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The prevalent conception of vision-for-categorization suggests an interplay of two dynamic flows of information within the occipito-ventral pathway. The bottom-up flow progressively reduces the high-dimensional input into a lower-dimensional representation that is compared with memory to produce categorization behavior. The top-down flow predicts category information (i.e. features) from memory that propagates down the same hierarchy to facilitate input processing and behavior. However, the neural mechanisms that support such dynamic feature propagation up and down the visual hierarchy and how they facilitate behavior remain unclear. Here, we studied them using a prediction experiment that cued participants (N = 11) to the spatial location (left vs. right) and spatial frequency (SF, Low, LSF, vs. High, HSF) contents of an upcoming Gabor patch. Using concurrent MEG recordings of each participant’s neural activity, we compared the top-down flow of representation of the predicted Gabor contents (i.e. left vs. right; LSF vs. HSF) to their bottom-up flow. We show (1) that top-down prediction improves speed of categorization in all participants, (2) the top-down flow of prediction reverses the bottom-up representation of the Gabor stimuli, going from deep right fusiform gyrus sources down to occipital cortex sources contra-lateral to the expected Gabor location and (3) that predicted Gabors are better represented when the stimulus is eventually shown, leading to faster categorizations. Our results therefore trace the dynamic top-down flow of a predicted visual content that chronologically and hierarchically reversed bottom-up processing, further facilitates visual representations in early visual cortex and subsequent categorization behavior.

The cemento-enamel junction (CEJ) is an important reference point for various clinical measurements in oral health assessment. Identifying CEJ in ultrasound images is a challenging task for dentists. In this study, a computer-assisted detection method is proposed to identify the CEJ in ultrasound images, based on the curvature change of the junction outlining the upper edge of the enamel and cementum at the cementum–enamel intersection. The technique consists of image preprocessing steps for image enhancement, segmentation, and edge detection to locate the boundary of the enamel and cementum. The effects of the image preprocessing and the sizes of the bounding boxes enclosing the CEJ were studied. For validation, the algorithm was applied to 120 images acquired from human volunteers. The mean difference of the best performance between the proposed method and the two raters’ measurements was an average of 0.25 mm with reliability ≥ 0.98. The proposed method has the potential to assist dental professionals in CEJ identification on ultrasonographs to provide better patient care.