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1,222 results on '"Lah, James"'

1. A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer's in asymptomatic individuals

Author

Wei, Yuxiang, Abrol, Anees, Lah, James, Qiu, Deqiang, and Calhoun, Vince D.

Subjects
Computer Science - Computational Engineering, Finance, and Science
Abstract

Alzheimer's disease (AD) progresses from asymptomatic changes to clinical symptoms, emphasizing the importance of early detection for proper treatment. Functional magnetic resonance imaging (fMRI), particularly dynamic functional network connectivity (dFNC), has emerged as an important biomarker for AD. Nevertheless, studies probing at-risk subjects in the pre-symptomatic stage using dFNC are limited. To identify at-risk subjects and understand alterations of dFNC in different stages, we leverage deep learning advancements and introduce a transformer-convolution framework for predicting at-risk subjects based on dFNC, incorporating spatial-temporal self-attention to capture brain network dependencies and temporal dynamics. Our model significantly outperforms other popular machine learning methods. By analyzing individuals with diagnosed AD and mild cognitive impairment (MCI), we studied the AD progression and observed a higher similarity between MCI and asymptomatic AD. The interpretable analysis highlights the cognitive-control network's diagnostic importance, with the model focusing on intra-visual domain dFNC when predicting asymptomatic AD subjects., Comment: Accepted by EMBC 2024

Published
2024

2. Model-based T1, T2* and Proton Density Mapping Using a Bayesian Approach with Parameter Estimation and Complementary Undersampling Patterns

Author

Huang, Shuai, Lah, James J., Allen, Jason W., and Qiu, Deqiang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Purpose: To achieve automatic hyperparameter estimation for the joint recovery of quantitative MR images, we propose a Bayesian formulation of the reconstruction problem that incorporates the signal model. Additionally, we investigate the use of complementary undersampling patterns to determine optimal undersampling schemes for quantitative MRI. Theory: We introduce a novel nonlinear approximate message passing framework, referred to as ``AMP-PE'', that enables the simultaneous recovery of distribution parameters and quantitative maps. Methods: We employed the variable flip angle multi-echo (VFA-ME) method to acquire measurements. Both retrospective and prospective undersampling approaches were utilized to obtain Fourier measurements using variable-density and Poisson-disk patterns. Furthermore, we extensively explored various undersampling schemes, incorporating complementary patterns across different flip angles and/or echo times. Results: AMP-PE adopts a model-based joint recovery strategy, it outperforms the $l_1$-norm minimization approach that follows a decoupled recovery strategy. A comparison with an existing joint-recovery approach further demonstrates the advantageous outcomes of AMP-PE. For quantitative $T_1$ mapping using VFA-ME, employing identical k-space sampling patterns across different echo times produced the best performance. Whereas for $T_2^*$ and proton density mappings, using complementary sampling patterns across different flip angles yielded the best performance. Conclusion: AMP-PE is equipped with built-in parameter estimation, and works naturally in clinical settings with varying acquisition protocols and scanners. It also achieves improved performance by combining information from the MR signal model and the sparse prior on images.

Published
2023

3. Integration of plasma and CSF metabolomics with CSF proteomic reveals novel associations between lipid mediators and central nervous system vascular and energy metabolism.

Author

Borkowski, Kamil, Seyfried, Nicholas, Arnold, Matthias, Lah, James, Levey, Allan, Hales, Chadwick, Dammer, Eric, Blach, Colette, Louie, Gregory, Kaddurah-Daouk, Rima, and Newman, John

Subjects
Humans, Alzheimer Disease, Epoxide Hydrolases, Proteomics, Central Nervous System, Energy Metabolism, Metabolomics, Bile Acids and Salts, Endocannabinoids
Abstract

Integration of the omics data, including metabolomics and proteomics, provides a unique opportunity to search for new associations within metabolic disorders, including Alzheimers disease. Using metabolomics, we have previously profiled oxylipins, endocannabinoids, bile acids, and steroids in 293 CSF and 202 matched plasma samples from AD cases and healthy controls and identified both central and peripheral markers of AD pathology within inflammation-regulating cytochrome p450/soluble epoxide hydrolase pathway. Additionally, using proteomics, we have identified five cerebrospinal fluid protein panels, involved in the regulation of energy metabolism, vasculature, myelin/oligodendrocyte, glia/inflammation, and synapses/neurons, affected in AD, and reflective of AD-related changes in the brain. In the current manuscript, using metabolomics-proteomics data integration, we describe new associations between peripheral and central lipid mediators, with the above-described CSF protein panels. Particularly strong associations were observed between cytochrome p450/soluble epoxide hydrolase metabolites, bile acids, and proteins involved in glycolysis, blood coagulation, and vascular inflammation and the regulators of extracellular matrix. Those metabolic associations were not observed at the gene-co-expression level in the central nervous system. In summary, this manuscript provides new information regarding Alzheimers disease, linking both central and peripheral metabolism, and illustrates the necessity for the omics data integration to uncover associations beyond gene co-expression.

