Individual Functional ROI Optimization Via Maximization of Group-Wise Consistency of Structural and Functional Profiles

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain.

[1]  Vijaya L. Melnick,et al.  Alzheimer’s Dementia , 1985, Contemporary Issues in Biomedicine, Ethics, and Society.

[2]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[3]  F. Bloom,et al.  Locating the central sulcus: comparison of MR anatomic and magnetoencephalographic functional methods. , 1993, AJNR. American journal of neuroradiology.

[4]  R. Edelman,et al.  Magnetic resonance imaging (2) , 1993, The New England journal of medicine.

[5]  Mirko Krivánek,et al.  Simulated Annealing: A Proof of Convergence , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  D. V. van Essen,et al.  A tension-based theory of morphogenesis and compact wiring in the central nervous system. , 1997, Nature.

[7]  M. Raichle.  Handbook of Functional Neuroimaging of Cognition , 2001 .

[8]  Klaas E. Stephan,et al.  The anatomical basis of functional localization in the cortex , 2002, Nature Reviews Neuroscience.

[9]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[10]  Dinggang Shen,et al.  HAMMER: hierarchical attribute matching mechanism for elastic registration , 2002, IEEE Transactions on Medical Imaging.

[11]  Karl J. Friston,et al.  Multivariate Autoregressive Modelling of fMRI time series , 2003 .

[12]  Rainer Goebel,et al.  Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. , 2003, Magnetic resonance imaging.

[13]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[14]  Guido Gerig,et al.  Analysis Tool for Diffusion Tensor MRI , 2003, MICCAI.

[15]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[16]  Timothy Edward John Behrens,et al.  Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Yingli Lu,et al.  Regional homogeneity approach to fMRI data analysis , 2004, NeuroImage.

[18]  J. Pekar,et al.  Alcohol Intoxication Effects on Simulated Driving: Exploring Alcohol-Dose Effects on Brain Activation Using Functional MRI , 2004, Neuropsychopharmacology.

[19]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[20]  Isabelle Bloch,et al.  From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations , 1995, Journal of Mathematical Imaging and Vision.

[21]  Lee Friedman,et al.  Report on a multicenter fMRI quality assurance protocol , 2006, Journal of magnetic resonance imaging : JMRI.

[22]  Jean Gotman,et al.  Anatomically informed interpolation of fMRI data on the cortical surface , 2006, NeuroImage.

[23]  D. V. Essen,et al.  Surface-Based and Probabilistic Atlases of Primate Cerebral Cortex , 2007, Neuron.

[24]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[25]  N. Ayache,et al.  Clinical DT-MRI Estimation, Smoothing, and Fiber Tracking With Log-Euclidean Metrics , 2007 .

[26]  Lei Guo,et al.  Brain tissue segmentation based on DTI data , 2007, NeuroImage.

[27]  Nicholas Ayache,et al.  Clinical DT-MRI estimation, smoothing and fiber tracking with Log-Euclidean metrics , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[28]  M. P. van den Heuvel,et al.  Normalized Cut Group Clustering of Resting-State fMRI Data , 2008, PloS one.

[29]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[30]  Jeonghun Ku,et al.  Artificial shifting of fMRI activation localized by volume- and surface-based analyses , 2008, NeuroImage.

[31]  Lei Guo,et al.  Automatic cortical sulcal parcellation based on surface principal direction flow field tracking , 2009, NeuroImage.

[32]  Jan Derrfuss,et al.  Lost in localization: The need for a universal coordinate database , 2009, NeuroImage.

[33]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[34]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[35]  Karl J. Friston.  Modalities, Modes, and Models in Functional Neuroimaging , 2009, Science.

[36]  Martin J. McKeown,et al.  Discovering Sparse Functional Brain Networks Using Group Replicator Dynamics (GRD) , 2009, IPMI.

[37]  Mark W. Woolrich,et al.  Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models , 2009, NeuroImage.

[38]  Dinggang Shen,et al.  TIMER: Tensor Image Morphing for Elastic Registration , 2009, NeuroImage.

[39]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[40]  Polina Golland,et al.  Combining spatial priors and anatomical information for fMRI detection , 2010, Medical Image Anal..

[41]  David N. Kennedy,et al.  Making Connections in the Connectome Era , 2010, Neuroinformatics.

[42]  Karl J. Friston,et al.  Dynamic causal modeling , 2010, Scholarpedia.

[43]  Bryan R. Conroy,et al.  Function-based Intersubject Alignment of Human Cortical Anatomy , 2009, Cerebral cortex.

[44]  Archana Venkataraman,et al.  Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. , 2010, Journal of neurophysiology.

[45]  Mert R. Sabuncu,et al.  Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex , 2010, IEEE Transactions on Medical Imaging.

[46]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[47]  Kaiming Li,et al.  Cortical surface based identification of brain networks using high spatial resolution resting state FMRI data , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[48]  Olaf Sporns,et al.  MR connectomics: Principles and challenges , 2010, Journal of Neuroscience Methods.

[49]  Degang Zhang,et al.  Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles , 2010, NIPS.

[50]  E. Bullmore,et al.  Functional Connectivity and Brain Networks in Schizophrenia , 2010, The Journal of Neuroscience.

[51]  Degang Zhang,et al.  Automatic cortical surface parcellation based on fiber density information , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[52]  Owen Carmichael,et al.  Update on the Magnetic Resonance Imaging core of the Alzheimer's Disease Neuroimaging Initiative , 2010, Alzheimer's & Dementia.

[53]  Jonathan D. Power,et al.  A Parcellation Scheme for Human Left Lateral Parietal Cortex , 2010, Neuron.

[54]  Tianming Liu.  A few thoughts on brain ROIs , 2011, Brain Imaging and Behavior.

[55]  Degang Zhang,et al.  Complex span tasks and hippocampal recruitment during working memory , 2011, NeuroImage.

[56]  Russell A. Poldrack,et al.  The future of fMRI in cognitive neuroscience , 2012, NeuroImage.