Resting-state functional connectivity MRI (rs-fcMRI) has emerged as a key noninvasive technique for mapping the functional organization of the brain. This organization, or functional connectivity, is often altered in neurological or mental disorders. Rs-fcMRI is poised to have a significant clinical impact, since a large number of functionally relevant brain networks can be characterized without requiring the subject to perform any specific tasks. This is important, since many clinical or research populations are either limited by their ability to perform tasks in the MRI scanner or have differences in task performance. In addition, rs-fcMRI holds great promise for determining the basis of many mental and neurological disorders. However, a critical problem is that subject motion and physiological noise (from variations in respiration depth and rate in particular) can cause regions of the brain to appear as if they are functionally connected. While a number of techniques have been developed to correct for motion and physiological noise, there is currently no consensus on which processing steps to apply. More importantly, even with available techniques, false positives resulting from motion and physiological noise remain. It is therefore unclear whether observed differences in functional connectivity across time or between subjects are truly neuronal in origin, or merely the result of differences in subject motion or physiological noise. This presents a significant barrier to the future utility of rs-fcMRI. The goal of the proposed research is to reduce the influence of subject head motion and physiological noise in order to improve the accuracy and consistency of mapping functional brain connectivity. Two complementary strategies are proposed to achieve this goal: 1) the development of a novel MRI acquisition scheme that is more robust to motion and physiological fluctuations;and 2) the development of new processing methods to better model and account for motion and respiration induced artifacts. These new processing techniques consist of methods that use externally-acquired information, as well as methods that are applicable to existing datasets, which is important given the wealth of data already acquired. The expected outcome of this study is a significant improvement in the ability to map differences in the functional organization of the brain across time and between subjects. This information will have a significant impact on hundreds of current and future rs- fcMRI studies. It is a critical step towards the successful use of resting-state functional connectivity in clinical investigations, as well as research studies aimed at improving our understanding of brain development, the disrupted brain organization in mental and neurological disorders, and the alterations in developmental trajectories that lead to (and are a risk factor for) later psychopathology.
The proposed research is relevant to public health because it will allow us to accurately measure the strength of functional connections in the brain, which change during the course of development and may be altered in various mental and neurological disorders. Consequently, the organization and strength of functional connections are an important indicator of the health of the brain. The proposed research is relevant to NIH's mission, because it will improve our ability to determine the causes of mental disorders and their developmental trajectories.
|Patriat, RÃ©mi; Molloy, Erin K; Birn, Rasmus M (2015) Using Edge Voxel Information to Improve Motion Regression for rs-fMRI Connectivity Studies. Brain Connect 5:582-95|
|Birn, Rasmus M; Cornejo, Maria Daniela; Molloy, Erin K et al. (2014) The influence of physiological noise correction on test-retest reliability of resting-state functional connectivity. Brain Connect 4:511-22|