Visual Cortex Research Articles

Dependence of Contextual Modulation in Macaque V1 on Interlaminar Signal Flow.

In visual cortex, neural correlates of subjective perception can be generated by modulation of activity from beyond the classical receptive field (CRF). In macaque V1, activity generated by nonclassical receptive field (nCRF) stimulation involves different intracortical circuitry than activity generated by CRF stimulation, suggesting that interactions between neurons across V1 layers differ under CRF and nCRF stimulus conditions. Using Neuropixels probes, we measured border ownership modulation within large, local populations of V1 neurons. We found that neurons in single columns preferred the same side of objects located outside of the CRF. In addition, we found that cross-correlations between pairs of neurons situated across feedback/horizontal and input layers differed between CRF and nCRF stimulation. Furthermore, independent of the comparison with CRF stimulation, we observed that the magnitude of border ownership modulation increased with the proportion of information flow from feedback/horizontal layers to input layers. These results demonstrate that the flow of signals between layers covaries with the degree to which neurons integrate information from beyond the CRF.

Magnetogenetic stimulation inside MRI induces spontaneous and evoked changes in neural circuits activity in rats.

The ability to modulate specific neural circuits and simultaneously visualize and measure brain activity with MRI would greatly impact our understanding of brain function in health and disease. The combination of neurostimulation methods and functional MRI in animal models have already shown promise in elucidating fundamental mechanisms associated with brain activity. We developed an innovative magnetogenetics neurostimulation technology that can trigger neural activity through magnetic fields. Similar to other genetic-based neuromodulation methods, magnetogenetics offers cell-, area-, and temporal-specific control of neural activity. The magnetogenetic protein-Electromagnetic Perceptive Gene (EPG)-is activated by non-invasive magnetic fields, providing a unique way to target neural circuits by the MRI static and gradient fields while simultaneously measuring their effect on brain activity. EPG was expressed in rat's visual cortex and the amplitude of low-frequency fluctuation, resting-state functional connectivity (FC), and sensory activation was measured using a 7T MRI. The results demonstrate that EPG-expressing rats had significantly higher signal fluctuations in the visual areas and stronger FC in sensory areas consistent with known anatomical visuosensory and visuomotor connections. This new technology complements the existing neurostimulation toolbox and provides a means to study brain function in a minimally-invasive way which was not possible previously.

Alignable kernel network

To enhance the adaptability and performance of Convolutional Neural Networks (CNN), we present an adaptable mechanism called Alignable Kernel (AliK) unit, which dynamically adjusts the receptive field (RF) dimensions of a model in response to varying stimuli. The branches of AliK unit are integrated through a novel align transformation softmax attention, incorporating prior knowledge through rank ordering constraints. The attention weightings across the branches establish the effective RF scales, leveraged by neurons in the fusion layer. This mechanism is inspired by neuroscientific observations indicating that the RF dimensions of neurons in the visual cortex vary with the stimulus, a feature often overlooked in CNN architectures. By aggregating successive AliK ensembles, we develop a deep network architecture named the Alignable Kernel Network (AliKNet). AliKNet with interdisciplinary design improves the network’s performance and interpretability by taking direct inspiration from the structure and function of human neural systems, especially the visual cortex. Empirical evaluations in the domains of image classification and semantic segmentation have demonstrated that AliKNet excels over numerous state-of-the-art architectures, achieving this without increasing model complexity. Furthermore, we demonstrate that AliKNet can identify target objects across various scales, confirming their ability to dynamically adapt their RF sizes in response to the input data.

Local stimulation of pyramidal neurons in deep cortical layers of anesthetized rats enhances cortical visual information processing

In the primary visual cortex area V1 activation of inhibitory interneurons, which provide negative feedback for excitatory pyramidal neurons, can improve visual response reliability and orientation selectivity. Moreover, optogenetic activation of one class of interneurons, parvalbumin (PV) positive cells, reduces the receptive field (RF) width. These data suggest that in V1 the negative feedback improves visual information processing. However, according to information theory, noise can limit information content in a signal, and to the best of our knowledge, in V1 signal-to-noise ratio (SNR) has never been estimated following either pyramidal or inhibitory neuron activation. Therefore, we optogenetically activated pyramidal or PV neurons in the deep layers of cortical area V1 and measured the SNR and RF area in nearby pyramidal neurons. Activation of pyramidal or PV neurons increased the SNR by 267% and 318%, respectively, and reduced the RF area to 60.1% and 77.5%, respectively, of that of the control. A simple integrate-and-fire neuron model demonstrated that an improved SNR and a reduced RF area can increase the amount of information encoded by neurons. We conclude that in V1 activation of pyramidal neurons improves visual information processing since the location of the visual stimulus can be pinpointed more accurately (via a reduced RF area), and more information is encoded by neurons (due to increased SNR).

