Binocular integration of retinal motion information underlies optic flow processing by the cortex

Transcranial magnetic stimulation-based neuroplasticity in the treatment of amblyopia

BACKGROUND

In recent years, repetitive transcranial magnetic stimulation (rTMS) has shown positive clinical effects in improving neuroplasticity by modulating cortical neural activity, particularly the functional connectivity of visual-related brain regions. This research was aimed to investigate the effects of rTMS on visual function in adult amblyopia and to assess changes in brain neuronal activity before and following remedy using resting-state functional magnetic resonance imaging (rs-fMRI).

NEW METHOD

A total of 148 patients with anisometropic amblyopia were enrolled and randomly divided into: intraocular lens (ICL) group and ICL+rTMS group, with 74 cases in each group. All patients received detailed perioperative care. Best-corrected visual acuity (BCVA) and random dot stereopsis were assessed using the Vision Perception Test System before treatment and 3 months following remedy, and brain functional status was evaluated using rs-fMRI.

RESULTS

As against pre-treatment levels, both ICL group and ICL+rTMS group suggested visible improvements in BCVA and random dot stereopsis 3 months following remedy, with ICL+rTMS group exhibiting better outcomes than ICL group (P < 0.05). The rs-fMRI revealed distinct patterns of neural plasticity: ICL group exhibited an increase in the ALFF of the ipsilateral frontal lobe, while ICL+rTMS group showed a decrease in the ALFF of the same-side cerebellum (P < 0.05). Compared to ICL alone, the combination of rTMS and ICL significantly reduced the ReHo in the ipsilateral frontal lobe and superior frontal gyrus, decreased the fALFF in the contralateral temporal lobe, and increased the fALFF in the contralateral occipital lobe (P < 0.05).

COMPARISON WITH EXISTING METHODS

The application of rTMS to directly regulate neural plasticity provides a non-invasive and precise treatment method. Compared with traditional therapies, rTMS can more effectively promote the reorganization of visual cortex function in amblyopia patients, improve treatment efficacy, and have fewer side effects, thus having high clinical application potential.

CONCLUSION

rTMS can effectively improve visual function in adult amblyopia patients by modulating neuronal activity and enhancing visual cortical neuroplasticity to correct the interocular excitation differences and exert therapeutic effects.

A competitive disinhibitory network for robust optic flow processing in Drosophila

Many animals navigate using optic flow, detecting rotational image velocity differences between their eyes to adjust direction. Forward locomotion produces strong symmetric translational optic flow that can mask these differences, yet the brain efficiently extracts these binocular asymmetries for course control. In Drosophila melanogaster, monocular horizontal system neurons facilitate detection of binocular asymmetries and contribute to steering. To understand these functions, we reconstructed horizontal system cells' central network using electron microscopy datasets, revealing convergent visual inputs, a recurrent inhibitory middle layer and a divergent output layer projecting to the ventral nerve cord and deeper brain regions. Two-photon imaging, GABA receptor manipulations and modeling, showed that lateral disinhibition reduces the output's translational sensitivity while enhancing its rotational selectivity. Unilateral manipulations confirmed the role of interneurons and descending outputs in steering. These findings establish competitive disinhibition as a key circuit mechanism for detecting rotational motion during translation, supporting navigation in dynamic environments.

Neural mechanisms to incorporate visual counterevidence in self-movement estimation

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can be spuriously triggered by visual motion created by objects moving in the world. Here, we show that stationary patterns on the retina, which constitute evidence against observer rotation, suppress inappropriate stabilizing rotational behavior in the fruit fly Drosophila. In silico experiments show that artificial neural networks (ANNs) that are optimized to distinguish observer movement from external object motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's local motion and optic-flow detectors. Our results show how the fly brain incorporates negative evidence to improve heading stability, exemplifying how a compact brain exploits geometrical constraints of the visual world.

