Jingyuan E. Chen^1,2, Hesamoddin Jahanian², and Gary H. Glover^1,2
1Electrical Engineering, Stanford University, Stanford, CA, United States, ²Radiology, Stanford University, Stanford, CA, United States

A growing number of studies using fast sampling have demonstrated the persistence of functional connectivity (FC) in resting state (RS) networks beyond the conventional 0.1 Hz. However, some RS studies have reported frequencies (e.g., up to 5 Hz) not easily supported by canonical hemodynamic response functions. Here, we investigated the influence of a common preprocessing step – whole-band (the entire frequency band resolved by a short TR) linear nuisance regression (LNR) – on RSFC. We demonstrated via both simulation and real data that LNR can introduce network structures in HF bands, which may largely account for the observations of HF-RSFC.

Xiaowei Zhuang¹, Zhengshi Yang¹, Tim Curran², and Dietmar Cordes^1,2
1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, ²Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States

A family constrained CCA (cCCA) method was introduced to improve the accuracy of activation detection in noisy fMRI data. The cCCA was converted into a constrained multivariate multiple regression problem and solved efficiently with a numerical optimization algorithm. Results from both simulated data and real episodic memory data indicated that a higher detection sensitivity for a fixed specificity can be achieved with the proposed cCCA method as compared to the widely used mass-univariate or other conventional multivariate (CCA) approaches.

Andrea Nordio^1,2,3, Denis Peruzzo², Filippo Arrigoni², Fabio Triulzi^2,4, and Alessandra Bertoldo^1,2
1Department of Information Engineering (DEI), University of Padova, Padova, Italy, ²IRCCS E.Medea, Bosisio Parini, Lecco, Italy, ³IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Foggia, Italy, ⁴IRCCS Cà Granda Ospedale Maggiore, Policlinico, Milano, Italy

Echo Planar Imaging (EPI) sequences used for acquiring fMRI time series data have a high temporal resolution but are also highly sensitive to the magnetic field inhomogeneity resulting in geometric distortions. In this work we propose an approach for correction of EPI distortion in fMRI sequences.  Our method takes advantage of a non-distorted T2-weighted (T2W) 3D sequence as intermediate step between the acquired fMRI data and the anatomical image. This strategy allows to use non-linear registration functions. We validated our method on a group of healty subjects during finger-tapping task, proving that the proposed method significantly improves the group analysis results of functional data.

David Provencher¹, Alexandre Bizeau¹, Yves Bérubé-Lauzière², and Kevin Whittingstall^1,3
1Radiation Sciences and Biomedical Imaging, Université de Sherbrooke, Sherbrooke, QC, Canada, ²Electrical and Computer Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada, ³Diagnostic Radiology, Université de Sherbrooke, Sherbrooke, QC, Canada

We previously showed that venous density correlates with BOLD signal amplitude¹. Since the BOLD contrast inherently originates in veins, we hypothesized that its temporal dynamics would also be affected by venous density. Here, we use fast multi-band fMRI imaging (TR=0.45s), SWIp vein reconstruction and different visual stimuli yielding co-localized activation, yet different BOLD dynamics. From this, we assess the effects of venous density on BOLD timing. Results show a robust association between higher vein density and shorter hemodynamic delay when comparing activated and deactivated regions. BOLD response timing differences may thus not entirely reflect neural activity, but also structural differences.

Michael Hütel^1,2, Andrew Melbourne¹, David L Thomas^1,2, Jonathan Rohrer², and Sebastien Ourselin^1,2
1Translational Imaging Group, University College London, London, United Kingdom, ²Dementia Research Centre, University College London, London, United Kingdom

Previous studies have shown that slow variations in the cardiac cycle are coupled with signal changes in the blood-oxygen level dependent (BOLD) contrast. The detection of neurophysiological hemodynamic changes, driven by neuronal activity, is hampered by such physiological noise. It is therefore of great importance to model and remove these physiological artefacts. The cardiac cycle causes pulsatile arterial blood flow. This pulsation is translated into brain tissue and fluids bounded by the cranial cavity. We exploit this pulsality effect and provide evidence that the heart rate is inherent in BOLD fMRI images.

