Yurui Gao^1,2, Feng Wang^2,3, Iwona Stepniewska⁴, Ann S Choe^1,2, Kurt G Schilling^1,2, Landman A Bennett^2,5, Adam W Anderson^1,2, Zhaohua Ding^2,3, Limin Chen^2,3, and John C Gore^2,3
1Department of Biomedical Engeneering, Vanderbilt University, Nashville, Tennessee, United States, ²Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States, ³Department of Radiology and Radiological Science, Vanderbilt University, Nashville, Tennessee, United States, ⁴Department of Psychology, Vanderbilt University, Tennessee, United States, ⁵Department of Electrical Engeneering, Vanderbilt University, Nashville, Tennessee, United States

This study aims to compare functional connectivity acquired using resting state MRI against the anatomical connectivity revealed by neurotracers injected into primary motor cortex (M1). BOLD timecourse correlation, CBV-weighted timecourse correlation and histological fiber strength were calculated, spacial normalized and compared. The comparison indicates the relationship between function and anatomy in motor network of squirrel monkey during resting state.

Tanzil Mahmud Arefin^1,2, Anna Mechling^2,3, Thomas Bienert², Hsu-Lei Lee², Sami Ben Hamida⁴, Dominik V. Elverfeldt², Jürgen Hennig², Brigitte Kieffer^5,6, and Laura-Adela Harsan^2
1Computational Neuroscience, Bernstein Center Freiburg, University of Freiburg, Freiburg, Baden - Württemberg, Germany, ²Diagnostic Radiology, Medical Physics, University Hospital Freiburg, Freiburg, Baden - Württemberg, Germany, ³Faculty of Biology, University of Freiburg, Freiburg, Baden - Württemberg, Germany, ⁴4Institut de Génétique et de Biologie Moléculaire et Cellulaire, Strasbourg, France, ⁵Douglas Research Center, McGill University, Montreal, Canada, ⁶Institut de Génétique et de Biologie Moléculaire et Cellulaire, Strasbourg, France

Functional communication between brain regions plays a key role in complex cognitive process. Emerging studies show that rsfMRI can reveal the modifications in functional brain connectivity due to psychiatric disorders or drug effects. This study was aimed to scrutinize the functional connectivity modifications in GPR88 Knock Out (KO) mice using rsfMRI technique, which has not been reported yet and might be interesting in the perspective of neurological or psychiatric disorders and drug research.

Adam Liska^1,2, Alberto Galbusera¹, Adam J. Schwarz³, and Alessandro Gozzi^1
1Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, TN, Italy, ²Center for Mind/Brain Sciences, University of Trento, Rovereto, TN, Italy, ³Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States

Human brain imaging studies have revealed the presence of functionally specialized sub-systems interlinked by highly-connected “hub nodes”. However, the extent to which a similar functional architecture exists in the mouse brain remains to be fully elucidated. We have computed voxel-scale whole-brain functional connectivity maps to generate a comprehensive mouse brain “functional connectome”. Consistent with human studies, we show that mouse brain contains mutually-interconnected connector hubs in several sub-regions of a “default mode network”, and in well-known integrative cortical structures. Our findings suggest the presence of evolutionarily-conserved, mutually-interconnected functional modules and cortical hubs as a fundamental feature of the mammal brain.

Alessandra Griffa^1,2, Kirell Benzi³, Benjamin Ricaud³, Xavier Bresson³, Pierre Vandergheynst³, Patric Hagmann^1,2, and Jean-Philippe Thiran^1,2
1Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ²Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland, ³Signal Processing Laboratory 2 (LTS2), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Magnetic resonance imaging allows inferring overall brain structural and functional networks. A growing body of recent literature suggests that a static description of functional connectivity (e.g. with simple correlation measures) might by over simplistic. In the present work we propose a mathematically sound and flexible method for the mapping of dynamic spatio-temporal resting state patterns. Our framework is based on the representation of data on a spatio-temporal graph and exploits structural (diffusion-based) and functional information in a complementary manner. Nodes within isolated functional sub-networks are simultaneously close in space (the space of the anatomical connectivity substrate) and time (temporally co-active).

