Structural and molecular basis of choline uptake into the brain by FLVCR2.

Choline is an essential nutrient that the human body needs in vast quantities for cell membrane synthesis, epigenetic modification and neurotransmission. The brain has a particularly high demand for choline, but how it enters the brain remains unknown1-3. The major facilitator superfamily transporter FLVCR1 (also known as MFSD7B or SLC49A1) was recently determined to be a choline transporter but is not highly expressed at the blood-brain barrier, whereas the related protein FLVCR2 (also known as MFSD7C or SLC49A2) is expressed in endothelial cells at the blood-brain barrier4-7. Previous studies have shown that mutations in human Flvcr2 cause cerebral vascular abnormalities, hydrocephalus and embryonic lethality, but the physiological role of FLVCR2 is unknown4,5. Here we demonstrate both in vivo and in vitro that FLVCR2 is a BBB choline transporter and is responsible for the majority of choline uptake into the brain. We also determine the structures of choline-bound FLVCR2 in both inward-facing and outward-facing states using cryo-electron microscopy. These results reveal how the brain obtains choline and provide molecular-level insights into how FLVCR2 binds choline in an aromatic cage and mediates its uptake. Our work could provide a novel framework for the targeted delivery of therapeutic agents into the brain.

Gradiate: A radial sweep approach to measuring detailed contrast sensitivity functions from eye movements.

The contrast sensitivity function (CSF) is an informative measure of visual health, but the practical difficulty of measuring it has impeded detailed analyses of its relationship to different visual disorders. Furthermore, most existing tasks cannot be used in populations with cognitive impairment. We analyzed detailed CSFs measured with a nonverbal procedure called "Gradiate," which efficiently infers visibility from eye movements and manipulates stimulus appearance in real time. Sixty observers of varying age (38 with refractive error) were presented with moving stimuli. Stimulus spatial frequency and contrast advanced along 15 radial sweeps through CSF space in response to stimulus-congruent eye movements. A point on the CSF was recorded when tracking ceased. Gradiate CSFs were reliable and in high agreement with independent low-contrast acuity thresholds. Overall CSF variation was largely captured by two orthogonal factors ("radius" and "slope") or two orthogonal shape factors when size was normalized ("aspect ratio" and "curvature"). CSF radius was highly predictive of LogMAR acuity, as were aspect ratio and curvature together, but only radius was predictive of observer age. Our findings suggest that Gradiate holds promise for assessing spatial vision in both verbal and nonverbal populations and indicate that variation between detailed CSFs can reveal useful information about visual health.

Curveball: A tool for rapid measurement of contrast sensitivity based on smooth eye movements.

The contrast sensitivity function (CSF) is an informative measure of visual function, but current tools for assessing it are limited by the attentional, motor, and communicative abilities of the participant. Impairments in these abilities can prevent participants from engaging with tasks or following an experimenter's instructions. Here, we describe an efficient new tool for measuring contrast sensitivity, Curveball, and empirically validate it with a sample of healthy adults. The Curveball algorithm continuously infers stimulus visibility through smooth eye tracking instead of perceptual report, and rapidly lowers stimulus contrast in real time until a threshold is found. The procedure requires minimal instruction to administer and takes only five minutes to estimate a full CSF, which is comparable to the best existing methods available for healthy adults. Task repeatability was high: the coefficients of repeatability were 0.275 (in log10 units of RMS contrast) within the same session and 0.227 across different days. We also present evidence that the task is robust across illumination changes, well correlated with results from conventional psychophysical methods, and highly sensitive to improvements in visual acuity from refractive correction. Our findings indicate that Curveball is a promising means of accurately assessing contrast sensitivity in previously neglected populations.

Illusory optical defocus generated by shaded surface texture.

