The Missing Temporal Crescent

Purpose We studied clinically the representation of the monocular temporal crescent in the human visual cortex and noted the importance of using the perimetric techniques best suited to detect this visual field defect and to study patients in whom the temporal crescent is missing. Methods Goldmann perimetry and high-resolution magnetic resonance imaging were performed in two patients with vascular lesions located in the anterior striate cortex. Results A monocular visual field defect, the missing temporal crescent, was found on the side contralateral to the lesion. Conclusions The perimetric-magnetic resonance imaging correlation is in exquisite agreement with recent information about the representation of the visual field in the human primary visual cortex. Reports of this specific perimetric finding are rare, in part because of underdetection with currently used perimetric techniques that concentrate on the central 30 degrees of the visual field.

In vivo functional localization of the temporal monocular crescent representation in human primary visual cortex

Perception Matches Selectivity in the Human Anterior Color Center

A paired-stimuli paradigm combined with fMRI was utilized to study the effect of gradient acoustic noise on fMRI response in the human primary visual cortex (V1) in terms of the auditory-visual cross-modal neural interaction. The gradient noise generated during the fMRI acquisition was used as the primary stimulus, and a single flashing light was used as the secondary stimulus. An interstimulus interval (ISI) separated the two. Six tasks were designed with different ISIs ranging from 50 to 700 ms. Both BOLD signal intensity and the number of activated pixels in V1 were analyzed and examined, and they showed a significant reduction when the gradient noise preceded the flashing light by approximately 300 ms. These results indicate that the gradient acoustic noise generated during fMRI acquisitions does interfere with neural behavior and the BOLD signal in the human visual cortex. This interference is modulated by the delay between the gradient noise and visual stimulation, and it can be studied quantitatively when the stimulation paradigm is designed appropriately. This study provides evidence of the auditory-visual interaction during fMRI studies, and the results should have an impact on fMRI applications.

Asymmetries in the response to visual patterns in the upper and lower visual fields (above and below the centre of gaze) have been associated with ecological factors relating to the structure of typical visual environments. Here, we investigated whether the content of the upper and lower visual field representations in low-level regions of human visual cortex are specialised for visual patterns that arise from the upper and lower visual fields in natural images. We presented image patches, drawn from above or below the centre of gaze of an observer navigating a natural environment, to either the upper or lower visual fields of human participants (n = 7) while we used functional magnetic resonance imaging (fMRI) to measure the magnitude of evoked activity in the visual areas V1, V2, and V3. We found a significant interaction between the presentation location (upper or lower visual field) and the image patch source location (above or below fixation); the responses to lower visual field presentation were significantly greater for image patches sourced from below than above fixation, while the responses in the upper visual field were not significantly different for image patches sourced from above and below fixation. This finding demonstrates an association between the representation of the lower visual field in human visual cortex and the structure of the visual input that is likely to be encountered below the centre of gaze.

The processing of orientations is at the core of our visual experience. Orientation selectivity in human visual cortex has been inferred from psychophysical experiments and more recently demonstrated with functional magnetic resonance imaging (fMRI). One method to identify orientation-selective responses is fMRI adaptation, in which two stimuli-either with the same or with different orientations-are presented successively. A region containing orientation-selective neurons should demonstrate an adapted response to the "same orientation" condition in contrast to the "different orientation" condition. So far, human primary visual cortex (V1) showed orientation-selective fMRI adaptation only in experimental designs using prolonged pre-adaptation periods (∼40 s) in combination with top-up stimuli that are thought to maintain the adapted level. This finding has led to the notion that orientation-selective short-term adaptation in V1 (but not V2 or V3) cannot be demonstrated using fMRI. The present study aimed at re-evaluating this question by testing three differently timed adaptation designs. With the use of a more sensitive analysis technique, we show robust orientation-selective fMRI adaptation in V1 evoked by a short-term adaptation design.

