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Chang-Jin Huang¹, Hong-Yi Wu¹, Changwei Wu², Shen-Mou Hsu³, and Jyh-Hong Chen^4
1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, ²Research Center for Brain and Consciousness, Taipei Medical University, Taipei, Taiwan, ³Imaging Center for Integrated Body, Mind and Culture Research, National Taiwan University, Taipei, Taiwan, ⁴Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

Keywords: Data Analysis, Quantitative Susceptibility mapping

Functional quantitative susceptibility mapping (fQSM) providing complementary quantitative information for fMRI, has been applied to study brain functions. However, the sensitivity of fQSM suffers from the QSM reconstruction, especially solving an ill-posed deconvolution. To improve the sensitivity of fQSM, we first applied MEDI+0 in fQSM study. This method using cerebrospinal fluid as a zero reference regularization has been proven to reduce the variability of the susceptibility maps from rescan. The higher common voxel ratio and cosine similarity scores were obtained by MEDI+0 than by MEDI. Activated voxels were successfully detected by MEDI+0 during high cognition task from standard fMRI acquisition.

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Kevin Marcus Rosenberg^1,2,3 and Stefan Posse^3,4,5
1Lovelace Medical System, Albuquerque, NM, United States, ²University of New Mexico, Albuquerque, NM, United States, ³Neurinsight LLC, Albuquerque, NM, United States, ⁴Neurology, University of New Mexico, Albuquerque, NM, United States, ⁵Physics and Astronomy, University of New Mexico, Albuquerque, NM, United States

Keywords: Machine Learning/Artificial Intelligence, fMRI (resting state)

In this study, we developed a model-free seed selection approach using deep learning-based tumor tissue segmentation in combination with iterative subject-specific seed-optimization which improves the specificity of peri-tumoral seed selection. The methodology automates seed placement in the vicinity of the tumor in the zone that is at risk during surgical resection without relying on neurofunctional brain atlases. Evaluation of cortical eloquence in different tumor subregions, such as edematous and infiltrative regions was feasible using a single MRI contrast. This computationally efficient approach was integrated into a real-time resting-state fMRI analysis pipeline to characterize peri-tumoral connectivity in patients with glioblastomas.

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Rongjie Hu¹, Jie Liang¹, Yiwen Ding², Shuang Jian², Xiuwen Wu¹, Yanming Wang¹, Yong Zhang³, Zhen Liang², Bensheng Qiu¹, and Xiaoxiao Wang^1
1Center for Biomedical Imaging, University of Science and Technology of China, Hefei, China, ²Anhui Medical University, Hefei, China, ³GE Healthcare, Shanghai, China

Keywords: Machine Learning/Artificial Intelligence, fMRI, eye movement

Eye movements reflect changes in human behavior and thought to some extent, but many functional magnetic resonance imaging (fMRI) studies are limited by equipment and do not perform eye movement tracking. Recently, a deep learning method has been proposed for the regression problem of a single volume's gaze point. In this paper, we propose an end-to-end pipeline called MRGazerⅡ, which includes eye signal extraction, eye-movement behavior recognition and gaze point regression from fMRI scanning slices. The method was tested on the human connectome project (HCP) fMRI dataset and achieved the desired performance.

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Katherine Breedlove^1,2, Hector Arciniega^1,2, Sylvain Bouix³, Martha Shenton^1,2, and Alexander Lin^1,2
1Brigham and Women's Hospital, Boston, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³University of Quebec, Montreal, QC, Canada

Keywords: Software Tools, fMRI (resting state)

Multisite studies are an important feature of modern neuroimaging research as they allow the study of larger and more diverse populations, which is essential to obtain clinically relevant insight and sensitivity to subtle effects. However, due to the variability among scanner platforms, even when utilizing the same acquisition protocols, harmonization is needed to remove site-to-site variability in order to improve signal-to-noise ratio and improve statistical power. By implementing CovBat as a BIDS application, we have created a turnkey application that quickly minimizes confounding signals in multisite fMRI studies.

