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Geon-Ho Jahng¹, Soonchan Park¹, Sue Min Jung², Mun Bae Lee³, Hak Young Rhee⁴, Chang-Woo Ryu¹, A- Rang Cho⁵, and Oh In Kwon^3
1Radiology, Kyung Hee University Hospital at Gangdong, Seoul, Korea, Republic of, ²Biomedical Engineering, Kyung Hee University, Yongin-si, Korea, Republic of, ³Mathematics, Konkuk University, Seoul, Korea, Republic of, ⁴Neurology, Kyung Hee University Hospital at Gangdong, Seoul, Korea, Republic of, ⁵Psychiatry, Kyung Hee University Hospital at Gangdong, Seoul, Korea, Republic of

Keywords: Alzheimer's Disease, Electromagnetic Tissue Properties

The objective of this study was to investigate high-frequency conductivity (HFC) obtained using magnetic resonance electrical property tomography (MREPT) in participants with Alzheimer’s disease (AD), amnestic mild cognitive impairment (MCI), and cognitively normal (CN) elderly controls. High-frequency conductivity (HFC) values in the brain are significantly increased in Alzheimer’s disease (AD) patients compared to cognitively normal (CN) elderly people, are negatively associated with Mini-Mental State Examination (MMSE) scores, and therefore can be used as an imaging biomarker to improve the differentiation of AD from CN.

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Diana L. Giraldo^1,2,3, Robert E. Smith⁴, Hanne Struyfs⁵, Ellis Niemantsverdriet⁵, Ellen De Roeck⁵, Sebastiaan Engelborghs⁵, Eduardo Romero², Jan Sijbers^1,3, and Ben Jeurissen^1,3,6
1imec-Vision Lab, University of Antwerp, Antwerp, Belgium, ²Computer Imaging and Medical Applications Laboratory - Cim@Lab, Universidad Nacional de Colombia, Bogota, Colombia, ³µNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium, ⁴The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, ⁵Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium, ⁶Lab for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium

Keywords: Alzheimer's Disease, Data Analysis

In this work, we compare the results of analyzing group differences in Alzheimer’s Disease (AD) with two different models for multi-shell diffusion MRI: the Diffusion Kurtosis Tensor (DKT) and Multi-Tissue Constrained Spherical Deconvolution (MT-CSD). Separate analysis for DKT metrics and measures derived from MT-CSD were performed to investigate differences between control subjects and patients with mild cognitive impairment and dementia due to AD. Statistical analyses included strong family-wise error correction for multiple comparisons. Results indicate that analyses with MT-CSD capture the differences that are detectable with DKT while offering more specific details that facilitate the interpretation of those differences.

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Qian Chen^1,2 and Bing Zhang^1,2
1Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China, ²Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China

Keywords: Alzheimer's Disease, Gray Matter, structural covariance network

We aimed to investigate whether spatial navigation could reveal subcortical structural alterations and the risk of clinical progression in subjective cognitive decline (SCD). SCD subjects were divided into SCD-good (G-SCD) and SCD-bad (B-SCD) groups according to their navigation performance. The B-SCD group showed decreased volumes in the basal forebrain and had a larger shortest path length in the subcortical structural covariance network than the G-SCD group. Follow-up data suggested that the B-SCD group had a higher conversion rate to MCI than the G-SCD group. This study may provide new insights for the risk assessment and early intervention for SCD subjects.

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Liangdong Zhou¹, Thanh Nguyen¹, Xiuyuan H Wang¹, Gloria C Chiang¹, Mony de Leon¹, and Yi Li^1
1Weill Cornell Medicine, New York, NY, United States

Keywords: Alzheimer's Disease, Neurofluids

Glymphatic clearance has recently gained increasing attention as a major driver of neurodegenerative diseases like Alzheimer’s disease. Perivascular spaces is believed to reflect glymphatic clearance such that enlargement of the PVS is believed to reflect blocked glymphatic clearance. We present preliminary work on a MR-based FAST-T2 CSF water fraction map to study glymphatic clearance. Our results show a quadratic relationship between CSFF and PiB PET SUVR in AD-specific regions. This indicates that CSFF could be a biomarker of dysfunctional glymphatic clearance, leading to early beta amyloid accumulation, since early on, CSFF increases without a significant change in PiB PET SUVR.