Traditionally, human primary visual cortex (V1) has been thought to mature within the first few years of life, based on anatomical studies of synapse formation, and establishment of intracortical and intercortical connections. Human vision, however, develops well beyond the first few years. Previously, we found prolonged development of some GABAergic proteins in human V1 (Pinto et al., 2010). Yet as >80% of synapses in V1 are excitatory, it remains unanswered whether the majority of synapses regulating experience-dependent plasticity and receptive field properties develop late, like their inhibitory counterparts. To address this question, we used Western blotting of postmortem tissue from human V1 (12 female, 18 male) covering a range of ages. Then we quantified a set of postsynaptic glutamatergic proteins (PSD-95, GluA2, GluN1, GluN2A, GluN2B), calculated indices for functional pairs that are developmentally regulated (GluA2:GluN1; GluN2A:GluN2B), and determined interindividual variability. We found early loss of GluN1, prolonged development of PSD-95 and GluA2 into late childhood, protracted development of GluN2A until ∼40 years, and dramatic loss of GluN2A in aging. The GluA2:GluN1 index switched at ∼1 year, but the GluN2A:GluN2B index continued to shift until ∼40 year before changing back to GluN2B in aging. We also identified young childhood as a stage of heightened interindividual variability. The changes show that human V1 develops gradually through a series of five orchestrated stages, making it likely that V1 participates in visual development and plasticity across the lifespan.SIGNIFICANCE STATEMENT Anatomical structure of human V1 appears to mature early, but vision changes across the lifespan. This discrepancy has fostered two hypotheses: either other aspects of V1 continue changing, or later changes in visual perception depend on extrastriate areas. Previously, we showed that some GABAergic synaptic proteins change across the lifespan, but most synapses in V1 are excitatory leaving unanswered how they change. So we studied expression of glutamatergic proteins in human V1 to determine their development. Here we report prolonged maturation of glutamatergic proteins, with five stages that map onto life-long changes in human visual perception. Thus, the apparent discrepancy between development of structure and function may be explained by life-long synaptic changes in human V1.

Functional magnetic resonance imaging (fMRI) studies of early sensory cortex often measure stimulus-driven increases in the blood oxygenation level-dependent (BOLD) signal. However, these positive responses are frequently accompanied by reductions in the BOLD signal in adjacent regions of cortex. Although this negative BOLD response (NBR) is thought to result from neuronal suppression, the precise relationship between local activity, suppression and perception remains unknown. By measuring BOLD signals in human primary visual cortex while varying the baseline contrast levels in the region affected by the NBR, we tested three physiologically-plausible computational models of neuronal modulation which could explain this phenomenon: a subtractive model, a response gain model and a contrast gain model. We also measured the ability of isoluminant contrast to generate an NBR. We show that the NBR can be modeled as a pathway-specific contrast gain modulation that is strongest outside the fovea. We found a similar spatial bias in a psychophysical study using identical stimuli, although these data indicated a response- rather than a contrast-gain mechanism. We reconcile these findings by proposing 1) that the NBR is associated with a long-range suppressive mechanism that hyperpolarizes a subset of magnocellularly-driven neurons at the input to V1; 2) that this suppression is broadly-tuned to match the spatial features of the mask region; 3) that increasing the baseline contrast in the suppressed region drives all neurons in the input layer, reducing the relative contribution of the suppressing subpopulation in the fMRI signal.

Visual perception depends strongly on spatial context. A classic example is the tilt illusion where the perceived orientation of a central stimulus differs from its physical orientation when surrounded by tilted spatial contexts. Here we show that such contextual modulation of orientation perception exhibits trait-like interindividual diversity that correlates with interindividual differences in effective connectivity within human primary visual cortex. We found that the degree to which spatial contexts induced illusory orientation perception, namely, the magnitude of the tilt illusion, varied across healthy human adults in a trait-like fashion independent of stimulus size or contrast. Parallel to contextual modulation of orientation perception, the presence of spatial contexts affected effective connectivity within human primary visual cortex between peripheral and foveal representations that responded to spatial context and central stimulus, respectively. Importantly, this effective connectivity from peripheral to foveal primary visual cortex correlated with interindividual differences in the magnitude of the tilt illusion. Moreover, this correlation with illusion perception was observed for effective connectivity under tilted contextual stimulation but not for that under iso-oriented contextual stimulation, suggesting that it reflected the impact of orientation-dependent intra-areal connections. Our findings revealed an interindividual correlation between intra-areal connectivity within primary visual cortex and contextual influence on orientation perception. This neurophysiological-perceptual link provides empirical evidence for theoretical proposals that intra-areal connections in early visual cortices are involved in contextual modulation of visual perception.

The distribution of the calcium-binding protein calbindin D-28K (CB) was investigated in human fetal primary visual cortex. CB is present in Cajal-Retzius cells of layer I, in sparse neurons of the ventricular and intermediate zones (VZ, IZ), and in tangential fibres in IZ by 15 weeks (W) of gestation. Cajal-Retzius cells lose their staining by 30W. CB appears in layers II-VI mainly from 26W, following an inside-outside sequence. Until 34W, CB labelling is in somata and neuropil located primarily in layers IVA, IVC and V. Then reactive perikarya and puncta increase in layers II-IVA and deep IVB and C, but are reduced in infragranular layers from 34W to term. From 30W positive somata form clusters in the cell-rich bands in layers IV and V and labelled neuropil in layers III and IV has a periodic pattern from 34W. Also from 34W, numerous lightly reactive pyramidal cells are present in layers II to IVA in primary, but not secondary, visual cortex. Our results show precocious expression of CB before full laminar differentiation of the cortex and that some of this expression is transient.

