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Tatiana Wolfe¹, Carlos Ernesto Garrido Salmon², G. Andrew James¹, Laura B. Dunn¹, and Clint Kilts^1
1Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR, United States, ²Physics, University of Sao Paulo, Ribeirao Preto, Brazil

Keywords: White Matter, Neuroscience, Neurodegeneration, Cognitive Flexibility, Late Life, Psychiatric Disorders' Risk Factors.

The interplay between brain white matter health and cognitive flexibility in late life is intimate. Understanding the age-related patterns of white matter disintegration along the cingulum network will aid in elucidating factors underlying individual susceptibility to psychiatric illness related cognitive flexibility impairment. We evaluated Bayesian Pearson correlations between a measurement of T1/T2-FLAIR kurtosis and psychiatric risk factors available in a UK Biobank sample. Our findings strongly suggest (BF₁₀>100) that MRI apparent integrity loss in the cingulum, uncinate fascicles and corticothalamic fibers is a significant corollary of an older-age exclusive effect of increased perceived rejection that co-occurs with cognitive flexibility decline.

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Rajikha Raja¹, Ruitian Song¹, John Glass¹, and Wilburn E Reddick^1
1Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States

Keywords: Brain Connectivity, Normal development, Connectome

We investigated the associations between structural connectivity and neurocognitive development in healthy children using MRI and behavioral data from 42 subjects belonging to Lifespan Human Connectome Project Development dataset. Diffusion MRI data were analysed using multi-shell multi-tissue constrained spherical deconvolution model to compute fiber orientation distribution functions before probabilistic fiber tracking was performed to obtain whole brain tractograms. Connectivity matrices were computed based on streamline density using nodes from HCP-MMP1 parcellation. Multiple significant correlations between connectivity and cognitive scores measuring working memory, processing speed and executive functioning were identified in this study.

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Tommy Boshkovski¹, Óscar Peña-Nogales¹, Paulo Rodrigues¹, Vesna Prčkovska¹, and Kire Trivodaliev^1
1QMENTA Inc., Boston, MA, United States

Keywords: Brain Connectivity, Software Tools

FiberMapper 2.0 facilitates automatic (pre-) processing of MRI data, reconstructs major white matter tracts and derives imaging biomarkers along the tracts sensitive to a wide range of microstructural properties. Additionally, Q-FiberMapper 2.0 implements tract lesion burden analysis which together with the quantitative MR imaging biomarkers could be very useful for early diagnosis, evaluation and monitoring of different neurodegenerative and neuroinflammatory diseases that affect the white matter such as traumatic brain injury, multiple sclerosis, and others.

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Bingyang Cai¹, Shize Jiang², Hui Huang¹, Jiwei Li¹, Siyu Yuan¹, Ya Cui¹, Lihong Tang¹, Liang Chen², and Jie Luo^1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ²Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China

Keywords: Brain Connectivity, Diffusion Tensor Imaging

Using high quality atlas of the structural connectome, and direct recording of neuronal activity by stereotactic-EEG (SEEG), this study investigated the relationship between neuronal signal connectivity at different seizure stage and the white matter scaffold. The results show electrical signal spectral coherence and phase synchronization are significantly stronger between structurally connected nodes within short distance, while at the time of pre-seizure and seizure onset, high frequency (>80Hz) signal may propagate through longer distance with structural connections.

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Maëlig Chauvel¹, Marco Pascucci¹, Ivy Uszynski¹, Bastien Herlin¹, Jean-François Mangin¹, William Donald Hopkins², and Cyril Poupon^1
1Université Paris-Saclay, CEA, CNRS, UMR 9027, BAOBAB, NeuroSpin, , Gif-sur-Yvette, France, Saclay, France, ²Michele E Keeling Center for Comparative Medicine and Research, The University of Texas MD Anderson Cancer Center, Bastrop, Texas, Texas, TX, United States

Keywords: Brain Connectivity, Neuroscience, chimpanzee, white matter atlas

The role of the superficial brain connections remains unclear. They appear to be important in the structural organization of the connectivity between the different brain gyri and are usually described as 'U-fibers' referring to their shape. Using an isomap algorithm, we explored in finer detail the superficial connectivity of the chimpanzee and human brains, still poorly known. This method allowed us to better capture the variability of the different shapes of the bundles that we observed and to overcome many bias due to subjective preconceptions.

