Visual Cortex Research Articles

Recall requirements can drastically modulate working memory representations in human visual cortex

Recall requirements can drastically modulate working memory representations in human visual cortex

Responses in human early visual cortex are more sensitive to task difficulty than object category.

Responses in human early visual cortex are more sensitive to task difficulty than object category.

Delayed divisive normalisation accounts for a wide range of temporal dynamics of neural responses in human visual cortex

Delayed divisive normalisation accounts for a wide range of temporal dynamics of neural responses in human visual cortex

Spatial Frequency Tuning in Early Visual Cortex is Not Scale Invariant

Spatial Frequency Tuning in Early Visual Cortex is Not Scale Invariant

Visual cortex encodes spatially specific reward information during closed-loop naturalistic interaction

Visual cortex encodes spatially specific reward information during closed-loop naturalistic interaction

Neuronal interactivity scales with stimulus complexity, not hierarchical structure in mouse visual cortex

Neuronal interactivity scales with stimulus complexity, not hierarchical structure in mouse visual cortex

How to estimate noise ceilings for computational models of visual cortex

How to estimate noise ceilings for computational models of visual cortex

Isolating the koniocellular contribution to aversive learning in human visual cortex

Isolating the koniocellular contribution to aversive learning in human visual cortex

Leveraging Vision and Language Generative Models to Understand the Visual Cortex

Leveraging Vision and Language Generative Models to Understand the Visual Cortex

Gain control of sensory input across polysynaptic circuitries in mouse visual cortex by a single G protein-coupled receptor type (5-HT2A)

Response gain is a crucial means by which modulatory systems control the impact of sensory input. In the visual cortex, the serotonergic 5-HT2A receptor is key in such modulation. However, due to its expression across different cell types and lack of methods that allow for specific activation, the underlying network mechanisms remain unsolved. Here we optogenetically activate endogenous G protein-coupled receptor (GPCR) signaling of a single receptor subtype in distinct mouse neocortical subpopulations in vivo. We show that photoactivation of the 5-HT2A receptor pathway in pyramidal neurons enhances firing of both excitatory neurons and interneurons, whereas 5-HT2A photoactivation in parvalbumin interneurons produces bidirectional effects. Combined photoactivation in both cell types and cortical network modelling demonstrates a conductance-driven polysynaptic mechanism that controls the gain of visual input without affecting ongoing baseline levels. Our study opens avenues to explore GPCRs neuromodulation and its impact on sensory-driven activity and ongoing neuronal dynamics.

Parallel gain modulation mechanisms set the resolution of color selectivity in human visual cortex.

Color discrimination is fundamental to human behavior. We find bananas by coarsely searching for yellow but then differentiate nuances of yellow to pick the best exemplars. How does the brain adjust the resolution of color selectivity to our changing needs? Here, we analyze the brain magnetic response in the human visual cortex to show that color selectivity is adaptively set by coarse- and fine-resolving processes running in parallel at different hierarchical levels. Those include a gain enhancement in the higher-lever cortex of color units tuned away from the target to resolve very similar colors and a coarsely resolving gain enhancement in the mid-level cortex of units tuned to the target. Our findings suggest that attention operates on a form of multiresolution representation of color at different levels in the visual hierarchy, which keeps selectivity adaptive to a changing resolution context.

The Role of Population Receptive Field Sizes in Higher-Order Visual Dysfunction.

Population receptive field (pRF) modeling is an fMRI technique used to retinotopically map visual cortex, with pRF size characterizing the degree of spatial integration. In clinical populations, most pRF mapping research has focused on damage to visual system inputs. Herein, we highlight recent work using pRF modeling to study high-level visual dysfunctions. Larger pRF sizes, indicating coarser spatial processing, were observed in homonymous visual field deficits, aging, and autism spectrum disorder. Smaller pRF sizes, indicating finer processing, were observed in Alzheimer's disease and schizophrenia. In posterior cortical atrophy, a unique pattern was found in which pRF size changes depended on eccentricity. Changes to pRF properties were observed in clinical populations, even in high-order impairments, explaining visual behavior. These pRF changes likely stem from altered interactions between brain regions. Furthermore, some studies suggested that pRF sizes change as part of cortical reorganization, and they can point towards future prognosis.