The human visual system is tasked with the problem of extracting information about the world from images that contain a conflated mixture of environmental sources and optical artifacts generated by the focal properties of our eyes. In most contexts, our brains manage to distinguish these sources, but this is not always the case. Recent work showed that shading gradients generated by smooth three-dimensional (3D) surfaces can elicit strong illusory percepts of optical defocus1,2 - the perception of illusory blur is only eliminated when the surface appears attached to self-occluding contours3, surface discontinuities1, or sharp specular reflections1,2, which all generate sharp ('high spatial frequency') image structure. This suggests that it should also be possible to eliminate the illusory blur elicited by shaded surfaces by altering the surface geometry to include small-scale surface relief, which would also generate high-frequency image structure. We report the surprising result here that this manipulation fails to eliminate the perception of blur; the fine texture fails to perceptually 'bind' to the low-frequency image structure when there is a sufficient gap between the spatial scales of the fine and coarse surface structure. These findings suggest that discontinuous 'gaps' in the spatial scale of textures are a segmentation cue the visual system uses to extract multiple causes of image structure.

Tracking-Based Interactive Assessment of Saccades, Pursuits, Visual Field, and Contrast Sensitivity in Children With Brain Injury.

Visual deficits in children that result from brain injury, including cerebral/cortical visual impairment (CVI), are difficult to assess through conventional methods due to their frequent co-occurrence with cognitive and communicative disabilities. Such impairments hence often go undiagnosed or are only determined through subjective evaluations of gaze-based reactions to different forms, colors, and movements, which limits any potential for remediation. Here, we describe a novel approach to grading visual health based on eye movements and evidence from gaze-based tracking behaviors. Our approach-the "Visual Ladder"-reduces reliance on the user's ability to attend and communicate. The Visual Ladder produces metrics that quantify spontaneous saccades and pursuits, assess visual field responsiveness, and grade spatial visual function from tracking responses to moving stimuli. We used the Ladder to assess fourteen hospitalized children aged 3 to 18 years with a diverse range of visual impairments and causes of brain injury. Four children were excluded from analysis due to incompatibility with the eye tracker (e.g., due to severe strabismus). The remaining ten children-including five non-verbal children-were tested multiple times over periods ranging from 2 weeks to 9 months, and all produced interpretable outcomes on at least three of the five visual tasks. The results suggest that our assessment tasks are viable in non-communicative children, provided their eyes can be tracked, and hence are promising tools for use in a larger clinical study. We highlight and discuss informative outcomes exhibited by each child, including directional biases in eye movements, pathological nystagmus, visual field asymmetries, and contrast sensitivity deficits. Our findings indicate that these methodologies will enable the rapid, objective classification and grading of visual impairments in children with CVI, including non-verbal children who are currently precluded from most vision assessments. This would provide a much-needed differential diagnostic and prognostic tool for CVI and other impairments of the visual system, both ocular and cerebral.

audiomath: A neuroscientist's sound toolkit.

In neuroscientific experiments and applications, working with auditory stimuli demands software tools for generation and acquisition of raw audio, for composition and tailoring of that material into finished stimuli, for precisely timed presentation of the stimuli, and for experimental session recording. Numerous programming tools exist to approach these tasks, but their differing specializations and conventions demand extra time and effort for integration. In particular, verifying stimulus timing requires extensive engineering effort when developing new applications. This paper has two purposes. The first is to present audiomath (https://pypi.org/project/audiomath), a sound software library for Python that prioritizes the needs of neuroscientists. It minimizes programming effort by providing a simple object-oriented interface that unifies functionality for audio generation, manipulation, visualization, decoding, encoding, recording, and playback. It also incorporates specialized tools for measuring and optimizing stimulus timing. The second purpose is to relay what we have learned, during development and application of the software, about the twin challenges of delivering stimuli precisely at a certain time, and of precisely measuring the time at which stimuli were delivered. We provide a primer on these problems and the possible approaches to them. We then report audio latency measurements across a range of hardware, operating systems and settings, to illustrate the ways in which hardware and software factors interact to affect stimulus presentation performance, and the resulting pitfalls for the programmer and experimenter. In particular, we highlight the potential conflict between demands for low latency, low variability in latency ("jitter"), cooperativeness, and robustness. We report the ways in which audiomath can help to map this territory and provide a simplified path toward each application's particular priority. By unifying audio-related functionality and providing specialized diagnostic tools, audiomath both simplifies and potentiates the development of neuroscientific applications in Python.