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Oun Al-iedani^1,2, Stasson Lea², Abdulaziz Alshehri^2,3,4, Vicki E. Maltby^2,5,6, Rodney Lea^2,7, Saadallah Ramadan^2,3, and Jeannette Lechner-Scott^2,5,6
1School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, Australia, ²Hunter Medical Research Institute, New Lambton Heights, Australia, ³School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, Australia, ⁴Department of Radiology, King Fahad University Hospital, Dammam, Saudi Arabia, ⁵Department of Neurology, John Hunter Hospital, New Lambton Heights, Australia, ⁶School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, Australia, ⁷School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia

Keywords: Multiple Sclerosis, Radiomics

This novel longitudinal study evaluates multiparametric MRI signature for predicting cognitive decline in multiple sclerosis (MS) cohort followed for 5-years using a penalised regression machine learning approach (GLMnet). 43 MS participants were assessed at baseline and 5-years follow-up. Baseline (input) data consisted of 76 multiparametric MRI measures for different brain regions and tissues. The best performing model was for a change in tARCS (15 features; r=0.7±0.07), which was substantially higher than that for SDMT (r=0.496±0.08). These findings highlight the importance of using measures from multiple MR modalities analysed in combination with machine learning techniques when assessing cognitive decline.

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Tatiana Wolfe¹, Ashley Pike¹, Sienna Colonese², Carlos Ernesto Garrido Salmon³, Laura B. Dunn¹, R. Lee Archer⁴, Clint D. Kilts¹, and G. Andrew James^1
1Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR, United States, ²Neurosciences, Colorado State University, Denver, CO, United States, ³Physics, University of Sao Paulo, Ribeirao Preto, Brazil, ⁴Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, United States

Keywords: Multiple Sclerosis, Brain Connectivity, Myelination integrity, cognitive impairment

Impairment of higher-order cognition is a difficult aspect of MS pathology that poses a long-standing challenge for patient care  advancement. Understanding how myelination integrity in the cingulum network – a brain pathway implicated in cognition – is related to lost or preserved cognitive functions is fundamental to enable comprehensive MS care and personalized remyelination therapeutics. We investigated if myelination integrity data has the potential to aid in characterizing individual differences in information processing in MS patients. Our findings support that myelination structure-function measured by SHIFT MRI is elucidatory variable to describe individual differences in higher-order cognitive function in MS patients.

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Cristian Montalba^1,2,3, Raul Caulier-Cisterna^1,3, Pamela Franco^1,3, Tomás Labbé², Marcelo E Andia^1,2,3, Miguel Guevara⁴, Jean-François Mangin⁵, Juan Pablo Cruz², Ethel Ciampi^6,7, Claudia Cárcamo^7,8, Pamela Guevara⁴, Rodrigo Salas^3,9,10, and Sergio Uribe^1,2,3
1Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, ²Radiology Department, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile, ³Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Pontificia Universidad Catolica de Chile, Santiago, Chile, ⁴Faculty of Engineering, Universidad de Concepción, Concepción, Chile, ⁵UNATI, Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France, ⁶Neurology Service, Hospital Dr. Sótero del Río, Santiago, Chile, ⁷Neurology Department, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile, ⁸Interdisciplinary Center of Neurosciences, Pontificia Universidad Catolica de Chile, Santiago, Chile, ⁹Faculty of Engineering, Universidad de Valparaíso, Valparaíso, Chile, ¹⁰Biomedical Engineering School, Universidad de Valparaíso, Valparaíso, Chile

Keywords: Multiple Sclerosis, Machine Learning/Artificial Intelligence

Radiological biomarkers of cognitive impairment in Multiple Sclerosis (MS) are still scarce. This study aimed to identify subcortical white matter biomarkers of cognitive impairment related to verbal episodic memory in MS patients and healthy controls using a Machine Learning approach.

