Yu Veronica Sui¹, Arjun V Masurkar^2,3,4, Karyn Marsh², Barry Reisberg⁵, Thomas Wisniewski^2,3,5,6, Henry Rusinek^1,5, and Mariana Lazar^1
1Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ²Center for Cognitive Neurology, Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States, ³Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States, ⁴Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, United States, ⁵Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States, ⁶Department of Pathology, New York University Grossman School of Medicine, New York, NY, United States

Identifying early changes associated with Alzheimer’s Disease (AD) and separating pathological from normal brain aging is a key step in AD treatment development. It has been proposed that a neuropathological retrogenesis heralded by cortical demyelination underlies the behavioral retrogenesis characterizing AD. As one of the earliest behavioral manifestations of AD, subjective cognitive decline (SCD) is a major research target, however, few studies have discovered reliable neural correlates at this stage. Here, using a novel cortical profile approach, we examined the cortical gradient of T1w/T2w, a putative myelin marker, in SCD and healthy control participants and its association with cognition.
 

Khalid Saifullah¹, Mahir Tazwar¹, Arnold M. Evia², Ashish A. Tamhane², David A. Bennett², Julie A Schneider², and Konstantinos Arfanakis^1,2
1Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States, ²Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States

The purpose of this work was to develop an MRI-based classifier of neurofibrillary tangles by combining ex-vivo MRI and detailed neuropathology in brain autopsies from a large number (N=878) of community-based older adults. The ex-vivo classifier was trained on volumetric, cortical thickness, subcortical shape, diffusion, and R2 measurements, as well as age and sex. The average AUC of the final classifier was 0.87. Future work will convert the ex-vivo classifier to in-vivo and test it in-vivo in independent cohorts of older adults.

Leonardo A Rivera-Rivera¹, Karly A Cody¹, Tobey A Betthauser¹, Rebecca Koscik¹, Erin Jonaitis ¹, Robert Cadman¹, Bruce Hermann¹, Howard A Rowley¹, Cynthia M Carlsson¹, Nathaniel Chin¹, Laura Eisenmenger¹, Sterling C Johnson¹, and Kevin M Johnson^1
1University of Wisconsin, Madison, Madison, WI, United States

In this study we investigated cerebrovascular health and vascular contributions to cognitive impairment in the presence and absence of amyloidosis. We employed various neuroimaging techniques including 4D-Flow, multi-delay ASL, T2-FLAIR, and structural T1 for vascular and structural biomarkers. β-amyloid (Aβ) burden was determined from PET imaging data. Data supports the notion that vascular dysfunction occurs in the presence and absence of Aβ, albeit with differing manifestations leading to cognitive decline.

Catherine A Morgan^1,2,3, Xingfeng Shao⁴, Jane Govender², Tabitha Manson⁵, Vinod Suresh^5,6, Deidre Jansson⁷, Danny JJ Wang⁴, David L Thomas^8,9, Lynette Tippett^1,2, and Michael Dragunow^7
1School of Psychology and Centre for Brain Research, University of Auckland, Auckland, New Zealand, ²Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand, Auckland, New Zealand, ³Centre for Advanced MRI, University of Auckland, Auckland, New Zealand, ⁴Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ⁵Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand, ⁶Department of Engineering Science, University of Auckland, Auckland, New Zealand, ⁷Department of Pharmacology and Centre for Brain Research, University of Auckland, Auckland, New Zealand, ⁸Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁹Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom

Blood-brain barrier (BBB) dysfunction has been observed in multiple neurodegenerative conditions, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). However recent concerns on the repeated use of Gadolinium based contrast agents (GBCAs), prompted us to investigate alternative, non-invasive methods for measuring BBB health. Diffusion-prepared arterial spin labelling (DP-ASL) imaging was implemented at 3T to determine water exchange rates (Kw) in 55 participants, comprising MCI, early AD and control participants. We found Kw to be associated with cognitive performance, suggesting it may be a useful imaging biomarker of early AD pathology.

