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1. LATE-NC risk alleles (in TMEM106B, GRN, and ABCC9 genes) among persons with African ancestry.

Author

Katsumata, Yuriko, Fardo, David W, Shade, Lincoln MP, Bowen, James D, Crane, Paul K, Jarvik, Gail P, Keene, C Dirk, Larson, Eric B, McCormick, Wayne C, McCurry, Susan M, Mukherjee, Shubhabrata, Kowall, Neil W, McKee, Ann C, Honig, Robert A, Lawrence, S, Vonsattel, Jean Paul, Williamson, Jennifer, Small, Scott, Burke, James R, Hulette, Christine M, Welsh-Bohmer, Kathleen A, Gearing, Marla, Lah, James J, Levey, Allan I, Wingo, Thomas S, Apostolova, Liana G, Farlow, Martin R, Ghetti, Bernardino, Saykin, Andrew J, Spina, Salvatore, Albert, Marilyn S, Lyketsos, Constantine G, Troncoso, Juan C, Frosch, Matthew P, Green, Robert C, Growdon, John H, Hyman, Bradley T, Tanzi, Rudolph E, Potter, Huntington, Dickson, Dennis W, Ertekin-Taner, Nilufer, Graff-Radford, Neill R, Parisi, Joseph E, Petersen, Ronald C, Duara, Ranjan, Buxbaum, Joseph D, Goate, Alison M, Sano, Mary, Masurkar, Arjun V, Wisniewski, Thomas, Bigio, Eileen H, Mesulam, Marsel, Weintraub, Sandra, Vassar, Robert, Kaye, Jeffrey A, Quinn, Joseph F, Woltjer, Randall L, Barnes, Lisa L, Bennett, David A, Schneider, Julie A, Yu, Lei, Henderson, Victor, Fallon, Kenneth B, Harrell, Lindy E, Marson, Daniel C, Roberson, Erik D, DeCarli, Charles, Jin, Lee-Way, Olichney, John M, Kim, Ronald, LaFerla, Frank M, Monuki, Edwin, Head, Elizabeth, Sultzer, David, Geschwind, Daniel H, Vinters, Harry V, Chesselet, Marie-Francoise, Galasko, Douglas R, Brewer, James B, Boxer, Adam, Karydas, Anna, Kramer, Joel H, Miller, Bruce L, Rosen, Howard J, Seeley, William W, Burns, Jeffrey M, Swerdlow, Russell H, Abner, Erin, Van Eldik, Linda J, Albin, Roger L, Lieberman, Andrew P, Paulson, Henry L, Arnold, Steven E, Trojanowski, John Q, Van Deerlin, Vivianna M, Hamilton, Ronald L, Kamboh, M Ilyas, Lopez, Oscar L, and Becker, James T

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Brain Disorders, Neurodegenerative, Alzheimer's Disease, Acquired Cognitive Impairment, Prevention, Aging, Minority Health, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Health Disparities, Genetics, 2.1 Biological and endogenous factors, Aetiology, Humans, Alleles, Polymorphism, Single Nucleotide, Alzheimer Disease, TDP-43 Proteinopathies, Progranulins, Membrane Proteins, Nerve Tissue Proteins, Sulfonylurea Receptors, Alzheimer’s Disease Genetics Consortium, KCNMB2, Diversity, Epidemiology, FTLD, Genome-Wide Association Studies, KATP, Neurology & Neurosurgery, Clinical sciences
Abstract

Limbic-predominant age-related TDP-43 encephalopathy (LATE) affects approximately one-third of older individuals and is associated with cognitive impairment. However, there is a highly incomplete understanding of the genetic determinants of LATE neuropathologic changes (LATE-NC) in diverse populations. The defining neuropathologic feature of LATE-NC is TDP-43 proteinopathy, often with comorbid hippocampal sclerosis (HS). In terms of genetic risk factors, LATE-NC and/or HS are associated with single nucleotide variants (SNVs) in 3 genes-TMEM106B (rs1990622), GRN (rs5848), and ABCC9 (rs1914361 and rs701478). We evaluated these 3 genes in convenience samples of individuals of African ancestry. The allele frequencies of the LATE-associated alleles were significantly different between persons of primarily African (versus European) ancestry: In persons of African ancestry, the risk-associated alleles for TMEM106B and ABCC9 were less frequent, whereas the risk allele in GRN was more frequent. We performed an exploratory analysis of data from African-American subjects processed by the Alzheimer's Disease Genomics Consortium, with a subset of African-American participants (n = 166) having corroborating neuropathologic data through the National Alzheimer's Coordinating Center (NACC). In this limited-size sample, the ABCC9/rs1914361 SNV was associated with HS pathology. More work is required concerning the genetic factors influencing non-Alzheimer disease pathology such as LATE-NC in diverse cohorts.

