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The IMOvifa™ perimeter with a 24plus (1-2) testing mode has additional measurement points within the central 10 degrees, which may help evaluate the visual field within this area. Here, we comparatively evaluated the IMOvifa™ 24plus (1-2) and HFA 10-2 for the first time. We included 30 patients (48 eyes) who underwent HFA 24-2 Swedish Interactive Threshold Algorithm Standard and IMOvifa™ 24plus (1-2) Ambient Interactive Zippy Estimated tests on the same day and HFA 10-2 within six months. We used Spearman's rank correlation coefficient to analyze the mean deviation (MD) and pattern standard deviation (PSD) between HFA 10-2 and IMOvifa™. The central 10-degree visual field was divided into four sectors, and concordance of visual field defects between IMOvifa™ 24plus (1-2) and HFA 10-2 was evaluated using kappa analysis. Additionally, all sectors showing a sensitivity of 0 dB on the HFA 24-2 were assessed for the presence and agreement of residual visual field in HFA 10-2 and IMOvifa™ 24plus (1-2). The MD (0.843/0.804) and PSD (0.852/0.763) of IMOvifa™ 24plus (1-2) and HFA 24-2 correlated strongly with those of HFA 10-2. Regarding the ability to detect visual field defects within the central 10 degrees, agreement with HFA 10-2 was κ = 0.715 (0.611,0.819) and 0.754 (0.654,0.854) for IMOvifa™ 24plus (1-2) and HFA 24-2, respectively. In the evaluation of residual visual field, IMOvifa™ 24plus (1-2) detected residual visual function in 100% of cases where HFA 10-2 indicated residual function. The IMOvifa™ 24plus (1-2) may have a higher ability to detect defects in certain areas of the visual field, compared with HFA 24-2, and may also detect residual visual function. However, the IMOvifa™ 24plus (1-2) is difficult to substitute for the 10-2 test, as the 10-2 test is necessary for evaluating visual field defects within the central 10 degrees.

Mapping the organization of excitatory inputs onto the dendritic spines of individual mouse visual cortex neurons reveals how inputs representing features from the extended visual scene are organized and establishes a computational unit suited to amplify contours and elongated edges. Synapses set the scene Processing a visual stimulus requires various connections between neurons, with each encoding for particular features that integrate and generate the overall representation of a scene. The precise logic of this connectivity and what information an individual neuron receives regarding various parts of the visual field are unknown. Here, Sonja Hofer and colleagues mapped the organization of excitatory inputs onto the dendritic spines of individual mouse visual cortex neurons. Inputs representing similar visual features in similar visual field positions were more likely to cluster on neighbouring spines and inputs beyond the receptive field of the observed neuron were located on higher-order dendritic branches. Connections between neurons with dissimilar receptive fields were more likely when these fields were spatially displaced. These arrangements establish a computational unit suited to amplify contours and elongated edges, features that are common elements of our visual space. How a sensory stimulus is processed and perceived depends on the surrounding sensory scene. In the visual cortex, contextual signals can be conveyed by an extensive network of intra- and inter-areal excitatory connections that link neurons representing stimulus features separated in visual space 1 , 2 , 3 , 4 . However, the connectional logic of visual contextual inputs remains unknown; it is not clear what information individual neurons receive from different parts of the visual field, nor how this input relates to the visual features that a neuron encodes, defined by its spatial receptive field. Here we determine the organization of excitatory synaptic inputs responding to different locations in the visual scene by mapping spatial receptive fields in dendritic spines of mouse visual cortex neurons using two-photon calcium imaging. We find that neurons receive functionally diverse inputs from extended regions of visual space. Inputs representing similar visual features from the same location in visual space are more likely to cluster on neighbouring spines. Inputs from visual field regions beyond the receptive field of the postsynaptic neuron often synapse on higher-order dendritic branches. These putative long-range inputs are more frequent and more likely to share the preference for oriented edges with the postsynaptic neuron when the receptive field of the input is spatially displaced along the axis of the receptive field orientation of the postsynaptic neuron. Therefore, the connectivity between neurons with displaced receptive fields obeys a specific rule, whereby they connect preferentially when their receptive fields are co-oriented and co-axially aligned. This organization of synaptic connectivity is ideally suited for the amplification of elongated edges, which are enriched in the visual environment, and thus provides a potential substrate for contour integration and object grouping.

