How is the cortical navigation network reorganized by the Likova Cognitive-Kinesthetic Navigation Training? We measured Granger-causal connectivity of the frontal-hippocampal-insular-retrosplenial-V1 network of cortical areas before and after this one-week training in the blind. Primarily top-down influences were seen during two tasks of drawing-from-memory (drawing complex maps and drawing the shortest path between designated map locations), with the dominant role being congruent influences from the egocentric insular to the allocentric spatial retrosplenial cortex and the amodal-spatial sketchpad of V1, with concomitant influences of the frontal cortex on these areas. After training, and during planning-from-memory of the best on-demand path, the hippocampus played a much stronger role, with the V1 sketchpad feeding information forward to the retrosplenial region. The inverse causal influences among these regions generally followed a recursive feedback model of the opposite pattern to a subset of congruent influences. Thus, this navigational network reorganized its pattern of causal influences with task demands and the navigation training, which produced marked enhancement of the navigational skills.
Radiologists and pathologists frequently make highly consequential perceptual decisions. For example, visually searching for a tumor and recognizing whether it is malignant can have a life-changing impact on a patient. Unfortunately, all human perceivers-even radiologists-have perceptual biases. Because human perceivers (medical doctors) will, for the foreseeable future, be the final judges of whether a tumor is malignant, understanding and mitigating human perceptual biases is important. While there has been research on perceptual biases in medical image perception tasks, the stimuli used for these studies were highly artificial and often critiqued. Realistic stimuli have not been used because it has not been possible to generate or control them for psychophysical experiments. Here, we propose to use Generative Adversarial Networks (GAN) to create vivid and realistic medical image stimuli that can be used in psychophysical and computer vision studies of medical image perception. Our model can generate tumor-like stimuli with specified shapes and realistic textures in a controlled manner. Various experiments showed the authenticity of our GAN-generated stimuli and the controllability of our model.
A Supervised Learning Approach for Dynamic Sampling (SLADS) addresses traditional issues with the incorporation of stochastic processes into a compressed sensing method. Statistical features, extracted from a sample reconstruction, estimate entropy reduction with regression models, in order to dynamically determine optimal sampling locations. This work introduces an enhanced SLADS method, in the form of a Deep Learning Approach for Dynamic Sampling (DLADS), showing reductions in sample acquisition times for high-fidelity reconstructions between ~ 70-80% over traditional rectilinear scanning. These improvements are demonstrated for dimensionally asymmetric, high-resolution molecular images of mouse uterine and kidney tissues, as obtained using Nanospray Desorption ElectroSpray Ionization (nano-DESI) Mass Spectrometry Imaging (MSI). The methodology for training set creation is adjusted to mitigate stretching artifacts generated when using prior SLADS approaches. Transitioning to DLADS removes the need for feature extraction, further advanced with the employment of convolutional layers to leverage inter-pixel spatial relationships. Additionally, DLADS demonstrates effective generalization, despite dissimilar training and testing data. Overall, DLADS is shown to maximize potential experimental throughput for nano-DESI MSI.
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