Published
2023

2. Neurobiology of Dementia and Other Neurodegenerative Disorders

Author

Huey, Edward D., Hardy, John, Scholz, Sonja W., Traynor, Bryan J., Small, Scott A., Schulze, Thomas G., Section editor, Laje, Gonzalo, Section editor, Tasman, Allan, editor, Riba, Michelle B., editor, Alarcón, Renato D., editor, Alfonso, César A., editor, Kanba, Shigenobu, editor, Lecic-Tosevski, Dusica, editor, Ndetei, David M., editor, Ng, Chee H., editor, and Schulze, Thomas G., editor

Published
2024
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3. Detecting Schizophrenia with 3D Structural Brain MRI Using Deep Learning

Author

Zhang, Junhao, Rao, Vishwanatha M., Tian, Ye, Yang, Yanting, Acosta, Nicolas, Wan, Zihan, Lee, Pin-Yu, Zhang, Chloe, Kegeles, Lawrence S., Small, Scott A., and Guo, Jia

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI., Comment: 13 pages, 6 figures

Published
2022

7. Identifying the trajectory of normal recovery following knee arthroplasty via physical activity monitoring

Author

Small, Scott Richard, Khalid, Sara, Doherty, Aiden, and Price, Andrew

Subjects
Total knee replacement, Activity trackers (Wearable technology), Orthopaedics
Abstract

Current orthopaedic practice lacks low-burden tools for objectively quantifying patient recovery in the first weeks following knee replacement surgery. Postoperative functional tests are burdensome to the clinical care team and unsuited for assessment of at-home recovery, while most patient reported outcome measures are not validated for perioperative use and may not reflect objective improvement in postoperative mobility. The aim of this thesis is to establish a methodology for objectively assessing the trajectory of early postoperative recovery following knee arthroplasty. A scoping review was conducted to evaluate the use of wearable sensors in knee arthroplasty clinical care, finding wearable-based gait analysis and physical activity monitoring as the most common applications of wearables in this area. This review highlights a significant missed opportunity to leverage physical activity monitoring for longitudinal assessment of patient recovery in the early postoperative period. Initially, fifty-four healthy volunteers were recruited for 24 hours of free-living activity monitoring. Reduced sampling rate was found to underestimate overall physical activity at both the wrist and hip body placements using the Axivity AX3 accelerometer. Transformations were subsequently generated to enable the direct comparison of accelerometer data collected at 25 and 100 Hz. Ground truth steps of thirty-nine healthy volunteers were recorded during 1 hour of free-living activity. A hybrid machine learning model was developed to count steps from a wrist-based accelerometer. In external validation, this model predicted steps with a 0.8% mean bias and 5.1% mean absolute percent error, compared to >20% error common to most current wrist and hip-based sensors. When step counts were derived for UK Biobank participants, daily steps and peak cadence were significant factors differentiating participants with lower limb arthritis and non arthritic matched controls. In a clinical study, patients undergoing primary knee arthroplasty were recruited from the Nuffield Orthopaedic Centre. Baseline preoperative physical activity data was collected from 141 patients, while 105 patients contributed up to 42 days of postoperative activity data, with high wear compliance during both preoperative and postoperative monitoring periods. Increase in week-to-week postoperative physical activity was documented, culminating in a median step count and acceleration of 3,983 steps/day and 19.5 mg, respectively, during the sixth postoperative week. Significantly increased recovery of physical activity was observed in patients undergoing unicompartmental versus total knee arthroplasty. Postoperative physical activity monitoring was highly acceptable in the clinical population and demonstrated utility for tracking recovery at the individual level and differentiating outcomes between surgical cohorts. Further implementation of activity monitoring in the clinical orthopaedic setting can be a useful means for assessing early postoperative patient recovery while additionally serving as an objective clinical trial outcome measure.

Published
2022

8. Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data

Author

Zhu, Nanyan, Liu, Chen, Feng, Xinyang, Sikka, Dipika, Gjerswold-Selleck, Sabrina, Small, Scott A., and Guo, Jia

