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Yalin Yu¹, Yue Qiu², Chuanjun Tong³, and Zhifeng Liang^1
1Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China, Shanghai, China, ²Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China, Shanghai, China, ³School of Biomedical Engineering, Southern Medical University, Guangzhou, China, Shanghai, China

Keywords: Data Analysis, Brain

Simultaneous electrophysiology and fMRI could provide both macroscopic and microscopic observations, but it is highly technically challenging and not widely used in sleep research. We developed mouse sleep fMRI based on simultaneous electrophysiology at 9.4T and allowed manifestation of the full sleep cycle (NREM/REM) during fMRI. The results revealed global state-dependent patterns. Rich state transition epochs demonstrated that state transitions were global, irreversible and sequential phenomenon, which can be predicted using LSTM RNN models. Importantly, simultaneous hippocampal recording revealed enhanced sharp-wave ripple triggered global patterns during NREM than awake state, which may attribute to co-occurrence of spindle events.

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Daniel Schache¹, Ajay Peddi¹, Ali Nahardani², Cornelius Faber¹, and Verena Hoerr^1,2
1Translational Research Imaging Center, Clinic of Radiology, University of Münster, Münster, Germany, ²Heart Center Bonn, Department of Internal Medicine II, University Hospital Bonn, Bonn, Germany

Keywords: Data Analysis, CEST & MT

Cardiac CEST MRI in mice rely on the application of very short saturation pulses. These pulses cause disturbing signal modulation in the CEST spectrum preventing a consistent data analysis. To correct for this, an additional approach named DOSE was established in the data analysis pipeline. The DOSE-corrected analysis was validated in vitro on a glucose concentration series as well as in vivo on cardiac glucoCEST experiments of nine healthy mice. Hereby, oscillations in the CEST spectra could be reduced substantially ensuring a consistent quantification.

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Robin Laven¹, Sam Richardson¹, Nadine Hidalgo², Dale Justin Sasis², Eryn Kwon^3,4, Samantha Holdsworth^3,4, and Poul Nielsen^1,2
1Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand, ²Department of Engineering Science, University of Auckland, Auckland, New Zealand, ³Department of Anatomy and Medical Imaging & Center for Brain Research, University of Auckland, Auckland, New Zealand, ⁴Mātai Medical Research Institute, Tairāwhiti-Gisborne, New Zealand

Keywords: Data Processing, Brain, Chiari 1 Malformation

Pulsatile brain motion is a valuable tool in understanding injury and congenital disorders. These pulsatile motions have a typical magnitude of < 1 pixel.  Amplified MRI (aMRI) amplifies the pulsatile motion to the point of visibility, enabling qualitative examination. However, the amplification in frequency space results in nonlinear distortion of motion preventing the recovery of unaugmented displacements. Here, we propose using phase-based subpixel registration. This methodology enables the recovery of displacement quantification of pulsatile brain motion.

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Wen Jin^1,2, Yibo Zhao^1,2, Yudu Li^1,3, Rong Guo^1,4, Ruihao Liu^1,5, Jie Luo⁵, Yao Li⁵, and Zhi-Pei Liang^1,2
1Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ²Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴Siemens Medical Solutions USA, Inc., Urbana, IL, United States, ⁵School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Keywords: Data Processing, Spectroscopy

J-resolved ¹H-MRSI provides a unique capability to quantify both brain neurotransmitters and metabolites. This paper presents a novel method to effectively remove water and lipid signals from J-resolved ¹H-MRSI data with limited spatial encodings and without lipid suppression. The proposed method has been validated using data from a healthy subject and a brain tumor patient, producing impressive results.

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Yibo Zhao^1,2, Yudu Li^1,3, Wen Jin^1,2, Rong Guo^1,4, Wenli Li⁵, Yao Li⁵, Jie Luo⁵, and Zhi-Pei Liang^1,2
1Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ²Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴Siemens Medical Solutions USA, Inc., Urbana, IL, United States, ⁵School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Keywords: Data Analysis, Spectroscopy, Macromolecule

Separation of macromolecules and metabolites in ultrashort-TE MRSI data has been very difficult due to limited SNR and strong spectral overlap. In this work, we proposed a new solution to the problem using a subspace-based approach aided with long-TE navigator signals. Physics-based prior information was incorporated through pre-learned spectral bases and probability distributions of spatial coefficients. The proposed method has been validated using experimental data from healthy and brain tumor subjects, producing impressive results.

