Joint Annual Meeting ISMRM-ESMRMB 2014 10-16 May 2014 Milan, Italy

Functional Connectivity

Tuesday 13 May 2014
Red 1 & 2  16:00 - 18:00 Moderators: Mark J. Lowe, Ph.D., Stephen M. Smith, D.Phil.

16:00 0437.   No reversal of ketamine-induced functional connectivity changes in the rat brain after acute dosing of antipsychotics
Dany D'Souza1, Andreas Bruns2, Basil Künnecke2, Daniel Alberati3, Edilio Borroni4, Markus von Kienlin2, Annemie Van der Linden5, and Thomas Mueggler2
1Pharma Research & Early Development Informatics, Disease & Translational Informatics, F. Hoffmann-La Roche Ltd., Basel, Basel Stadt, Switzerland,2Pharma Research & Early Development, DTA Neuroscience, Behaviour Pharmacology & Preclinical Imaging, F. Hoffmann-La Roche Ltd., Basel Stadt, Switzerland, 3Pharma Research & Early Development, DTA Neuroscience, Functional Neuroscience, F. Hoffmann-La Roche Ltd., Basel Stadt, Switzerland,4Pharma Research & Early Development, DTA Neuroscience, Biomarkers and Clinical Imaging, F. Hoffmann-La Roche Ltd., Basel Stadt, Switzerland, 5Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium

In the present resting-state fMRI study we investigated whether an acute dose of a first or second generation antipsychotic (haloperidol, clozapine, & risperidone) or an mGlu2/3 agonist (LY354740) can reverse the hyper functional connectivity specifically across cortical areas in the rat brain elicited by acute treatment with ketamine. While acute antipsychotic dosing failed to block the effect of the NMDA antagonist (ketamine) we identified a set of brain regions, i.e. substantia nigra and motor cortex, commonly modulated by haloperidol, clozapine, or LY354740 likely reflecting the direct or indirect modulation of dopamine transmission originating in the nigrostriatal pathway, respectively.

16:12 0438.   
Weaker Brain Dynamics during Sustained Working Memory Task: Perspectives from Co-activation Patterns
Jingyuan Chen1 and Gary Glover1
1Stanford University, Stanford, CA, United States

In a prior study, we have demonstrated less variability of Pearson correlations with respect to the default-mode network (DMN) during working memory (WM) state compared to rest. Here, we attempt to employ co-activation patterns (CAPs) analysis to examine the fundamental changes in brain repertoires that underlie the macroscopic decrease in correlation variations during WM task, as shown by sliding-window analysis.

16:24 0439.   
Investigating the neural basis of the default mode network using blind hemodynamic deconvolution of resting state fMRI data
Sreenath Pruthviraj Kyathanahally1,2, Karthik R Sreenivasan1, Daniele Marinazzo3, Guorong Wu3,4, and Gopikrishna Deshpande1,5
1AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, United States, 2Department of Clinical Research, Unit for MR Spectroscopy and Methodology, University of Bern, Bern, Switzerland, 3Department of Data Analysis, Ghent University, Ghent, Belgium, 4School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China, 5Department of Psychology, Auburn University, Auburn, Alabama, United States

Since the fMRI time series at each voxel is the convolution of an underlying neural signal with the hemodynamic response, there is a debate on whether the Default mode network(DMN) has a neural origin or is at least in part (or at most fully) a consequence of hemodynamic processes and physiological noise arising due to cardiac pulsation and respiration. In order to investigate this, we performed blind hemodynamic deconvolution of resting state fMRI data that was acquired with different TR and magnetic field strength. Subsequently functional connectivity maps were found using seed based correlation analysis on latent neuronal signals with a posterior cingulate seed in order to identify the DMN.

16:36 0440.   Dynamic thalamus parcellation based on resting-state fMRI data
Bing Ji1,2, Zhihao Li1, Kaiming Li1, Longchuan Li1,3, and Xiaoping Hu1
1Wallace H. Coulter Dept. of Biomedical Engineering, Emory University School of Medicine, Atlanta, GA, United States, 2University of Shanghai for Science & Technology, 200093, Shanghai, China, 3Department of Pediatrics, Marcus Autism Center, Children's Healthcare of Altanta, Emory University, Atlanta, GA, United States

In my study, we exploit the dynamic nature of FC in the parcellation of thalamus based its connectivities with the cortex and identify two dominant states, then derive two similar but distint parcellations results depending on these states. These parcellations, examined separately or combined, provide better correspondence with anatomic landmarks.

16:48 0441.   Spatially coupled functional and vascular networks
Molly G Bright1 and Kevin Murphy1
1CUBRIC, School of Psychology, Cardiff University, Cardiff, Cardiff, United Kingdom

Independent component analysis (ICA) can identify network structure in resting-state fMRI data. However, it is challenging to determine the origin of signal fluctuations in these networks. We apply breath-hold tasks that drive a global BOLD signal increase via the vascular response to hypercapnia. Using ICA, we demonstrate that neural and vascular networks can be identified and isolated, including those that share a similar network structure (e.g., default mode network). Our results suggest that vascular networks may be organised to support functional networks.