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Current neuroimaging techniques provide paths to investigate the structure and function of the brain in vivo and have made great advances in understanding Alzheimer's disease (AD). However, the group-level analyses prevalently used for investigation and understanding of the disease are not applicable for diagnosis of individuals. More recently, deep learning, which can efficiently analyze large-scale complex patterns in 3D brain images, has helped pave the way for computer-aided individual diagnosis by providing accurate and automated disease classification. Great progress has been made in classifying AD with deep learning models developed upon increasingly available structural MRI data. The lack of scale-matched functional neuroimaging data prevents such models from being further improved by observing functional changes in pathophysiology. Here we propose a potential solution by first learning a structural-to-functional transformation in brain MRI, and further synthesizing spatially matched functional images from large-scale structural scans. We evaluated our approach by building computational models to discriminate patients with AD from healthy normal subjects and demonstrated a performance boost after combining the structural and synthesized functional brain images into the same model. Furthermore, our regional analyses identified the temporal lobe to be the most predictive structural-region and the parieto-occipital lobe to be the most predictive functional-region of our model, which are both in concordance with previous group-level neuroimaging findings. Together, we demonstrate the potential of deep learning with large-scale structural and synthesized functional MRI to impact AD classification and to identify AD's neuroimaging signatures., Comment: Published in IEEE ISBI 2021. Available at https://ieeexplore.ieee.org/document/9433808

Published
2021
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11. Substituting Gadolinium in Brain MRI Using DeepContrast

Author

Sun, Haoran, Liu, Xueqing, Feng, Xinyang, Liu, Chen, Zhu, Nanyan, Gjerswold-Selleck, Sabrina J., Wei, Hong-Jian, Upadhyayula, Pavan S., Mela, Angeliki, Wu, Cheng-Chia, Canoll, Peter D., Laine, Andrew F., Vaughan, J. Thomas, Small, Scott A., and Guo, Jia

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Cerebral blood volume (CBV) is a hemodynamic correlate of oxygen metabolism and reflects brain activity and function. High-resolution CBV maps can be generated using the steady-state gadolinium-enhanced MRI technique. Such a technique requires an intravenous injection of exogenous gadolinium based contrast agent (GBCA) and recent studies suggest that the GBCA can accumulate in the brain after frequent use. We hypothesize that endogenous sources of contrast might exist within the most conventional and commonly acquired structural MRI, potentially obviating the need for exogenous contrast. Here, we test this hypothesis by developing and optimizing a deep learning algorithm, which we call DeepContrast, in mice. We find that DeepContrast performs equally well as exogenous GBCA in mapping CBV of the normal brain tissue and enhancing glioblastoma. Together, these studies validate our hypothesis that a deep learning approach can potentially replace the need for GBCAs in brain MRI.

Published
2020

12. Estimating brain age based on a healthy population with deep learning and structural MRI

Author

Feng, Xinyang, Lipton, Zachary C., Yang, Jie, Small, Scott A., and Provenzano, Frank A.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods
Abstract

Numerous studies have established that estimated brain age, as derived from statistical models trained on healthy populations, constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale heterogeneous dataset (N = 10,158, age range 18 - 97) of structural brain MRIs in a healthy population from multiple publicly-available sources, upon which we train a deep learning model for brain age estimation. The availability of the large-scale dataset enables a more uniform age distribution across adult life-span for effective age estimation with no bias toward certain age groups. We demonstrate that the age estimation accuracy, evaluated with mean absolute error (MAE) and correlation coefficient (r), outperforms previously reported methods in both a hold-out test set reflective of the custom population (MAE = 4.06 years, r = 0.970) and an independent life-span evaluation dataset (MAE = 4.21 years, r = 0.960) on which a previous study has evaluated. We further demonstrate the utility of the estimated age in life-span aging analysis of cognitive functions. Furthermore, we conduct extensive ablation tests and employ feature-attribution techniques to analyze which regions contribute the most predictive value, demonstrating the prominence of the frontal lobe as well as pattern shift across life-span. In summary, we achieve superior age estimation performance confirming the efficacy of deep learning and the added utility of training with data both in larger number and more uniformly distributed than in previous studies. We demonstrate the regional contribution to our brain age predictions through multiple routes and confirm the association of divergence between estimated and chronological brain age with neuropsychological measures., Comment: 32 pages, 9 figures, 6 tables

Published
2019

14. List of contributors

Author

Anagnostou, Emmanouil, primary, Anquetin, Sandrine, additional, Astitha, Marina, additional, Ayalew, Tibebu, additional, Calovi, Martina, additional, Cannon, Forest, additional, DeCastro, Amy, additional, Cerrai, Diego, additional, Cervone, Guido, additional, Chen, Dan, additional, Doan, Quang-Van, additional, Clemente, Laura, additional, Eghdami, Masih, additional, Di Giuseppe, F., additional, Juliano, Timothy W., additional, Muñoz-Esparza, Domingo, additional, Di Napoli, C., additional, Fonley, Morgan, additional, Frediani, Maria, additional, Hu, Weiming, additional, Jadidoleslam, Navid, additional, Jimenez, Pedro, additional, Knievel, Jason C., additional, Khan, Sana, additional, Kirschbaum, Dalia B., additional, Kosovi, Branko, additional, Krajewski, Witold F., additional, Kusaka, Hiroyuki, additional, Magnusson, Linus, additional, Mantilla, Ricardo, additional, Monache, Luca Delle, additional, Nikolopoulos, Efthymios, additional, Pappenberger, F., additional, Prudhomme, C., additional, Quintero, Felipe, additional, Ramamurthy, Prathap, additional, Ray, Pallav, additional, Ruin, Isabelle, additional, Small, Scott, additional, Siems-Anderson, Amanda, additional, Terti, Galateia, additional, Tewari, Mukul, additional, Velasquez, Nicolas, additional, and Wang, Zhihua, additional