Feedback needs experience

Visual perception requires aligned feedforward and feedback processing, yet the role of experience remains unclear. The study by Dias etal.1 in this issue of Neuron shows that the retinotopic organization of orientation-tuned feedback from higher to primary visual cortex is learned in mice.

Impact of transcranial Direct Current Stimulation on stereoscopic vision and retinal structure in adult amblyopic rodents.

The impact of visual deprivation on retinal structure is widely debated. Experimental models, like monocular deprivation through lid suture, provide insights into the consequences of lacking visual experience during development. This deprivation delays primary visual cortex (CV1) maturation due to improper neural connection consolidation, which remains plastic beyond the critical period. However, few studies have used Optical Coherence Tomography (OCT) to investigate structural alterations in the retina of animal models following monocular deprivation. Instead, some studies have focused on the ganglion cell layer using post-mortem histological techniques in amblyopia models induced by monocular deprivation. In this study, we used Cliff test to assess stereoscopic vision and spectral domain optical coherence tomography (SD-OCT) to evaluate retinal changes in an in vivo model of visual deprivation treated with Transcranial Direct Current Stimulation (tDCS). The depth perception test initially revealed differences between individuals with amblyopia and the control group. However, after 8 tDCS sessions, amblyopic subjects matched the control group's performance, which remained stable Additionally, significant changes were observed in retinal structures post-tDCS treatment. Specifically, the thickness of the Nerve Fiber Layer + Ganglion Cell Layer + Inner Plexiform Layer (NFL+GCL+IPL) increased significantly in amblyopic eyes (p<0.001). Moreover, significant retinal thickening, including the Nerve Fiber Layer + Ganglion Cell Layer + Inner Plexiform Layer (NFL+GCL+IPL) and the entire retina, was observed post-tDCS treatment (p<0.05), highlighting the critical role of tDCS in ameliorating amblyopia. Additionally, treated animals exhibited reduced thickness in the Inner Nuclear Layer (INL) and Outer Nuclear Layer (ONL). tDCS treatment effectively restores amblyopic individuals' stereoscopic vision, aligning their performance with controls, while impacting retinal structure, highlighting its potential in ameliorating amblyopia's visual deficits.

Adaptation and exogenous attention interact in the early visual cortex: A TMS study

Adaptation and exogenous attention interact in the early visual cortex: A TMS study

Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method.

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

TMS-evoked potentials unveil occipital network involvement in patients diagnosed with Parkinson’s disease within 5 years of inclusion

Distinguishing Parkinson’s disease (PD) subgroups may be achieved by observing network responses to external stimuli. We compared TMS-evoked potential (TEP) measures from stimulation of bilateral motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and visual cortex (V1) between 62 PD patients (age: 69.9 ± 7.5) and 76 healthy controls (age: 69.2 ± 4.3) using a TMS–EEG protocol. TEP measures were analyzed using two-way ANCOVA adjusted for MOCA. PD patients were divided into tremor dominant (TD), non-tremor dominant (NTD) and rapid disease progression (RDP) subgroups. PD patients showed lower wide-waveform adherence (wWFA) (p = 0.025) and interhemispheric connectivity (IHCCONN) (p < 0.001) compared to healthy controls. Lower occipital IHCCONN correlated with advanced disease stage (r = −0.37, p = 0.0039). The RDP and NTD groups showed lower wWFA in response to occipital stimulation than the TD group (p = 0.005). Occipital TEP measures identified RDP patients with 85% accuracy. These findings demonstrate occipital network involvement in early PD stages, suggesting that TEP measures offer insights into altered networks in PD subgroups.

Brain Functional Alterations in Patients With Benign Paroxysmal Positional Vertigo Demonstrate the Visual-Vestibular Interaction and Integration.