Ádám Kettinger^1,2, Christian Windischberger³, Christopher Hill⁴, and Zoltán Nagy^4
1Department of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary, ²Brain Imaging Centre, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary, ³Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria, ⁴Laboratory for Social and Neural Systems Research, University of Zurich, Zurich, Switzerland

Multi-echo EPI acquisitions are used in fMRI research due to their superior BOLD sensitivity. Several advanced methods of echo combinations have been proposed. We confirmed, using dual-echo data, that CNR weighting is the optimal combination on a single subject level. However, we have shown that these advantages do not carry over to a group analysis where a simple averaging of the echos provides equally good statistical results. This is likely due to the increase of inter-subject variance of contrast-to-noise ratio. Future work aims to quantitatively compare inter-subject and intra-subject variance of dual-echo data in group studies.

Ashish Kaul Sahib¹, Klaus Mathiak², Michael Erb¹, Adham Elshahabi³, Silke Klamer³, Klaus Scheffler⁴, Niels Focke³, and Thomas Ethofer^1
1Biomedical magnetic resonance, University of tuebingen, Tuebingen, Germany, ²5Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital Aachen, Aachen, Germany, ³Department of Neurology/Epileptology, University of tuebingen, Tuebingen, Germany, ⁴Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany

To assess the impact of colored noise on statistics and determine optimal imaging parameters in event-related fMRI (visual stimulation using checkerboards) acquired by simultaneous multi-slice imaging enabling repetition times (TR) between 2.64 to 0.26s. Optimal statistical power was obtained for a TR of 0.33s, but short TRs required higher-order autoregressive (AR) models to achieve stable statistics.  Colored noise in event-related fMRI obtained at short TRs calls for more sophisticated correction of serial autocorrelations.

Ru Kong¹, Alexander Schaefer¹, Avram J. Holmes², Simon B. Eickhoff^3,4, Xi-Nian Zuo⁵, and B.T. Thomas Yeo^1
1Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore, Singapore, ²Department of Psychology, Yale University, New Haven, CT, United States, ³Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany, ⁴Institute for Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany, ⁵Lab for Functional Connectome and Development Division of Cognitive and Developmental Psychology, CAS, Beijing, China, People's Republic of

We propose a hidden Markov Random Field (MRF) model to parcellate the cerebral cortex of individual subjects using resting-state fMRI (rs-fMRI). Our MRF model imposes a smoothness prior on the individual-specific parcellation, while imposing group-level population priors that capture inter-subject variability in both functional connectivity profiles and spatial distribution of functional brain networks. Experiments on a test-retest dataset suggest that the resulting parcellation estimates are better than alternative approaches at capturing stable properties of individual subjects’ intrinsic brain organization, instead of transient noise or session-dependent variations.

Sriranga Kashyap¹, Dimo Ivanov¹, Martin Havlícek¹, Benedikt A Poser¹, and Kâmil Uludag^1
1Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands

Cortical-depth dependent fMRI usually relies on the definition of depths on an anatomical image (eg. MPRAGE). The geometric dissimilarities of the functional compared to the anatomical data require further spatial processing of the functional data to ensure good co-registration. We propose an alternative approach that uses an optimised inversion-recovery EPI derived T₁ image, whose resolution and readout, hence distortions, are identical to that of the functional data, in order to delineate cortical depths. As a result, the cortical-depth specific fMRI data can be analysed in the native space without any spatial confounds stemming from distortion correction and inaccurate registration.

Wietske van der Zwaag¹, Pieter Buur¹, Maarten Versluis², and José P. Marques^3
1Spinoza Centre for Neuroimaging, Amsterdam, Netherlands, ²Philips Healthcare, Best, Netherlands, ³Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands

Achieving sufficiently good quality co-registration between the anatomical and functional images is currently a large stumbling block for laminar fMRI. Here, we present a distortion-matched T₁weighted/T₁-estimation mapping approach using two 3D-EPI readouts per inversion, following the MP2RAGE signal combination. 0.7mm isotropic T₁ data with matching distortions to a 0.7mm isotropic fMRI protocol can be acquired in less than two minutes.