Omer Burak Demirel^1,2, Burhaneddin Yaman^1,2, Steen Moeller², Logan Dowdle², Luca Vizioli², Kendrick Kay², Essa Yacoub², John Strupp², Cheryl Olman², Kâmil Uğurbil², and Mehmet Akçakaya^1,2
1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, ²Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

There are significant benefits to HCP-style fMRI acquisitions, which acquires high spatial and temporal resolution across the whole brain in an effort to better understand the human brain. This can be achieved through simultaneous multi-slice (SMS)/Multiband (MB) imaging, which provides rapid whole-brain coverage using high acceleration rates albeit with increased noise amplification. Deep learning reconstruction techniques have recently gained substantial interest in improving accelerated MRI. Here we utilize a physics-guided self-supervised deep learning reconstruction on a 5-fold SMS and 2-fold in-plane accelerated whole brain 7T fMRI acquisition to reduce the reconstruction noise without altering the subsequent fMRI result.     

Andrew Palmera Leynes^1,2, Nikou Louise Damestani³, David John Lythgoe³, Ana Beatriz Solana⁴, Brice Fernandez⁵, Brian Burns^1,6, Steven Charles Rees Williams³, Fernando Zelaya³, Peder E.Z. Larson^1,2, and Florian Wiesinger^3,4
1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ²UC Berkeley - UC San Francisco Joint Graduate Program in Bioengineering, Berkeley and San Francisco, CA, United States, ³King's College London, London, United Kingdom, ⁴GE Healthcare, Munich, Germany, ⁵GE Healthcare, Paris, France, ⁶GE Healthcare, Menlo Park, CA, United States

The Looping Star pulse sequence was recently introduced as an acoustically silent alternative to EPI-based fMRI pulse sequences. In this abstract, we present improvements to the spatiotemporal resolution of Looping Star using the “extreme MRI” approach, without sacrificing functional sensitivity. We demonstrate the application of silent fMRI with increased temporal resolution and increased spatial resolution using extreme Looping Star, in comparison with standard Looping Star, on a motor task and a visual task across two sites.

Wiktor Olszowy^1,2 and Ileana O Jelescu^1,2
1CIBM Center for Biomedical Imaging, Lausanne, Switzerland, ²Animal Imaging and Technology, EPFL, Lausanne, Switzerland

Diffusion fMRI (dfMRI) is an alternative to BOLD fMRI. Here, we present the first dfMRI study in humans attempting to minimize all sources of BOLD contamination and comparing functional responses at two field strengths, both for task and resting-state fMRI. Our study benefits from unprecedented high spatiotemporal resolution. We observed task-induced water diffusivity decreases in the perfusion-free b-value regime. Furthermore, we found that positive correlations were largely preserved while anti-correlations were suppressed in dfMRI functional connectivity compared to BOLD. We conclude that dfMRI contrast is genuine and distinct from BOLD mechanisms.

Xi Chen¹, Wenchuan Wu¹, and Mark Chiew^1
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

Multi-shot 3D EPI is one of the most popular 3D imaging techniques for fMRI, which can provide higher SNR than 2D single-shot EPI. However, the vulnerability to inter-shot signal variations arising from physiological fluctuations, like respiration-related phase variations, has limited the tSNR benefits of 3D multi-shot acquisitions. To improve the temporal stability of 3D multi-shot EPI for fMRI at 7T, we investigated both the optimization of sampling trajectories which show varying vulnerabilities to the inter-shot inconsistencies, and the use of a low-rank annihilating filter constrained reconstruction which can reduce unwanted temporal variance induced by the inter-shot phase inconsistencies.

Andrew Lithen^1,2, Albert Tamashausky³, Berkin Bilgic^1,4, Kawin Setsompop⁵, Bryan Kennedy¹, Lilianne Mujica-Parodi^1,2, Lawrence Wald^1,4, Shahin Nasr^1,4, and Jason Stockmann^1,4
1A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Dept. of Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ³Asbury Carbons, Asbury, NJ, United States, ⁴Harvard Medical School, Boston, MA, United States, ⁵Dept. of Electrical Engineering, Stanford University, Stanford, CA, United States

Neuroimaging of the human brain temporal lobes has long been impeded by severe B₀ inhomogeneity arising from air-tissue susceptibility interfaces around the ear canals and temporal bone. Here, we propose localized passive shimming using graphite-embedded silicone ear plugs, which replace standard ear plugs used during MRI. We further synergistically combine the passive shims with active multi-coil B₀ shimming to boost shim performance.  Our results show improvements in B₀ homogeneity as assessed using field maps and signal loss and distortion in gradient-echo EPI slices.  

