Deep and superficial amygdala nuclei projections revealed in vivo by probabilistic tractography

Copyright © 2011 Society for Neuroscience and the authors. The The Journal of Neuroscience uses a Creative Commons Attribution-NonCommercial-ShareAlike licence: http://creativecommons.org/licenses/by-nc-sa/4.0/.Despite a homogenous macroscopic appearance on magnetic resonance images, subregions of the amygdala express distinct functional profiles as well as corresponding differences in connectivity. In particular, histological analysis shows stronger connections for superficial (i.e., centromedial and cortical), compared with deep (i.e., basolateral and other), amygdala nuclei to lateral orbitofrontal cortex and stronger connections of deep compared with superficial, nuclei to polymodal areas in the temporal pole. Here, we use diffusion weighted imaging with probabilistic tractography to investigate these connections in humans. We use a data-driven approach to segment the amygdala into two subregions using k-means clustering. The identified subregions are spatially contiguous and their location corresponds to deep and superficial nuclear groups. Quantification of the connection strength between these amygdala clusters and individual target regions corresponds to qualitative histological findings in non-human primates, indicating such findings can be extrapolated to humans. We propose that connectivity profiles provide a potentially powerful approach for in vivo amygdala parcellation and can serve as a guide in studies that exploit functional and anatomical neuroimaging.The Wellcome Trust, a Max Planck Research Award and Swiss National Science Foundation

SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning

In fisheye images, rich distinct distortion patterns are regularly distributed in the image plane. These distortion patterns are independent of the visual content and provide informative cues for rectification. To make the best of such rectification cues, we introduce SimFIR, a simple framework for fisheye image rectification based on self-supervised representation learning. Technically, we first split a fisheye image into multiple patches and extract their representations with a Vision Transformer (ViT). To learn fine-grained distortion representations, we then associate different image patches with their specific distortion patterns based on the fisheye model, and further subtly design an innovative unified distortion-aware pretext task for their learning. The transfer performance on the downstream rectification task is remarkably boosted, which verifies the effectiveness of the learned representations. Extensive experiments are conducted, and the quantitative and qualitative results demonstrate the superiority of our method over the state-of-the-art algorithms as well as its strong generalization ability on real-world fisheye images.Comment: Accepted to ICCV 202