Visual evoked potentials (VEPs) can provide important diagnostic information regarding the functional integrity of the visual system. This document updates the ISCEV standard for clinical VEP testing and supersedes the 2009 standard. The main changes in this revision are the acknowledgment that pattern stimuli can be produced using a variety of technologies with an emphasis on the need for manufacturers to ensure that there is no luminance change during pattern reversal or pattern onset/offset. The document is also edited to bring the VEP standard into closer harmony with other ISCEV standards. The ISCEV standard VEP is based on a subset of stimulus and recording conditions that provide core clinical information and can be performed by most clinical electrophysiology laboratories throughout the world. These are: (1) Pattern-reversal VEPs elicited by checkerboard stimuli with large 1 degree (°) and small 0.25° checks. (2) Pattern onset/offset VEPs elicited by checkerboard stimuli with large 1° and small 0.25° checks. (3) Flash VEPs elicited by a flash (brief luminance increment) which subtends a visual field of at least 20°. The ISCEV standard VEP protocols are defined for a single recording channel with a midline occipital active electrode. These protocols are intended for assessment of the eye and/or optic nerves anterior to the optic chiasm. Extended, multi-channel protocols are required to evaluate postchiasmal lesions.

A catalogue of neuronal cell types has often been called a ‘parts list’ of the brain 1 , and regarded as a prerequisite for understanding brain function 2 , 3 . In the optic lobe of Drosophila , rules of connectivity between cell types have already proven to be essential for understanding fly vision 4 , 5 . Here we analyse the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. Most new cell types contain 10 to 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi) 6 – 10 at least double in number of types. A new serpentine medulla (Sm) interneuron family contains more types than any other. Three families of cross-neuropil types are revealed. The consistency of types is demonstrated by analysing the distances in high-dimensional feature space, and is further validated by algorithms that select small subsets of discriminative features. We use connectivity to hypothesize about the functional roles of cell types in motion, object and colour vision. Connectivity with ‘boundary types’ that straddle the optic lobe and central brain is also quantified. We showcase the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity and reduction of the connectome to a substantially simpler wiring diagram of cell types, with immediate relevance for brain function and development. An analysis of the Drosophila connectome yields all cell types intrinsic to the optic lobe, and their rules of connectivity.

The aims of this randomized observational case control study were to quantify fixation behavior during standard automated perimetry (SAP) with different fixation targets and to evaluate the relationship between fixation behavior and threshold variability at each test point in healthy young participants experienced with perimetry. SAP was performed on the right eyes of 29 participants using the Octopus 900 perimeter, program 32, dynamic strategy. The fixation targets of Point, Cross, and Ring were used for SAP. Fixation behavior was recorded using a wearable eye-tracking glass. All participants underwent SAP twice with each fixation target in a random fashion. Fixation behavior was quantified by calculating the bivariate contour ellipse area (BCEA) and the frequency of deviation from the fixation target. The BCEAs (deg2) of Point, Cross, and Ring targets were 1.11, 1.46, and 2.02, respectively. In all cases, BCEA increased significantly with increasing fixation target size (p < 0.05). The logarithmic value of BCEA demonstrated the same tendency (p < 0.05). A positive correlation was identified between fixation behavior and threshold variability for the Point and Cross targets (ρ = 0.413-0.534, p < 0.05). Fixation behavior increased with increasing fixation target size. Moreover, a larger fixation behavior tended to be associated with a higher threshold variability. A small fixation target is recommended during the visual field test.

The human visual system is organized as a hierarchy of maps that share the topography of the retina. Known retinotopic maps have been identified using simple visual stimuli under strict fixation, conditions different from everyday vision which is active, dynamic, and complex. This means that it remains unknown how much of the brain is truly visually organized. Here I demonstrate widespread stable visual organization beyond the traditional visual system, in default-mode network and hippocampus. Detailed topographic connectivity with primary visual cortex during movie-watching, resting-state, and retinotopic-mapping experiments revealed that visual–spatial representations throughout the brain are warped by cognitive state. Specifically, traditionally visual regions alternate with default-mode network and hippocampus in preferentially representing the center of the visual field. This visual role of default-mode network and hippocampus would allow these regions to interface between abstract memories and concrete sensory impressions. Together, these results indicate that visual–spatial organization is a fundamental coding principle that structures the communication between distant brain regions.