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Jannette Nassar¹, Oliver C. Kiersnowski¹, Patrick Fuchs¹, and Karin Shmueli^1
1Medical Physics and Biomedical Engineering, University College London, London, United Kingdom

Keywords: Data Analysis, Brain, Quantitative Susceptiblity Mapping, fQSM, fMRI

Functional QSM (fQSM) detects changes in blood oxygenation in response to neuronal activation, providing complementary information to conventional magnitude-based fMRI. For standard structural gradient-echo QSM, multi-echo (ME) acquisitions are more accurate than single-echoes. Preliminary work suggests this holds for ME-EPI. Previous fQSM studies used single-echo EPI with physiological noise correction but with ME-EPI, we observed fQSM activations in the visual cortex with a visual stimulus without physiological noise correction. ME and single-echo EPI fQSM were compared, showing that ME-EPI might be preferable. fQSM activations were weaker (maximum T-score=4 compared to 10 in fMRI) and more localised than fMRI, as expected.      

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Haowei Xiang¹, Jeffrey A. Fessler¹, and Douglas C. Noll^2
1EECS, University of Michigan, Ann Arbor, MI, United States, ²Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

Keywords: Image Reconstruction, Brain

Looping star is a silent MRI pulse sequence that can be used for quantitative susceptibility mapping (QSM), T2*-weighted imaging and fMRI. However, the sparse radial sampling of looping-star limits its spatial and temporal resolution in fMRI studies. This work proposes a customized looping-star fMRI protocol and a spatial temporal reconstruction method that removes the undersampling artifact from the repeating sampling pattern and improves the temporal resolution and quality of the time course and activation map.

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Khaled Talaat¹, Bruno Sa De La Rocque Guimaraes², and Stefan Posse^3,4
1Nuclear Engineering, University of New Mexico, Albuquerque, NM, United States, ²Nucelar Engineering, University of New Mexico, Albuquerque, NM, United States, ³Neurology, University of New Mexico, Albuquerque, NM, United States, ⁴Physics and Astronomy, University of New Mexico, Albuquerque, NM, United States

Keywords: Data Processing, fMRI (resting state)

Regression of filtering residuals is introduced to spectrally segmented regression of nuisance parameters in high-speed fMRI to enable the application of finite impulse response filters for spectral segmentation of regressors. This extension of our previously introduced method of spectrally and temporally segmented regression improves the removal of noise and mitigates the introduction of artefactual correlations in high frequency resting-state fMRI. Simulations and in-vivo data demonstrate significant advantages of spectrally segmented regression compared to whole-band regression when frequency-dependent errors are present in the regression model. High-frequency resting-state connectivity is detected with high sensitivity during normo-, hypo- and hypercapnic state.

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Christine Sze Wan Law¹, Ken Weber¹, Merve Kaptan¹, Dario Pfyffer¹, Sean Mackey¹, and Gary Glover^1
1Stanford University, Stanford, CA, United States

Keywords: Data Processing, Spinal Cord, fmri, multiecho, brain

Functional activation within brain has been studied extensively using fMRI.  But limiting investigation to only the brain provides a truncated view of the human central nervous system as it does not capture information exchange between brain and body periphery through spinal cord.  Simultaneous brain-spinal cord fMRI provides a means to measure motor and pain activity across the central nervous system. BOLD signal, especially in spinal cord, usually suffers from poor signal-to-noise ratio (SNR) which can cause difficulty in detecting activation.  Here, we demonstrate that multiecho acquisition improves spinal cord BOLD detection.