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Sana Vaziri¹, Adam W Autry¹, Marisa Lafontaine¹, Janine Lupo¹, Susan Chang², and Yan Li^1
1Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States

Keywords: Data Processing, Spectroscopy

The use of deep learning models for frequency and phase correction of spectral data was evaluated to reduce processing time of 3D MRSI datasets acquired from patients with glioma. Models for frequency and phase offset estimation were trained for data prior to coil-combination. Separate models for baseline removal of coil-combined data were similarly trained and evaluated. The voxel-wise peaks for spectra processed using the standard approach were evaluated and compared to the spectra processed using the proposed deep learning approach. Compared to standard processing, deep learning processing produced spectra with comparable SNR and linewidths in a shorter processing time.

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Rui Chen¹, Yuncai Ran¹, Junxia Niu¹, Yanglei Wu², Yong Zhang¹, and Jingliang Cheng^1
1Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²MR Collaborations, Siemens Healthineers Ltd., Beijing, China

Keywords: Visualization, Nerves, Cinematic Volume Rendering Technique (cVRT);Brachial plexus nerve

Cinematic Volume Rendering Technique (cVRT) can clearly display the contours of the tumor, brachial plexus, and peripheral blood vessels, and the extent of their involvement while simultaneously imaging them. The three-dimensional anatomical effect is more realistic, playing a direct role in guiding the surgical plan.

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Fatimah M. Alkaabi^1,2, Jiayue Cao¹, Angela Yee¹, Xiaokai Wang¹, Ulrich Scheven¹, Chih Hsuan Tsai¹, Ana C. Saavedra¹, and Zhongming Liu^1,3
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ²Biomedical Engineering, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia, ³Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States

Keywords: Data Analysis, Animals

Gastric Electrical Stimulation (GES) is an FDA approved therapy for gastroparesis with unspecified working mechanisms. One plausible mechanism is that it activates the vagal afferents to engage the brain’s interoceptive network in regulating the stomach. Orientation and location-specific stimulation that targets the vagal-gastric receptors can effectively activate the brainstem. We asked whether and how this type of GES can engage interoceptive regions and modulate their interactions, especially the anterior cingulate (ACC) and insular cortices (IC), the primary visceromotor and viscerosensory processing regions, respectively. Therefore, we evaluated the functional connectivity in the rat brain before, during, and after targeted GES.

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Dillip K. Senapati^1,2, Helge J. Zöllner^1,2, Ipek Özdemir ^1,2, Georg Oeltzschner^1,2, Doris D.M. Lin^1,2, and Peter B. Barker^1,2
1Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Keywords: Data Analysis, Spectroscopy, GABA, MR Spectroscopy, MRSI, Reproducibility, Brain

Reproducibility of both conventional and GABA-edited multi-slice MRSI of the human brain recorded at 3T was investigated in 11 healthy adult volunteers. GABA-edited data were post-processed using a retrospective motion compensation (MoCo) scheme. Test-retest coefficients of variation(CV) were calculated. Overall reproducibility was good for conventional MRSI for selected brain regions, but edited-MRSI data showed significant variations. Edited-MRSI scans may benefit from both improved post-doc correction schemes to minimize subtraction artifacts, as well as better B₀ field homogeneity over the brain, and possibly also prospective MoCo schemes.

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Hasan Atakan Bedel^1,2, Irmak Şıvgın^1,2, Onat Dalmaz^1,2, Salman Ul Hassan Dar^1,2, and Tolga Çukur^1,2,3
1Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, ²National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, ³Neuroscience Program, Bilkent University, Ankara, Turkey

Keywords: Data Analysis, fMRI (resting state)

Functional MRI (fMRI) experiments serve a key role in advancing our understanding of human brain function during normal and disease states. Analysis of high-dimensional fMRI data can significantly benefit from recent deep learning approaches, yet existing methods are insufficiently sensitive to the contextual representations in fMRI data across diverse time scales. Here, we present a novel transformer model for fMRI analysis that effectively captures local and global dependencies in fMRI data. Comprehensive demonstrations are provided that show the superior performance of BolT in gender and disease detection against state-of-the-art learning-based methods.

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Alessandro Nigi¹ and Jeroen C.W. Siero^1
1Radiology, UMCU, Utrecht, Netherlands

Keywords: Data Processing, Data Processing, denoising, NORDIC, BOLD fMRI, thermal noise

Submillimetre BOLD fMRI enables studying brain function at the mesoscopic level but is limited by low SNR. The NORDIC PCA algorithm reduces thermal noise levels in fMRI in a patch-wise manner via singular value thresholding (SVT). However, the NORDIC patch formation uses adjacent pixels that often contain signals from multiple tissues, which can degrade the denoising performance. We propose an alternative patch formation using the similarity between non-local pixels, dubbed pixel-matching (PM) NORDIC. PM-NORDIC outperforms standard NORDIC in terms of temporal SNR and spatial smoothness estimates. 

