Functional Connectivity and Baseline Networks of the White Matter Brain: Development and Dissemination of Algorithms and Tools

PROJECT ABSTRACT The discovery of functional brain connectivity (FC) and functional networks (FNs) have propelled the neuroimaging field, particularly in functional magnetic resonance imaging (fMRI), which has experienced an exponential growth in recent years. FNs have allowed us to better understand extrinsic and intrinsic brain properties in various disease and healthy states, leading to better characterization of neuropsychiatric disorders. However, current fMRI analyses are constrained to the gray matter (GM) region of the brain and fMRI data from the white matter (WM) region are often discarded, which makes up approximately 50% of the brain by volume. Many brain disorders have been associated with WM deficiencies, since WM is critical for the transmission of information to the GM cortical areas. Despite findings of blood-oxygen-level-dependent (BOLD) signals in the WM, WM-FNs are yet to be fully characterized, and neither the mechanism by which WM-FNs may affect GM-FNs, nor how WM-FNs are associated with phenotypic traits are known. The long-term goal of this project is to better understand the effect of WM-FNs on normal cognitive functions of the human brain and apply fMRI data from various healthy and diseased populations for more reliable diagnostics and monitoring. The rationale for this study is based on our preliminary studies which investigated WM-FNs using the Human Connectome Project dataset. We found that WM-FNs are correlated with subregions of the corpus callosum, a critical WM region relaying information between the two cortical hemispheres. Furthermore, we determined an overlap between the WM-FNs and tracts from diffusion tensor imaging (DTI). In this study we will examine WM-FNs of the whole brain using resting fMRI data from two large independent cohorts. We hypothesize that the FN measures derived from WM will be similar to that of GM and the metrics can be used to reliably predict phenotypic traits. The hypothesis will be tested with the following specific aims: Aim1: To develop and evaluate the time-series, FC and FN characteristics of WM of the whole - brain; Aim 2: To investigate WM-phenotype associations and the predictability of phenotypes using WM-FNs; and Aim 3: To develop and disseminate a WM-FN toolbox. To the best of our knowledge, this study will be the first to examine the reliability and validity of WM-FNs in resting fMRI data, and its relation to brain function. The proposed work holds significant contribution since it will facilitate the use of WM-FN methods for the neuroimaging community, which currently lacks the necessary analytic tools to reliably characterize WM function. This study will provide a strong foundation f or future clinical use of both WM-FNs and GM-FNs, to understand brain function more comprehensively, in addition to facilitating the use of reliable and reproducible WM-FC methods.