Published
2023

4. 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

6. Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits

Author

Tian, Ganzhong, Hanfelt, John, Lah, James, and Risk, Benjamin

Subjects
Statistics - Applications
Abstract

There is no gold standard for the diagnosis of Alzheimer's disease (AD), except from autopsies. Unsupervised learning can provide insight into the pathophysiology of AD. A mixture of regressions can simultaneously identify clusters from multiple biomarkers while accounting for within-cluster demographic effects. Cerebrospinal fluid (CSF) biomarkers for AD have detection limits, which create additional challenges. We apply a mixture of regressions with a multivariate truncated Gaussian distribution (also called a censored multivariate Gaussian mixture of regressions or a mixture of multivariate tobit regressions) to over 3,000 participants from the Emory Goizueta Alzheimer's Disease Research Center and Emory Healthy Brain Study to examine amyloid-beta peptide 1-42 (Abeta42), total tau protein and phosphorylated tau protein in CSF with known detection limits. We address three gaps in the literature on mixture of regressions with a truncated multivariate Gaussian distribution: software availability; inference; and clustering accuracy. We discovered three clusters that tend to align with an AD group, a normal control profile and non-AD pathology. The CSF profiles differed by race, gender and the genetic marker ApoE4, highlighting the importance of considering demographic factors in unsupervised learning with detection limits. Notably, African American participants in the AD-like group had significantly lower tau burden.

Published
2023

7. Robust Quantitative Susceptibility Mapping via Approximate Message Passing with Parameter Estimation

Author

Huang, Shuai, Lah, James J., Allen, Jason W., and Qiu, Deqiang

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

Purpose: For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion. We propose a probabilistic Bayesian approach for QSM with built-in parameter estimation, and incorporate the nonlinear formulation of the dipole inversion to achieve a robust recovery of the susceptibility maps. Theory: From a Bayesian perspective, the image wavelet coefficients are approximately sparse and modelled by the Laplace distribution. The measurement noise is modelled by a Gaussian-mixture distribution with two components, where the second component is used to model the noise outliers. Through probabilistic inference, the susceptibility map and distribution parameters can be jointly recovered using approximate message passing (AMP). Methods: We compare our proposed AMP with built-in parameter estimation (AMP-PE) to the state-of-the-art L1-QSM, FANSI and MEDI approaches on the simulated and in vivo datasets, and perform experiments to explore the optimal settings of AMP-PE. Reproducible code is available at https://github.com/EmoryCN2L/QSM_AMP_PE Results: On the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE, DFCM and the highest SSIM, while MEDI achieved the lowest HFEN. On the in vivo datasets, AMP-PE is robust and successfully recovers the susceptibility maps using the estimated parameters, whereas L1-QSM, FANSI and MEDI typically require additional visual fine-tuning to select or double-check working parameters. Conclusion: AMP-PE provides automatic and adaptive parameter estimation for QSM and avoids the subjectivity from the visual fine-tuning step, making it an excellent choice for the clinical setting., Comment: Keywords: Approximate message passing, Compressive sensing, Outlier modelling, Parameter estimation, Quantitative susceptibility mapping

Published
2022

8. Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer’s disease

Author

Johnson, Erik C. B., Bian, Shijia, Haque, Rafi U., Carter, E. Kathleen, Watson, Caroline M., Gordon, Brian A., Ping, Lingyan, Duong, Duc M., Epstein, Michael P., McDade, Eric, Barthélemy, Nicolas R., Karch, Celeste M., Xiong, Chengjie, Cruchaga, Carlos, Perrin, Richard J., Wingo, Aliza P., Wingo, Thomas S., Chhatwal, Jasmeer P., Day, Gregory S., Noble, James M., Berman, Sarah B., Martins, Ralph, Graff-Radford, Neill R., Schofield, Peter R., Ikeuchi, Takeshi, Mori, Hiroshi, Levin, Johannes, Farlow, Martin, Lah, James J., Haass, Christian, Jucker, Mathias, Morris, John C., Benzinger, Tammie L. S., Roberts, Blaine R., Bateman, Randall J., Fagan, Anne M., Seyfried, Nicholas T., and Levey, Allan I.