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Tim Schmidt^1,2, Johanna Vannesjo³, Stefan Sommer^4,5,6, and Zoltan Nagy^1
1Laboratory for Social and Neural Systems Research (SNS Lab), University of Zürich, Zürich, Switzerland, ²Institute for Biomedical Engineering, ETH Zürich and University of Zürich, Zürich, Switzerland, ³Norwegian University of Science and Technology, Trondheim, Norway, ⁴Siemens Healthineers International AG, Zürich, Switzerland, ⁵Swiss Center for Musculoskeletal Imaging (SCMI), Balgrist Campus, Zürich, Switzerland, ⁶Advanced Clinical Imaging Technology (ACIT), Siemens Healthineers International AG, Lausanne, Switzerland

Keywords: Data Processing, fMRI

By combining an fMRI data shuffling method and multiband accelerated acquisition, we were able to measure the hemodynamic response in the primary motor and visual cortices with 75ms temporal resolution while maintaining high SNR. With the addition of multiband imaging, we were able to achieve whole-brain coverage in a feasible scan time, and use appropriate event-related stimulus paradigm to map the BOLD response in the primary motor and visual cortices in a combined experiment.

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Ru Zhang¹, Kay Jann¹, Danny J.J. Wang¹, Christina J. Duval², and Stuart B. Murray^2
1Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States, ²Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, CA, United States

Keywords: Data Analysis, fMRI (resting state)

The current study used resting state functional magnetic resonance imaging to measure the complexity of pre-adolescents with ADHD but no other comorbidities. We ran a factorial 2 (Group: ADHD, Control) by 2 (Sex: Male, Female) analyses of covariance on the multiscale entropy (MSE) maps with the pubertal development status as a covariate. It revealed MSE values were significantly lower in the ADHD group than the control group in various regions, including bilateral superior frontal gyrus, bilateral precuneus, right inferior/middle frontal gyrus/postcentral gyrus/insular, and left precentral gyrus/middle cingulate gyrus (t = -3.131 to -2.445, p < 0.015). 

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Wei Zhu¹, Guangle Zhang¹, Xiao-Hong Zhu¹, and Wei Chen^1
1University of Minnesota, Minneapolis, MN, United States

Keywords: Data Processing, fMRI

High-resolution BOLD fMRI has become an essential tool for studying neural circuit and hemodynamic changes at mesoscopic scale. Nevertheless, it is more prone to the poor sensitivity and non-neural signal contamination as the spatial resolution increases. Group-level analysis also imposes new requirements on the subject alignment accuracy. To deal with these challenges, we developed a fMRI preprocessing pipeline featured in random matrix theory-based PCA denoising, one-time image voxel shift correction, and enhanced subject-level alignment. We applied this pipeline to the high-resolution mouse resting state fMRI and achieved high-quality hierarchical connectomes from large brain regions to thin cortical layers.

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Alice Giubergia¹, Sara Mascheretti², Valentina Lampis³, Tommaso Ciceri¹, Martina Villa^4,5,6, Chiara Andreola⁷, Filippo Arrigoni⁸, and Denis Peruzzo^1
1Neuroimaging Unit, IRCCS Eugenio Medea, Bosisio Parini (LC), Italy, ²Department of Brain and Behavioral Sciences, University of Pavia, Pavia (PV), Italy, ³Child Psychopathology Unit, IRCCS Eugenio Medea, Bosisio Parini (LC), Italy, ⁴Department of Psychological Sciences, University of Connecticut, Storrs, CT, United States, ⁵Institute for the Brain and Cognitive Sciences (IBACS), University of Connecticut, Storrs, CT, United States, ⁶Haskins Laboratories, New Haven, CT, United States, ⁷Laboratoire de Psychologie de Développement et de l’Éducation de l’Enfant (LaPsyDÉ), Université Paris Cité, Paris, France, ⁸Paediatric Radiology and Neuroradiology Department, V. Buzzi Children’s Hospital, Milano (MI), Italy

Keywords: Data Processing, Brain Connectivity

Functional connectomics investigates how brain regions are functionally associated. Studies in literature seek to infer a “pseudo-resting” state from task data, however it should be tested whether resting-state-like connectivity can be inferred by task-fMRI data. This work investigates several preprocessings of task-evoked connectivity performing classification experiments, to test their ability to reproduce a real “resting-state” connectivity and their impact on a clinical context, namely the comparison of Typical Readers and Developmental Dyslexics. Our results suggest that a task-free “pseudo-resting” connectivity cannot be inferred from task-fMRI data and that signal preprocessing does not influence the way the classification rules are inferred.

