A Unified Theory of Neuro-MRI Data Shows Scale-Free Nature of Connectivity Modes.
A primary goal of many neuroimaging studies that use magnetic resonance imaging (MRI) is to deduce the structure-function relationships in the human brain using data from the three major neuro-MRI modalities: high-resolution anatomical, diffusion tensor imaging, and functional MRI. To date, the general procedure for analyzing these data is to combine the results derived independently from each of these modalities. In this article, we develop a new theoretical and computational approach for combining these different MRI modalities into a powerful and versatile framework that combines our recently developed methods for morphological shape analysis and segmentation, simultaneous local diffusion estimation and global tractography, and nonlinear and nongaussian spatial-temporal activation pattern classification and ranking, as well as our fast and accurate approach for nonlinear registration between modalities. This joint analysis method is capable of extracting new levels of information that is not achievable from any of those single modalities alone. A theoretical probabilistic framework based on a reformulation of prior information and available interdependencies between modalities through a joint coupling matrix and an efficient computational implementation allows construction of quantitative functional, structural, and effective brain connectivity modes and parcellation. This new method provides an overall increase of resolution, accuracy, level of detail, and information content and has the potential to be instrumental in the clinical adaptation of neuro-MRI modalities, which, when jointly analyzed, provide a more comprehensive view of a subject's structure-function relations, while the current standard, wherein single-modality methods are analyzed separately, leaves a critical gap in an integrated view of a subject's neuorphysiological state. As one example of this increased sensitivity, we demonstrate that the jointly estimated structural and functional dependencies of mode power follow the same power law decay with the same exponent.
Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92093-0854, U.S.A., and Electrical and Computer Engineering Department, University of California at San Diego, La Jolla, CA 92093-0407, U.S.A. vit@ucsd.edu.
Vitaly L
VL
Galinsky
Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92093-0854, U.S.A.; Department of Radiology, University of California at San Diego, La Jolla, CA 92093-0854, U.S.A.; and VA San Diego Healthcare System, San Diego, CA 92161, U.S.A. lfrank@ucsd.edu.
Lawrence R
LR
Frank
D000222:Adaptation, Physiological; D001921:Brain / Q000166:cytology* / Q000000981:diagnostic imaging*; D001931:Brain Mapping; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000502:physiology; D009474:Neurons / Q000502:physiology*