Published
2023
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19. Stabilizing the Retromer Complex in a Human Stem Cell Model of Alzheimer’s Disease Reduces TAU Phosphorylation Independently of Amyloid Precursor Protein

Author

Young, Jessica E, Fong, Lauren K, Frankowski, Harald, Petsko, Gregory A, Small, Scott A, and Goldstein, Lawrence SB

Subjects
Biochemistry and Cell Biology, Biological Sciences, Aging, Neurosciences, Brain Disorders, Dementia, Stem Cell Research, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Alzheimer's Disease, Acquired Cognitive Impairment, 2.1 Biological and endogenous factors, 5.1 Pharmaceuticals, Aetiology, Development of treatments and therapeutic interventions, Neurological, Alzheimer Disease, Amyloid Precursor Protein Secretases, Amyloid beta-Peptides, Amyloid beta-Protein Precursor, Animals, Cells, Cultured, Endosomes, Humans, Induced Pluripotent Stem Cells, Mice, Neurons, Phosphorylation, Protein Transport, tau Proteins, Alzheimer’s disease, human induced pluripotent stem cells, retromer complex stabilization, Clinical Sciences, Biochemistry and cell biology
Abstract

Developing effective therapeutics for complex diseases such as late-onset, sporadic Alzheimer's disease (SAD) is difficult due to genetic and environmental heterogeneity in the human population and the limitations of existing animal models. Here, we used hiPSC-derived neurons to test a compound that stabilizes the retromer, a highly conserved multiprotein assembly that plays a pivotal role in trafficking molecules through the endosomal network. Using this human-specific system, we have confirmed previous data generated in murine models and show that retromer stabilization has a potentially beneficial effect on amyloid beta generation from human stem cell-derived neurons. We further demonstrate that manipulation of retromer complex levels within neurons affects pathogenic TAU phosphorylation in an amyloid-independent manner. Taken together, our work demonstrates that retromer stabilization is a promising candidate for therapeutic development in AD and highlights the advantages of testing novel compounds in a human-specific, neuronal system.

Published
2018

24. Device-Measured Physical Activity in 3506 Individuals with Knee or Hip Arthroplasty.

Author

SMALL, SCOTT R., KHALID, SARA, PRICE, ANDREW J., and DOHERTY, AIDEN

Subjects
*JOINT physiology, *CROSS-sectional method, *SELF-evaluation, *TOTAL hip replacement, *ACCELEROMETERS, *SEDENTARY lifestyles, *DIGITAL health, *FUNCTIONAL status, *MULTIVARIATE analysis, *GAIT in humans, *DATA analytics, *DESCRIPTIVE statistics, *LONGITUDINAL method, *TOTAL knee replacement, *JOINT pain, *OSTEOARTHRITIS, *ELECTRONIC health records, *SLEEP, *CONFIDENCE intervals, *COMPARATIVE studies, *PHYSICAL activity, *PHYSICAL mobility, *REGRESSION analysis, *TIME, *ACTIVITIES of daily living
Abstract

Purpose: Hip and knee arthroplasty aims to reduce joint pain and increase functional mobility in patients with osteoarthritis; however, the degree to which arthroplasty is associated with higher physical activity is unclear. The current study sought to assess the association of hip and knee arthroplasty with objectively measured physical activity. Methods: This cross-sectional study analyzed wrist-worn accelerometer data collected in 2013-2016 from UK Biobank participants (aged 43-78 yr). Multivariable linear regression was performed to assess step count, cadence, overall acceleration, and activity behaviors between nonarthritic controls, end-stage arthritic, and postoperative cohorts, controlling for demographic and behavioral confounders. From a cohort of 94,707 participants with valid accelerometer wear time and complete self-reported data, electronic health records were used to identify 3506 participants having undergone primary or revision hip or knee arthroplasty and 68,389 nonarthritic controls. Results: End-stage hip or knee arthritis was associated with taking 1129 fewer steps per day (95% confidence interval (CI), 811-1447; P < 0.001) and having 5.8 fewer minutes per day (95% CI, 3.0-8.7; P < 0.001) of moderate-to-vigorous activity compared with nonarthritic controls. Unilateral primary hip and knee arthroplasties were associated with 877 (95% CI, 284-1471; P = 0.004) and 893 (95% CI, 232-1554; P = 0.008) more steps than end-stage osteoarthritic participants, respectively. Postoperative unilateral hip arthroplasty participants demonstrated levels of moderate-to-vigorous physical activity and daily step count equivalent to nonarthritic controls. No difference in physical activity was observed between any cohorts in terms of overall acceleration, or time spent in daily light activity, sedentary behavior, or sleep. Conclusions: Hip and knee arthroplasties are associated with higher levels of physical activity compared with participants with end-stage arthritis. Unilateral hip arthroplasty patients, in particular, demonstrate equivalence to nonarthritic peers at more than 1 yr after surgery. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning.