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Vahid Malekian¹, Nadège Corbin^1,2, Michael Moutoussis^1,3, and Martina F. Callaghan^1
1Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ²Centre de Résonance Magnétique des Systèmes Biologiques, CNRS‐University Bordeaux, Bordeaux, France, ³Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, United Kingdom

Keywords: Data Acquisition, fMRI

Multi-echo fMRI can boost BOLD sensitivity relative to conventional single-echo fMRI, especially in high-susceptibility brain regions like orbito-frontal cortex (OFC). Another option is to optimise slice-tilts, z-shimming and k-space traversal to minimise susceptibility effects. In this study, we sought to determine if multi-echo EPI, which requires the use of parallel imaging to achieve reasonable echo times, would remain optimal in OFC when compared to an OFC-optimised single echo alternative. The relative performance is quantified via BOLD contrast-to-noise ratio and an additional comparison is made by incorporating the TE Dependent ANAlysis (TEDANA) denoising approach. Multi-echo increased BOLD CNR, particularly following denoising.

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Alexander Jaffray¹, Michelle Medina¹, Christian Kames¹, and Alexander Rauscher^1,2
1Physics, UBC MRI Research Centre, Vancouver, BC, Canada, ²Department of Pediatrics, UBC, Vancouver, BC, Canada

Keywords: Data Processing, fMRI (resting state), Breathing Belt, Respiratory Phase

Functional Magnetic Resonance Imaging (fMRI) is routinely acquired using gradient-echo sequences with long echo times and short repetition time. Such sequences encode information about the magnetic field in the often discarded image phase. We demonstrate a method for processing the phase of reconstructed fMRI data to isolate temporal fluctuations in the harmonic fields associated with respiration by solving a blind source separation problem. Computed respiratory phase from the fMRI-derived field fluctuations was shown to be in strong agreement with breathing belt data acquired during the same scan. This work thus presents a hardware free measurement of respiratory phase.

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Venkatagiri Krishnamurthy^1,2, Alec Reinhardt^3,4, Serena Song², Joo Han², M. Lawson Meadows², Bruce Crosson², and Suprateek Kundu^3,4
1Dept. of Medicine, Emory University, Atlanta, GA, United States, ²Atlanta VA Medical Center, Decatur, GA, United States, ³Dept. of Biostatistics, Emory University, Atlanta, GA, United States, ⁴Dept. of Biostatistics, UT MD Anderson Cancer Center, Houston, TX, United States

Keywords: Data Analysis, fMRI (task based), Neuroplasticity

Stroke is inherently complex due to the heterogeneity in lesion location, size in addition to other clinical comorbidities. Further, longitudinal three-dimensional fMRI datasets add more complexity towards estimating sensitive and robust biomarkers of neuroplasticity. In this study we propose an innovative extension of Bayesian Tensor Response Regression (BTRR) approach to estimate neuroplasticity that is more sensitive, accurate and reliable compared to traditional voxel-wise approach. Results from our longitudinal aphasia-treatment study not only show that the BTRR approach is more superior but is also able to derive plasticity estimates that are sensitive to treatment differences that are subject and time-point specific. 

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Di Hu¹, Yanqiu Lv¹, Dandan Zheng², Geli Hu², Peng Sun², and Yun Peng^1
1The Department of Radiology,, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China, ²Clinical & Technical Support, Philips Healthcare, Beijing, China

Keywords: Data Analysis, Reproductive

We measured imaging reproducibility in structural, resting state fMRI and diffusion tensor scans across different time points and scanners in healthy volunteers. First, we assessed structural imaging variability by calculating volume for seven subcortical structures. Second, we evaluated across-scanner and across-time reliability of rsfMRI by assessing temporal signal-to-noise ratio of five networks. Finally, we assessed variability in diffusion metrics across scanners and time points. Our results provide statistical validations for longitudinal work on multiple systems, especially for structural study. The influence of different equipment in rsfMRI and DTI related research may be considered, especially DMN and GCC analysis involved. 