Double dissociation of visuomotor interaction mediated by visual feedback during continuous de novo motor learning

While the sensorimotor cortices are central neural substrates for motor control and learning, how the interaction between their subregions with visual cortices contributes to acquiring de novo visuomotor skills is poorly understood. We design a continuous visuomotor task in fMRI where participants control a cursor using their fingers while learning an arbitrary finger-to-cursor mapping. To investigate visuomotor interaction in the de novo motor task, we manipulate visual feedback of a cursor such that they learn to control using fingers under two alternating conditions: online cursor feedback is available or unavailable except when a target is reached. As a result, we find double dissociation of fMRI activity in subregions of the sensorimotor and visual cortices. Specifically, motor and late visual cortices are more active with online cursor feedback, and somatosensory and early visual cortices are more active without online cursor feedback. We also find a significant reduction in functional connectivity between somatosensory cortices and early visual cortices, which is highly correlated with performance improvement. These findings support the distinct interaction between subregions of sensorimotor cortices and visual cortices, while the connectivity analysis highlights the critical role of somatosensory cortices during de novo motor learning.

Molecular programs guiding arealization of descending cortical pathways.

Layer 5 extratelencephalic (ET) neurons are present across neocortical areas and send axons to multiple subcortical targets1-6. Two cardinal subtypes exist7,8: (1) Slco2a1-expressing neurons (ETdist), which predominate in the motor cortex and project distally to the pons, medulla and spinal cord; and (2) Nprs1- or Hpgd-expressing neurons (ETprox), which predominate in the visual cortex and project more proximally to the pons and thalamus. An understanding of how area-specific ETdist and ETprox emerge during development is important because they are critical for fine motor skills and are susceptible to spinal cord injury and amyotrophic lateral sclerosis9-12. Here, using cross-areal mapping of axonal projections in the mouse neocortex, we identify the subtype-specific developmental dynamics of ET neurons. Whereas subsets of ETprox emerge by pruning of ETdist axons, others emerge de novo. We outline corresponding subtype-specific developmental transcriptional programs using single-nucleus sequencing. Leveraging these findings, we use postnatal in vivo knockdown of subtype-specific transcription factors to reprogram ET neuron connectivity towards more proximal targets. Together, these results show the functional transcriptional programs driving ET neuron diversity and uncover cell subtype-specific gene regulatory networks that can be manipulated to direct target specificity in motor corticofugal pathways.

Oscillations in an artificial neural network convert competing inputs into a temporal code.

The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and complex cells in early visual cortex. However, the computational relevance of oscillatory dynamics experimentally observed in the visual system are typically not considered in artificial neural networks (ANNs). Computational models of neocortical dynamics, on the other hand, rarely take inspiration from computer vision. Here, we combine methods from computational neuroscience and machine learning to implement multiplexing in a simple ANN using oscillatory dynamics. We first trained the network to classify individually presented letters. Post-training, we added temporal dynamics to the hidden layer, introducing refraction in the hidden units as well as pulsed inhibition mimicking neuronal alpha oscillations. Without these dynamics, the trained network correctly classified individual letters but produced a mixed output when presented with two letters simultaneously, indicating a bottleneck problem. When introducing refraction and oscillatory inhibition, the output nodes corresponding to the two stimuli activate sequentially, ordered along the phase of the inhibitory oscillations. Our model implements the idea that inhibitory oscillations segregate competing inputs in time. The results of our simulations pave the way for applications in deeper network architectures and more complicated machine learning problems.

Toxoplasma gondii Infection of Alzheimer's Disease Mice Reduces Brain Amyloid Density Globally and Regionally.

Toxoplasma gondii infection of Alzheimer's disease model mice decreases amyloid β plaques. We aimed to determine if there is a brain regional difference in amyloid β reduction in the brains of T. gondii-infected compared to control mice. Three-month-old 5xFAD (AD model) mice were injected with T. gondii or with phosphate-buffered saline as a control. Intact brains were harvested at 6 weeks postinfection, optically cleared using iDISCO+, and brain-wide amyloid burden was visualized using volumetric light-sheet imaging. Amyloid signal was quantified across each brain and computationally mapped to the Allen Institute Brain Reference Atlas to determine amyloid density in each region. A brain-wide analysis of amyloid in control and T. gondii-infected 5xFAD mice revealed that T. gondii infection decreased amyloid burden in the brain globally as well as in the cortex and hippocampus, and many daughter regions. Daughter regions that showed reduced amyloid burden included the prelimbic cortex, visual cortex, and retrosplenial cortex. The olfactory tubercle, a region known to have increased monocytes following T. gondii infection, also showed reduced amyloid after infection. T. gondii infection of AD mice reduces amyloid burden in a brain region-specific manner that overlaps with known regions of T. gondii infection and peripheral immune cell infiltration.