Author

Ma, David J., Yang, Yanting, Harguindeguy, Natalia, Tian, Ye, Small, Scott A., Liu, Feng, Rothman, Douglas L., and Guo, Jia

Subjects
NUCLEAR magnetic resonance spectroscopy, DEEP learning, PARIETAL lobe, IMAGE registration, RECORDING & registration
Abstract

Background: Deep learning‐based methods have been successfully applied to MRI image registration. However, there is a lack of deep learning‐based registration methods for magnetic resonance spectroscopy (MRS) spectral registration (SR). Purpose: To investigate a convolutional neural network‐based SR (CNN‐SR) approach for simultaneous frequency‐and‐phase correction (FPC) of single‐voxel Meshcher–Garwood point‐resolved spectroscopy (MEGA‐PRESS) MRS data. Study Type: Retrospective. Subjects: Forty thousand simulated MEGA‐PRESS datasets generated from FID Appliance (FID‐A) were used and split into the following: 32,000/4000/4000 for training/validation/testing. A 101 MEGA‐PRESS medial parietal lobe data retrieved from the Big GABA were used as the in vivo datasets. Field Strength/Sequence: 3T, MEGA‐PRESS. Assessment: Evaluation of frequency and phase offsets mean absolute errors were performed for the simulation dataset. Evaluation of the choline interval variance was performed for the in vivo dataset. The magnitudes of the offsets introduced were −20 to 20 Hz and −90° to 90° and were uniformly distributed for the simulation dataset at different signal‐to‐noise ratio (SNR) levels. For the in vivo dataset, different additional magnitudes of offsets were introduced: small offsets (0–5 Hz; 0–20°), medium offsets (5–10 Hz; 20–45°), and large offsets (10–20 Hz; 45–90°). Statistical Tests: Two‐tailed paired t‐tests for model performances in the simulation and in vivo datasets were used and a P‐value <0.05 was considered statistically significant. Results: CNN‐SR model was capable of correcting frequency offsets (0.014 ± 0.010 Hz at SNR 20 and 0.058 ± 0.050 Hz at SNR 2.5 with line broadening) and phase offsets (0.104 ± 0.076° at SNR 20 and 0.416 ± 0.317° at SNR 2.5 with line broadening). Using in vivo datasets, CNN‐SR achieved the best performance without (0.000055 ± 0.000054) and with different magnitudes of additional frequency and phase offsets (i.e., 0.000062 ± 0.000068 at small, −0.000033 ± 0.000023 at medium, 0.000067 ± 0.000102 at large) applied. Data Conclusion: The proposed CNN‐SR method is an efficient and accurate approach for simultaneous FPC of single‐voxel MEGA‐PRESS MRS data. Evidence Level: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Published
2024
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33. Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifiesLRRC4C, LHX5-AS1and nominates ancestry-specific lociPTPRK,GRB14, andKIAA0825as novel risk loci for Alzheimer’s disease: the Alzheimer’s Disease Genetics Consortium

