyramid model to test for nonlinear contributions of normalization and contrast saturation. Overall, our findings suggest that the feedforward responses of V1 neurons can be well explained by assuming only a few nonlinear processing stages.

A strong direct link from the layer 3/4 border to layer 6 of cat primary visual cortex.

The cat primary visual cortex (V1) is a cortical area for which we have one of the most detailed estimates of the connection 'weights' (expressed as number of synapses) between different neural populations in different layers (Binzegger et al in J Neurosci 24:8441-8453, 2004). Nevertheless, the majority of excitatory input sources to layer 6, the deepest layer in a local translaminar excitatory feedforward loop, was not accounted for by the known neuron types used to generate the quantitative Binzegger diagram. We aimed to fill this gap by using a retrograde tracer that would label neural cell bodies in and outside V1 that directly connect to layer 6 of V1. We found that more than 80% of labeled neurons projecting to layer 6 were within V1 itself. Our data indicate that a substantial fraction of the missing input is provided by a previously unidentified population of layer 3/4 border neurons, laterally distributed and connecting more strongly to layer 6 than the typical superficial layer pyramidal neurons considered by Binzegger et al. (Binzegger et al in J Neurosci 24:8441-8453, 2004). This layer 3/4 to layer 6 connection may be a parallel route to the layer 3 - layer 5 - layer 6 feedforward pathway, be associated with the fast-conducting, movement-related Y pathway and provide convergent input from distant (5-10 degrees) regions of the visual field.

A large-scale neuronal network modelling study: Stimulus size modulates gamma oscillations in the primary visual cortex by long-range connections.

Stimulus size modulation of neuronal firing activity is a fundamental property of the primary visual cortex. Numerous biological experiments have shown that stimulus size modulation is affected by multiple factors at different spatiotemporal scales, but the exact pathways and mechanisms remain incompletely understood. In this paper, we establish a large-scale neuronal network model of primary visual cortex with layer 2/3 to study how gamma oscillation properties are modulated by stimulus size and especially how long-range connections affect the modulation as realistic neuronal properties and spatial distributions of synaptic connections are considered. It is shown that long-range horizontal synaptic connections are sufficient to produce dimensional modulation of firing rates and gamma oscillations. In particular, with increasing grating stimulus size, the firing rate increases and then decreases, the peak frequency of gamma oscillations decreases and the spectral power increases. These are consistent with biological experimental observations. Furthermore, we explain in detail how the number and spatial distribution of long-range connections affect the size modulation of gamma oscillations by using the analysis of neuronal firing activity and synaptic current fluctuations. Our results provide a mechanism explanation for size modulation of gamma oscillations in the primary visual cortex and reveal the important and unique role played by long-range connections, which contributes to a deeper understanding of the cognitive function of gamma oscillations in visual cortex.