TRADITIONAL POSTER - ismrm

Poster Sessions
contrast mechanisms behind these changes are not fully understood, they may be a confound in calibration studies, or a novel way to rapidly induce
calibration-useful global BOLD signal changes.
1137. Hypoxia and Hyperoxia Alter Brain Metabolism in Awake Human
Feng Xu 1 , Uma Yezhuvath 1 , Peiying Wang 1 , Hanzhang Lu 1
1 Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States
Many studies tried to understand how neural activity change vascular parameters, but little attention was received to whether gas content changes in blood
would reversely alter neural activity. To investigate such an effect, we used a recently developed MRI technique to quantify global cerebral metabolic rate of
oxygen (CMRO2) under hypoxia and hyperoxia. Our data suggest that a change in arterial oxygen content can modulate brain metabolism in a dosedependent
manner, with hypoxia increasing CMRO2 and hyperoxia decreasing it. Therefore, in addition to the well-known “forward” neurovascular
coupling, the “reverse” coupling may be important in the regulation of brain function.
1138. Hyperoxic (HO) Versus Hypercapnic (HC) BOLD Calibration Under Precise Control of End-Tidal
Carbon Dioxide and Oxygen
Clarisse Ildiko Mark 1 , M. Slessarev 2 , S. Ito 3 , J. Han 2 , J. A. Fisher 2 , G. B. Pike 1
1 McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; 2 Department of
Anaesthesiology, University Health Network, Univeristy of Toronto,, Toronto, Ontario, Canada; 3 Department of Anaesthesiology and
Medical Crisis Management, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
Manual HC calibration depends on intrinsically low signal-to-noise perfusion imaging and individual vascular architecture, with resulting calibration (M)-
values prone to large intra- and inter-subject variations that may bias oxygen metabolism studies. We thereby sough to investigate HO as a calibration
alternative under rigorous control of end-tidal partial pressures of CO 2 (PetCO 2 ) and O 2 (PetO 2 ). Our findings suggest the viability of precisely controlling
HO stimulation to provide more precise per-subject and per-brain-region M-estimates, based on high SNR PaO 2 measurements and the removal of the
confound of vascular variation in population observed under HC-calibration.
fMRI Analysis Methods
Hall B Thursday 13:30-15:30
1139. Unbiased Group-Level Statistical Assessment of Independent Component Maps by Means of
Automated Retrospective Matching
Dave Langers 1,2
1 Otorhinolaryngology, University Medical Center Groningen, Groningen, Netherlands; 2 Eaton-Peabody Laboratory, Massachusetts
Eye and Ear Infirmary, Boston, MA, United States
Spatial Independent Component Analysis (sICA) is increasingly being used for the analysis of fMRI datasets with unpredictable response dynamics, like in
resting state experiments. However, group-level statistical assessments are difficult, and proper statistical characterization and validation under the nullhypothesis
are so far lacking. In the current study, a novel method is proposed that is based on retrospective matching of individual component maps to
aggregate group maps. Selection bias is analytically predicted and explicitly corrected for. It is shown that valid outcomes are obtained, in the sense that the
achieved specificity does not violate the imposed confidence levels, only if bias-correction is applied. Sensitivity and discriminatory power remain
acceptable, and only moderately smaller than those of a biased method. Finally, it is shown that the method is able to identify significant effects of interest in
an actual dataset, proving its applicability as a group-level sICA fMRI data analysis method.
1140. Eigenvector Centrality Mapping as a New Model-Free Method for Analyzing FMRI Data
Gabriele Lohmann 1 , Daniel S. Margulies 1 , Dirk Goldhahn 1 , Annette Horstmann 1 , Burkhard Pleger 1 ,
Joeran Lepsien 1 , Arno Villringer 1 , Robert Turner 1
1 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
We introduce a new assumption- and parameter-free method for the analysis of fMRI resting state data based on „eigenvector centrality”. Eigenvector
centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are
themselves central within the network. Google's PageRank algorithm is a variant of eigenvector centrality. We tested eigenvector centrality mapping (ECM)
on two resting state scans of 35 subjects, and found a network of hubs including precuneus, thalamus and sensorimotor areas of the marginal ramus of the
cingulate and mid-cingulate cortex.
1141. ROI Atlas Generated from Whole Brain Parcellation of Resting State FMRI Data
Richard Cameron Craddock 1,2 , George Andrew James 3 , Paul Edgar Holtzheimer 2 , Xiaoping P. Hu 3 , Helen
S. Mayberg 2
1 Electrical and Computer Engineering, Georiga Institute of Technology, Atlanta, GA, United States; 2 Psychiatry, Emory University,
Atlanta, GA, United States; 3 Biomedical Imaging Technology Center, Emory University/Georgia Institute of Technology, Atlanta,
GA, United States
Network analysis of resting state fMRI data requires the specification of ROIs. This is a difficult process fraught with error. We propose a method for
developing an ROI atlas by whole brain parcellation of resting state data in functinally homogenous, contiguous regions.