ISMRM 21st Annual Meeting & Exhibition 20-26 April 2013 Salt Lake City, Utah, USA

fMRI Connectivity: Mechanisms & Analysis
Monday 22 April 2013
Room 255 EF  10:45 - 12:45 Moderators: Xiaoping P. Hu, Ed X. Wu

10:45 0032.   
Anatomical/Axonal Basis and Plasticity of Resting-State fMRI Connectivity in an Experimental Model of Corpus Callosum Transection
Iris Y. Zhou1,2, Y. X. Liang3, Russell W. Chan1,2, Shujuan Fan1,2, Patrick P. Gao1,2, Joe S. Cheng1,2, K. F. So3, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China, 3Department of Anatomy, The University of Hong Kong, Hong Kong SAR, China

This study explored the role of anatomical/axonal connections in resting-state fMRI connectivity and the plasticity of resting-state networks. Animal models of complete and partial corpus callosum (CC) transection were studied with rsfMRI in conjunction with intracortical EEG recording and Mn2+ tracing of axonal connections. At post-surgery day 7, resting-state connectivity significantly decreased in the cortical areas whose callosal connections were severed. At post-surgery day 28, disrupted connectivity was partly restored in partial transection group, likely through the spared pathways in remaining CC. These rsfMRI findings were paralleled by EEG recording and. Mn2+ tracing results. These results directly support the primary and indispensable role of anatomical/axonal connections via CC in resting-state fMRI connectivity, and that anatomical connection based resting-state networks can be plastic.

10:57 0033.   
Caffeine-Induced Reductions in the Resting-State fMRI Global Signal Reflect Increases in EEG Vigilance Measures
Chi Wah Wong1, Valur Olafsson1, Omer Tal1, and Thomas Liu1
1Center for Functional MRI, University of California San Diego, La Jolla, CA, United States

A prior study has shown that caffeine reduces the amplitude of the global signal in resting-state fMRI and enhances the anti-correlation between the Default Mode Network (DMN) and Task Positive Network (TPN). In this study, we used simultaneous EEG-fMRI to investigate the neural-electrical basis of these caffeine-related effects. We found that the caffeine-induced changes in the global signal amplitude are negatively correlated with changes in vigilance derived using EEG.

11:09 0034.   
Clustered Spontaneous Coordinated Network Events Contribute to Functional Connectivity
Thomas Allan1, Matthew J. Brookes1, Susan T. Francis1, and Penelope A. Gowland1
1SPMMRC, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom

The origins of functional connectivity are unknown but contributions from spontaneous neural events may have an effect of typical measures of functional connectivity. These spontaneous events, whether they are externally stimulated or internally driven can evoke bilateral responses in multiple network nodes. Here we show that spontaneous BOLD events, detected using paradigm free mapping, have a significant contribution to functional connectivity on a short window correlation analysis and that these events are clustered in space and do not require the entire network to perform a task, highlighting substructures within large scale networks.

11:21 0035.   
Multiple Time Scale Complexity Analysis of Resting State Fluctuations
Robert Smith1
1Neurology, UCLA, Los Angeles, CA, United States

The present study explores multi-scale entropy (MSE) analysis to investigate the entropy of resting state fMRI signals across multiple time scales. MSE analysis distinguishes random noise from complex signals since the entropy of the former decreases with larger time scales while the latter signal maintains its entropy due to “self-similarity" across time scales. The results show enhanced contrast in entropy between gray and white matter, as well as between age groups using MSE analysis.

11:33 0036.   
Finite Number of Brain Network Configurations Revealed from Time-Varying Connectivity Assessment of Resting State fMRI
Hao Jia1, Xiaoping P. Hu2, and Gopikrishna Deshpande1,3
1AU MRI research center, ECE dept., Auburn University, Auburn, AL, United States, 2Coulter Dept. of Biomedical Engineering, Georgia Institute of Technology & Emory Univeristy, Atlanta, GA, United States, 3Dept. of Psychology, Auburn University, Auburn, AL, United States

We assume connectivity dynamics derived from fMRI have finite, quasi-stable configurations based on previous EEG/fMRI evidence. We tested this using a unified framework involving dynamic estimation of whole brain functional connectivity (FC) and effective connectivity (EC), evolutionary clustering and segmentation into finite number of patterns. Sliding window method was used to determine FC and dynamic granger method was used for EC. Result evidenced above hypothesis and there are 2-3 dominant modes for both FC and EC. Main FC modes feature default mode network, visual, sub-cortical and motor networks, while sensory regions to frontal cortex interaction is revealed by EC modes.