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Zaineb Amor¹, Caroline Le Ster¹, Chaithya G.R.^1,2, Guillaume Daval-Frérot^1,2,3, Bertrand Thirion^1,2, Nicolas Boulant¹, Franck Mauconduit¹, Christian Mirkes⁴, Philippe Ciuciu^1,2, and Alexandre Vignaud^1
1Université Paris-Saclay, CEA, NeuroSpin, CNRS, Gif-sur-Yvette, France, ²Université Paris-Saclay, Inria, MIND, Palaiseau, France, ³Siemens Healthineers, Saint-Denis, France, ⁴Skope Magnetic Resonance Technologies AG, Zurich, Switzerland

Keywords: Data Acquisition, fMRI

Static and dynamic ∆B₀ field imperfections are detrimental for fMRI applications as they degrade the temporal SNR (tSNR) and the sensitivity to the BOLD contrast. In this work we propose an experimental protocol for field imperfection monitoring and correction on 3D-SPARKLING fMRI data using the Skope Clip-on field camera in an alternative setting challenging its long TR constraint. We demonstrate the viability of our protocol and the reproducible gain in image quality, tSNR and retinotopic maps when correcting static and dynamic field imperfections on resting-state and task-based fMRI data for 3 healthy volunteers

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Ru Zhang¹, Zhifeng Chen², Chengyang Zhao¹, Danny J.J. Wang¹, and Kay Jann^1
1Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States, ²Monash Biomedical Imaging, Department of Data Science & AI, Faculty of IT, Monash University, Clayton, Australia

Keywords: Image Reconstruction, Image Reconstruction

We quantitatively evaluate the performance of NORDIC and KWIA methods in denoising of high-resolution task-fMRI data from 7T in comparison to the standard manufacturer reconstruction.  We found that both techniques improve single run statistics as well as improved consistency across runs. Analysis of tSNR confirmed improved data quality in the time domain that facilitates time-series analysis for task-fMRI.

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Fanny Munsch¹, Manuel Taso¹, Daniel H. Wolf², Daniel Press¹, Stephanie Buss¹, John A. Detre², and David C. Alsop^1
1Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States, ²University of Pennsylvania, Philadelphia, PA, United States

Keywords: Data Analysis, fMRI (resting state), Pharmacological MRI | Drugs | GABA | Brain connectivity

Neural activity reflects a complex interplay between excitatory and inhibitory signaling. Yet, the BOLD signal is typically interpreted as related to excitatory activity. However, GABAergic neurotransmission also requires metabolism and BOLD may be sensitive to this inhibitory activity that can promote coordinated activity across networks. Here, we evaluated the changes in BOLD rsfMRI and rsCBF metrics (amplitude of low frequency fluctuations (ALFF), intrinsic connectivity (IC) and local correlations (LC)) after oral administration of alprazolam, a GABA agonist, in healthy volunteers. We observed a highly significant increase of BOLD ALFF across the cortex that suggests increasing inhibition can increase BOLD signals.

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Zidan Yu¹, Christoph Rettenmeier¹, and V. Andrew Stenger^1
1Department of Medicine, University of Hawaii, Honolulu, HI, United States

Keywords: Data Acquisition, fMRI

Multi-echo fMRI is of current interest due to its potential for more accurate BOLD contrast with reduced artifacts. Furthermore, spiral trajectories are advantageous for fMRI because of high sampling efficiency. However, images are susceptible to blurring due to B0 inhomogeneity. In this study, we present a 3D multi-echo spiral sequence with a model-based reconstruction method that combines under sampled spiral data from different echoes for increased sampling efficiency and reduced B0 artifacts. Multi-echo and single-echo spiral fMRI data including T2* maps were acquired and compared at 3T demonstrating the method. 