Priya Patel¹, Aneurin James Kennerley¹, Luke Boorman¹, Myles Jones¹, and Jason Berwick^1
1Psychology, University of Sheffield, Sheffield, South Yorks, United Kingdom

Slow cerebral oscillations, 0.1 Hz, termed as vasomotion, could confound neurovascular coupling within resting state fMRI BOLD signal, therefore inferring connectivity changes from BOLD fMRI signal in disease states problematic. We aim to utilize a systematic analysis of the BOLD fMRI signal and 2 - dimensional optical imaging spectroscopy (2D-OIS) data to examine the magnitude and spatial correlations of fluctuations in BOLD fMRI signals and hemodynamics following manipulation of the systemic blood pressure in anesthetized rodents; this will in part emulate physiological conditions such as in disease states. Changing the systemic blood pressure modulated the 0.1Hz vasomotion signal and we have seen a difference in the inferred connectivity maps before and after this change.

Peiying Liu¹, Babu G Welch², Darlene King², Yang Li¹, Marco Pinho^1,3, and Hanzhang Lu^1
1Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States, ²Neurological Surgery Clinic, University of Texas Southwestern Medical Center, Dallas, Texas, United States, ³Department of Radiology, University of Texas Southwestern Medical Center, Texas, United States

Hypercapnia inhalation is commonly used to map cerebrovascular reactivity(CVR) in clinical research studies. However, gas inhalation may not be feasible for acute patients (e.g., acute stoke, traumatic brain injury). In this work, we aim to explore an alternative approach without gas inhalation. We hypothesize that global BOLD signal can serve as a reliable regressor for CVR estimation from the resting state BOLD data. We showed that the resting CVR generated by this method is reproducible, and of similar physiological origin as CO2-inhalation-derived CVR. This method might be a reliable surrogate of CVR mapping when hypercapnia inhalation is not feasible.

Sandro Nunes¹, Marta Bianciardi², Afonso Dias¹, Rodolfo Abreu¹, Juliana Rodrigues¹, L. Miguel Silveira³, Lawrence L. Wald², and Patricia Figueiredo^1
1Institute for Systems and Robotics and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Lisbon, Portugal,²Department of Radiology, A.A. Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, Boston, MA, United States, ³INESC-ID and Department of Electrical and Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Lisbon, Portugal

A number of strategies have been proposed for modeling physiological noise in rs-fMRI, including different models of the respiratory volume per time (RVT) and heart rate (HR) contributions. We performed a systematic comparison of RVT and HR models within an extended RETROICOR model of physiological noise in rs-fMRI at 7T. We found that a dual-lag model with subject-specific lag optimization explained significantly more variance than single-lag or convolutions models, or group optimization. We conclude that taking into account the high inter-subject variability of RVT/HR responses significantly improves physiological noise modeling, and it should hence reduce inter-subject variability of rs-fMRI studies.

Thomas Welton¹, Dorothee P Auer¹, and Robert A Dineen^1
1Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom

We tested the between-scanner reliability of graph-theoretic brain network properties derived from resting-state fMRI data. This is a crucial prerequisite to the wider application of graph metrics in clinical neuroscience and, in particular, to future adoption in multicentre treatment trials. Using data from five healthy subjects scanned in 10 different scanners, we show that overall reliability was poor. Scanner field strength was not associated with reliability, but averaging over repeat scans did improve reliability. Graph metrics derived from human brain fMRI data are not yet reliable enough between scanners for use as surrogate outcomes in multicentre clinical trials.

Michael J. Tobia¹, David Gallagher¹, Rahul Dewal¹, Prasanna Karunanayaka¹, Sebastien Rupprecht¹, and Qing X. Yang^1
1Center for NMR Research, Penn State University, Hershey, PA, United States

This experiment investigated anisotropic local functional connectivity (LFC) in GE-EPI time series in phantom and human in vivo resting state fMRI data. LFC was computed in a neighborhood radius of 2 voxels using Pearson’s correlation. In vivo, LFC anisotropy varied across gray and white matter sites, resembling DTI in some aspects, while differing on others. Phantom experiments showed that fluctuating electric current is sufficient to generate anisotropic LFC proximal to the current-carrying filament. In conclusion, anisotropic correlations of fMRI time series may arise from an alternative non-BOLD contrast mechanism, potentially related to an electric current effect on Bo.

Garth John Thompson¹, Valentin Riedl^2,3, Timo Grimmer^3,4, Alexander Drzezga⁵, Peter Herman¹, and Fahmeed Hyder^1,6
1Diagnostic Radiology, Magnetic Resonance Research Center, Yale University, New Haven, CT, United States, ²Neuroradiology, Nuclear Medicine, Universität München, München, Germany, ³Technische, Universität München - Neuroimaging Center, München, Germany, ⁴Psychiatry, Universität München, München, Germany, ⁵Nuclear Medicine, Uniklinikum, Koeln, Germany, ⁶Biomedical Engineering, Yale University, New Haven, CT, United States