Published
2023
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11. A Probabilistic Bayesian Approach to Recover $R_2^*$ map and Phase Images for Quantitative Susceptibility Mapping

Author

Huang, Shuai, Lah, James J., Allen, Jason W., and Qiu, Deqiang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Purpose: Undersampling is used to reduce the scan time for high-resolution 3D magnetic resonance imaging. In order to achieve better image quality and avoid manual parameter tuning, we propose a probabilistic Bayesian approach to recover $R_2^*$ map and phase images for quantitative susceptibility mapping (QSM), while allowing automatic parameter estimation from undersampled data. Theory: Sparse prior on the wavelet coefficients of images is interpreted from a Bayesian perspective as sparsity-promoting distribution. A novel nonlinear approximate message passing (AMP) framework that incorporates a mono-exponential decay model is proposed. The parameters are treated as unknown variables and jointly estimated with image wavelet coefficients. Results: The proposed AMP with parameter estimation (AMP-PE) approach successfully recovers $R_2^*$ maps and phase images for QSM across various undersampling rates. It is more computationally efficient, and performs better than the state-of-the-art $l_1$-norm regularization (L1) approach in general, except a few cases where the L1 approach performs as well as AMP-PE. Conclusion: AMP-PE achieves better performance by drawing information from both the sparse prior and the mono-exponential decay model. It does not require parameter tuning, and works with a clinical, prospective undersampling scheme where parameter tuning is often impossible or difficult due to the lack of ground-truth image., Comment: Keywords: Approximate Message Passing, Compressive Sensing, Parameter Estimation, Quantitative Susceptibility Mapping, R2* mapping, T2* mapping, Undersampling

Published
2021
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12. Association of plasma and CSF cytochrome P450, soluble epoxide hydrolase, and ethanolamide metabolism with Alzheimer’s disease

Author

Borkowski, Kamil, Pedersen, Theresa L, Seyfried, Nicholas T, Lah, James J, Levey, Allan I, Hales, Chadwick M, Dammer, Eric B, Blach, Colette, Louie, Gregory, Kaddurah-Daouk, Rima, and Newman, John W

Subjects
Medical Biochemistry and Metabolomics, Biomedical and Clinical Sciences, Aging, Acquired Cognitive Impairment, Alzheimer's Disease, Brain Disorders, Dementia, Neurosciences, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aetiology, 2.1 Biological and endogenous factors, Neurological, Alzheimer Disease, Cytochrome P-450 Enzyme System, Epoxide Hydrolases, Fatty Acids, Humans, Oxylipins, Alzheimer's disease, Lipid mediators, Endocannabinoids, Cognition, Alzheimer’s Disease Metabolomics Consortium, Alzheimer’s disease, Medical and Health Sciences, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundAlzheimer's disease, cardiovascular disease, and other cardiometabolic disorders may share inflammatory origins. Lipid mediators, including oxylipins, endocannabinoids, bile acids, and steroids, regulate inflammation, energy metabolism, and cell proliferation with well-established involvement in cardiometabolic diseases. However, their role in Alzheimer's disease is poorly understood. Here, we describe the analysis of plasma and cerebrospinal fluid lipid mediators in a case-control comparison of ~150 individuals with Alzheimer's disease and ~135 healthy controls, to investigate this knowledge gap.MethodsLipid mediators were measured using targeted quantitative mass spectrometry. Data were analyzed using the analysis of covariates, adjusting for sex, age, and ethnicity. Partial least square discriminant analysis identified plasma and cerebrospinal fluid lipid mediator discriminates of Alzheimer's disease. Alzheimer's disease predictive models were constructed using machine learning combined with stepwise logistic regression.ResultsIn both plasma and cerebrospinal fluid, individuals with Alzheimer's disease had elevated cytochrome P450/soluble epoxide hydrolase pathway components and decreased fatty acid ethanolamides compared to healthy controls. Circulating metabolites of soluble epoxide hydrolase and ethanolamides provide Alzheimer's disease predictors with areas under receiver operator characteristic curves ranging from 0.82 to 0.92 for cerebrospinal fluid and plasma metabolites, respectively.ConclusionsPrevious studies report Alzheimer's disease-associated soluble epoxide hydrolase upregulation in the brain and that endocannabinoid metabolism provides an adaptive response to neuroinflammation. This study supports the involvement of P450-dependent and endocannabinoid metabolism in Alzheimer's disease. The results further suggest that combined pharmacological intervention targeting both metabolic pathways may have therapeutic benefits for Alzheimer's disease.