Emerging computational motifs: Lessons from the retina

The retinal neuronal circuit is the first stage of visual processing in the central nervous system. The efforts of scientists over the last few decades indicate that the retina is not merely an array of photosensitive cells, but also a processor that performs various computations. Within a thickness of only ∼200 µm, the retina consists of diverse forms of neuronal circuits, each of which encodes different visual features. Since the discovery of direction-selective cells by Horace Barlow and Richard Hill, the mechanisms that generate direction selectivity in the retina have remained a fascinating research topic. This review provides an overview of recent advances in our understanding of direction-selectivity circuits. Beyond the conventional wisdom of direction selectivity, emerging findings indicate that the retina utilizes complicated and sophisticated mechanisms in which excitatory and inhibitory pathways are involved in the efficient encoding of motion information. As will become evident, the discovery of computational motifs in the retina facilitates an understanding of how sensory systems establish feature selectivity.

Neural mechanisms to incorporate visual counterevidence in self motion estimation

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can confuse the movement of external objects with genuine self motion. Here, we show that stationary patterns on the retina, which constitute negative evidence against self rotation, are used by the fruit fly Drosophila to suppress inappropriate stabilizing rotational behavior. In silico experiments show that artificial neural networks optimized to distinguish self and world motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's motion- and optic flow-detectors. Our results exemplify how the compact brain of the fly incorporates negative evidence to improve heading stability, exploiting geometrical constraints of the visual world.

Cortical Integration of Vestibular and Visual Cues for Navigation, Visual Processing, and Perception

Despite increasing evidence of its involvement in several key functions of the cerebral cortex, the vestibular sense rarely enters our consciousness. Indeed, the extent to which these internal signals are incorporated within cortical sensory representation and how they might be relied upon for sensory-driven decision-making, during, for example, spatial navigation, is yet to be understood. Recent novel experimental approaches in rodents have probed both the physiological and behavioral significance of vestibular signals and indicate that their widespread integration with vision improves both the cortical representation and perceptual accuracy of self-motion and orientation. Here, we summarize these recent findings with a focus on cortical circuits involved in visual perception and spatial navigation and highlight the major remaining knowledge gaps. We suggest that vestibulo-visual integration reflects a process of constant updating regarding the status of self-motion, and access to such information by the cortex is used for sensory perception and predictions that may be implemented for rapid, navigation-related decision-making.

Best-corrected visual acuity results facilitate molecular diagnosis of infantile nystagmus patients harboring FRMD7 mutations

The visual function of patients with infantile nystagmus (IN) can be significantly decreased owing to constant eye movement. While, reaching a definitive diagnosis becomes a challenge due to genetic heterozygous of this disease. To address it, we investigated whether best-corrected visual acuity (BCVA) results can facilitate the molecular diagnosis of IN patients harboring FRMD7 mutations. 200 patients with IN from 55 families and 133 sporadic cases were enrolled. Mutations were comprehensively screened by direct sequencing using gene-specific primers for FRMD7. We also retrieved related literature to verify the results based on our data. We found that the BCVA of patients with IN harboring FRMD7 mutations was between 0.5 and 0.7, which was confirmed by data retrieved from the literature. Our results showed that BCVA results facilitate the molecular diagnosis of patients with IN harboring FRMD7 mutations. In addition, we identified 31 FRMD7 mutations from the patients, including six novel mutations, namely, frameshift mutation c.1492_1493insT (p.Y498LfsTer14), splice-site mutation c.353C  >  G, three missense mutations [c.208C  >  G (p.P70A), c.234G > A (p.M78I), and c.1109G > A (p.H370R)], and nonsense mutation c.1195G > T (p.E399Ter). This study demonstrates that BCVA results may facilitate the molecular diagnosis of IN patients harboring FRMD7 mutations.

