fMRI Connectivity Mechanisms & Methods
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Monday 7 May 2012
Room 201  16:30 - 18:30 Moderators: Shella D. Keilholz, Alard F. Roebroeck

16:30 0132.   
The Electrophysiological Basis of Resting State Networks
Matthew Jon Brookes1, Mark Woolrich2, Henry Luckhoo2, Darren Price1, Joanne Hale1, Mary Stephenson1, Gareth Barnes3, Stephen Smith4, and Peter Morris1
1Sir Peter Mansfield Magnetic Resonance Centre, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom, 2Oxford centre for human brain activity, University of Oxford, Oxford, 3Wellcome trust centre for neuroimaging, University College London, London, 4Oxford Centre for functional MRI of the brain, University of Oxford, Oxford

BOLD fMRI is capable of delineating functional brain networks with unparalleled spatial resolution. However, it is an indirect measure of ‘brain activity’ and neither rapid temporal dynamics nor the electrophysiological basis of network function can be assessed using fMRI alone. Here, we report the results of a resting state magnetoencephalography (MEG) study that independently identifies multiple brain networks in MEG data. The networks elucidated exhibit significant spatial similarity to networks that have been well characterised by previous fMRI studies. These results confirm the electrophysiological basis of resting-fMRI networks and highlight the utility of a multi-modal approach for future studies.

16:42 0133.   Resing state fMRI slow fluctuations correlate with the activity of fast cortico-cortical physiological connections
Giacomo Koch1,2, Marco Bozzali3, Sonia Bonni1, Viola Giacobbe1, Carlo Caltagirone1,2, and Mara Cercignani3,4
1Laboratory of Clinical and Behavioural Neurology, Santa Lucia Foundation, Rome, Italy, 2Department of Neuroscience, University of Rome Tor Vergata, Rome, Italy, 3Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy, 4CISC, Brighton and Sussex Medical School, Brighton, United Kingdom

Multifocal TMS allows the investigation of the causal neurophysiological interactions occurring in specific cortico-cortical connections, and the aim of this work is assessing the correlation between measures of brain connectivity obtained with TMS and resting state fMRI. Results showed that the activity of fast cortico-cortical physiological interactions occurring in the millisecond range correlated selectively with the coupling of fMRI slow oscillations within the same cortical areas that form part of the dorsal attention network. We conclude that resting-state fMRI slow fluctuations are likely to reflect the interaction of underlying physiological cortico-cortical connections

16:54 0134.   
Coupling between BOLD and electrophysiological brain network measurements
Joanne R Hale1, Susan T Francis1, Matthew J Brookes1, and Peter G Morris1
1SPMMRC, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom

fMRI allows identification of functional brain networks with unparalleled spatial resolution. However, BOLD is an in-direct measure of ‘brain activity’ and thus cannot probe neither the electrophysiological basis nor the most rapid temporal dynamics of network activity. Here we employ parallel BOLD and magnetoencephalography (MEG) experiments to assess the relationship between haemodynamic and electrodynamic measures of network activity during an N-back working memory task. Specifically, we explore coupling between BOLD and â/ã band neural oscillatory signals in the default mode network. Results are in agreement with electrophysiological studies and highlight the benefits of a multi-modal approach to network elucidation.

17:06 0135.   The relationship between functional connectivity strength and cerebral blood flow
Xia Liang1,2, Qihong Zou3, Yong He2, and Yihong Yang1
1Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States, 2State Key Laboratory of Cognitive Neuroscience, Beijing Normal University, Beijing, China, 3MRI Research Center and Beijing City Key Lab for Medical Physics and Engineering, Peking University, Beijing, China

We investigated the relationship between functional connectivity strength and cerebral blood flow (CBF) by analyzing a set of resting-state functional BOLD and ASL imaging data collected on the same subjects to test the hypothesis that brain regions with stronger functional connectivity demand more metabolic supply. Our results show that functional connectivity and CBF are highly correlated across voxels as well as across subjects.

17:18 0136.   
Neural Origin of Specificity Change of Functional Connectivity at Different Anesthesia Levels
Xiao Liu1, Xiao-Hong Zhu1, Yi Zhang1, and Wei Chen1
1Radiology, Center for Magnetic Resonance Research, Minneapolis, MN, United States

To further understand the mechanism of the specificity change of functional connectivity across anesthesia levels, EEG signals were recorded from rats under different anesthesia conditions using isoflurane. EEG power correlations between electrodes located at different brain regions demonstrated very similar dependencies on anesthesia as BOLD signal correlations observed previously: the correlation strength increased while the spatial specificity decreased from the light to deep anesthesia. The finding provides strong evidence for the neural origin of the change of functional connectivity specificity across different anesthesia levels.