Author

Rajabli, Farid, primary, Benchek, Penelope, additional, Tosto, Giuseppe, additional, Kushch, Nicholas, additional, Sha, Jin, additional, Bazemore, Katrina, additional, Zhu, Congcong, additional, Lee, Wan-Ping, additional, Haut, Jacob, additional, Hamilton-Nelson, Kara L., additional, Wheeler, Nicholas R., additional, Zhao, Yi, additional, Farrell, John J., additional, Grunin, Michelle A., additional, Leung, Yuk Yee, additional, Kuksa, Pavel P., additional, Li, Donghe, additional, Lucio da Fonseca, Eder, additional, Mez, Jesse B., additional, Palmer, Ellen L., additional, Pillai, Jagan, additional, Sherva, Richard M., additional, Song, Yeunjoo E., additional, Zhang, Xiaoling, additional, Iqbal, Taha, additional, Pathak, Omkar, additional, Valladares, Otto, additional, Kuzma, Amanda B., additional, Abner, Erin, additional, Adams, Perrie M., additional, Aguirre, Alyssa, additional, Albert, Marilyn S., additional, Albin, Roger L., additional, Allen, Mariet, additional, Alvarez, Lisa, additional, Apostolova, Liana G., additional, Arnold, Steven E., additional, Asthana, Sanjay, additional, Atwood, Craig S., additional, Ayres, Gayle, additional, Baldwin, Clinton T., additional, Barber, Robert C., additional, Barnes, Lisa L., additional, Barral, Sandra, additional, Beach, Thomas G., additional, Becker, James T., additional, Beecham, Gary W., additional, Beekly, Duane, additional, Benitez, Bruno A., additional, Bennett, David, additional, Bertelson, John, additional, Bird, Thomas D., additional, Blacker, Deborah, additional, Boeve, Bradley F., additional, Bowen, James D., additional, Boxer, Adam, additional, Brewer, James, additional, Burke, James R., additional, Burns, Jeffrey M., additional, Buxbaum, Joseph D., additional, Cairns, Nigel J., additional, Cantwell, Laura B., additional, Cao, Chuanhai, additional, Carlson, Christopher S., additional, Carlsson, Cynthia M., additional, Carney, Regina M., additional, Carrasquillo, Minerva M., additional, Chasse, Scott, additional, Chesselet, Marie-Francoise, additional, Chin, Nathaniel A., additional, Chui, Helena C., additional, Chung, Jaeyoon, additional, Craft, Suzanne, additional, Crane, Paul K., additional, Cribbs, David H., additional, Crocco, Elizabeth A., additional, Cruchaga, Carlos, additional, Cuccaro, Michael L., additional, Cullum, Munro, additional, Darby, Eveleen, additional, Davis, Barbara, additional, De Jager, Philip L., additional, DeCarli, Charles, additional, DeToledo, John, additional, Dick, Malcolm, additional, Dickson, Dennis W., additional, Dombroski, Beth A., additional, Doody, Rachelle S., additional, Duara, Ranjan, additional, Ertekin-Taner, NIlüfer, additional, Evans, Denis A., additional, Faber, Kelley M., additional, Fairchild, Thomas J., additional, Fallon, Kenneth B., additional, Fardo, David W., additional, Farlow, Martin R., additional, Fernandez-Hernandez, Victoria, additional, Ferris, Steven, additional, Foroud, Tatiana M., additional, Frosch, Matthew P., additional, Fulton-Howard, Brian, additional, Galasko, Douglas R., additional, Gamboa, Adriana, additional, Gearing, Marla, additional, Geschwind, Daniel H., additional, Ghetti, Bernardino, additional, Gilbert, John R., additional, Goate, Alison M., additional, Grabowski, Thomas J., additional, Graff-Radford, Neill R., additional, Green, Robert C., additional, Growdon, John H., additional, Hakonarson, Hakon, additional, Hall, James, additional, Hamilton, Ronald L., additional, Harari, Oscar, additional, Hardy, John, additional, Harrell, Lindy E., additional, Head, Elizabeth, additional, Henderson, Victor W., additional, Hernandez, Michelle, additional, Hohman, Timothy, additional, Honig, Lawrence S., additional, Huebinger, Ryan M., additional, Huentelman, Matthew J., additional, Hulette, Christine M., additional, Hyman, Bradley T., additional, Hynan, Linda S., additional, Ibanez, Laura, additional, Jarvik, Gail P., additional, Jayadev, Suman, additional, Jin, Lee-Way, additional, Johnson, Kim, additional, Johnson, Leigh, additional, Kamboh, M. Ilyas, additional, Karydas, Anna