Jason Langley¹, Sumanth Dara², Ilana Bennett³, and Xiaoping Hu^1,2
1Center for Advanced Neuroimaging, University of California Riverside, Riverside, CA, United States, ²Department of Bioengineering, University of California Riverside, Riverside, CA, United States, ³Department of Psychology, University of California Riverside, Riverside, CA, United States

We use tissue susceptibility to control for iron-related off-target binding effects in ¹⁸F-AV1451 PET and examine the impact of APOE-ε4 carrier status on striatal tau-PET signal in mild cognitive impairment. We found significant increases in tau-PET SUVR in the putamen (p=0.01) and caudate nucleus (p=0.046) of APOE-ε4 positive participants compared to APOE-ε4 negative participants. Controlling for striatal iron, significant correlations were seen between striatal tau-PET SUVR memory measures in the control (putamen: r=0.435; caudate: r=0.623) and APOE-ε4 positive MCI (putamen: r=0.403; caudate: r=0.648) groups with greater tau burden correlated with greater memory impairment.

Fatemeh Mohammadian¹, Maryam Noroozian², Arash Zare Sadeghi³, Hanieh Mobarak Salari⁴, Hassan Hashemi⁵, and Hamid Reza Saligheh Rad^1,6
1Department of Medical physics and Biomedical Engineering, Tehran university of medical sciences, TEHRAN, Iran (Islamic Republic of), ²Department of Psychiatry, Tehran university of medical sciences, TEHRAN, Iran (Islamic Republic of), ³Iran University of Medical Sciences, TEHRAN, Iran (Islamic Republic of), ⁴Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, TEHRAN, Iran (Islamic Republic of), ⁵Tehran university of medical sciences, TEHRAN, Iran (Islamic Republic of), ⁶Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of)

Millions of people around the world suffer from Alzheimer's disease (AD), the most common neurodegenerative disease. Due to the progressive cognitive decline of the disease, early diagnosis can play an important role in the treatment and prevention of disease progression. Because functional and physiological changes occur before structural changes, functional imaging-based biomarkers such as Functional connectivity (FC) can show these changes well.

Fatemeh Adelnia^1,2, Taylor L Davis², Lealani Mae Acosta³, Amanda Puckett³, Feng Wang^1,2, Zhongliang Zu^1,2, Kevin D Harkins^1,2, and John C Gore^1,2,4
1Vanderbilt University Institute of Imaging Science, NASHVILLE, TN, United States, ²Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, NASHVILLE, TN, United States, ³Department of Neurology, Vanderbilt University Medical Center, NASHVILLE, TN, United States, ⁴Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States

R_1ρ dispersion at weak locking fields (R_1ρ^Diff) has the potential to reveal information on microvascular geometry and density. Our results show ΔR_1ρ of white matter extracted from R_1ρ^Diff dispersion is significantly greater in subjects with lower RBANS and MoCA scores. R₂ or R_1p values measured at a single locking field amplitude have no significant correlation with cognitive impairment scores. This work supports the hypothesis that microvascular impairment in white matter may be one of the causal factors in the progression of cognitive impairment in older adults.

Safa Sanami^1,2, Brittany N Intzandt^2,3,4, Julia Huck^1,2, PREVENT-AD Research Group⁵, and Claudine J Gauthier^1,2,4
1Physics, Concordia University, Montreal, QC, Canada, ²PERFORM Centre, Concordia University, Montreal, QC, Canada, ³Centre de Recherche de l’Institut Universitaire de G´eriatrie de Montr´eal, Montreal, QC, Canada, ⁴Centre de Recherche de l’Institut de Cardiologie de Montr´eal, Montreal, QC, Canada, ⁵StoP-AD, Douglas Mental Health Institute, Montreal, QC, Canada

Alzheimer’s Disease (AD) is the most common form of dementia, preceded by a mild cognitive impairment (MCI) prodromal stage. There are sex differences in the prevalence and severity of MCI and AD. However, the presence of sex differences in cerebral hemodynamics during the transition from normal cognition to MCI is unknown. This is the first study to assess whether sex differences exist in cerebral blood flow (CBF) in the years prior to MCI diagnosis in patients with a familial history of AD.

Juin W. Zhou¹, Minos Kritikos^2,3, Chuan Huang^4,5, Sean A. P. Clouston^2,3, Roberto G. Lucchini⁶, Samuel E. Gandy^7,8, Benjamin J. Luft^9,10, and Evelyn J. Bromet^5
1Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ²Program in Public Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States, ³Department of Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States, ⁴Department of Radiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States, ⁵Department of Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States, ⁶Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ⁷Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ⁸Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ⁹Stony Brook World Trade Center Wellness Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States, ¹⁰Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States

A larger than expected number of World Trade Center (WTC) responders, now at midlife, are experiencing chronic post-traumatic stress disorder (PTSD) and incidence of mild cognitive impairment (MCI). A previous study reported that WTC responders with MCI have altered white matter connectivity when compared to unimpaired responders. However, the effects of PTSD on fiber integrity in this population was not studied due to a limited sample size. In the present study, we analyzed 97 WTC responders with/without CI and/or PTSD. Our results suggest that PTSD may influence the neuropathology of CI within this population.