Photogeometric Cues to Perceived Surface Shading.

The human visual system is remarkably adept at extracting the three-dimensional (3D) shape of surfaces from images of smoothly shaded surfaces (shape from shading). Most research into this remarkable perceptual ability has focused on understanding how the visual system derives a specific representation of 3D shape when it is known (or assumed) that shading and self-occluding contours are the sole causes of image structure [1-11]. But there is an even more fundamental problem that must be solved before any such analysis can take place: how does the visual system determine when it's viewing a shaded surface? Here, we present theoretical analyses showing that there is statistically reliable information generated along the bounding contours of smoothly curved surfaces that the visual system uses to identify surface shading. This information can be captured by two photogeometric constraints that link the shape of bounding contours to the distributions of shading intensity along the contours: one that links shading intensity to the local orientations along bounding contours and a second that links shading intensity to bounding contour curvature. We show that these constraints predict the perception of shading for surfaces with smooth self-occluding contours and a widely studied class of bounding contours (planar cuts). The results provide new insights into the information that the visual system exploits to distinguish surface shading from other sources of image structure and offer a coherent explanation of the influence of bounding contours on the perception of surface shading and 3D shape.

Shady: A software engine for real-time visual stimulus manipulation.

BACKGROUND: Precise definition, rendering and manipulation of visual stimuli are essential in neuroscience. Rather than implementing these tasks from scratch, scientists benefit greatly from using reusable software routines from freely available toolboxes. Existing toolboxes work well when the operating system and hardware are painstakingly optimized, but may be less suited to applications that require multi-tasking (for example, closed-loop systems that involve real-time acquisition and processing of signals). NEW METHOD: We introduce a new cross-platform visual stimulus toolbox called Shady (https://pypi.org/project/Shady)-so called because of its heavy reliance on a shader program to perform parallel pixel processing on a computer's graphics processor. It was designed with an emphasis on performance robustness in multi-tasking applications under unforgiving conditions. For optimal timing performance, the CPU drawing management commands are carried out by a compiled binary engine. For configuring stimuli and controlling their changes over time, Shady provides a programmer's interface in Python, a powerful, accessible and widely-used high-level programming language. RESULTS: Our timing benchmark results illustrate that Shady's hybrid compiled/interpreted architecture requires less time to complete drawing operations, exhibits smaller variability in frame-to-frame timing, and hence drops fewer frames, than pure-Python solutions under matched conditions of resource contention. This performance gain comes despite an expansion of functionality (e.g. "noisy-bit" dithering as standard on all pixels and all frames, to enhance effective dynamic range) relative to previous offerings. CONCLUSIONS: Shady simultaneously advances the functionality and performance available to scientists for rendering visual stimuli and manipulating them in real time.

Specular image structure modulates the perception of three-dimensional shape.

Retinal images are produced by interactions between a surface's 3D shape, material properties, and surrounding light field. In order to recover the 3D geometry of a surface, the visual system must somehow separate aspects of image structure generated by a surface's shape from structure generated by its material properties or the light field in which it is embedded. Attributing image structure to the wrong physical source would cause the visual system to interpret changes in one physical property (such as reflectance) as changes in another (such as shape). Many previous studies have shown that the visual system does not conflate image structure generated by specular reflectance with 3D shape, but they did not assess the physical conditions where it would be computationally most difficult to disentangle these different sources of image structure. Here, we show that varying the specular roughness and curvature of surfaces embedded in natural light fields can strongly modulate perceived shape. Despite the complexity of these interactions, we show how an image's gradient structure mediates its interpretation as a specular reflection or a change in 3D shape. Our findings provide a coherent explanation of when and why specular reflections impact perceived shape and reveal how the static surface properties, simplified light fields, and experimental methods used in previous studies may explain their inconsistent results.