Published
2021

13. Fast Nonconvex $T_2^*$ Mapping Using ADMM

Author

Huang, Shuai, Lah, James J., Allen, Jason W., and Qiu, Deqiang

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

Magnetic resonance (MR)-$T_2^*$ mapping is widely used to study hemorrhage, calcification and iron deposition in various clinical applications, it provides a direct and precise mapping of desired contrast in the tissue. However, the long acquisition time required by conventional 3D high-resolution $T_2^*$ mapping method causes discomfort to patients and introduces motion artifacts to reconstructed images, which limits its wider applicability. In this paper we address this issue by performing $T_2^*$ mapping from undersampled data using compressive sensing (CS). We formulate the reconstruction as a nonconvex problem that can be decomposed into two subproblems. They can be solved either separately via the standard approach or jointly via the alternating direction method of multipliers (ADMM). Compared to previous CS-based approaches that only apply sparse regularization on the spin density $\boldsymbol X_0$ and the relaxation rate $\boldsymbol R_2^*$, our formulation enforces additional sparse priors on the $T_2^*$-weighted images at multiple echoes to improve the reconstruction performance. We performed convergence analysis of the proposed algorithm, evaluated its performance on in vivo data, and studied the effects of different sampling schemes. Experimental results showed that the proposed joint-recovery approach generally outperforms the state-of-the-art method, especially in the low-sampling rate regime, making it a preferred choice to perform fast 3D $T_2^*$ mapping in practice. The framework adopted in this work can be easily extended to other problems arising from MR or other imaging modalities with non-linearly coupled variables.