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Yi Chen^1,2, Zachary Fernandez^3,4, Norman Scheel³, Mahsa Gifani⁵, Chunqi Qian³, Anne M. Dorrance⁶, Scott E. Counts^4,5,7,8, and David C. Zhu^3
1Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, ²Department of Radiology, Michigan State University, East Lansing, MI, United States, ³Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI, United States, ⁴Neuroscience Program, Michigan State University, East Lansing, MI, United States, ⁵Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI, United States, ⁶Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI, United States, ⁷Department of Family Medicine, Michigan State University, Grand Rapids, MI, United States, ⁸Hauenstein Neurosciences Center, Mercy Health Saint Mary’s Hospital, Grand Rapids, MI, United States

Keywords: Alzheimer's Disease, Animals

Extensive studies have revealed that the entorhinal cortex (EC) plays a critical role in Alzheimer’s disease (AD) development. However, EC functional connectivity and its associated network abnormalities are understudied, especially in rodent models. To the best of our knowledge, this is the first study to report disrupted EC functional connectivity in hypertensive AD rodents using resting-state fMRI. Our results may provide new insights into the impaired EC connectivity patterns and enable the search for novel preclinical EC-based fMRI biomarkers for AD studies. In addition, our novel animal model provides new information to understand the link between hypertension and AD.

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Anna Marie Chen¹, Martin Gajdošík¹, Rosemary Peralta¹, Dishari Azad², Helena Zheng¹, Mia Gajdošík¹, Ajax George¹, Sinyeob Ahn³, Mickael Tordjman^1,4, Julia Zabludovsky¹, Yuxin Zhang⁵, LianLian Chen⁵, Henry Rusinek^1,2, Guillaume Madelin¹, Ricardo Osorio², and Ivan Kirov^1,6,7
1Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ²Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States, ³Siemens Medical Solutions USA Inc., Malvern, PA, United States, ⁴Department of Radiology, Cochin Hospital, Paris, France, ⁵Department of Biostatistics, New York University School of Global Public Health, New York, NY, United States, ⁶Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States, ⁷Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States

Keywords: Alzheimer's Disease, Gray Matter

¹H-MRSI can examine spatiotemporal characteristics of metabolic dysfunction in multiple brain regions in early Alzheimer’s disease (AD) pathology. Since both cortical and subcortical gray matter structures have shown vulnerability to AD neurodegeneration, we tested whether regional gray matter metabolic abnormalities were associated with (i) APOE4 genotype, a risk factor for amyloid burden, (ii) CSF p-tau181, an indicator of tau burden, and (iii) morphometry metrics (volume, cortical thickness) indicative of neurodegeneration, in cognitively unimpaired elderly. We found lower caudate Cho and lower posterior cingulate Glx in APOE4 carriers compared to non-carriers. There were no metabolite relationships with CSF p-tau181 or morphometry.

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Jarrod J. Eisma¹, Colin D. McKnight², Ciaran M. Considine¹, Kilian Hett¹, Alexander K. Song¹, Caleb J. Han¹, Jason Elenberger¹, Daniel O. Claassen¹, and Manus J. Donahue^1,3
1Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States, ²Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ³Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States

Keywords: Alzheimer's Disease, Neurofluids, Aging, Perfusion, Arterial spin labelling

The goal of this work is to characterize changes in the choroid plexus (ChP) and aqueductal cerebrospinal fluid dynamics in patients with Alzheimer’s disease utilizing arterial spin labeling (ASL) and phase contrast (PC), respectively. We observed that across all participants, ChP hypertrophy was associated with reduced ChP perfusion. However, we did not observe differences in ChP perfusion or net CSF flow between AD and healthy cohorts. The observed increase in peak CSF flow with age, despite the general decrease in ChP perfusion with age, suggests that the ChP performs functions beyond simply increasing or decreasing CSF production.

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Coreylyn A. deBettencourt¹, Kirubel Kentiba¹, Alexander Atalay², Benjamin T. Newman³, John D. Van Horn⁴, and T. Jason Druzgal^3
1University of Virginia School of Medicine, Charlottesville, VA, United States, ²University of Virginia, Charlottesville, VA, United States, ³Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States, ⁴Department of Psychology, University of Virginia, Charlottesville, VA, United States

Keywords: Alzheimer's Disease, Alzheimer's Disease

Density of the cholinergic nuclei of the basal forebrain has been shown to be associated with disease and symptom progression in Parkinson’s Disease. This project was aimed at determining if there is a similar effect of these brain regions in predicting cognitive impairment and disease progression in Alzheimer’s Disease. By using a longitudinal multicenter dataset of Alzheimer's patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we analyzed the relationship between grey matter density of cholinergic basal forebrain and disease progression. We demonstrated that patients with increased disease severity had significantly lower density in the cholinergic nuclei of the basal forebrain.

