Matthew Jon Brookes¹, Mark Woolrich², Henry Luckhoo², Darren Price¹, Joanne Hale¹, Mary Stephenson¹, Gareth Barnes³, Stephen Smith⁴, and Peter Morris^1
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.

Giacomo Koch^1,2, Marco Bozzali³, Sonia Bonni¹, Viola Giacobbe¹, Carlo Caltagirone^1,2, and Mara Cercignani^3,4
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

Joanne R Hale¹, Susan T Francis¹, Matthew J Brookes¹, and Peter G Morris^1
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.

Xia Liang^1,2, Qihong Zou³, Yong He², and Yihong Yang^1
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.

Xiao Liu¹, Xiao-Hong Zhu¹, Yi Zhang¹, and Wei Chen^1
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.

Dany V. D'Souza¹, Elisabeth Jonckers¹, Andreas Bruns², Basil Kuennecke², Marleen Verhoye¹, Markus von Kienlin², Annemie van der Linden¹, and Thomas Mueggler^2
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.

Kajo van der Marel¹, Willem M Otte^1,2, Umesh S Rudrapatna¹, Annette van der Toorn¹, and Rick M Dijkhuizen^1
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.

Fernando Calamante^1,2, Richard Andrew James Masterton¹, Jacques-Donald Tournier^1,2, Robert Elton Smith^1,2, Lisa Willats¹, David Raffelt¹, and Alan Connelly^1,2
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.

John McGonigle^1,2, Majid Mirmehdi², Laurence Reed¹, and Andrea Malizia^3
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.

Thomas Allan¹, Cesar Caballero-Gaudes², Matthew Brookes¹, Susan Francis¹, and Penny Gowland^1
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.