Ratnamanjuri Devi¹, Toralf Mildner¹, Torsten Schlumm¹, and Harald E. Möller^1
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

ME-DEPICTING, a 2D multi-echo readout achieving short echo times was combined with SS-SI-VASO for simultaneous CBV and BOLD measurements at 3T. Results were compared with a traditional ME-EPI readout. Uncorrected time courses of the first echo of ME-DEPICTING demonstrated high sensitivity to functional CBV changes without further BOLD correction, while time courses of S0 and R2* of the multi-echo fit proved almost equivalent for both readouts. Remarkably, a TE dependence was observed in the BOLD-corrected VASO signals obtained by dynamic division with the corresponding control images of VASO indicating a more reliable CBV measure from the S0 fit.

Icaro A.F. Oliveira^1,2, Jeroen C.W. Siero^1,3, Serge O. Dumoulin^1,2,4, and Wietske van der Zwaag^1
1Spinoza Centre for Neuroimaging, Amsterdam, Netherlands, ²Experimental and Applied Psychology, VU University, Amsterdam, Netherlands, ³Radiology, University Medical Centre Utrecht, Utrecht, Netherlands, ⁴Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands

We used sub-millimeter resolution VASO fMRI at 7T to simultaneously map CBV-VASO and BOLD activation in the sensorimotor and somatosensory cortex during a finger digit mapping task. We reliably showed digit representation maps and quantified digit selectivity for both VASO-CBV and BOLD activations. VASO-CBV demonstrated a higher digit selectivity, confirming the higher spatial specificity of VASO-CBV.

Alejandro Monreal¹, Ruoyun Emily Ma¹, Renzo Huber¹, Denizhan Kurban¹, Nicolas Boulant², and Benedikt A Poser^1
1Maastricht University, Maastricht, Netherlands, ²CEA NeuroSpin, Gif-sur-Yvette, France

VASO fMRI can provide beneficial localization specificity and quantifiability compared to the commonly used BOLD contrast. Previous work has also shown the benefits of using spiral readouts compared to Cartesian. 

In this work, we explore the benefits of 3D stack of spirals readouts and compare it with the current state of the art 3D EPI readouts for VASO fMRI. The sequence implementation is done using Pulseq, images were reconstructed using gpuNUFFT; functional analysis with an openly available pipeline. We find that a tSNR efficiency improvement of a factor of 2.5 over EPI is achieved using the proposed spiral implementation.

Benedetta Franceschiello^1,2, Simone Rumac¹, Tom Hilbert^1,3,4, Christopher W. Roy¹, Giulio Degano⁵, Anna Gaglianese¹, Jerome Yerly^1,2, Matthias Stuber^1,2, Tobias Kober^1,3,4, Ruud B. van Heeswijk¹, Micah M. Murray^1,2,6,7, and Eleonora Fornari^1,2,7
1Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ²CIBM Center for Biomedical Imaging, Lausanne, Switzerland, ³Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, ⁴LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁵Department of Psychology, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland, ⁶Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN, United States, ⁷These authors provided equal last-authorship contribution, Lausanne, Switzerland

We demonstrate the efficacy of a new free-running framework for quantitatively measuring blood-oxygen-level-dependent functional MRI (BOLD-fMRI) signals. Whole-brain data were acquired uninterruptedly during a blocked-design ON/OFF visual paradigm (checkerboard vs. grey image). 5-D volumes were reconstructed (i.e. the three spatial dimensions (x,y,z),  the trial dimension (N=33), and the ON vs. OFF dimension (N=2)). BOLD-activated regions were measured as the difference between volumes reconstructed from all read-outs acquired during the ON vs. OFF condition, rather than resulting from a canonical, model-based, GLM analysis. The framework enables high temporal T2* k-space sampling and opens up new horizons for BOLD-fMRI.