17:30 0137.   Functional Connectivity Hubs and Modules in Resting-state Rat Brain
Dany V. D'Souza1, Elisabeth Jonckers1, Andreas Bruns2, Basil Kuennecke2, Marleen Verhoye1, Markus von Kienlin2, Annemie van der Linden1, and Thomas Mueggler2
1Bio-Imaging Lab, University of Antwerp, Wilrijk, Antwerp, Belgium, 2Translational Neuroscience, CNS, Roche, Switzerland

Graph analysis of resting state fMRI (rs-fMRI) data enables characterization of the properties of large-scale brain functional networks both in humans and small animals. Graph measures bearing neurobiological importance are often computed in networks of strong positive associations among brain regions, neglecting the negative associations. We performed rs-fMRI experiments in rats, and constructed a fully connected network of 30 brain regions by retaining all functional connections irrespective of their sign and strength. Applying graph measures we found that rat functional network is segregated into 6 modules associated with known brain functions, and exhibits hubs which might form a network core.

17:42 0138.   
Serial resting-state fMRI functional connectivity analysis of normal rat brain development
Kajo van der Marel1, Willem M Otte1,2, Umesh S Rudrapatna1, Annette van der Toorn1, and Rick M Dijkhuizen1
1Biomedical MR Imaging and Spectroscopy Group, Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, 2Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, Netherlands

Brain function maturation is increasingly studied with resting-state fMRI functional connectivity (RSFC) analysis, to further our understanding of developmental alterations underlying neuropsychiatric illness. As RSFC is routinely measured in rodents, we extended human cross-sectional studies by characterizing functional development from serial RSFC measurements in normally developing rats through adolescence into adulthood. Linear mixed-effects regression of homotopic RSFC revealed region-specific development trajectories. Nonlinear regression could predict individual brain maturity, and classification accurately distinguished adolescent from adult RSFC. Normal brain maturation profiles based on RSFC may thus provide valuable benchmarks for identifying and characterizing neurodevelopmental disturbances in rodent models of neuropsychiatric disease.

17:54 0139.   Super-resolution track-weighted functional connectivity (TW-FC): a tool for characterizing the structural-functional connections in the brain
Fernando Calamante1,2, Richard Andrew James Masterton1, Jacques-Donald Tournier1,2, Robert Elton Smith1,2, Lisa Willats1, David Raffelt1, and Alan Connelly1,2
1Brain Research Institute, Florey Neuroscience Institutes, Heidelberg, Victoria, Australia, 2Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia

We apply the recently proposed super-resolution track-weighted imaging (TWI) methodology, to combine whole-brain fibre-tracking data (the so-called tractogram) with resting state functional connectivity (FC) data, to generate track-weighted (TW) FC maps of a given FC network. The method was assessed on data from 8 healthy volunteers. The TW-FC technique provides an approach for the fusion of structural and functional data into a single quantitative image. A potential important application of this methodology is for quantitative voxel-wise group comparison.

18:06 0140.   Methodological issues in comparing brain connectivity between groups
John McGonigle1,2, Majid Mirmehdi2, Laurence Reed1, and Andrea Malizia3
1Neuropsychopharmacology Unit, Imperial College London, London, United Kingdom, 2Computer Science, University of Bristol, Bristol, United Kingdom,3Psychopharmacology Unit, University of Bristol, Bristol, United Kingdom

When examining functional connectivity in the brain it is common to compare the synchrony of the mean time courses of spatially separated regions of interest and model these as edges between nodes in a graph. However, in creating a node, due to the commutative nature of the averaging, the quality of the time course can be driven by the number of voxels in the region in the native space of the subject. We explore this issue using real and simulated data and find that differences in apparent connectivity between groups with systematically different structure and volume may be artefactual.

18:18 0141.   
Assessing high frequency functional connectivity networks
Thomas Allan1, Cesar Caballero-Gaudes2, Matthew Brookes1, Susan Francis1, and Penny Gowland1
1SPMMRC, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom, 2Department of Radiology and Medical Informatics, Hopitaux Universitaire de Genève, Genève, Switzerland

We investigate the signal fluctuations behind functional connectivity to determine what contribution high frequency signals (greater than 0.01Hz) and haemodynamic events have on functional correlations. We also consider how the number of events found during rest periods, using paradigm free mapping, changes following a task (motor and 2-back task) and how these events modulate functional networks. We show that events and high frequency oscillations are a significant contributor to network connectivity, and removing these events changes the correlation between distinct brain regions.