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Théodore Soulier¹, Emanuele Morena², Arya Yazdan Panah³, Matteo Tonietto⁴, Mariem Hamzaoui⁵, Vito Ricigliano⁶, Giacomo Boffa⁵, Michel Bottlaender⁷, Benedetta Bodini⁸, and Bruno Stankoff^8
1Institut du Cerveau (ICM) = Paris Brain Institute - Sorbonne Université, Paris, France, ²Sapienza University of Rome, Rome, Italy, ³Paris Brain Institute - Sorbonne Université - INRIA, Paris, France, ⁴Centre Hospitalier Frédéric Joliot - CEA, Orsay, France, ⁵Paris Brain Institute - Sorbonne Université, Paris, France, ⁶Paris Brain Institute - Sorbonne Université - Hôpital Pitié Salpêtrière APHP, Paris, France, ⁷Centre Hospitalier Frédéric Joliot - CEA - Université Paris Saclay, Orsay, France, ⁸Paris Brain Institute - Sorbonne Université - CNRS - INSERM - Hôpital Saint Antoine APHP, Paris, France

Keywords: Multiple Sclerosis, PET/MR, Prelesions, lesion formation

Studying prelesions, the healthy areas preceding lesion appearance, helps to understand the Multiple Sclerosis (MS) lesion formation process. We study here prelesions in a longitudinal cohort of 19 MS patients combining MRI with Positron Emission Tomography (PET). MRI allowed to identify prelesions, whereas [11C]-PiB PET allowed to quantify myelin content as well as perfusion. We found that the 69 analyzable prelesions of our cohort were heterogeneous regarding perfusion: 24.6% were hyperperfused and 18.8% hypoperfused. Hypoperfused prelesions showed multimodal evidence of demyelination whereas hyperperfused prelesions were not demyelinated yet, suggesting that hyperperfusion precedes demyelination in the MS lesion formation process.
 

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Rishab Agarwal¹, Gelareh Sadigh^2,3, Jason W. Allen³, David Sandlin⁴, and Candace C. Fleischer^1,3
1Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States, ²Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States, ³Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States, ⁴Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States

Keywords: Multiple Sclerosis, Health Care Economics, Financial Toxicity, Imaging Follow-up Time

Multiple sclerosis (MS) is a chronic condition relying heavily on repeated MR imaging, and patients are particularly vulnerable to “financial toxicity”, a component of healthcare economics defined as the effects of healthcare costs on patient well-being. Our goal was to explore the effects of financial and socioeconomic factors on MRI follow-up time in patients with MS. Financial liability, or patient cost after primary insurance coverage, was associated with longer MRI follow-up time, suggesting financial toxicity may contribute to imaging frequency in MS patients. Further investigation over a longer time period is warranted.
 

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Samantha Noteboom¹, Moritz Seiler², Claudia Chien², Roshan P. Rane², Eva M. M. Strijbis³, Friedemann Paul², Menno M. Schoonheim¹, and Kerstin Ritter^2
1MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, Netherlands, ²Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany, ³MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, Netherlands

Keywords: Multiple Sclerosis, Brain

Machine learning may aid in individualized prediction of disease progression in multiple sclerosis (MS). In this study, we used data from 354 patients with MS to evaluate the capability of different machine learning approaches at predicting future disease worsening. Multiple clinical end points of disease worsening were tested and different combinations of clinical and structural MRI measures were used as inputs. Machine learning models were capable of discriminating between patients with low and high disability but did not perform well in predicting future disease course.