This study aimed to analyze the features of resting-state functional magnetic resonance imaging (rs-fMRI) and clinical relevance in patients with benign paroxysmal positional vertigo (BPPV) that have undergone repositioning maneuvers. A total of 38 patients with BPPV who have received repositioning maneuvers and 38 matched healthy controls (HCs) were enrolled in the present study from March 2018 to August 2021. Imaging analysis software was employed for functional image preprocessing and indicator calculation, mainly including the amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), percent amplitude of fluctuation (PerAF), and seed-based functional connectivity (FC). Statistical analysis of the various functional indicators in patients with BPPV and HCs was also conducted, and correlation analysis with clinical data was performed. Patients with BPPV displayed decrease in ALFF, fALFF, and PerAF values, mainly in the bilateral occipital lobes in comparison with HCs. Additionally, their ALFF and fALFF values in the proximal vermis region of the cerebellum increased relative to HCs. The PerAF values in the bilateral paracentral lobules, the right supplementary motor area (SMA), and the left precuneus decreased in patients with BPPV and were negatively correlated with dizziness visual analog scale (VAS) scores 1 week after repositioning (W1). In addition, in the left fusiform gyrus and lingual gyrus, the PerAF values show a negative correlation with dizziness handicap inventory (DHI) scores at initial visit (W0). Seed-based FC analysis using the seeds from differential clusters of fALFF, ALFF, and PerAF showed reductions between the left precuneus and bilateral occipital lobe, the left precuneus and left paracentral lobule, and within the occipital lobes among patients with BPPV. The spontaneous activity of certain brain regions in the bilateral occipital and frontoparietal lobes of patients with BPPV was reduced, whereas the activity in the cerebellar vermis was increased. Additionally, there were reductions in FC between the precuneus and occipital cortex or paracentral lobule, as well as within the occipital cortex. The functional alterations in these brain regions may be associated with the inhibitory interaction and functional integration of visual, vestibular, and sensorimotor systems. The functional alterations observed in the visual cortex and precuneus may represent adaptive responses associated with residual dizziness.

An Electrolyte‐Gated InGaZnO Phototransistor that Emulates Visual Experience‐Dependent Plasticity

AbstractInspired by neurobiological learning rules, bionic devices that simulate the fundamental functions of synapses and neurons provide a highly effective approach to neuromorphic computing. Among various learning rules, the Bienenstock‐Cooper‐Munro (BCM) learning rule can explain the threshold sliding effect of synaptic weight modification in the visual cortex, which is difficult to explain with the classical Hebb's rule. Existing research mainly focuses on exploiting electrical stimulation to implement the BCM rule, while the optical implementation is still unexplored. In this paper, the light‐history‐dependent BCM learning rule is implemented with electrolyte‐gated InGaZnO (IGZO) transistors. The channel conductance can be modulated through light illumination and electrical stimulation. By utilizing the light‐history‐dependent property of the IGZO electrolyte‐gated transistor and following the triplet‐spike‐timing‐dependent plasticity (STDP) rules, the BCM learning rule is successfully emulated in a single device. Moreover, the light‐history‐dependent property enables a variety of bionic vision functions including image edge detection and associative memory. This work provides a paradigm for the novel implementation of the BCM rule and paves the way for further development of machine vision systems.

Anatomical and molecular development of the human primary visual cortex.

The human primary visual cortex (V1) development is pivotal to understanding cortical maturation and neuroplasticity. Theories on V1 development range from early maturation models, which emphasize the early peak of synapses in infancy, to those suggesting an extended developmental timeline where key plasticity mechanisms continue to mature well into adulthood. Classic histological approaches have supported early development, while recent molecular studies highlight prolonged or multiple windows of plasticity, indicating that V1 remains susceptible to experience-dependent modifications beyond childhood. This review consolidates findings from both anatomical and molecular studies, tracing the development of V1 from prenatal stages through aging. The evidence reveals that human V1 develops across multiple timescales, with some aspects maturing early and others gradually changing across the lifespan. Reflecting on Cajal's early work, this review underscores the importance of methodological advancements in revealing the intricate details of V1's development.