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Roël Matthijs Vrooman¹, Gabriel Desrosiers-Gregoire^2,3, Gabriel Devenyi^2,4, Yen-Yu Ian Shih^5,6,7, Sung-Ho Lee^5,6, Monica van den Berg^8,9, Georgios Keliris^8,9, and Joanes Grandjean^1,10
1Donders Institute for Brain, Cognition and Behaviour, RadboudUMC, Utrecht, Netherlands, ²Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, QC, Canada, ³Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada, ⁴Department of Psychiatry, McGill University, Montreal, QC, Canada, ⁵Center for Animal MRI, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ⁶Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ⁷Biomedical Engineering, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ⁸Bio-imaging lab, University of Antwerp, Antwerpen, Belgium, ⁹µNEURO Research Centre of Excellence, University of Antwerp, Antwerpen, Belgium, ¹⁰Department for Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands

Keywords: Data Acquisition, fMRI (resting state), Rat, Rodent

fMRI in rats is performed under diverse protocols that span different animal handling practices and preprocessing steps. The disparity in image acquisition and preprocessing hampers the interoperability and comparison of the results between studies. Importantly, it is unclear what factors lead to superior acquisition and detection of functional networks. We detail a consensus-based task-free fMRI acquisition protocol in the rat that has been validated in 21 datasets. Along with the animal handling protocol and MRI sequence, we provide a detailed path for data conversion, preprocessing, and analysis.

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Joo Han¹, Justin D. Sprick^2,3, Lisa C. Krishnamurthy^1,4, Serena Song¹, and Venkatagiri Krishnamurthy^1,5
1Center for Visual and Neurocognitive Rehabilitation, Atlanta VAMC, Decatur, GA, United States, ²Division of Renal Medicine, Department of Medicine, Emory University, Atlanta, GA, United States, ³University of North Texas Health Science Center, Denton, TX, United States, ⁴Department of Physics & Astronomy, Georgia State University, Atlanta, GA, United States, ⁵Division of Geriatrics and Gerontology, Department of Medicine, Emory University, Atlanta, GA, United States

Keywords: Signal Modeling, fMRI (task based)

The limitation of estimating Dynamic Cerebral Autoregulation (dCA) using Transcranial Doppler ultrasound is the lack of whole-brain estimation capabilities. In this study, the data acquisition of whole-brain task-fMRI scan during a novel Passive Cyclical Leg Raise (PCLR) task allows us to induce blood pressure (BP) changes. A surrogate BP signal was acquired from the brain stem and depicts expected fluctuations based on the PCLR-task design. The whole-brain dCA was estimated using voxel-wise TFA to obtain the transfer gain, coherence, and phase-offset. These results show similar significant brain regions but with distinct values based on the participant’s varying level of constitution.

Govinda Poudel^1
1Australian Catholic University, Melbourne, Australia

Keywords: Data Analysis, fMRI (resting state)

Timeseries can be mapped into graphs by linking visibility of the signal at each time-point with respect to others – an approach known as visibility graph. We used visibility graph analysis to convert fMRI data into complex networks. We then evaluated the graph theoretical properties of this network and characterised their robustness to motion and test-retest reliability between sessions.  We show that time-series network features such as average degree, average path length, and clustering coefficient are highly sensitive to motion. However, on a low-motion dataset they have a good reliability between sessions.

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Inês Esteves¹, Ana R. Fouto¹, Amparo Ruiz-Tagle¹, Gina Caetano¹, and Patrícia Figueiredo^1
1ISR-Lisboa and Department of Bioengineering, Instituto Superior Técnico – Universidade de Lisboa, Lisbon, Portugal

Keywords: fMRI, Multimodal, ECG, respiration

Physiological signal acquisition during fMRI may be used for multiple purposes, though it usually requires additional setup which may increase complexity and cause subject discomfort. Since ECG is modulated by respiration, an ECG-derived respiration (EDR) may be obtained without needing extra equipment for EEG-fMRI studies, which inherently use the ECG. In this work, EDR signals were computed for resting-state and two respiratory challenges modulating respiration patterns, to validate their use in the MR environment. Their performance for estimating physiological regressors of BOLD-fMRI signals was similar to the one obtained by using concurrently acquired respiratory signals.