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Colette Clare Milbourn¹, Seyedmohammad J Shams², and Jean J Chen^2,3,4,5
1School of Life Sciences, University of Nottingham, Nottingham, United Kingdom, ²Research, Rotman Research Institute, Toronto, ON, Canada, ³Research, Baycrest Health Sciences Centre, Toronto, ON, Canada, ⁴Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, ⁵Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada

Keywords: Data Analysis, Neuroscience, blood vessels, cerebrovascular reactivity, deconvolution Secondary category: contrast mechanism

Cerebrovascular Reactivity (CVR) was measured in healthy, young and older adults. Specifically, dynamic CVR (dCVR) was measured as the carbon dioxide (CO₂)-response function. This was estimated by deconvolving a sinusoidal hypercapnic time course from simultaneously acquired blood oxygen level dependent (BOLD) fMRI data using a canonical-correlation (CCA) based approach. The results demonstrate differences in dCVR amplitude and rise time between age groups. This proof-of-concept study establishes the feasibility of dCVR modelling for the study of aging.

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Zhengshi Yang¹, Xiaowei Zhuang², Mark Lowe³, and Cordes Dietmar^1
1Lou Ruvo Center for Brain Health, Cleveland Clinic, Las Vegas, NV, United States, ²Cleveland Clinic, Las Vegas, NV, United States, ³Cleveland Clinic, Cleveland, OH, United States

Keywords: Data Analysis, fMRI, Task fMRI; reigstration

In this study, we have established a reliable hippocampal subfield segmentation and activation analysis pipeline to probe the role of hippocampal subfields in pattern separation task by using 7T fMRI data, which could be able to identify the role of subfield dysfunction in cognitive impairment in prodromal AD.

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Hyemin Um¹, Virginia Hill², Marwa Ismail³, Sushant Puri⁴, Ameya Nayate⁵, Prateek Prasanna⁶, Lisa Rogers⁵, Jennifer Yu⁷, and Pallavi Tiwari^3
1Case Western Reserve University, Cleveland, OH, United States, ²Northwestern Medicine, Chicago, IL, United States, ³University of Wisconsin-Madison, Madison, WI, United States, ⁴Johns Hopkins Medicine, Baltimore, MD, United States, ⁵University Hospitals Cleveland Medical Center, Cleveland, OH, United States, ⁶Stony Brook University, Stony Brook, NY, United States, ⁷Cleveland Clinic, Cleveland, OH, United States

Keywords: Quantitative Imaging, Machine Learning/Artificial Intelligence

A significant challenge in the management of metastatic brain tumors following radiation therapy is distinguishing radiation necrosis from tumor recurrence. Differential diagnosis is difficult on routine MRI and patients are subject to invasive procedures to confirm the absence of disease. We explored the feasibility of deformation features from the normal parenchyma to identify disease recurrence versus radiation effects. Our results suggest that measurements of the subtle tissue deformations in the normal-appearing brain regions may elucidate differences in the tissue microarchitecture of radionecrosis and tumor recurrence and may serve as surrogate markers to non-invasively characterize treatment response in brain metastases.

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Li Jiang¹, Jiachen Zhuo¹, Steven Roys¹, Chandler Sours Rhodes², Prashant Raghavan Raghavan¹, and Rao Gullapalli^1
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Baltimore, Baltimore, MD, United States, ²2National Intrepid Center of Excellence, Walter Read National Military Medical Center, Rockville, MD, United States

Keywords: Data Analysis, Traumatic brain injury

Mild traumatic brain injury (mTBI) account for 85% of all TBIs. Working memory impairment is one of the most common symptoms in mTBI patients and may persist years post-mTBI. In this study, we used N-back fMRI to investigate the varying N-back task loads on the functional activation pattern in patients with mild TBI and age-matched healthy controls (HCs) as well as the longitudinal changes within mTBI group. Our results suggested that appropriate task load would be more sensitive in detecting the subtle brain functional changes, even though differences in behavioral performance between mTBI and HCs were absent.