Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution

Objective.Objective of this work is the development and evaluation of a cortical parcellation framework based on tractography-derived brain structural connectivity.Impact Statement.The proposed framework utilizes novel spatial-graph representation learning methods for solving the task of cortical parcellation,an important medical image analysis and neuroscientific problem.Introduction.The concept of“connectional fingerprint”has motivated many investigations on the connectivity-based cortical parcellation,especially with the technical advancement of diffusion imaging.Previous studies on multiple brain regions have been conducted with promising results.However,performance and applicability of these models are limited by the relatively simple computational scheme and the lack of effective representation of brain imaging data.Methods.We propose the Spatial-graph Convolution Parcellation(SGCP)framework,a two-stage deep learning-based modeling for the graph representation brain imaging.In the first stage,SGCP learns an effective embedding of the input data through a self-supervised contrastive learning scheme with the backbone encoder of a spatial-graph convolution network.In the second stage,SGCP learns a supervised classifier to perform voxel-wise classification for parcellating the desired brain region.Results.SGCP is evaluated on the parcellation task for 5 brain regions in a 15-subject DWI dataset.Performance comparisons between SGCP,traditional parcellation methods,and other deep learning-based methods show that SGCP can achieve superior performance in all the cases.Conclusion.Consistent good performance of the proposed SGCP framework indicates its potential to be used as a general solution for investigating the regional/subregional composition of human brain based on one or more connectivity measurements.

Functional Neuroimaging in the New Era of Big Data

The field of functional neuroimaging has substantially advanced as a big data science in the past decade,thanks to international collaborative projects and community efforts.Here we conducted a literature review on functional neuroimaging,with focus on three general challenges in big data tasks:data collection and sharing,data infrastructure construction,and data analysis methods.The review covers a wide range of literature types including perspectives,database descriptions,methodology developments,and technical details.We show how each of the challenges was proposed and addressed,and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community.Furthermore,based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries,we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure,methodology development toward improved learning capability,and multi-discipline translational research framework for this new era of big data.