Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke

Loss of vision across large parts of the visual field is a common and devastating complication of cerebral strokes. In the clinic, this loss is quantified by measuring the sensitivity threshold across the field of vision using static perimetry. These methods rely on the ability of the patient to report the presence of lights in particular locations. While perimetry provides important information about the intactness of the visual field, the approach has some shortcomings. For example, it cannot distinguish where in the visual pathway the key processing deficit is located. In contrast, brain imaging can provide important information about anatomy, connectivity, and function of the visual pathway following stroke. In particular, functional magnetic resonance imaging (fMRI) and analysis of population receptive fields (pRF) can reveal mismatches between clinical perimetry and maps of cortical areas that still respond to visual stimuli after stroke. Here, we demonstrate how information from different brain imaging modalities—visual field maps derived from fMRI, lesion definitions from anatomical scans, and white matter tracts from diffusion weighted MRI data—provides a more complete picture of vision loss. For any given location in the visual field, the combination of anatomical and functional information can help identify whether vision loss is due to absence of gray matter tissue or likely due to white matter disconnection from other cortical areas. We present a combined imaging acquisition and visual stimulus protocol, together with a description of the analysis methodology, and apply it to datasets from four stroke survivors with homonymous field loss (two with hemianopia, two with quadrantanopia). For researchers trying to understand recovery of vision after stroke and clinicians seeking to stratify patients into different treatment pathways, this approach combines multiple, convergent sources of data to characterize the extent of the stroke damage. We show that such an approach gives a more comprehensive measure of residual visual capacity—in two particular respects: which locations in the visual field should be targeted and what kind of visual attributes are most suited for rehabilitation.

How lesions at different locations along the visual pathway influence pupillary reactions to chromatic stimuli

Purpose To examine systematically how prechiasmal, chiasmal, and postchiasmal lesions along the visual pathway affect the respective pupillary responses to specific local monochromatic stimuli. Methods Chromatic pupil campimetry (CPC) was performed in three patient groups (10 subjects with status after anterior ischemic optic neuropathy, 6 with chiasmal lesions, and 12 with optic tract or occipital lobe lesions (tumor, ischemia)) using red, low-intensity red, and blue local stimuli within the central 30° visual field. Affected areas - as determined by visual field defects revealed using conventional static perimetry - were compared with non-affected areas. Outcome parameters were the relative maximal constriction amplitude (relMCA) and the latency to constriction onset of the pupillary responses. Results A statistically significant relMCA reduction was observed in the affected areas of postchiasmal lesions with red (p = 0.004) and low-intensity red stimulation (p = 0.001). RelMCA reduction in the affected areas seemed more pronounced for low-intensity red stimulation (46.5% mean reduction compared to non-affected areas; 36% for red stimulation), however statistically not significant. In prechiasmal lesions, a statistically significant latency prolongation could be demonstrated in the affected areas with low-intensity red stimulation (p = 0.015). Conclusion Our results indicate that the choice of stimulus characteristics is relevant in detecting defects in the pupillary pathway of impairment along the visual pathway, favoring red stimuli of low intensity over blue stimuli. Such knowledge opens the door for further fundamental research in pupillary pathways and is important for future clinical application of pupillography in neuro-ophthalmologic patients.

Histological and Top-Down Proteomic Analyses of the Visual Pathway Using the Cuprizone Demyelination Model

A change in visual perception is a frequent early symptom of multiple sclerosis (MS), the pathoetiology of which remains unclear. Following a slow demyelination process caused by 12 weeks of low-dose (0.1%) cuprizone (CPZ) consumption, histology and proteomics were used to investigate components of the visual pathway in young adult mice. Histological investigation did not identify demyelination or gliosis in the optic tracts, pretectal nuclei, superior colliculi, lateral geniculate nuclei or visual cortices. However, top-down proteomic assessment of the optic nerve/tract revealed a significant change in the abundance of 34 spots in high-resolution 2D gels. Subsequent liquid chromatography-tandem mass spectrometry analysis identified alterations in 75 proteoforms. Literature mining revealed the relevance of these proteoforms in terms of proteins previously implicated in animal models and human MS. Importantly, 24 proteoforms were not previously described in any animal models of MS or MS itself. Bioinformatic analysis indicated involvement of these proteoforms in cytoskeleton organization, metabolic dysregulation, protein aggregation, and axonal support. Collectively, these results indicate that continuous CPZ-feeding, which evokes a slow demyelination, results in proteomic changes that precede any clear histological changes in the visual pathway and that these proteoforms may be potential early markers of degenerative demyelinating conditions.

Face detection in untrained deep neural networks

Face-selective neurons are observed in the primate visual pathway and are considered as the basis of face detection in the brain. However, it has been debated as to whether this neuronal selectivity can arise innately or whether it requires training from visual experience. Here, using a hierarchical deep neural network model of the ventral visual stream, we suggest a mechanism in which face-selectivity arises in the complete absence of training. We found that units selective to faces emerge robustly in randomly initialized networks and that these units reproduce many characteristics observed in monkeys. This innate selectivity also enables the untrained network to perform face-detection tasks. Intriguingly, we observed that units selective to various non-face objects can also arise innately in untrained networks. Our results imply that the random feedforward connections in early, untrained deep neural networks may be sufficient for initializing primitive visual selectivity.

The dorsal visual pathway represents object-centered spatial relations for object recognition

Although there is mounting evidence that input from the dorsal visual pathway is crucial for object processes in the ventral pathway, the specific functional contributions of dorsal cortex to these processes remains poorly understood. Here, we hypothesized that dorsal cortex computes the spatial relations among an object's parts — a processes crucial for forming global shape percepts — and transmits this information to the ventral pathway to support object categorization. Using multiple functional localizers, we discovered regions in the intraparietal sulcus (IPS) that were selectively involved in computing object-centered part relations. These regions exhibited task-dependent functional connectivity with ventral cortex, and were distinct from other dorsal regions, such as those representing allocentric relations, 3D shape, and tools. In a subsequent experiment, we found that the multivariate response of posterior IPS, defined on the basis of part-relations, could be used to decode object category at levels comparable to ventral object regions. Moreover, mediation and multivariate connectivity analyses further suggested that IPS may account for representations of part relations in the ventral pathway. Together, our results highlight specific contributions of the dorsal visual pathway to object recognition. We suggest that dorsal cortex is a crucial source of input to the ventral pathway and may support the ability to categorize objects on the basis of global shape.