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Christian Tinauer¹, Stefan Ropele¹, and Christian Langkammer^1
1Medical University of Graz, Graz, Austria

Keywords: Alzheimer's Disease, Machine Learning/Artificial Intelligence

Recent studies have shown Clever Hans effects in e.g. a widely used MRI tumor dataset. Clever Hans was a horse in the early 20th century that could supposedly do some arithmetic, although it only reacted to unintended cues of its owner. Inspired by these insights, we used T1w images from the ADNI database and removed the texture using threshold binarization. By using a standard deep neural network we separated images from Alzheimer's patients (n=404) from normal controls (n=905). This study revealed that volumetric measures are overwhelmingly relevant for the classification, while textures (T1w-contrast variations) are neglectable.

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Scott Peltier¹, Michelle Karker², Luis Hernandez¹, Henry Paulson³, Bruno Giordani⁴, Benjamin M Hampstead⁴, and Doug Noll^2
1Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States, ²Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ³Neurology, University of Michigan, Ann Arbor, MI, United States, ⁴Psychiatry, University of Michigan, Ann Arbor, MI, United States

Keywords: Alzheimer's Disease, Data Analysis

A recent technique, Connectome-based Predictive Modeling (CPM), has shown promise in relating imaging-derived measures to clinical/behavioral observations.  In this work, we adapt this method to relate volumetric CBF data to continuous composite measures of memory, learning, and language in subjects with MCI and DAT, along with healthy subjects.  Models using the learning and memory composite scores had the largest effect sizes and statistical significance using permutation testing. Regions in the feature masks indicate involvement of brain regions that may be impacted in DAT. This demonstrates the utility of ASL-based perfusion measurement as a predictor of cognitive status in older subjects. 

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Chih-Chien Tsai¹, YI-Chou Hou², Yao-Liang Chen³, and Jiun-Jie Wang^4
1Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan, ²Department of Nephrology, Cardinal Tien Hospital at Xindian, New Taipei City, Taiwan, ³Department of Diagnostic Radiology, Chang Gung Memorial Hospital at Keelung, Keelung, Taiwan, ⁴Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan

Keywords: Dementia, Kidney

Patients with end-stage kidney disease are vulnerable to cognitive impairment or dementia. Previous studies indicated that end-stage kidney disease subjects manifest prominent white matter hyperintensities. We use fixel-based analysis to examine the tract-specific differences in fixel-based metrics between end-stage kidney disease patients and normal participants. The results demonstrate the pattern of white matter degeneration in end-stage kidney disease patients. Our findings suggest that end-stage kidney disease patients with cognitive impairment exhibit white matter abnormalities. In clinical, the fixel-based analysis may serve as a potential biomarker for monitoring white matter change caused by disease.

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Katherine Anne Koenig¹, Stephen Ruedrich², Se-Hong Oh¹, Mark J Lowe¹, and James B Leverenz^1
1The Cleveland Clinic, Cleveland, OH, United States, ²University Hospitals, Cleveland, OH, United States

Keywords: Alzheimer's Disease, Genetics, Down Syndrome

Down syndrome (DS) is a genetic condition that is associated with early onset Alzheimer’s disease (AD). Individuals with DS show changes in neuroanatomy, including regional increases in cortical thickness. Here, we assess cortical thickness in ten non-demented adults with DS. We focus on regions that show cortical thinning in AD and relate cortical thickness to scores on a measure of dementia. We find that regional thickness measures are related to the 4.6 year change in a measure of dementia, suggesting that the magnitude of early DS-related neuroanatomical differences may relate to aspects of dementia onset.

Wei Zheng^1,2, Ronghua Mu¹, Xiaoyan Qin¹, Xin Li¹, Fuzhen Liu¹, Zeyu Zhuang^1,2, Peng Yang¹, Jian Lv¹, Xiqi Zhu¹, and Kan Deng^3
1the Nanxishan Hospital, Guangxi Zhuang Autonomous Region, guilin, China, ²Graduate School of Guilin Medical University, guilin, China, ³Philips Healthcare, Guangzhou, China

Keywords: Alzheimer's Disease, Radiomics

Mild cognitive impairment (MCI) is a transitional state between normal aging and dementia disorders, especially Alzheimer's disease (AD). In this study, we aimed to develop a radiomics model based on 3D-T1W imaging to distinguish amnestic mild cognitive impairment (aMCI) patients from the normal elderly population by measuring changes in the frontal lobe. And the result shows that a quantitative nomogram based on clinical feature and radiomic features of MRI imaging can be used to distinguish aMCI and CN with excellent predictive ability, which can be served as a potential decision support tool to assist clinicians in Screening community aMCI population.

 

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Fenghua Long¹, Qian Zhang¹, Yaxuan Wang¹, Qian Li¹, Youjin Zhao¹, and Fei Li^1
1Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China, Chengdu, China

Keywords: Dementia, MR Value

We performed a meta-analysis by including published studies using machine learning for unrestricted modalities and interventions on magnetic resonance imaging (MRI) data to predict the effectiveness of treatment for patients with major depressive disorder (MDD). The results showed that the resting-state functional MRI (rs-fMRI) had higher predictive performance in the modality subgroups, suggesting brain rs-fMRI may have an advantage in prediction performance. Using machine-learning analysis to predict treatment effectiveness is promising, but should not yet be implemented into clinical practice.