Learning to segment self-generated from externally caused optic flow through sensorimotor mismatch circuits

Efficient sensory detection requires the capacity to ignore task-irrelevant information, for example when optic flow patterns created by egomotion need to be disentangled from object perception. To investigate how this is achieved in the visual system, predictive coding with sensorimotor mismatch detection is an attractive starting point. Indeed, experimental evidence for sensorimotor mismatch signals in early visual areas exists, but it is not understood how they are integrated into cortical networks that perform input segmentation and categorization. Our model advances a biologically plausible solution by extending predictive coding models with the ability to distinguish self-generated from externally caused optic flow. We first show that a simple three neuron circuit produces experience-dependent sensorimotor mismatch responses, in agreement with calcium imaging data from mice. This microcircuit is then integrated into a neural network with two generative streams. The motor-to-visual stream consists of parallel microcircuits between motor and visual areas and learns to spatially predict optic flow resulting from self-motion. The second stream bidirectionally connects a motion-selective higher visual area (mHVA) to V1, assigning a crucial role to the abundant feedback connections to V1: the maintenance of a generative model of externally caused optic flow. In the model, area mHVA learns to segment moving objects from the background, and facilitates object categorization. Based on shared neurocomputational principles across species, the model also maps onto primate visual cortex. Our work extends Hebbian predictive coding to sensorimotor settings, in which the agent actively moves - and learns to predict the consequences of its own movements.

Computational model of layer 2/3 in mouse primary visual cortex explains observed visuomotor mismatch response.

Activity in layer 2/3 of the mouse primary visual cortex has been shown to depend both on visual input and the mouse's locomotion. Moreover, this activity is altered by a mismatch between the observed visual flow and the predicted visual flow from locomotion. Here, I present a simple computational model that explains previously reported recordings from layer 2/3 neurons in mice. In my model, layer 2/3 encodes the velocity difference between the estimate from visual flow and the prediction from locomotion using a neural population code. Moreover, I describe a hypothesized mechanism for how the brain may carry out computations of variables encoded in population codes. This mechanism may point to a general principle for computing any mathematical function in the brain.

Efficient Neural Decoding Based on Multimodal Training.

Neural decoding methods are often limited by the performance of brain encoders, which map complex brain signals into a latent representation space of perception information. These brain encoders are constrained by the limited amount of paired brain and stimuli data available for training, making it challenging to learn rich neural representations. To address this limitation, we present a novel multimodal training approach using paired image and functional magnetic resonance imaging (fMRI) data to establish a brain masked autoencoder that learns the interactions between images and brain activities. Subsequently, we employ a diffusion model conditioned on brain data to decode realistic images. Our method achieves high-quality decoding results in semantic contents and low-level visual attributes, outperforming previous methods both qualitatively and quantitatively, while maintaining computational efficiency. Additionally, our method is applied to decode artificial patterns across region of interests (ROIs) to explore their functional properties. We not only validate existing knowledge concerning ROIs but also unveil new insights, such as the synergy between early visual cortex and higher-level scene ROIs, as well as the competition within the higher-level scene ROIs. These findings provide valuable insights for future directions in the field of neural decoding.

Tracking neurons across days with high-density probes.

Neural activity spans multiple time scales, from milliseconds to months. Its evolution can be recorded with chronic high-density arrays such as Neuropixels probes, which can measure each spike at tens of sites and record hundreds of neurons. These probes produce vast amounts of data that require different approaches for tracking neurons across recordings. Here, to meet this need, we developed UnitMatch, a pipeline that operates after spike sorting, based only on each unit's average spike waveform. We tested UnitMatch in Neuropixels recordings from the mouse brain, where it tracked neurons across weeks. Across the brain, neurons had distinctive inter-spike interval distributions. Their correlations with other neurons remained stable over weeks. In the visual cortex, the neurons' selectivity for visual stimuli remained similarly stable. In the striatum, however, neuronal responses changed across days during learning of a task. UnitMatch is thus a promising tool to reveal both invariance and plasticity in neural activity across days.

Contrastive learning explains the emergence and function of visual category-selective regions.

Modular and distributed coding theories of category selectivity along the human ventral visual stream have long existed in tension. Here, we present a reconciling framework-contrastive coding-based on a series of analyses relating category selectivity within biological and artificial neural networks. We discover that, in models trained with contrastive self-supervised objectives over a rich natural image diet, category-selective tuning naturally emerges for faces, bodies, scenes, and words. Further, lesions of these model units lead to selective, dissociable recognition deficits, highlighting their distinct functional roles in information processing. Finally, these pre-identified units can predict neural responses in all corresponding face-, scene-, body-, and word-selective regions of human visual cortex, under a highly constrained sparse positive encoding procedure. The success of this single model indicates that brain-like functional specialization can emerge without category-specific learning pressures, as the system learns to untangle rich image content. Contrastive coding, therefore, provides a unifying account of object category emergence and representation in the human brain.

Potential Biomarkers of Dysmenorrhea Relief: A MEG Study of Hinoki Aromatherapy and Working Memory.