M., additional, Katz, Mindy J., additional, Kauwe, John S., additional, Kaye, Jeffrey A., additional, Keene, C. Dirk, additional, Khaleeq, Aisha, additional, Kim, Ronald, additional, Knebl, Janice, additional, Kowall, Neil W., additional, Kramer, Joel H., additional, Kukull, Walter A., additional, LaFerla, Frank M., additional, Lah, James J., additional, Larson, Eric B., additional, Lerner, Alan, additional, Leverenz, James B., additional, Levey, Allan I., additional, Lieberman, Andrew P., additional, Lipton, Richard B., additional, Logue, Mark, additional, Lopez, Oscar L., additional, Lunetta, Kathryn L., additional, Lyketsos, Constantine G., additional, Mains, Douglas, additional, Margaret, Flanagan E., additional, Marson, Daniel C., additional, Martin, Eden R R., additional, Martiniuk, Frank, additional, Mash, Deborah C., additional, Masliah, Eliezer, additional, Massman, Paul, additional, Masurkar, Arjun, additional, McCormick, Wayne C., additional, McCurry, Susan M., additional, McDavid, Andrew N., additional, McDonough, Stefan, additional, McKee, Ann C., additional, Mesulam, Marsel, additional, Miller, Bruce L., additional, Miller, Carol A., additional, Miller, Joshua W., additional, Montine, Thomas J., additional, Monuki, Edwin S., additional, Morris, John C., additional, Mukherjee, Shubhabrata, additional, Myers, Amanda J., additional, Nguyen, Trung, additional, O’Bryant, Sid, additional, Olichney, John M., additional, Ory, Marcia, additional, Palmer, Raymond, additional, Parisi, Joseph E., additional, Paulson, Henry L., additional, Pavlik, Valory, additional, Paydarfar, David, additional, Perez, Victoria, additional, Peskind, Elaine, additional, Petersen, Ronald C., additional, Pierce, Aimee, additional, Polk, Marsha, additional, Poon, Wayne W., additional, Potter, Huntington, additional, Qu, Liming, additional, Quiceno, Mary, additional, Quinn, Joseph F., additional, Raj, Ashok, additional, Raskind, Murray, additional, Reiman, Eric M., additional, Reisberg, Barry, additional, Reisch, Joan S., additional, Ringman, John M., additional, Roberson, Erik D., additional, Rodriguear, Monica, additional, Rogaeva, Ekaterina, additional, Rosen, Howard J., additional, Rosenberg, Roger N., additional, Royall, Donald R., additional, Sager, Mark A., additional, Sano, Mary, additional, Saykin, Andrew J., additional, Schneider, Julie A., additional, Schneider, Lon S., additional, Seeley, William W., additional, Slifer, Susan H., additional, Small, Scott, additional, Smith, Amanda G., additional, Smith, Janet P., additional, Sonnen, Joshua A., additional, Spina, Salvatore, additional, St George-Hyslop, Peter, additional, Stern, Robert A., additional, Stevens, Alan B., additional, Strittmatter, Stephen M., additional, Sultzer, David, additional, Swerdlow, Russell H., additional, Tanzi, Rudolph E., additional, Tilson, Jeffrey L., additional, Trojanowski, John Q., additional, Troncoso, Juan C., additional, Tsuang, Debby W., additional, Van Deerlin, Vivianna M., additional, van Eldik, Linda J., additional, Vance, Jeffery M., additional, Vardarajan, Badri N., additional, Vassar, Robert, additional, Vinters, Harry V., additional, Vonsattel, Jean-Paul, additional, Weintraub, Sandra, additional, Welsh-Bohmer, Kathleen A., additional, Whitehead, Patrice L., additional, Wijsman, Ellen M., additional, Wilhelmsen, Kirk C., additional, Williams, Benjamin, additional, Williamson, Jennifer, additional, Wilms, Henrik, additional, Wingo, Thomas S., additional, Wisniewski, Thomas, additional, Woltjer, Randall L., additional, Woon, Martin, additional, Wright, Clinton B., additional, Wu, Chuang-Kuo, additional, Younkin, Steven G., additional, Yu, Chang-En, additional, Yu, Lei, additional, Zhu, Xiongwei, additional, Kunkle, Brian W., additional, Bush, William S., additional, Wang, Li-San, additional, Farrer, Lindsay A., additional, Haines, Jonathan L., additional, Mayeux, Richard, additional, Pericak-Vance, Margaret A., additional, Schellenberg, Gerard D., additional, Jun, Gyungah R., additional, Reitz, Christiane, additional, and Naj, Adam C., additional