Neural responses to visual stimuli are modulated by spatial and temporal context. For example, in primary visual cortex (V1), responses to an oriented target stimulus will be suppressed when embedded within an oriented surround stimulus. This suppression is orientation-specific, with the largest suppression observed when stimuli in the neuron's classical receptive field and surround are of similar orientation. In human psychological experiments, the tilt illusion and tilt aftereffect demonstrate an effect of context on perceived orientation of a target stimulus. Similar to the neurophysiological data, the strength of these effects is modulated by the orientation difference between the target stimulus and context. It has been hypothesized that the neural mechanism underlying both the tilt illusion and tilt aftereffect involves orientation-tuned inhibition in V1. However, to date there is no direct evidence linking human perception of these illusions with measurements of inhibition from human visual cortex. Here, we measured context-induced suppression of neural responses in human visual cortex using functional magnetic resonance imaging (fMRI). In the same participants, we also measured magnitudes of their tilt illusion and tilt aftereffect. Our data revealed a significant relationship between the magnitude of neural suppression in V1 and size of the tilt illusion and tilt aftereffect. That is, participants who showed stronger blood oxygenation level dependent (BOLD) suppression in V1 also perceived stronger shifts in illusory tilt. This agreement between perception and neural responses in human V1 suggests a shared inhibitory mechanism that mediates both spatial and temporal effects of context in human perception.

In the human brain, the incoming light to the retina is transformed into meaningful representations that allow us to interact with the world. In a similar vein, the RGB pixel values are transformed by a deep neural network (DNN) into meaningful representations relevant to solving a computer vision task it was trained for. Therefore, in my research, I aim to reveal insights into the visual representations in the human visual cortex and DNNs solving vision tasks. In the previous decade, DNNs have emerged as the state-of-the-art models for predicting neural responses in the human and monkey visual cortex. Research has shown that training on a task related to a brain region’s function leads to better predictivity than a randomly initialized network. Based on this observation, we proposed that we can use DNNs trained on different computer vision tasks to identify functional mapping of the human visual cortex. To validate our proposed idea, we first investigate a brain region occipital place area (OPA) using DNNs trained on scene parsing task and scene classification task. From the previous investigations about OPA’s functions, we knew that it encodes navigational affordances that require spatial information about the scene. Therefore, we hypothesized that OPA’s representation should be closer to a scene parsing model than a scene classification model as the scene parsing task explicitly requires spatial information about the scene. Our results showed that scene parsing models had representation closer to OPA than scene classification models thus validating our approach. We then selected multiple DNNs performing a wide range of computer vision tasks ranging from low-level tasks such as edge detection, 3D tasks such as surface normals, and semantic tasks such as semantic segmentation. We compared the representations of these DNNs with all the regions in the visual cortex, thus revealing the functional representations of different regions of the visual cortex. Our results highly converged with previous investigations of these brain regions validating the feasibility of the proposed approach in finding functional representations of the human brain. Our results also provided new insights into underinvestigated brain regions that can serve as starting hypotheses and promote further investigation into those brain regions. We applied the same approach to find representational insights about the DNNs. A DNN usually consists of multiple layers with each layer performing a computation leading to the final layer that performs prediction for a given task. Training on different tasks could lead to very different representations. Therefore, we first investigate at which stage does the representation in DNNs trained on different tasks starts to differ. We further investigate if the DNNs trained on similar tasks lead to similar representations and on dissimilar tasks lead to more dissimilar representations. We selected the same set of DNNs used in the previous work that were trained on the Taskonomy dataset on a diverse range of 2D, 3D and semantic tasks. Then, given a DNN trained on a particular task, we compared the representation of multiple layers to corresponding layers in other DNNs. From this analysis, we aimed to reveal where in the network architecture task-specific representation is prominent. We found that task specificity increases as we go deeper into the DNN architecture and similar tasks start to cluster in groups. We found that the grouping we found using representational similarity was highly correlated with grouping based on transfer learning thus creating an interesting application of the approach to model selection in transfer learning. During previous works, several new measures were introduced to compare DNN representations. So, we identified the commonalities in different measures and unified different measures into a single framework referred to as duality diagram similarity. This work opens up new possibilities for similarity measures to understand DNN representations. While demonstrating a much higher correlation with transfer learning than previous state-of-the-art measures we extend it to understanding layer-wise representations of models trained on the Imagenet and Places dataset using different tasks and demonstrate its applicability to layer selection for transfer learning. In all the previous works, we used the task-specific DNN representations to understand the representations in the human visual cortex and other DNNs. We were able to interpret our findings in terms of computer vision tasks such as edge detection, semantic segmentation, depth estimation, etc. however we were not able to map the representations to human interpretable concepts. Therefore in our most recent work, we developed a new method that associates individual artificial neurons with human interpretable concepts. Overall, the works in this thesis revealed new insights into the representation of the visual cortex and DNNs...

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