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Letizia Losa¹, Denis Peruzzo¹, Graeme Bydder², Andrea Salmaggi³, and Nivedita Agarwal^4
1CESNE, IRCCS E. Medea, Bosisio Parini, Italy, ²Department of Radiology, University of California, San Diego, San Diego, CA, United States, ³Neurology, Manzoni Hospital, Lecco, Lecco, Italy, ⁴Neuroradiology, IRCCS E. Medea, Bosisio Parini, Italy

Keywords: Multiple Sclerosis, Quantitative Imaging, T1 mapping

Multiple Sclerosis (MS) is a demyelinating progressive chronic disease. MR imaging is a key diagnostic tool that allows long-term monitoring of disease progression. Since T1 values can identify clinically relevant tissue properties such as increased water content, myelin loss and changes in microstructure, we assessed the whole brain using Multiplied, Added, Subtracted and/or Divided Inversion Recovery (MASDIR) in patients with MS in an attempt to improve contrast and quantify T1 of lesions. We show that MASDIR is a clinically reliable, easy to implement technique that greatly increases T1 contrast and simultaneously provides quantitative T1 maps.

Alexey Dimov¹, Thanh Nguyen¹, Shun Zhang², Yi Wang¹, and Susan Gauthier^3
1Radiology, Weill Cornell Medicine, New York, NY, United States, ²Tongji Hospital, Wuhan, China, ³Neurology, Weill Cornell Medicine, New York, NY, United States

Keywords: Multiple Sclerosis, Susceptibility, Quantitative Susceptibility Mapping, susceptibility source separation

De- and remyelination are important treatment targets for multiple sclerosis therapies. In this study, we utilize susceptibility source separation to study differences in longitudinal trajectories of two important lesion types, paramagnetic rim lesions (rim+) and non-rim (rim-) lesions. We report significant difference in myelin content: while rim- lesions tend to demonstrate slow remyelination, rim+ tend to remain stable or demyelinate over time.

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Nikolai I Lesack¹, Sarah Levy², James Sumowski², Stephanie S. G. Brown³, Sam Horng², and Rebecca Emily Feldman^1
1Medical Physics, University of British Columbia, Kelowna, BC, Canada, ²Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³University of Cambridge, Cambridge, United Kingdom

Keywords: Multiple Sclerosis, Diffusion/other diffusion imaging techniques, fixed based analysis, DTI, brain, lesions, peri-lesion, normal appearing white matter

Diffusion Tensor Imaging and Fixel Based Analysis (FBA) techniques were used to analyze microstructural properties of multiple sclerosis (MS) lesions and normal appearing white matter (NAWM) in relapsing-remitting multiple sclerosis (RRMS) subjects. FBA metric values were similar in peri-lesion white matter and MS lesions compared with bulk NAWM which may indicate FBA metric sensitivity to the infiltration of RRMS lesion pathology into surrounding NAWM. FBA fibre density in bulk NAWM was significantly different between subjects treated with Copaxone vs. Tysabri, which may indicate FBA metric sensitivity to NAWM differences between disease modifying therapies.

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Loredana Storelli¹, Elisabetta Pagani¹, Patrizia Pantano^2,3, Gioacchino Tedeschi⁴, Nicola De Stefano⁵, Maria Assunta Rocca^1,6,7, and Massimo Filippi^1,6,7,8,9
1Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy, ²Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy, ³IRCCS NEUROMED, Pozzilli, Italy, ⁴Department of Advanced Medical and Surgical Sciences, and 3T MRI-Center, University of Campania “Luigi Vanvitelli”, Naples, Italy, ⁵Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy, ⁶Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy, ⁷Vita-Salute San Raffaele University, Milan, Italy, ⁸Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy, ⁹Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy

Keywords: Multiple Sclerosis, Neuroinflammation

Thalamic atrophy has been found since the earliest phases of multiple sclerosis (MS). However, this measure is not included in clinical practice, due to the time-consuming manual segmentation and technical challenges. By comparing thalamic segmentations from three available automatic methods in a multicenter dataset, we found that the inclusion of fractional anisotropy maps facilitated the automatic identification of thalamic boundaries increasing robustness of the results. In particular, the multimodal approach (FSL-MIST) showed a better capability to detect small longitudinal variations of thalamic volumes in MS patients and a better correlation with another relevant MRI measure such as the lesion volume.

