Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity

Abstract

Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.

Keywords: Atlas registration; Atlas segmentation; Brain mapping; Image processing; Mesoscale connectivity; Neuron reconstruction.

Fig. 1

Workflow of image processing for connecto-informatics at mesoscale. (a) Schematics diagram of key image processing steps for neural data obtained from imaging. (b) Whole brain images obtained in 2D are aligned into a 3D stack and is registered to the Allen CCFv3 [26]. (c) The registered neural images are segmented using the annotated Allen CCFv3. (d) Noisy images are processed using denoising algorithm to remove unwanted artifacts that can arise from numerous factors [65]. (e) Cell segmentation using CNN allow automatic detection and segmentation of cells in neural images, allowing cellular level connectivity analysis [97]. (f) 3D reconstruction of neuron using neuTube 1.0 shows synaptic connectivity of hippocampal region with mGRASP labeled synapses [110]. All scale bar represents 1000μm

Fig. 2

Comparison of mouse brain atlases. Rebuilt illustration using publicly available atlases for comparison between the enhanced and unified anatomical atlas, and the molecular atlas of the mouse brain combined. (a) The left hemisphere is the Allen reference atlas [26] and the right hemisphere is the enhance and unified mouse brain atlas that combines labels from the Franklin-Paxinos atlas and the common coordinate framework from the Allen Institute to create a unified mouse brain atlas [27]. (b) The left hemisphere is the Allen reference atlas and the right hemisphere is the molecular atlas of the adult mouse brain that shows anatomical divisions based molecular composition [29]. (c) Comparison of the hippocampus region delineation between mouse brain atlases. Scale bars for (a-b) represents 1000μm; scale bar for (c) represents 500μm