In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters. A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma. The two popular pre-trained DL models: ResNet18 and VGG16, were used to train the PD images using five-fold cross-validation (CV) and observed the performance using balanced, pre-augmented data (n = 476 images), imbalanced original data (n = 265) and feature extraction. The trained images were further investigated using the Grad-CAM visualization technique. Moreover, four ML models were trained from the global indices: mean deviation (MD), pattern standard deviation (PSD) and visual field index (VFI), using five-fold CV to compare the classification performance with the DL model's result. The DL model, ResNet18 trained from balanced, pre-augmented PD images, achieved high accuracy in classifying the groups with an overall F1-score: 96.8%, precision: 97.0%, recall: 96.9%, and specificity: 99.0%. The highest F1 score was 87.8% for ResNet18 with the original dataset and 88.7% for VGG16 with feature extraction. The DL models successfully localized the affected VF loss in PD plots. Among the ML models, the random forest (RF) classifier performed best with an F1 score of 96%. The DL model trained from PD plots was promising in differentiating normal and glaucomatous groups and performed similarly to conventional global indices. Hence, the evidence-based DL model trained from PD images demonstrated that the DL model could stage glaucoma using only PD plots like Mills criteria. This automated DL model will assist clinicians in precision glaucoma detection and progression management during extensive glaucoma screening.

BackgroundMacular Integrity Assessment (MAIA) microperimetry is increasingly used in clinical and research settings to assess point retinal sensitivity and fixation stability. Testing occurs under mesopic conditions, commonly after a period of dark adaptation. Our aim was to identify the minimum length of adaptation required to optimise microperimetry performance.MethodsMAIA microperimetry using the 10-2 grid was performed on 40 right eyes of 40 healthy participants aged 18–73 with no ocular pathology and vision of at least 0.1 logMAR after ambient light exposure, with 0, 5, 10, 15, 20 and 30 min of adaptation in mesopic settings. Ten right eyes of 10 participants with choroideremia were also tested following 0 and 20 min of adaptation. We further tested 10 right eyes of 10 healthy participants after bright light exposure, with 0, 10 and 20 min of adaptation. We compared changes in threshold sensitivity and fixation stability across time points.ResultsMicroperimetry performance did not improve with increasing adaptation time in healthy participants or patients with choroideremia after ambient light exposure. After bright light exposure, we found microperimetry thresholds improved after 10 min of adaptation, but did not improve further at 20 min.ConclusionMesopic adaptation is not required before MAIA microperimetry after exposure to ambient light. Ten minutes of adaptation is sufficient after exposure to a bright light stimulus, such as ophthalmoscopy or retinal imaging. The brief time of dark adaptation required corresponds to cone adaptation curves and provides further evidence for cone-mediated central retinal function under mesopic conditions.

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

In this manuscript we develop a deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma. A generalized variational auto-encoder (VAE) was trained to learn a low-dimensional representation of standard automated perimetry (SAP) visual fields using 29,161 fields from 3,832 patients. The VAE was trained on a 90% sample of the data, with randomization at the patient level. Using the remaining 10%, rates of progression and predictions were generated, with comparisons to SAP mean deviation (MD) rates and point-wise (PW) regression predictions, respectively. The longitudinal rate of change through the VAE latent space (e.g., with eight dimensions) detected a significantly higher proportion of progression than MD at two (25% vs. 9%) and four (35% vs 15%) years from baseline. Early on, VAE improved prediction over PW, with significantly smaller mean absolute error in predicting the 4 th , 6 th and 8 th visits from the first three (e.g., visit eight: VAE8: 5.14 dB vs. PW: 8.07 dB; P < 0.001). A deep VAE can be used for assessing both rates and trajectories of progression in glaucoma, with the additional benefit of being a generative technique capable of predicting future patterns of visual field damage.