Luca Vizioli^1,2, Steen Moeller¹, logan T Dowdle¹, Mehmet Akcakaya¹, Federico De Martino³, Essa Yacoub¹, and Kamil Ugurbil^1
1CMRR, University of Minnesota, minneapolis, MN, United States, ²Department of Neurosurgery, University Of Minnesota, minneapolis, MN, United States, ³University Of Maastricht, Maastricht, Netherlands

Functional imaging with the BOLD contrast is an essential tool to investigate human brain functions, however the contribution of thermal noise compromises the signal, particularly at higher resolutions. A recently developed technique, NORDIC, aims to suppress this noise source. Here we show that NORDIC produces data with substantially higher signal-to-noise and functional contrast to noise ratios, resulting in improved detection of functional activation with no change in spatial precision. One run of denoised data is equivalent to combining 3 to 5 runs of conventional data. These effects create new opportunities for functional neuroimaging, while reducing participant or patient burden.

David Bancelin¹, Beata Bachrata^1,2, Pedro Lima Cardoso¹, Siegfried Trattnig^1,2, and Simon Daniel Robinson^1,3,4
1High-Field MR Centre, Medical University of Vienna, Vienna, Austria, ²Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria, ³Department of Neurology, Medical University of Graz, Graz, Austria, ⁴Centre for Advanced Imaging, University of Queensland, Queensland, Australia

External physiological recordings can be used to filter out cardiac and respiration fluctuations in fMRI data but these can be unreliable. We propose an unsupervised method which derives physiological noise regressors, including cardiac fluctuations with a period much less than the volume TR, from phase and magnitude fMRI data. We compare its efficacy with a correction method which uses external recordings (RETROICOR), and a rival physiological data-free method (PESTICA).

Eneko Uruñuela¹, Stefano Moia¹, and César Caballero-Gaudes^1
1Basque Center on Cognition, Brain and Language, Donostia - San Sebastián, Spain

Functional MRI deconvolution algorithms are gaining popularity to study the dynamicss of functional brain activity and connectivity at short timescales. This work sheds light on our understanding of two state-of-the-art approaches based on L1-norm regularized estimators: Paradigm Free Mapping (synthesis model) and Total Activation (analysis model). Through simulations with varying signal-to-noise ratios, and an experimental motor task dataset, we demonstrate that both formulations produce identical estimates of the innovation and activity-inducing signals underlying BOLD events when identical hemodynamic response and regularization parameters are used. These observations open up the possibility for future developments without questioning their core formulation and performance.

Ali Golestani¹, Nichole R Bouffard¹, Morgan D Barense^1,2, and Morris Moscovitch^1,2
1Department of Psychology, University of Toronto, Toronto, ON, Canada, ²Rotman Research Institute at Baycrest, Toronto, ON, Canada

The autocorrelation (AC) of the fMRI signal is assumed irrelevant to the brain function and is eliminated by fMRI preprocessing. Recent findings have suggested that the brain function may alter the AC value of the fMRI signal. We used fMRI data acquired during cognitive processes of working memory (WM), mathematical computations, video watching, and resting-state, and showed that cognitively demanding tasks decrease the AC values in functionally related brain regions. Decrease in AC is related to performance on the target tasks. The AC of the fMRI signal is affected by cognitive processes and can provide complementary information about brain function.

Renzo Huber¹, Benedikt Poser¹, Peter A Bandettini², Kabir Arora¹, Konrad Wagstyl³, Shinho Cho⁴, Jozien Goense⁵, Andrew T Morgan^2,5, Nils Nothnagel⁵, Anna K Mueller⁶, Job van den Hurk⁷, Richard C Reynolds², Daniel R Glen², Rainer Goebel^1,8, and Omer Faruk Gulban^8
1Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands, ²NIH, Bethesda, MD, United States, ³UCL, London, United Kingdom, ⁴CMRR, Minneapolis, MN, United States, ⁵University of Glasgow, Glasgow, United Kingdom, ⁶Uni Mainz, Mainz, Germany, ⁷Scannexus, Maastricht, Netherlands, ⁸Brain Innovation, Maastricht, Netherlands

• A new software toolbox is introduced for layer-specific functional MRI: LayNii.
• LayNii is a suite of command-line executable C++ programs. It can be easily installed via source code and binaries for Linux, Windows, and macOS.
• LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and facilitates user-dependent parameter tweaking.
• LayNii performs layerification, columnar distance estimation, and cortical unfolding in the native voxel space of functional data.
• LayNii performs layer-smoothing, GE-BOLD deveining, fMRI quality assessment, and VASO analysis.