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Gradient mapping is an important technique to summarize high dimensional biological features as low dimensional manifold representations in exploring brain structure-function relationships at various levels of the cerebral cortex. While recent studies have characterized the major gradients of functional connectivity in several brain structures using this technique, very few have systematically examined the correspondence of such gradients across structures under a common systems-level framework. Using resting-state functional magnetic resonance imaging, here we show that the organizing principles of the isocortex, and those of the cerebellum and hippocampus in relation to the isocortex, can be described using two common functional gradients. We suggest that the similarity in functional connectivity gradients across these structures can be meaningfully interpreted within a common computational framework based on the principles of predictive processing. The present results, and the specific hypotheses that they suggest, represent an important step toward an integrative account of brain function. Analysis of functional MRI data from the Human Connectome Project and Brain Genomics Superstruct Project reveals common functional gradients among the human isocortex, cerebellum, and hippocampus.

Motion blur is a known phenomenon in full-field digital mammography that arises during image acquisition. It has been reported to reduce lesion detection performance and mask small microcalcifications, resulting in failure to detect smaller abnormalities until they reach more advanced stages. It is estimated that 20% of screening mammograms show elements of blur. Not only does patient movement cause motion blur, but also the compression paddle during the clamping phase of the mammography exam has been found to move slightly in the vertical direction, resulting in tissue motion during image acquisition (up to 1.5 mm of motion). We propose using machine-learning algorithms to automatically detect motion blur, which could support the clinical decision-making process during the mammography exam by allowing for an immediate retake, thereby preventing unnecessary expense, time, and patient anxiety.Blur was simulated mathematically to mimic the real blur seen in clinical practice. The blur point-spread-function mask is generated by distributing pixel intensity of an image pixel moving under random motion within the range of blur effect (the maximum amount of tissue motion allowed). The random motion trajectory vector is generated on a super-sampled image frame to accommodate smaller substeps; the vector was then sampled on a regular pixel grid using subpixel linear interpolation to generate the blur point-spread-function (PSF) mask. This randomly-generated motion trajectory is constrained by several factors: the effects of variations in tissue elasticity, imaging exposure time, and size of blur effect (motion boundary in millimeters) were examined. The blur mask is convolved with a mammogram to create blur. Five motion blur magnitudes (0.1, 0.25, 0.5, 1.0, and 1.5 mm) were simulated on 244 and 428 mammograms from INbreast and DDSM databases, respectively. Blur was quantified using 28 blur measure operators for each mammogram and at each blur level. The data were assigned to training (70%) and testing (30%) datasets to train three machine-learning classifiers: Ensemble Bagged Trees, Fine Gaussian SVM, and Weighted KNN, to distinguish five levels of blurred from unblurred mammograms, using six-way classification. For INbreast, the average classification accuracies were 87.7%, 85.66% and 85.68% for Ensemble Bagged Trees, Fine Gaussian SVM, and Weighted KNN, respectively, and the average classification accuracies for DDSM were 93.50%, 93.64%, and 92.70% for Ensemble Bagged Trees, Fine Gaussian SVM, and Weighted KNN, respectively.As of this date, no other study has investigated the ability of machine-learning classifiers and blur measure operators to detect motion blur in mammograms yet. Our preliminary results show the potential to detect simulated blur automatically using those methods. Although limited work has been done to quantify the effects of motion blur on radiologists' performance, there is evidence that motion blur might not be detected visually by a human observer, which potentially can reduce diagnostic performance.

Breast cancer is the second leading cause of death for women in the U.S. Early detection of breast cancer is key to higher survival rates of breast cancer patients. We are investigating infrared (IR) thermography as a noninvasive adjunct to mammography for breast cancer screening. IR imaging is radiation-free, pain-free, and non-contact. Automatic segmentation of the breast area from the acquired full-size breast IR images will help limit the area for tumor search, as well as reduce the time and effort costs of manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (C-DCNN) had been applied to automatically segment the breast area in IR images in previous studies. In this study, we applied a state-of-the-art deep-learning segmentation model, MultiResUnet, which consists of an encoder part to capture features and a decoder part for precise localization. It was used to segment the breast area by using a set of breast IR images, collected in our pilot study by imaging breast cancer patients and normal volunteers with a thermal infrared camera (N2 Imager). The database we used has 450 images, acquired from 14 patients and 16 volunteers. We used a thresholding method to remove interference in the raw images and remapped them from the original 16-bit to 8-bit, and then cropped and segmented the 8-bit images manually. Experiments using leave-one-out cross-validation (LOOCV) and comparison with the ground-truth images by using Tanimoto similarity show that the average accuracy of MultiResUnet is 91.47%, which is about 2% higher than that of the autoencoder. MultiResUnet offers a better approach to segment breast IR images than our previous model.

Predictive processing is emerging as a common computational hypothesis to account for diverse psychological functions subserved by a brain, providing a systems-level framework for characterizing structure-function relationships of its distinct substructures. Here, we contribute to this framework by examining gradients of functional connectivity as a low dimensional spatial representation of functional variation in the brain and demonstrating their computational implications for predictive processing. Specifically, we investigated functional connectivity gradients in the cerebral cortex, the cerebellum, and the hippocampus using resting-state functional MRI data collected from large samples of healthy young adults. We then evaluated the degree to which these structures share common principles of functional organization by assessing the correspondence of their gradients. We show that the organizing principles of these structures primarily follow two functional gradients consistent with the existing hierarchical accounts of predictive processing: A model-error gradient that describes the flow of prediction and prediction error signals, and a model-precision gradient that differentiates regions involved in the representation and attentional modulation of such signals in the cerebral cortex. Using these gradients, we also demonstrated triangulation of functional connectivity involving distinct subregions of the three structures, which allows characterization of distinct ways in which these structures functionally interact with each other, possibly subserving unique and complementary aspects of predictive processing. These findings support the viability of computational hypotheses about the functional relationships between the cerebral cortex, the cerebellum, and the hippocampus that may be instrumental for understanding the brain's dynamics within its large-scale predictive architecture. Competing Interest Statement The authors have declared no competing interest.