Susana Martinez-Conde, PhD - US grants

How do we distinguish motion in the world from similar retinal image displacements due to eye movements? This problem has special importance in diseases such as vertigo and a variety of spatial orientation disorders, where deficits in motion perception?including the suppression of self-motion?lead to devastating conse- quences. Impaired balance and motion perception substantially impact people?s daily lives, hindering spatial judgments and impeding performance during bodily motion tasks, such as ambulating or driving a vehicle. Until we know how the brain differentiates self-motion from external motion, we will be unable to develop therapeutic advances to address such disorders. Pioneering research in the 1960's - 90's?indeed the first published awake non-human primate (NHP) vision study?asked whether early cortical neurons discerned ocular from external motion, with the majority concluding that primary visual cortex (V1) neurons responded similarly to either type of motion. These studies used different tasks for self-generated vs external motion conditions, however, meaning that the respective neural responses evoked by either motion were not directly comparable. Thus, no research to date has developed a model for how neurons in V1 respond to external vs. self-generated motion. Recent work from the MPIs' labs, and others, has begun to use novel methods to directly compare self- vs real-motion responses in V1. We propose a transformative study to leverage these new techniques to evaluate the responses of V1 neurons to saccadic eye movements of all sizes under equivalent stimuli motions, with directly comparable viewing tasks in all conditions, in all layers of V1 simultaneously, and to develop a model that links the specific contributions of V1 circuits to perception. Our preliminary data suggests that V1 neurons can differentiate be- tween self-generated and external motion, driving our hypotheses: 1) V1 neurons distinguish between self- generated ocular motion vs. external retinal image motion, 2) an inhibitory feedback signal occurs during re- sponses to self-generated motion to drive the discrimination process, and 3) V1 responses to eye movements interact with responses driven by external motion in a nonlinear?though predictable?fashion, leading to both physiological and perceptual effects on the detection of retinal motion. By comparing neurophysiological re- sponses directly to perception in behaving NHPs, we will determine the contribution of V1 neurons to discerning external vs self-generated motion, as well as the provenance of any feedback (and/or perhaps feedforward) signals, using laminar analysis. These studies will establish the contributions of signals arriving to (or arising within) different V1 layers, so as to dissociate external vs self- motion. We will create quantitative models (based on our previously established models) using the new ground truth measurements from the proposed research, to determine the precise neural and perceptual consequences of each V1 circuit involved. The studies will elu- cidate loss of function in various oculomotor and neurological disorders and as such is directly relevant to the research priorities of the Strabismus, Amblyopia, and Visual Processing program at the National Eye Institute.

PROGRAM SUMMARY Radiological imaging is often the first step of the diagnostic pathway for many devastating diseases; thus, an erroneous assessment of ?normal? can lead to death. Whereas a grayscale object in an image can be described by its first-order image statistics?such as contrast, spatial frequency, position, entropy, and orientation?none of these dimensions, by itself, indicates abnormal vs normal radiological findings. We are a highly diverse team proposing an empirical approach to determine the mixtures of the first-order statistics?the ?visual textures?? that radiology experts explicitly and implicitly use to identify the locations of potential abnormalities in medical images. Our innovative approach does not rely on assumptions about which textures may or may not be im- portant to abnormality detection. Instead, we will track the oculomotor behavior of expert radiologists to deter- mine their conscious and unconscious targeting choices, and thus ascertain which textures are empirically in- formative. The ability of expert radiologists to rapidly find abnormalities suggests that they may be able to first identify them in their retinal periphery. Peripheral visual analysis skills are therefore potentially critical to radio- logic performance, despite being understudied. We will measure these skills and leverage the results to develop perceptual learning heuristics to improve peripheral abnormality texture detection. By comparing novices to ex- perts we will determine whether the first are inexpert due to a lack of sensitivity to diagnostically relevant textures (texture informativeness), or to a lack of knowledge about which textures are abnormal, or to a combined lack of both sensitivity and knowledge. Radiology also requires the acquisition of oculomotor skills through practice and optimization. Radiologic expertise thus changes the oculomotor system in predictable and detectable ways, in much the same way that an athlete?s body and brain change as a function of expertise acquisition in their sport. We will therefore analyze both the consistency between experts? fixation choices in medical images, and the eye movement performance characteristics of experts vs novice radiologists, to create an objective oculomotor bi- omarker of radiological expertise. The differences between novices and experts will train a deep learning (DL) system, which will have human visual and oculomotor performance characteristics. Training the DL with the abnormalities identified by a panel of expert radiologists will allow it to pinpoint the possible solutions in the manner of a simulated human radiologist performing at peak accuracy, precision, and speed. The resulting rank- ordered list of possible optimal and suboptimal image-reading strategies will serve as a benchmarking tool to quantify the performance of actual clinicians and residents who read the same images, rested vs fatigued. Meas- uring the effects of both training and fatigue on radiology expertise will be a major interdisciplinary cross-cutting advance in performance assessment. Our proposal to quantify fatigue in terms of erosion of expertise represents a transformational advance towards objective fitness-for-duty and expertise measures in medicine and beyond.