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Sam Sedaghat¹, Hyungseok Jang¹, Jiyo Athertya¹, Yajun Ma¹, Jody Corey-Bloom¹, and Jiang Du^1
1University of California San Diego, San Diego, CA, United States

Keywords: Multiple Sclerosis, Neurodegeneration, UTE, MPRAGE, FLAIR, EDSS

We found a significant correlation between the signal variability of MS lesions on IR-UTE / MPRAGE sequences and the EDSS, while the IR-UTE sequence is superior. By using direct myelin imaging, the grade of disability in MS patients could be estimated more accurately. The signal variability of MS lesions on IR-UTE could be used as a novel imaging biomarker for patient’s disability.

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Samantha By¹, Jean-Christophe Houde², Chahin Pachai¹, Loika Maltais², Jorge A. Torres³, Gabrielle Grenier², Sujuan Huang⁴, and Daniel P. Bradley^3
1Bristol Myers Squibb, Lawrenceville, NJ, United States, ²Imeka Solutions, Inc., Sherbrooke, QC, Canada, ³Biogen, Cambridge, MA, United States, ⁴Cytel, Waltham, MA, United States

Keywords: Multiple Sclerosis, Diffusion/other diffusion imaging techniques

Free water (FW)-corrected metrics in diffusion MRI have shown promise in elucidating confounding pathological processes in multiple sclerosis (MS).  Here, we present a retrospective analysis of FW-corrected diffusion in a cohort of 287 MS participants from a multi-center clinical trial. Our preliminary results indicate the method’s ability to distinguish different lesion types and MS subtypes.

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Wanyong Shin¹, Balu Krishnan², Ajay Nemani¹, Daniel Ontaneda³, and Mark J Lowe^1
1Radiology, Cleveland Clinic, Cleveland, OH, United States, ²Eplepsy, Cleveland Clinic, Cleveland, OH, United States, ³Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, OH, United States

Keywords: Multiple Sclerosis, Multimodal, EEG

We investigated the change of EEG power, BOLD and CBF contrast during visual stimulation in multiple sclerosis patients and healthy controls. The result shows that cerebrovascular reactivity is disrupted in MS patients compared to controls. 

Ashley Stewart¹, Po-Jui Liu^2,3, Matthias Weigel^2,3, Steffen Bollmann¹, and Cristina Granziera^2,3
1School for Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia, ²Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicing, University Hospital of Basel and University of Basel, Basel, Switzerland, Basel, Switzerland, ³Neurological Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland, Basel, Switzerland

Keywords: Multiple Sclerosis, Susceptibility

In this work, an automated and scalable processing tool for Quantitative Susceptibility Mapping (QSM), QSMxT, is applied to a large dataset of 3D echo-planar imaging (3D-EPI) acquisitions of Multiple Sclerosis (MS) patients and volunteer controls. This is used to reconstruct QSMs for all subjects and generate a group space for the study. MS lesions are depicted and compared across multiple contrasts, including FLAIR, MP2RAGE, 3D-EPI magnitude and QSM. The group space is used to generate average QSM images for the MS and control groups along with a difference image to investigate iron accumulation and demyelination patterns.

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Jing Huang¹, Zhiwei Shen², and Jie Lu^1
1Xuanwu Hospital, Capital Medical University, Beijing, China, ²Philips Healthcare, Beijing, China

Keywords: Multiple Sclerosis, CEST & MT

APTw imaging can help us understand the pathological changes in MS more sensitively and accurately.