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Thomas Jue¹, James Graham², Usman Rehman¹, Keshav Datta³, Shie-Chau Liu³, Jane Chen⁴, Ralph Hurd³, and Daniel Spielman^3
1Biochemistry and Molecular Medicine, University of California, Davis, CA, United States, ²Nutrition, University of California, Davis, CA, United States, ³Radiology, Stanford University, Stanford, CA, United States, ⁴Center for Genomic Pathology Laboratory, University of California, Davis, CA, United States

Keywords: Alzheimer's Disease, Neurodegeneration, Hyperpolarized C13 MRI/MRS Metabolism

How type 2 diabetes (T2D) increases the risk of Alzheimer’s disease (AD) frames the central research question. The study has developed a rat model that exhibits both T2D and AD (T2D-AD +/-).  These hemizygous animals contain the genes APPswe and PS1ΔE9 and display amyloid burden as early as 6 mos of age.  Standard Tg344-AD +/- model shows amyloid plaque at 9 mos of age.

              AD impairs cognitive performance but brain metabolism as observed with Barnes Maze test and dynamic nuclear polarization experiments.

              MRS/MRI and physiological studies can now use the animal model to interrogate how insulin insensitivity accelerates AD onset/progression.

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Vaibhavi Sanjeet Itkyal¹, Ishaan Batta², Anees Abrol³, and Vince Calhoun^2
1Neuroscience, Emory University, Decatur, GA, United States, ²Georgia Tech, Decatur, GA, United States, ³Georgia State University, Decatur, GA, United States

Keywords: Alzheimer's Disease, Multimodal, Neurodegeneration, Deep Learning

In this project, we use different neuroimaging modalities in the ADNI data, including structural magnetic resonance imaging (sMRI) and temporal transformations of resting-state functional magnetic resonance imaging (rs-fMRI) data, for several classification (e.g., diagnosis) and regression (e.g., age and clinical assessments) objectives.

Sirong Piao¹, Keliang Chen¹, Na Wang¹, Yong Zhang², and Yuxin Li^1
1Huashan Hospital, Fudan University, Shanghai, China, ²GE Healthcare, Shanghai, China

Keywords: Alzheimer's Disease, fMRI (resting state)

The default mode network (DMN) could be further divided into three subsystems, and each subsystem of DMN serves different cognitive functions. In this study, we revealed that cognitive decline is driven by weakened functional connectivity within the core subsystems, but enhanced functional connectivity between the core and MTL subsystems as well as the core and DMPFC subsystems. This dissociation may reflect abnormal core-centered connection abnormalities, along with the disruption of the MTL subsystem and the DMPFC subsystem to some degree. Our findings provided  a promising imaging biomarker for the early diagnosis of AD.

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Joana Pinto¹, Sierra Sparks¹, Genevieve Hayes¹, and Daniel P. Bulte^1
1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom

Keywords: Alzheimer's Disease, Alzheimer's Disease

The influence of biological sex on clinical outcomes has been often neglected. Nevertheless, it is known that women have higher risk of dementia. Pregnancy might play an important role in this discrepancy, and studies have shown that this event is related to an increased risk of cerebrovascular impairment later in life. In this work we investigated the relation between the pregnancy related metrics and cognitive status and cerebral blood flow.  Our results confirm that the number of pregnancies might be an important factor to consider when studying AD. Baseline CBF is not related to cognitive status or number of pregnancies.

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Timothy Jaehyun Choi¹, Nagesh Adluru^1,2, Veena Nair¹, Daniel Chu¹, Camille Garcia-Ramos^3,4, Anusha Adluru¹, Shi-Jiang Li⁵, Andrew L Alexander^2,4, Barbara Bendlin⁶, and Vivek Prabhakaran^1
1Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, ²Waisman Center, University of Wisconsin-Madison, Madison, WI, United States, ³Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States, ⁴Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ⁵Medical College of Wisconsin, Milwaukee, WI, United States, ⁶Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States

Keywords: Alzheimer's Disease, Brain Connectivity

The prevalence of Alzheimer’s Dementia (AD) is on the rise, with an estimated economic burden to the US rivaling that of diabetes. Graph theory (GT) offers topological measures that can be used to detect connectivity changes due to aging and AD related processes. We aim to investigate the utility of GT measures based on multi-shell diffusion MRI (ms-dMRI) in discriminating aging individuals with cognitive impairment (CI) from healthy controls, and to find any intrinsic differences within the cognitively impaired subjects by clustering on the GT measures.