Distributed representations of behaviour-derived object dimensions in the human visual system.

Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorization. However, object vision supports diverse behavioural goals, suggesting basic limitations of this category-centric framework. To address these limitations, we mapped a series of dimensions derived from a large-scale analysis of human similarity judgements directly onto the brain. Our results reveal broadly distributed representations of behaviourally relevant information, demonstrating selectivity to a wide variety of novel dimensions while capturing known selectivities for visual features and categories. Behaviour-derived dimensions were superior to categories at predicting brain responses, yielding mixed selectivity in much of visual cortex and sparse selectivity in category-selective clusters. This framework reconciles seemingly disparate findings regarding regional specialization, explaining category selectivity as a special case of sparse response profiles among representational dimensions, suggesting a more expansive view on visual processing in the human brain.

Stimulus repetition induces a two-stage learning process in primary visual cortex.

Repeated stimulus exposure alters the brain's response to the stimulus. We investigated the underlying neural mechanisms by recording functional MRI data from human observers passively viewing 120 presentations of two Gabor patches (each Gabor repeating 60 times). We evaluated support for two prominent models of stimulus repetition, the fatigue model and the sharpening model. Our results uncovered a two-stage learning process in the primary visual cortex. In Stage 1, univariate BOLD activation in V1 decreased over the first twelve repetitions of the stimuli, replicating the well-known effect of repetition suppression. Applying MVPA decoding along with a moving window approach, we found that (1) the decoding accuracy between the two Gabors decreased from above-chance level (∼60% to ∼70%) at the beginning of the stage to chance level at the end of the stage (∼50%). This result, together with the accompanying weight map analysis, suggested that the learning dynamics in Stage 1 were consistent with the predictions of the fatigue model. In Stage 2, univariate BOLD activation for the remaining 48 repetitions of the two stimuli exhibited significant fluctuations but no systematic trend. The MVPA decoding accuracy between the two Gabor patches was at chance level initially and became progressively higher as stimulus repetition continued, rising above and staying above chance level starting at the ∼35th repetition. Thus, results from the second stage supported the notion that sustained and prolonged stimulus repetition prompts sharpened representations. Additional analyses addressed (1) whether the neural patterns within each learning stage remained stable and (2) whether new neural patterns were evoked in Stage 2 relative to Stage 1.

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.

Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of 'predatory'-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.

Patterns of cortical thickness alterations in degenerative cervical myelopathy: associations with dexterity and gait dysfunctions.

Degenerative cervical myelopathy (DCM) can lead to significant brain structural reorganization. The association between the cortical changes and specific motor symptoms in DCM has yet to be fully elucidated. We investigated the associations between cortical thickness changes with neurological symptoms, such as dexterity and gait abnormalities, in patients with DCM in a case-control study. A 3 Tesla MRI scanner was used to acquire high-resolution T1-weighted structural scans from 30 right-handed patients with DCM and 22 age-matched healthy controls. Pronounced cortical thinning was observed in DCM patients relative to healthy controls, particularly in the bilateral precentral and prefrontal gyri, left pars triangularis, left postcentral gyrus, right transverse temporal and visual cortices (P ≤ 0.04). Notably, cortical thickness in these regions showed strong correlations with objective motor deficits (P < 0.0001). Specifically, the prefrontal cortex, premotor area and supplementary motor area exhibited significant thickness reductions correlating with diminished dexterity (R2 = 0.33, P < 0.0007; R2 = 0.34, P = 0.005, respectively). Similarly, declines in gait function were associated with reduced cortical thickness in the visual motor and frontal eye field cortices (R2 = 0.39, P = 0.029, R2 = 0.33, P = 0.04, respectively). Interestingly, only the contralateral precuneus thickness was associated with the overall modified Japanese Orthopaedic Association (mJOA) scores (R2 = 0.29, P = 0.003). However, the upper extremity subscore of mJOA indicated an association with the visual cortex and the anterior prefrontal (R2 = 0.48, P = 0.002, R2 = 0.33, P = 0.0034, respectively). In conclusion, our findings reveal patterns of cortical changes correlating with motor deficits, highlighting the significance of combining objective clinical and brain imaging assessments for understanding motor network dysfunction in DCM.