Shota Hodono^1,2, Jonathan R Polimeni^3,4, and Martijn A Cloos^1,2
1Centre for Advanced Imaging,The University of Queensland, Brisbane, Australia, ²ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia, ³Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

A key feature of the diffusion-weighted fMRI (DfMRI) response is that it can detect activation with an earlier onset compared to the hemodynamic response measured with spin-echo Blood Oxygenation Level Dependent (SE-BOLD), which was taken as evidence that the DfMRI signal can track an aspect of neural activation that is distinct from hemodynamics. However, despite considerable efforts, it has been challenging to detect this early onset reliably. Here we revisit this question leveraging modern fMRI hardware and acquisition approaches to reduce unwanted hemodynamic contributions. Averaged across all subjects, we see a 1.6±0.2s earlier onset compared to SE-BOLD. 

Mario Serrano-Sosa¹, Jared X. Van Snellenberg^1,2,3, and Chuan Huang^1,2,4
1Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ²Psychiatry, Stony Brook Medicine, Stony Brook, NY, United States, ³Psychology, Stony Brook University, Stony Brook, NY, United States, ⁴Radiology, Stony Brook Medicine, Stony Brook, NY, United States

Interpretable Deep Learning(DL) models are the next step in establishing DL prediction models as accepted tools that provide researchers with data-driven methods to further understand neuroimaging data. In this work, we developed two interpretable DL models to predict Working Memory(WM) scores from task fMRI data to assess neural circuitry pertaining to WM; wherein a traditional Convolutional Neural Network(CNN)(1-3) contained fMRI activation data from cortical vertices as a single image, and the second contained cortical activation data from both hemispheres as separate channels. Overall, the interpretable DL model provided high quality saliency maps potentially displaying novel regions pertaining to WM.

Pinar S Özbay¹, Dante Picchioni¹, Hendrik Mandelkow¹, Jacco A de Zwart¹, Yicun Wang¹, Peter van Gelderen¹, and Jeff Duyn^1
1NINDS, NIH, Bethesda, MD, United States

According to recent research, slow waves may increase metabolic waste clearance during sleep, possibly through cerebrospinal fluid (CSF) pulsatile movement. Pulsations can also be caused by direct pressure effects from the cardiac and respiratory cycles, such as deep breaths. To examine this possibility, we designed a cued deep breathing experiment, which was previously demonstrated to result in widespread fMRI global signal reductions. In this work, our findings point to an alternative pathway for the creation of CSF pulsations that relies on autonomic changes rather than sleep.

Daniel E. P. Gomez^1,2,3, Nina E. Fultz^1,2,4, Jonathan R. Polimeni^1,3,5, and Laura D. Lewis^2
1Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Department of Biomedical Engineering, Boston University, Boston, MA, United States, ³Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁴Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark, ⁵Harvard-MIT Division of Life Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

Blood-oxygen-level-dependent (BOLD) signals can produce much faster (>0.2Hz) responses than predicted by canonical hemodynamic response models. The timing of hemodynamic responses depends on vascular anatomy, and whether these fast responses differ in veins and parenchyma is unknown. Linear models predict that with faster stimuli, venous responses should attenuate more than the parenchyma. We tested this hypothesis by imaging visual responses at high spatial resolution with 7T fMRI. We found that both veins and parenchyma produce larger fMRI responses to fast stimuli than predicted by linear models, suggesting that faster stimuli produce narrower hemodynamic responses in both compartments.

S Senthil Kumaran¹, Himanshu Singh¹, and A Ankeeta^1
1Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India

Cognitive studies using functional imaging are inherently associated with nonstimulant noise in MRI. Dissociating the noise aspect in functional imaging from cognitive traits is crucial in delineating auditory cognition. This study explores the association of noise in auditory cognition studies. we designed a functional Navon task paradigm with introduction of pink noise characteristics modelled as part of its memory phase. Variation of noise induced the cognitive masking associated with characteristics of stimuli across the local and global suppression.

