Joint representation of consistent structural and functional profiles for identification of common cortical landmarks

Abstract

In the brain mapping field, there have been significant interests in representation of structural/functional profiles to establish structural/functional landmark correspondences across individuals and populations. For example, from the structural perspective, our previous studies have identified hundreds of consistent DICCCOL (dense individualized and common connectivity-based cortical landmarks) landmarks across individuals and populations, each of which possess consistent DTI-derived fiber connection patterns. From the functional perspective, a large collection of well-characterized HAFNI (holistic atlases of functional networks and interactions) networks based on sparse representation of whole-brain fMRI signals have been identified in our prior studies. However, due to the remarkable variability of structural and functional architectures in the human brain, it is challenging for earlier studies to jointly represent the connectome-scale structural and functional profiles for establishing a common cortical architecture which can comprehensively encode both structural and functional characteristics across individuals. To address this challenge, we propose an effective computational framework to jointly represent the structural and functional profiles for identification of consistent and common cortical landmarks with both structural and functional correspondences across different brains based on DTI and fMRI data. Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified.

Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

“…In this review, we have discussed the possibility of a mapping between gyro-sulcal patterns and functions and suggest that these two anatomical patterns could serve as basic blocks for a complex building of brain functions. Although conventional definitions of gyri and sulci are used in these studies, it is thought provoking that definitions could be revised by integrating axonal wiring and function information (Zhu et al ., 2013 ; Jiang et al ., 2015b ; Lv et al ., 2014 , 2015 ; Zhang et al ., 2018b , 2019a , b ). For example, Jiang et al .…”

Joint Analysis of Functional and Structural Connectomes Between Preterm and Term Infant Brains via Canonical Correlation Analysis With Locality Preserving Projection

“…In recent years, integration of both brain function and structure has been widely adopted to study the brain's working mechanism (Groves et al, 2011;Adali et al, 2015;Calhoun and Sui, 2016) or to identify the brain alterations in diseases compared to the health controls (Calhoun et al, 2006;Arbabshirani et al, 2017;Qi et al, 2018). For example, Zhang et al (2018a;2018b;2019a) proposed a framework to integrate the DICCCOL system (structural perspective) into the HAFNI system (functional perspective), and successfully obtained the consistent common landmarks with both structural and functional consistency across different individual brains. Furthermore, hierarchical representation of such multimodal representation has been proposed to show the relationship between function and structure (Zhang et al, 2019a).…”

Discovering hierarchical common brain networks via multimodal deep belief network

“…Given the complementary information embedded in structural and functional connectomics data, it is natural and welljustified to combine multimodal information together to investigate brain connectivities and their relationships simultaneously (Chen et al, 2013;Zhu et al, 2014a). For instance , Zhang S et al 2017a andZhang S et al 2017b proposed novel multimodal fusion models to identify common and consistent cortical landmarks by jointly representing connectome-scale functional and structural profiles from the brain; Zhang S et al 2018a proposed a novel multimodal fusion framework to explore the relationship among cortical folding, structural connectivity and functional networks, and they observed that structural connectivity based brain parcellations and sparse dictionary learning derived functional networks exhibited deeply rooted regularity across individuals, although cortical folding patterns are substantially more variable. Zhang S et al 2018b also proposed a novel framework to explore fiber skeletons via joint representation of functional networks and structural connectivity.…”

“…So far, from the existing multimodal fusion studies (e.g. Zhu et al, 2014b;Zhang S et al 2017a;Zhang S et al 2017b;Zhang S et al 2018a;Sui et al, 2012;Rykhlevskaia et al, 2008), we strongly believe that multimodal brain connectomics research will revolutionize our fundamental understanding of the structure and function of the brain and their relationships, and eventually shed novel insights into how to treat and prevent many devastating brain disorders. However, multimodal integration of brain connectomics is also widely considered a challenge, due to the intrinsic nature of multiscale properties of connectivity and its significant spatial and temporal variability across individuals and populations (He et al, 2013).…”