17:00 0442.   Resting-state cerebral blood flow and functional connectivity in focal epilepsy as assessed by arterial spin labeling
Silvia Francesca Storti1, Ilaria Boscolo Galazzo1, Alessandra Del Felice1, Francesca Pizzini2, Chiara Arcaro1, Emanuela Formaggio3, Roberto Mai4, Alberto Beltramello2, and Paolo Manganotti1,3
1Department of Neurological and Movement Sciences, University of Verona, Verona, Italy, 2Department of Neuroradiology, AOUI of Verona, University of Verona, Verona, Italy, 3Department of Neurophysiology, Foundation IRCCS San Camillo Hospital, Venice, Italy, 4Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy

Arterial spin labeling can be very useful for the detection of perfusion changes in drug-resistant focal epilepsy. In order to identify regions related to the epileptic focus, quantification of CBF and a statistical analysis were computed in each patient and compared with a template of normal perfusion. A seed-driven connectivity was also used to identify networks regions that are differently organized in epileptic patients compared to healthy subjects. The investigation allowed us to correctly identify the epileptogenic zone in patients, in whom the results were confirmed by surgical resection and subsequent seizure freedom.

17:12 0443.   
Dynamic changes of Resting State Networks depict short-term plasticity of the brain
Gloria Castellazzi1,2, Fulvia Palesi2,3, Stefania Bruno4, Ahmed T. Toosy5, Egidio D'Angelo2,6, and Claudia A.M. Wheeler-Kingshott7
1Department of Industrial and Information Engineering, University of Pavia, Pavia, PV, Italy, 2Brain Connectivity Center, National Neurological Institute C.Mondino, Pavia, PV, Italy, 3Department of Physics, University of Pavia, Pavia, PV, Italy, 4Overdale Hospital, Jersey, United Kingdom, 5Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, United Kingdom, 6Department of Public Health, Neuroscience, Experimental Medicine, University of Pavia, Pavia, PV, Italy, 7NMR Research Unit, Department of Neuroinflammation, Queen Square MS, UCL Institute of Neurology, London, Italy

During the execution of complex “continuous” cognitive tasks, the brain elaborates information over multiple domains and time scales, integrating it across space and over time. In literature, only few rs-fMRI works report how resting state networks (RSNs) change over space and time when stimulated by external inputs. We investigated the dynamic changes in brain activity occurring in subjects listening to a narrated story. Results show that RSNs respond to the stimulus with specific dynamics of alteration and suggest the existence of a spatiotemporal hierarchy of changes, the levels of which depend on the specific activity each network is involved in.

17:24 0444.   Visual-motor connectivity relates to autism trait severity
Mary Beth Nebel1,2, Ani Eloyan3, Carrie Nettles1, Kristie Sweeney1, Katarina Ament1, Rebecca Ward1, Ann S Choe1,2, Anita D Barber1,2, Brian S Caffo3, James J Pekar1,2, and Stewart H Mostofsky1,2
1Kennedy Krieger Institute, Baltimore, MD, United States, 2Johns Hopkins School of Medicine, Baltimore, MD, United States, 3Johns Hopkins School of Public Health, Baltimore, MD, United States

One problem experienced by children with autism that is potentially critical for acquiring social skills is difficulty imitating others’ actions, which depends on visual-motor integration; however, it is unclear what brain mechanisms contribute to this deficit. Using resting state functional MRI, we show that children with autism exhibit significantly stronger anticorrelation between motor and visual areas compared to their typically developing (TD) peers, and the stronger the anticorrelation between motor and visual networks, the more severe their autistic traits. In TD children, motor-visual functional connectivity strength was correlated with imitation performance; children with stronger positive visual-motor coupling were better imitators.

17:36 0445.   
Exploration of Resting State Networks in Human Cervical Spinal Cord
Xiaojia Liu1,2, Xiang Li2, Fuqing Zhou2, Jiaolong Cui2, Adrian Tsang1,3, Iris Y. Zhou1,3, Ed X. Wu1,3, and Yong Hu2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China, 3Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

Resting state networks in the human cervical spinal cord has only been scarcely explored. In this study, we investigated the resting state network using a clinically relevant 3T whole body MRI scanner from 14 healthy subjects. The correlation coefficient computed from rsfMRI images between each ventral or dorsal horn of different segments was used to generate the correlation matrix. Segments C2 and C6 demonstrated stronger correlations with other segments. Segment C2 has a stronger inter-segment correlation than other segments. Functional connectivity distribution among segments was detected, which demonstrated the neural network in the human cervical spinal cord.

17:48 0446.   
Resting State fMRI in the moving fetus: a robust framework for motion and spin history correction.
Giulio Ferrazzi1,2, Maria Kuklisova Murgasova1,2, Tom Arichi3, Joanna Allsop1,2, Christina Malamateniou1,2, Mary Rutherford1,2, Shaihan Malik1,2, Paul Aljabar1,2, and Joseph V. Hajnal1,2
1Center for the Developing Brain, King's College London, London, United Kingdom, 2Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom, 3Department of Bioengineering, Imperial College London, London, United Kingdom

During typical fetal Resting State fMRI examinations, maternal respiration and fetal movement together result in large scale and unpredictable motion. Conventional fMRI processing methods, which assume that brain movements are infrequent or at least small, are not suitable. We seek to address the problem of fetal motion in fMRI using image registration to place the acquired data into a self-consistent anatomical space. Bias field and spin history corrections are also discussed, aiming at achieving a robust framework that allows as much as possible, ideally all acquired data, to be retained as part of the final network analysis.