Stefanie A Tremblay^1,2, Nathan Spreng^3,4,5, Amir Pirhadi⁶, Julia Huck¹, Christine Lucas Tardif^7,8,9, Mallar Chakravarty^10,11, PREVENT-AD Research Group¹², Sylvia Villeneuve^10,13,14,15, Ilana Ruth Leppert^7,8,9, Felix Carbonell¹⁶, Yasser Iturria-Medina^8,9,17, Christopher J Steele^18,19, and Claudine J Gauthier^1,2
1Physics, Concordia University, Montreal, QC, Canada, ²Montreal Heart Institute, Montreal, QC, Canada, ³Laboratory of Brain and Cognition, Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada, ⁴McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada, ⁵Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada, ⁶Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada, ⁷Biomedical Engineering, McGill University, Montreal, QC, Canada, ⁸McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada, ⁹Neurology and Neurosurgery, McGill University, Montreal, QC, Canada, ¹⁰StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montreal, QC, Canada, ¹¹McGill University, Montreal, QC, Canada, ¹²StoP-AD Centre, Douglas Mental Health Institute, Montreal, QC, Canada, ¹³McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, QC, Canada, ¹⁴McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada, ¹⁵CRIUGM - Université de Montreal, Montreal, QC, Canada, ¹⁶Biospective Inc., Montreal, QC, Canada, ¹⁷Ludmer Centre for NeuroInformatics and Mental Health, Montreal, QC, Canada, ¹⁸Psychology, Concordia University, Montreal, QC, Canada, ¹⁹Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Although the APOE4 genotype is known to increase the risk of developing Alzheimer’s disease (AD), the mechanisms through which this occurs are not fully understood. Here, we used a voxel-wise multivariate approach, the Mahalanobis distance (MhD), to quantify the extent to which the white matter microstructure of individuals with the APOE4 genotype differ from those without the E4 allele. The MhD of individuals with the E4 allele deviated significantly from our reference group in several regions, including tracts projecting to the hippocampus, a region known for its involvement in AD. Future work will investigate links with cognition and lifestyle factors.

Jasmin A. Keller¹, Sigurður Sigurdsson², Kelly Klaassen¹, Eveline Scholte¹, Lydiane Hirschler¹, Mark A. van Buchem¹, Lenore J. Launer³, Matthias J.P. van Osch¹, Vilmundor V. Gudnason², and Jeroen de Bresser^1
1Department of Radiology, Leiden University Medical Center, Leiden, Netherlands, ²Icelandic Heart Association, Kopavogur, Iceland, ³Laboratory of Epidemiology and Population Science, National Institute on Aging, Bethesda, MD, United States

Recently white matter hyperintensity (WMH) shape was introduced as a promising novel marker that may provide a more detailed characterization of WMH than volume alone. We aimed to investigate the association between WMH shape and the occurrence of dementia later in life in community dwelling older adults. WMH shape markers and WMH volumes were determined for periventricular/confluent, and deep WMH. A more complex shape of periventricular/confluent WMH (higher fractal dimension), as well as total and periventricular/confluent WMH volume, were associated with a greater risk of dementia. These results may indicate a prognostic value of WMH shape markers.

Jordan Colman¹, Olivia Goodkin^1,2, Michael Foster², Nima Mahmoudi³, Mike Wattjes³, Gabriel Gonzalez-Escamilla⁴, Sergiu Groppa⁴, Giuseppe Pontillo^2,5, Einar August Høgestøl^6,7, Lars T Westlye⁷, Silvia Messina⁸, Jacqueline Palace⁸, Rosa Cortese⁹, Nicola De Stefano⁹, Alex Rovira¹⁰, Jaume Sastre-Garriga¹⁰, Stefan Ropele¹¹, Mara Rocca¹², Massimo Filippi¹³, Ahmed Toosy², Olga Ciccarelli², Tarek Yousry^14,15, Ferran Prados^1,2,16, Frederik Barkhof^1,2,17,18, and James H Cole^1,18
1Centre for Medical Image Computing, University College London, London, United Kingdom, ²Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, United Kingdom, ³Medizinische Hochschule Hannover, Hannover, Germany, ⁴Movement Disorders, Neurostimulation and Neuroimaging, University Medicine Mainz, Mainz, Germany, ⁵Departments of Advanced Biomedical Sciences and Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy, ⁶Department of Neurology, Oslo University Hospital, Oslo, Norway, ⁷Department of Psychology, University of Oslo, Oslo, Norway, ⁸Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, United Kingdom, ⁹Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy, ¹⁰Hospital Universitari Vall d’Hebron, Barcelona, Spain, ¹¹Neuroimaging Research Unit, Department of Neurology, Medical University of Graz, Graz, Austria, ¹²Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy, ¹³Neurology Unit, Neurorehabilitation Unit, Neurophysiology Service, and Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy, ¹⁴Lysholm Department of Neuroradiology, UCLH National Hospital for Neurology and Neurosurgery, London, United Kingdom, ¹⁵Neuroradiological Academic Unit, 15. Neuroradiological Academic Unit, London, United Kingdom, ¹⁶E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain, ¹⁷Department of Radiology and Nuclear Medicine, VU Medical Centre, Amsterdam, Netherlands, ¹⁸Dementia Research Centre, UCL Queen Square Institute of Neurology, London, United Kingdom

Brain-predicted age difference (brain-PAD; brain-predicted age – chronological age) is a potential biomarker for neurodegenerative diseases, including multiple sclerosis (MS). Previous models generally rely on T1-weighted (T1w) MRI brain scans. Here, we developed a deep-learning brain-age prediction model on FLAIR MRI. Our Inception-ResNet-V2 model was more accurate than a current state-of-the-art architecture and the FLAIR based model is comparable to a T1w MRI model. We used saliency maps, showing that areas such as the thalamus and ventricles are salient for brain-age prediction. We applied the FLAIR model to patients with MS, finding significantly higher brain-PAD compared to healthy controls.