11:45 0037.   
Investigation of the Neural Basis of the Default Mode Network Using Parallel Independent Component Analysis of Simultaneous EEG/fMRI Data
Sreenath Pruthviraj Kyathanahally1, Nurhan Erbil1, Vince D. Calhoun2,3, and Gopikrishna Deshpande1,4
1AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, United States, 2The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, United States, 3Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, Alabama, United States, 4Department of Psychology, Auburn University, Auburn, Alabama, United States

Previous work using simultaneous EEG/fMRI has shown that the slow temporal dynamics of resting state networks (RSNs) obtained from fMRI are correlated with smoothed and down sampled versions of various EEG features such as microstates and band-limited power and these RSNs not only exist in the low frequency but also exist in high frequency. In this study, to test this critical hypothesis, we acquired a simultaneous EEG/fMRI from which involved fast fMRI sampling using multiband EPI and EPI acquisition. We found DMN in both MB-EPI and EPI acquisition however the correlation coefficient between two modalities in MB-EPI was higher.

11:57 0038.   
Intrinsic Connectivity Network Activity Revealed by the Independent Modelling of the Primary and Post-Stimulus Components of the BOLD Response
Karen J. Mullinger1, Stephen D. Mayhew2, Andrew P. Bagshaw2, Richard W. Bowtell1, and Susan T. Francis1
1SPMMRC, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom, 2BUIC, School of Psychology, University of Birmingham, Birmingham, United Kingdom

Intrinsic connectivity networks (ICNs) exist during both stimulation and rest, however little is known about the changes in their activity in the transition between task and rest. Simultaneous EEG-BOLD measures provide an interesting new method to study this transition. We demonstrate that during median-nerve stimulation BOLD modulations, indexed by EEG mu power, occur over a number of ICNs, whilst post-stimulus modulations are specific to the bilateral sensorimotor network. We hypothesize that the post-stimulus activity represents re-setting of the entire sensorimotor network, providing a mechanism for this ICN to return to resting-state activity from the lateralised activity driven by stimulation.

12:09 0039.   Spontaneous Co-Activation Patterns of the Brain Revealed by Selectively Averaging Resting-State fMRI Volumes
Xiao Liu1, Catie Chang1, and Jeff H. Duyn1
1Advanced MRI section, LFMI, NINDS, National Institutes of Health, Bethesda, MD, United States

In this study, we identified 30 spontaneous co-activation patterns (CAPs) by regrouping and then averaging resting-state fMRI volumes. The CAPs present interesting information regarding spontaneous brain activity at distinct time points: e.g., multiple default mode network (DMN) CAPs with distinct features, multiple “task-positive” CAPs anti-correlated with DMN region, very specific thalamocortical connections, and altered occurrence rates in different populations. The data-driven approach used here may serve as a novel method for analyzing and interpreting resting-state fMRI signals, complementary to conventional approaches.

12:21 0040.   
Resting Brain Networks Revealed by Independent Component Analysis of Cerebral Blood Flow
Senhua Zhu1,2, Zhuo Fang1,2, Siyuan Hu3, Marc Korczykowski2, Ze Wang2, John A. Detre2, and Hengyi Rao1,2
1Psychology, Sun Yat-sen University, Guangzhou, Guangdong, China, 2Center for Functional Neuroimaging, University of Pennsylvania, Philadelphia, PA, United States, 3State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, Beijing, China

The present study used independent component analysis to examine resting brain networks in a large cohort (n=149) of subjects with arterial spin labeling (ASL) perfusion MRI data. Ten CBF networks were consistently identified across the whole and sub-datasets, including the default mode network, bilateral attention networks, primary and second visual networks, auditory network, ventral-medial prefrontal network, dorsal-medial prefrontal network, and two limbic networks. These networks well replicated the resting-state BOLD networks from a sub-group (n=81) and support the feasibility of using CBF connectivity to examine resting brain function.

12:33 0041.   Resting-State fMRI at 4 Hz
Ying-Hua Chu1, Jyrki Ahveninen2, Tommi Raij2, Wen-Jui Kuo3, John W. Belliveau2, and Fa-Hsuan Lin1
1Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, 2A. A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States,3Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan

Resting-state fMRI studies reflecting functional connectivity have been typically limited to frequencies below 0.1 H. Here, we hypothesize that fMRI can detect interregional correlations in MRI time series at frequencies above 0.1 Hz as well. Using MR inverse imaging (InI) at a 10 Hz sampling rate, we studied interhemispheric correlations between primary sensorimotor and visual cortices. We found significant correlations at 4 Hz (average Z-score ~8) that were about 60% of those observed at 0.1 Hz.