Published
2023
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39. Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank

Author

Small, Scott R., primary, Chan, Shing, additional, Walmsley, Rosemary, additional, Fritsch, Lennart von, additional, Acquah, Aidan, additional, Mertes, Gert, additional, Feakins, Benjamin G., additional, Creagh, Andrew, additional, Strange, Adam, additional, Matthews, Charles E., additional, Clifton, David A., additional, Price, Andrew J., additional, Khalid, Sara, additional, Bennett, Derrick, additional, and Doherty, Aiden, additional

Published
2023
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42. In vivo cortical diffusion imaging relates to Alzheimer's disease neuropathology.

Author

Torso, Mario, Ridgway, Gerard R., Valotti, Michele, Hardingham, Ian, Chance, Steven A., Brewer, James, Lopez, Oscar, Hyman, Bradley, Grabowski, Thomas, Sano, Mary, Chui, Helena, Albert, Marilyn, Morris, John, Kaye, Jeffrey, Wisniewski, Thomas, Small, Scott, Trojanowski, John, DeCarli, Charles, Saykin, Andrew, and Bennett, David

Subjects
ALZHEIMER'S disease, CEREBRAL amyloid angiopathy, NEUROLOGICAL disorders, DIFFUSION magnetic resonance imaging, CHRONIC traumatic encephalopathy, LOCUS coeruleus, AMYLOID plaque
Abstract

Background: There has been increasing interest in cortical microstructure as a complementary and earlier measure of neurodegeneration than macrostructural atrophy, but few papers have related cortical diffusion imaging to post-mortem neuropathology. This study aimed to characterise the associations between the main Alzheimer's disease (AD) neuropathological hallmarks and multiple cortical microstructural measures from in vivo diffusion MRI. Comorbidities and co-pathologies were also investigated. Methods: Forty-three autopsy cases (8 cognitively normal, 9 mild cognitive impairment, 26 AD) from the National Alzheimer's Coordinating Center and Alzheimer's Disease Neuroimaging Initiative databases were included. Structural and diffusion MRI scans were analysed to calculate cortical minicolumn-related measures (AngleR, PerpPD+, and ParlPD) and mean diffusivity (MD). Neuropathological hallmarks comprised Thal phase, Braak stage, neuritic plaques, and combined AD neuropathological changes (ADNC—the "ABC score" from NIA-AA recommendations). Regarding comorbidities, relationships between cortical microstructure and severity of white matter rarefaction (WMr), cerebral amyloid angiopathy (CAA), atherosclerosis of the circle of Willis (ACW), and locus coeruleus hypopigmentation (LCh) were investigated. Finally, the effect of coexistent pathologies—Lewy body disease and TAR DNA-binding protein 43 (TDP-43)—on cortical microstructure was assessed. Results: Cortical diffusivity measures were significantly associated with Thal phase, Braak stage, ADNC, and LCh. Thal phase was associated with AngleR in temporal areas, while Braak stage was associated with PerpPD+ in a wide cortical pattern, involving mainly temporal and limbic areas. A similar association was found between ADNC (ABC score) and PerpPD+. LCh was associated with PerpPD+, ParlPD, and MD. Co-existent neuropathologies of Lewy body disease and TDP-43 exhibited significantly reduced AngleR and MD compared to ADNC cases without co-pathology. Conclusions: Cortical microstructural diffusion MRI is sensitive to AD neuropathology. The associations with the LCh suggest that cortical diffusion measures may indirectly reflect the severity of locus coeruleus neuron loss, perhaps mediated by the severity of microglial activation and tau spreading across the brain. Recognizing the impact of co-pathologies is important for diagnostic and therapeutic decision-making. Microstructural markers of neurodegeneration, sensitive to the range of histopathological features of amyloid, tau, and monoamine pathology, offer a more complete picture of cortical changes across AD than conventional structural atrophy. [ABSTRACT FROM AUTHOR]

Published
2023
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48. Real-time flood forecasting and information system for the State of Iowa

Author

Krajewski, Witold F., Ceynar, Daniel, Demir, Ibrahim, Goska, Radoslaw, Kruger, Anton, Langel, Carmen, Mantilla, Ricardo, Niemeier, James, Quintero, Felipe, Seo, Bong-Chul, Small, Scott J., Weber, Larry J., and Young, Nathan C.

Subjects
United States. National Weather Service -- Evaluation -- Innovations -- International economic relations, United States. Geological Survey -- Evaluation -- Innovations -- International economic relations, Floods, Flood forecasting, Rain, Soil moisture, Hydraulic flow, Business, Earth sciences
Abstract

The Iowa Flood Center (IFC), established following the 2008 record floods, has developed a real-time flood forecasting and information dissemination system for use by all Iowans. The system complements the [...]

Published
2017
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50. In vivo evidence that SORL1, encoding the endosomal recycling receptor SORLA, can function as a causal gene in Alzheimer’s Disease

Author

Andersen, Olav Michael, Bøgh, Nikolaj Hvidt, Landau, Anne, Pløen, Gro Grunnet, Jensen, Anne Mette Gissel, Monti, Giulia, Ulhøi, Benedicte Parm, Nyengaard, Jens Randel, Jacobsen, Kirsten Rosenmay, Jørgensen, Margarita Melnikova, Holm, Ida Elisabeth, Kristensen, Marianne Lundsgaard, Alstrup, Aage Kristian Olsen, Søvsø Szocska Hansen, Esben, Teunissen, Charlotte E, Breidenbach, Laura, Droescher, Mathias, Liu, Ying, Pedersen, Hanne Skovsgaard, Callesen, Henrik, Luo, Yonglun, Bolund, Lars, Brooks, David J, Laustsen, Christoffer, Small, Scott A, Mikkelsen, Lars F., and Sørensen, Charlotte Brandt

Published
2022

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