Visual Cortex Research Articles

Spatial context non-uniformly modulates inter-laminar information flow in the primary visual cortex.

Our visual experience is a result of the concerted activity of neuronal ensembles in the sensory hierarchy. Yet, how the spatial organization of objects influences this activity remains poorly understood. We investigate how inter-laminar information flow within the primary visual cortex (V1) is affected by visual stimuli in isolation or with flankers at spatial configurations that are known to cause non-uniform degradation of perception. By employing dimensionality reduction approaches to simultaneous, layer-specific population recordings, we establish that information propagation between cortical layers occurs along a structurally stable communication subspace. The spatial configuration of contextual stimuli differentially modulates inter-laminar communication efficacy, the balance of feedforward and effective feedback signaling, and contextual signaling in the superficial layers. Remarkably, these modulations mirror the spatially non-uniform aspects of perceptual degradation. Our results suggest a model of retinotopically non-uniform cortical connectivity in the output layers of V1 that influences information flow in the sensory hierarchy.

Mapping lesion-related human aggression to a common brain network

BackgroundAggression exacts a significant toll on human societies and is highly prevalent among neuropsychiatric patients for which neural mechanisms are unclear and treatment options are limited. MethodsUsing recently validated lesion network mapping technique, we derived an aggression associated network by analyzing 182 patients who had suffered penetrating head injuries during their service in the Vietnam War. To test whether damage to this lesion-derived network would increase the risk of aggression related neuropsychiatric symptoms, we used the Harvard Lesion Repository (N = 928). To explore potential therapeutic relevance of this network, we used an independent Deep brain stimulation dataset of 25 patients with epilepsy, in which irritability and aggression are known potential side effects. ResultsWe found that lesions associated with aggression occurred in many different brain locations but were characterized by a specific brain network defined by functional connectivity to a hub region in the right prefrontal cortex (PFC). This network involves positive connectivity to the ventromedial PFC, dorsolateral PFC, frontal pole, posterior cingulate cortex, anterior cingulate cortex, temporal-parietal junction, and lateral temporal lobe and negative connectivity to the amygdala, hippocampus, insula, and visual cortex. Among all 25 neuropsychiatric symptoms included in the Harvard Lesion Repository, criminality demonstrated the most alignment with our aggression associated network. DBS site connectivity to this same network was associated with increased irritability. ConclusionsWe conclude that brain lesions associated with aggression map to a specific human brain circuit, and the functionally connected regions in this circuit provide testable targets for therapeutic neuromodulation.

Structural and functional neural correlates of sensorimotor deficits in progression of hepatic encephalopathy

Hepatic encephalopathy (HE) is a neurological condition that occurs as a complication of liver dysfunction that involves sensorimotor symptoms in addition to cognitive and behavioral changes, particularly in cases of severe liver disease or cirrhosis. Previous studies have reported spatially distributed structural and functional abnormalities related to HE, but the exact relationship between the structural and functional alterations with respect to disease progression remains unclear. In this study, we performed surface-based cortical thickness comparisons and functional connectivity (FC) analyses between three cross-sectional groups: healthy controls (HC, N=51), patients with minimal hepatic encephalopathy (MHE, N=50), patients with overt hepatic encephalopathy (OHE, N=51). In addition to the distributed cortical thinning that is extensively thought to be associated with cognitive decline in HE, we found significant cortical thickening in the left parahippocampal gyrus cortex in the OHE group (p<0.001, p=0.009) as compared to the HC and MHE group respectively, which is further corroborated by the significant correlation between the cortical thickness and digit symbol test (DST) scores. Furthermore, the decreased FC between the right postcentral gyrus and several sensory regions (bilateral somatosensory and visual cortices) was found to be significant in MHE patients as compared to the HC group. Our results revealed cross-sectional structural and functional variations concerning disease progression across different subsystems (e.g., visual, motor and sensory), providing evidence that can potentially explain the mechanisms underlying the sensorimotor and cognitive deficits related to HE.

Decoding sound content in the early visual cortex of aphantasic participants.

Listening to natural auditory scenes leads to distinct neuronal activity patterns in the early visual cortex (EVC) of blindfolded sighted and congenitally blind participants.1,2 This pattern of sound decoding is organized by eccentricity, with the accuracy of auditory information increasing from foveal to far peripheral retinotopic regions in the EVC (V1, V2, and V3). This functional organization by eccentricity is predicted by primate anatomical connectivity,3,4 where cortical feedback projections from auditory and other non-visual areas preferentially target the periphery of early visual areas. In congenitally blind participants, top-down feedback projections to the visual cortex proliferate,5 which might account for even higher sound-decoding accuracy in the EVC compared with blindfolded sighted participants.2 In contrast, studies in participants with aphantasia suggest an impairment of feedback projections to early visual areas, leading to a loss of visual imagery experience.6,7 This raises the question of whether impaired visual feedback pathways in aphantasia also reduce the transmission of auditory information to early visual areas. We presented auditory scenes to 23 blindfolded aphantasic participants. We found overall decreased sound decoding in early visual areas compared to blindfolded sighted ("control") and blind participants. We further explored this difference by modeling eccentricity effects across the blindfolded control, blind, and aphantasia datasets, and with a whole-brain searchlight analysis. Our findings suggest that the feedback of auditory content to the EVC is reduced in aphantasic participants. Reduced top-down projections might lead to both less sound decoding and reduced subjective experience of visual imagery.

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.

A neural mechanism for optic flow parsing in macaque visual cortex.

For the brain to compute object motion in the world during self-motion, it must discount the global patterns of image motion (optic flow) caused by self-motion. Optic flow parsing is a proposed visual mechanism for computing object motion in the world, and studies in both humans and monkeys have demonstrated perceptual biases consistent with the operation of a flow-parsing mechanism. However, the neural basis of flow parsing remains unknown. We demonstrate, at both the individual unit and population levels, that neural activity in macaque middle temporal (MT) area is biased by peripheral optic flow in a manner that can at least partially account for perceptual biases induced by flow parsing. These effects cannot be explained by conventional surround suppression mechanisms or choice-related activity and have substantial neural latency. Together, our findings establish the first neural basis for the computation of scene-relative object motion based on flow parsing.

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).

Positive serial dependence in ratings of food images for appeal and calories.

Food is fundamental to survival, and our brains are highly attuned to rapidly process food stimuli. Neural signals show that foods can be discriminated as edible or inedible as early as 85ms after stimulus onset,1 distinguished as processed or unprocessed beginning at 130ms,2 and as high or low density from 165ms.3 Recent evidence revealed specialized processing of food stimuli in the ventral visual pathway,4,5,6 an area that underlies perception of faces and other important objects. For many visual objects, perception can be biased toward recent perceptual history (known as serial dependence7,8). We examined serial dependence for food in two large samples (n>300) who rated sequences of food images for either "appeal" or "calories." Ratings for calories were highly correlated between participants and were similar for males and females. Appeal ratings varied considerably between participants, consistent with the idiosyncratic nature of food preferences, and tended to be higher for males than females. High-calorie ratings were associated with high appeal, especially in males. Importantly, response biases showed clear positive serial dependences: higher stimulus values in the previous trials led to positive biases, and vice versa. The effects were similar for males and females and for calories and appeal ratings and were remarkably consistent across participants. These findings square with recently found food selectively in the visual temporal cortex, reveal a new mechanism influencing food decision-making, and suggest a new sensory-level component that could complement cognitive strategies in diet intervention.

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.

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.