Title
Exploring the Functional Difference of Gyri/Sulci via Hierarchical Interpretable Autoencoder
Abstract
Understanding the functional mechanism of human brain has been of intense interest in the brain mapping field. Recent studies suggested that cortical gyri and sulci, the two basic cortical folding patterns, play different functional roles based on various data-driven methods from local time scale to global perspective. However, given the evidence that the brain's neuronal organization follows a hierarchical principle both spatially and temporally, it is unclear whether there exists temporal and spatial hierarchical functional differences between gyri and sulci due to the lack of suitable analytical tools. To answer this question, in this paper, we proposed a novel Hierarchical Interpretable Autoencoder (HIAE) to explore the hierarchical functional difference between gyri and sulci. The core idea is that hierarchical features learned by autoencoder can be embedded into a one-dimensional vector which interprets the features as spatial-temporal patterns, with which the region-based analysis in gyri and sulci can be further performed. We evaluated our framework using the Human Connectome Project (HCP) fMRI dataset, and the experiments showed that our framework is effective in terms of revealing meaningful hierarchical spatial-temporal features. Analysis based on Activation Ratio (AR) metric suggested that gyri have more low-frequency/global features while sulci have more high-frequency/local features. Our study provided novel insights to understand the brain's folding-function relationship.
Year
DOI
Venue
2021
10.1007/978-3-030-87234-2_66
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VII
Keywords
DocType
Volume
Gyri/Sulci, Cortical folding, Hierarchical Interpretable Autoencoder, Functional difference, fMRI
Conference
12907
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Lin Zhao1366.09
Haixing Dai201.01
Xi Jiang331137.88
Tuo Zhang474.47
Dajiang Zhu532036.72
Tianming Liu61033112.95