Background/Objectives: This study explored the potential of Hinoki aromatherapy to induce biomarkers of dysmenorrhea relief through working memory. Structural magnetic resonance imaging and magnetoencephalography (MEG) were used to examine their effects on neurophysiological responses to a visual working memory (VWM) test. Behavioral performance was measured to understand its effects on the overall working memory. Methods: Twenty-four healthy participants completed the VWM task during nonmenstruation and menstruation. Behavioral (accuracy and reaction time) and neurophysiological (event-related fields, source estimation, and permutation t-test on source data) measures were assessed without and with Hinoki aromatherapy. Results: A significant difference in the ratio of accuracy to reaction time was found without and with aromatherapy in participants with dysmenorrhea, suggesting that aromatherapy may improve working memory performance in this population. MEG analysis revealed high temporal resolution of evoked latency and intensity during the VWM task. Source localization of the activation aimed to identify brain areas involved in dysmenorrhea. Aromatherapy reduced signals in these areas, which may also contribute to reducing dysmenorrhea-related visual signals. Conclusions: Based on these findings, Hinoki aromatherapy may be a promising treatment option for alleviating dysmenorrhea and improving related symptoms by reducing activity in brain pain processing regions. These regions include the left entorhinal cortex, inferior temporal gyrus, primary visual cortex, retrosplenial cortex, and presubiculum. Furthermore, decreased activity in these areas with aromatherapy suggests that they could be used as biomarkers of dysmenorrhea relief.

Subjective visual sensitivity in neurotypical adults: insights from a magnetic resonance spectroscopy study.

Altered subjective visual sensitivity manifests as feelings of discomfort or overload elicited by intense and irritative visual stimuli. This can result in a host of visual aberrations including visual distortions, elementary visual hallucinations and visceral responses like dizziness and nausea, collectively referred to as "pattern glare." Current knowledge of the underlying neural mechanisms has focused on overall excitability of the visual cortex, but the individual contribution of excitatory and inhibitory systems has not yet been quantified. In this study, we focus on the role of glutamate and γ-aminobutyric acid (GABA) as potential mediators of individual differences in subjective visual sensitivity, measured by a computerized Pattern Glare Test-a series of monochromatic square-wave gratings with three different spatial frequencies, while controlling for psychological variables related to sensory sensitivity with multiple questionnaires. Resting neurotransmitter concentrations in primary visual cortex (V1) and right anterior insula were studied in 160 healthy participants using magnetic resonance spectroscopy. Data showed significant differences in the perception of visual distortions (VD) and comfort scores between men and women, with women generally reporting more VD, and therefore the modulatory effect of sex was considered in a further examination. A general linear model analysis showed a negative effect of occipital glutamate on a number of reported visual distortions, but also a significant role of several background psychological traits. When assessing comfort scores in women, an important intervening variable was the menstrual cycle. Our findings do not support that baseline neurotransmitter levels have a significant role in overreactivity to aversive stimuli in neurotypical population. However, we demonstrated that biological sex can have a significant impact on subjective responses. Based on this additional finding, we suggest that future studies investigate aversive visual stimuli while examining the role of biological sex.

Retinal damage promotes mitochondrial transfer in the visual system of a mouse model of Leber hereditary optic neuropathy

Lenadogene nolparvovec is a gene therapy which has been developed to treat Leber hereditary optic neuropathy (LHON) caused by a point mutation in the mitochondrial NADH dehydrogenase 4 (ND4) gene. Clinical trials have demonstrated a significant improvement of visual acuity up to 5 years after treatment by lenadogene nolparvovec but, surprisingly, unilateral treatment resulted in bilateral improvement of vision. This contralateral effect – similarly observed with other gene therapy products in development for MT-ND4-LHON – is supported by the migration of viral vector genomes and their transcripts to the contralateral eye, as reported in animals, and post-mortem samples from two patients. In this study, we used an AAV2 encoding fluorescent proteins targeting mitochondria to investigate whether these organelles themselves could transfer from the treated eye to the fellow one. We found that mitochondria travel along the visual system (optic chiasm and primary visual cortex) and reach the contralateral eye (optic nerve and retina) in physiological conditions. We also observed that, in a rotenone-induced model of retinal damage mimicking LHON, mitochondrial transfer from the healthy to the damaged eye was accelerated and enhanced. Our results thus provide a further explanation for the contralateral beneficial effect observed during clinical studies with lenadogene nolparvovec.