| Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) holds promise as a clinical tool to characterize and monitor the phenotype of different neurological and psychiatric disorders. The most common analysis approach requires the definition of one or more regions-of-interest (ROIs). However this need for a priori ROI information makes rs-fMRI inadequate to survey functional connectivity differences associated with a range of neurological disorders where the ROI information may not be available. Recently introduced voxel-based connectivity measures overcome this need for a ROI. However, conventional measures of voxel-based connectivity rely on the need for an arbitrary correlation threshold to determine whether or not two areas are connected. This is problematic because in many cases the differences in tissue connectivity between disease groups and/or control subjects are threshold dependent. In this work we propose a novel voxel-based contrast mechanism for rs-fMRI, the Intrinsic Connectivity Distribution (ICD), that does not requires an arbitrary threshold to define a connection. We show the sensitivity of previous methods to the choice of connection thresholds and evaluate ICD on several clinical population groups.;Two extensions to the ICD approach are developed. The first, cross-hemisphere ICD (ch-ICD), assesses connectivity on a regional scale allowing for the examination of lateralization effects of connectivity. Typical voxel-based metrics including ICD describe a voxel's connectivity to every other region in the brain and, thus, provide global measures of connectivity. However, subtle differences in regional connectivity may be occluded when connectivity is assessed on this global scale. Ch-ICD fills this voids by examine lateralizing/regional difference in connectivity.;The second, coupled-ICD, analyzes paired resting-state fMRI (rs-fMRI) data collected under two different conditions and jointly models both conditions to incorporate additional spatial information into the connectivity metric. Rs-fMRI holds promise as a clinical tool to monitor clinical treatments, and study functional changes induced by drugs. As such, examining paired rs-fMRI data such as scans acquired pre- and post-intervention is an important application for rs-fMRI methodologically. When presented with data from paired conditions, conventional voxel-based methods analyze each condition separately. However, nonlinearities introduced during processing can cause this approach to underestimate differences between conditions. We show that commonly used methods can underestimate functional changes and subsequently introduce and evaluate our solution to this problem, the coupled-ICD metric.;Additionally, a "case-study" where ICD provides key evidence of the efficacy of neurofeedback via real-time fMRI as a mechanism for subjects to manipulate functional brain networks in order to reduce experienced anxiety is presented highlighting translational/clinical utility of rs-fMRI. Finally, we illustrate ongoing and unresolved issues with voxel-based methods for rs-fMRI. |