Emma Biondetti^1,2, Antonio Maria Chiarelli^1,2, Ilona Lipp^3,4, Rachael Stickland^3,5, Alessandro Villani^1,2, Eleonora Patitucci³, Valentina Tomassini^1,2,6,7,8, and Richard Wise^1,2
1Department of Neuroscience, Imaging, and Clinical Sciences, D'Annunzio University of Chieti-Pescara, Chieti, Italy, ²Institute for Advanced Biomedical Technologies, D'Annunzio University of Chieti-Pescara, Chieti, Italy, ³Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, United Kingdom, ⁴Department of Neurophysics, Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany, ⁵Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States, ⁶MS Centre, Neurology Unit, SS. Annunziata University Hospital, Chieti, Italy, ⁷Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom, ⁸Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, United Kingdom

 The representation of changes in neural activity by blood oxygenation level-dependent (BOLD) fMRI is affected by vascular properties including cerebral blood volume (CBV) and cerebral vascular reactivity (CVR). Here, we investigate these factors using a breath-hold BOLD fMRI experiment. We show that a group-level correction of the stimulus-evoked BOLD fMRI signal by these vascular properties reduces between-subject variability and improves sensitivity to the task response. Notably, correcting with a marker of relative CBV does not require individual recordings of exhaled carbon dioxide (in contrast with CVR) and resulted in the highest increase in sensitivity to the task.  

Zaki Ahmed¹, Kirk M Welker¹, Maria A Halverson¹, Nolan K Meyer¹, Daehun Kang¹, Myung-Ho In¹, Erin M Gray¹, David F Black¹, Joshua D Trzasko¹, John Huston III¹, Matt A Bernstein¹, and Yunhong Shu^1
1Department of Radiology, Mayo Clinic, Rochester, MN, United States

Multiband acquisition can improve the temporal resolution of task-based fMRI at the cost of increased noise and artifacts. The optimal multiband acceleration depends on the task being studied, specific imaging protocol, and hardware involved. In the current work, multiband acceleration factors of 4, 5, and 6 were compared for a rhyming-task fMRI study using a 32-channel head coil on a compact 3T scanner with high-performance gradients. Multiband factors of 4 and 6 provided significant improvement over no acceleration, whereas a multiband factor of 5 was inconsistent and did not provide a significant improvement in half of the volunteer cohort.

Roberta Maria Lorenzi¹, Adnan Alahmadi^2,3, Letizia Casiraghi^1,4, Anita Monteverdi^1,5, Egidio D'Angelo^1,5, Fulvia Palesi¹, and Claudia AM Gandini Wheeler-Kingshott^1,3,5
1Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ²Department of Diagnostic Radiology, College of Applied medical sciences, King Abdulaziz University, Jeddah, Saudi Arabia, ³NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom, ⁴Department of Mental Health and Dependence, ASST of Pavia, Pavia, Italy, ⁵Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy

Dynamic Causal Modelling(DCM) is a framework enabling the quantification of the causal relationship between functionally connected brain regions. Here we included the cerebellum in a visuomotor network to investigate its role in motion prediction and how different Grip-Force levels modulate the effective connectivity between cerebellum and primary visual cortex. We estimated different DCMs models and we used Bayesian Models Selection to assess the best model at group-level. This study paves the way for investigating the source of BOLD non-linearities when applying different grip-forces in terms of modulations of the cerebro-cerebellar connections.

Prasanna Karunanayaka¹, Andréa Hobkirk², Jianli Wang¹, Rommy Elyan¹, Lauren Spreen³, Ran Pang⁴, Samgan Kanekar¹, and Qing Yang^4
1Department of Radiology, Pennsylvania State University College of Medicine, Hershey, PA, United States, ²Department of Psychiatry, Pennsylvania State University College of Medicine, Hershey, PA, United States, ³Department of Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, United States, ⁴Department of Neurosurgery, Pennsylvania State University College of Medicine, Hershey, PA, United States

Odor-identification is thought to be subserved by a distributed brain network that includes both the medial and inferior temporal lobes, still, its neural substrate remains poorly understood. Odor identification deficits are one of the hallmark symptoms of early Alzheimer’s disease (AD). Here, we propose the development of an oddball olfactory fMRI paradigm to establish the neural basis of odor-identification in healthy subjects. This task may provide a basis for establishing relationships between olfactory deficits, neurodegeneration, and memory impairment for current AD research.