Luigi Lorenzini¹, Silvia Ingala¹, Alle Meije Wink¹, Joost PA Kuijer ¹, Viktor Wottschel ¹, Carole H Sudre^2,3,4,5, Sven Haller^6,7, José Luis Molinuevo ^8,9,10,11, Juan Domingo Gispert ^8,10,11,12, David M Cash³, David L Thomas ¹³, Sjoerd B Vos ^4,13, Prados Ferran^14,15,16, Jan Petr¹⁷, Robin Wolz¹⁸, Alessandro Palombit¹⁸, Adam J Schwarz ¹⁹, Gael Chételat ²⁰, Pierre Payoux ^21,22, Carol Di Perri²³, Cyril Pernet ²³, Giovanni Frisoni^24,25, Nick C Fox ³, Craig Ritchie²⁶, Joanna Wardlaw ^23,27, Adam Waldman^23,28, Frederik Barkhof^1,29, and Henk JMM Mutsaerts ^1,30
1Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam Neuroscience, Amsterdam, The Netherlands, Amsterdam, Netherlands, ²MRC unit for Lifelong Health and Ageing at UCL, London, UK, London, United Kingdom, ³Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK, London, United Kingdom, ⁴Centre for Medical Image Computing, University College London, London, UK, London, United Kingdom, ⁵School of Biomedical Engineering & Imaging Sciences, King’s College London, UK, London, United Kingdom, ⁶CIRD Centre d’Imagerie Rive Droite, Geneva, Switzerland, Geneva, Switzerland, ⁷Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden, Uppsala, Sweden, ⁸Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain, Barcelona, Spain, ⁹CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain, Madrid, Spain, ¹⁰IMIM (Hospital del Mar Medical Research Institute), Barcelona Spain, Barcelona, Spain, ¹¹Universitat Pompeu Fabra, Barcelona, Spain, Barcelona, Spain, ¹²CIBER Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain, Madrid, Spain, ¹³Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology London, UK, London, United Kingdom, ¹⁴Nuclear Magnetic Resonance Research Unit, Queen Square Multiple Sclerosis Centre, University College London Institute of Neurology, London, United Kingdom, London, United Kingdom, ¹⁵Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom, London, United Kingdom, ¹⁶e-Health Centre, Open University of Catalonia, Barcelona, Spain, Barcelona, United Kingdom, ¹⁷Helmholtz‐Zentrum Dresden‐Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany, Dresden, Germany, ¹⁸IXICO, London, UK, London, United Kingdom, ¹⁹Takeda Pharmaceuticals Ltd., Cambridge, MA, USA, Cambridge, ME, United States, ²⁰Université de Normandie, Unicaen, Inserm, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", institut Blood-and-Brain @ Caen-Normandie, Cyceron, 14000 Caen, France, Caen, France, ²¹Department of Nuclear Medicine, Toulouse CHU, Purpan University Hospital, Toulouse, France, Toulouse, France, ²²Toulouse NeuroImaging Center, University of Toulouse, INSERM, UPS, Toulouse, France, Toulouse, France, ²³Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK, Edinburgh, Scotland, ²⁴Laboratory Alzheimer’s Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy, Brescia, Italy, ²⁵University Hospitals and University of Geneva, Geneva, Switzerland, Geneva, Switzerland, ²⁶Centre for Dementia Prevention, The University of Edinburgh, Scotland, UK, Edinburgh, Scotland, ²⁷UK Dementia Research Institute at Edinburgh, University of Edinburgh, UK, Edinburgh, Scotland, ²⁸Department of Medicine, Imperial College London, London, UK, London, United Kingdom, ²⁹Institute of Neurology and Healthcare Engineering, University College London, London, UK, London, United Kingdom, ³⁰Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium, Ghent, Belgium

Image-derived phenotypes (IDPs) from multimodal MRI sequences constitute an important resource that allows the characterization of brain alterations in the early stages of Alzheimer diseases and other neurodegenerative conditions. Here, we showed the computation of multimodal IDPs from the European Prevention of Alzheimer Dementia (EPAD) cohort and assessed their relationship with non-imaging markers of neurodegeneration. We demonstrated the clinical relevance of IDPs to uncover early brain alteration in AD by showing expected association with non-imaging data.

Peyton L Delgorio¹, Lucy V Hiscox², Grace McIlvain¹, Alexis A Merritt¹, Alexa M Diano¹, Mary K Kramer¹, Kyra E Twohy¹, James M Ellison³, Alyssa Lanzi¹, Matthew L Cohen¹, Christopher R Martens¹, and Curtis L Johnson^1
1University of Delaware, Newark, DE, United States, ²University of Bath, Bath, United Kingdom, ³ChristianaCare Health System, Newark, DE, United States

The aim of this study was to determine whether magnetic resonance elastography (MRE) could sensitively detect mechanical property alterations in the hippocampal subfields due to amnestic mild cognitive impairment (aMCI) - a prodromal stage of Alzheimer’s disease (AD). Results show that entorhinal cortex viscoelasticity was significantly lower in aMCI participants. Further, the entorhinal cortex did not display significant volume differences due to aMCI, which demonstrates how MRE may yield more information about the health of a region known to harbor AD pathology. These results suggest that hippocampal subfield MRE measures show potential for use as an imaging biomarker of disease.