Walking humans and running mice: perception and neural encoding of optic flow during self-motion

Locomotion produces full-field optic flow that often dominates the visual motion inputs to an observer. The perception of optic flow is in turn important for animals to guide their heading and interact with moving objects. Understanding how locomotion influences optic flow processing and perception is therefore essential to understand how animals successfully interact with their environment. Here, we review research investigating how perception and neural encoding of optic flow are altered during self-motion, focusing on locomotion. Self-motion has been found to influence estimation and sensitivity for optic flow speed and direction. Nonvisual self-motion signals also increase compensation for self-driven optic flow when parsing the visual motion of moving objects. The integration of visual and nonvisual self-motion signals largely follows principles of Bayesian inference and can improve the precision and accuracy of self-motion perception. The calibration of visual and nonvisual self-motion signals is dynamic, reflecting the changing visuomotor contingencies across different environmental contexts. Throughout this review, we consider experimental research using humans, non-human primates and mice. We highlight experimental challenges and opportunities afforded by each of these species and draw parallels between experimental findings. These findings reveal a profound influence of locomotion on optic flow processing and perception across species. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

A Hypothesis From Metabolomics Analysis of Diabetic Retinopathy: Arginine-Creatine Metabolic Pathway May Be a New Treatment Strategy for Diabetic Retinopathy

Diabetic retinopathy is one of the serious complications of diabetes, which the leading causes of blindness worldwide, and its irreversibility renders the existing treatment methods unsatisfactory. Early detection and timely intervention can effectively reduce the damage caused by diabetic retinopathy. Metabolomics is a branch of systems biology and a powerful tool for studying pathophysiological processes, which can help identify the characteristic metabolic changes marking the progression of diabetic retinopathy, discover potential biomarkers to inform clinical diagnosis and treatment. This review provides an update on the known metabolomics biomarkers of diabetic retinopathy. Through comprehensive analysis of biomarkers, we found that the arginine biosynthesis is closely related to diabetic retinopathy. Meanwhile, creatine, a metabolite with arginine as a precursor, has attracted our attention due to its important correlation with diabetic retinopathy. We discuss the possibility of the arginine-creatine metabolic pathway as a therapeutic strategy for diabetic retinopathy.

Hierarchical and nonhierarchical features of the mouse visual cortical network

Neocortical computations underlying vision are performed by a distributed network of functionally specialized areas. Mouse visual cortex, a dense interareal network that exhibits hierarchical properties, comprises subnetworks interconnecting distinct processing streams. To determine the layout of the mouse visual hierarchy, we have evaluated the laminar patterns formed by interareal axonal projections originating in each of ten areas. Reciprocally connected pairs of areas exhibit feedforward/feedback relationships consistent with a hierarchical organization. Beta regression analyses, which estimate a continuous hierarchical distance measure, indicate that the network comprises multiple nonhierarchical circuits embedded in a hierarchical organization of overlapping levels. Single-unit recordings in anaesthetized mice show that receptive field sizes are generally consistent with the hierarchy, with the ventral stream exhibiting a stricter hierarchy than the dorsal stream. Together, the results provide an anatomical metric for hierarchical distance, and reveal both hierarchical and nonhierarchical motifs in mouse visual cortex.

Mouse visual cortex is a dense, interconnected network of distinct areas. D’Souza et al. identify an anatomical index to quantify the hierarchical nature of pathways, and highlight the hierarchical and nonhierarchical features of the network.

EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking

Eye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible.

Mechanism of Motion Direction Detection Based on Barlow’s Retina Inhibitory Scheme in Direction-Selective Ganglion Cells

Previous studies have reported that directionally selective ganglion cells respond strongly in their preferred direction, but are only weakly excited by stimuli moving in the opposite null direction. Various studies have attempted to elucidate the mechanisms underlying direction selectivity with cellular basis. However, these studies have not elucidated the mechanism underlying motion direction detection. In this study, we propose the mechanism based on Barlow’s inhibitory scheme for motion direction detection. We described the local motion-sensing direction-selective neurons. Next, this model was used to construct the two-dimensional multi-directional detection neurons which detect the local motion directions. The information of local motion directions was finally used to infer the global motion direction. To verify the validity of the proposed mechanism, we conducted a series of experiments involving a dataset with a number of images. The proposed mechanism exhibited good performance in all experiments with high detection accuracy. Furthermore, we compare the performance of our proposed system and traditional Convolution Neural Network (CNN) on motion direction prediction. It is found that the performance of our system is much better than that of CNN in terms of accuracy, calculation speed and cost.