Published
2020

14. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Author

Kaddurah-Daouk, Rima, Kueider-Paisley, Alexandra, Doraiswamy, P. Murali, Blach, Colette, Moseley, Arthur, Thompson, Will, St John-Williams, Lisa, Mahmoudiandehkhordi, Siamak, Tenenbaum, Jessica, Welsh-Balmer, Kathleen, Plassman, Brenda, Saykin, Andrew J., Nho, Kwangsik, Risacher, Shannon L., Kastenmüller, Gabi, Arnold, Matthias, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Kling, Mitchel, Shaw, Leslie, Trojanowski, John, van Duijin, Cornelia, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Price, Nathan, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Chang, Rui, Farrer, Lindsay, Au, Rhoda, Qiu, Wendy, Würtz, Peter, Koal, Therese, Mangravite, Lara, Krumsiek, Jan, Suhre, Karsten, Newman, John, Moreno, Herman, Foroud, Tatania, Sacks, Frank, Jansson, Janet, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Jr., Jagust, William, Trojanowki, John Q., Toga, Arthur W., Beckett, Laurel, Green, Robert C., Morris, John C., Perrin, Richard J., Shaw, Leslie M., Khachaturian, Zaven, Carrillo, Maria, Potter, William, Barnes, Lisa, Bernard, Marie, Gonzalez, Hector, Ho, Carole, Hsiao, John K., Jackson, Jonathan, Masliah, Eliezer, Masterman, Donna, Okonkwo, Ozioma, Perrin, Richard, Ryan, Laurie, Silverberg, Nina, Fleisher, Adam, Sacrey, Diana Truran, Fockler, Juliet, Conti, Cat, Veitch, Dallas, Neuhaus, John, Jin, Chengshi, Nosheny, Rachel, Ashford, Miriam, Flenniken, Derek, Kormos, Adrienne, Montine, Tom, Rafii, Michael, Raman, Rema, Jimenez, Gustavo, Donohue, Michael, Gessert, Devon, Salazar, Jennifer, Zimmerman, Caileigh, Cabrera, Yuliana, Walter, Sarah, Miller, Garrett, Coker, Godfrey, Clanton, Taylor, Hergesheimer, Lindsey, Smith, Stephanie, Adegoke, Olusegun, Mahboubi, Payam, Moore, Shelley, Pizzola, Jeremy, Shaffer, Elizabeth, Sloan, Brittany, Harvey, Danielle, Forghanian-Arani, Arvin, Borowski, Bret, Ward, Chad, Schwarz, Christopher, Jones, David, Gunter, Jeff, Kantarci, Kejal, Senjem, Matthew, Vemuri, Prashanthi, Reid, Robert, Fox, Nick C., Malone, Ian, Thompson, Paul, Thomopoulos, Sophia I., Nir, Talia M., Jahanshad, Neda, DeCarli, Charles, Knaack, Alexander, Fletcher, Evan, Tosun-Turgut, Duygu, Chen, Stephanie Rossi, Choe, Mark, Crawford, Karen, Yushkevich, Paul A., Das, Sandhitsu, Koeppe, Robert A., Reiman, Eric M., Chen, Kewei, Mathis, Chet, Landau, Susan, Cairns, Nigel J., Householder, Erin, Franklin, Erin, Bernhardt, Haley, Taylor-Reinwald, Lisa, Korecka, Magdalena, Figurski, Michal, Neu, Scott, Apostolova, Liana G., Shen, Li, Foroud, Tatiana M., Nudelman, Kelly, Faber, Kelley, Wilmes, Kristi, Thal, Leon, Silbert, Lisa C., Lind, Betty, Crissey, Rachel, Kaye, Jeffrey A., Carter, Raina, Dolen, Sara, Quinn, Joseph, Schneider, Lon S., Pawluczyk, Sonia, Becerra, Mauricio, Teodoro, Liberty, Dagerman, Karen, Spann, Bryan M., Vanderswag, Helen, Ziolkowski, Jaimie, Heidebrink, Judith L., Zbizek-Nulph, Lisa, Lord, Joanne L., Mason, Sara S., Albers, Colleen S., Knopman, David, Johnson, Kris, Villanueva-Meyer, Javier, Pavlik, Valory, Pacini, Nathaniel, Lamb, Ashley, Kass, Joseph S., Doody, Rachelle S., Shibley, Victoria, Chowdhury, Munir, Rountree, Susan, Dang, Mimi, Stern, Yaakov, Honig, Lawrence S., Mintz, Akiva, Ances, Beau, Winkfield, David, Carroll, Maria, Stobbs-Cucchi, Georgia, Oliver, Angela, Creech, Mary L., Mintun, Mark A., Schneider, Stacy, Geldmacher, David, Love, Marissa Natelson, Griffith, Randall, Clark, David, Brockington, John, Marson, Daniel, Grossman, Hillel, Goldstein, Martin A., Greenberg, Jonathan, Mitsis, Effie, Shah, Raj C., Lamar, Melissa, Samuels, Patricia, Duara, Ranjan, Greig-Custo, Maria T., Rodriguez, Rosemarie, Albert, Marilyn, Onyike, Chiadi, Farrington, Leonie, Rudow, Scott, Brichko, Rottislav, Kielb, Stephanie, Smith, Amanda, Raj, Balebail Ashok, Fargher, Kristin, Sadowski, Martin, Wisniewski, Thomas, Shulman, Melanie, Faustin, Arline, Rao, Julia, Castro, Karen M., Ulysse, Anaztasia, Chen, Shannon, Sheikh, Mohammed O., Singleton-Garvin, Jamika, Petrella, JeffreyR., James, Olga, Wong, Terence Z., Borges-Neto, Salvador, Karlawish, Jason H., Wolk, David A., Vaishnavi, Sanjeev, Clark, Christopher M., Arnold, Steven E., Smith, Charles D., Jicha, Gregory A., Raslau, Flavius D., Lopez, Oscar L., Oakley, MaryAnn, Simpson, Donna M., Porsteinsson, Anton P., Martin, Kim, Kowalski, Nancy, Keltz, Melanie, Goldstein, Bonnie S., Makino, Kelly M., Ismail, M. Saleem, Brand, Connie, Thai, Gaby, Pierce, Aimee, Yanez, Beatriz, Sosa, Elizabeth, Witbracht, Megan, Kelley, Brendan, Nguyen, Trung, Womack, Kyle, Mathews, Dana, Quiceno, Mary, Levey, Allan I., Lah, James J., Hajjar, Ihab, Cellar, Janet S., Burns, Jeffrey M., Swerdlow, Russell H., Brooks, William M., Silverman, Daniel H.S., Kremen, Sarah, Apostolova, Liana, Tingus, Kathleen, Lu, Po H., Bartzokis, George, Woo, Ellen, Teng, Edmond, Graff-Radford, Neill R., Parfitt, Francine, Poki-Walker, Kim, Farlow, Martin R., Hake, Ann Marie, Matthews, Brandy R., Brosch, Jared R., Herring, Scott, van, Christopher H., Mecca, Adam P., Good, Susan P., MacAvoy, Martha G., Carson, Richard E., Varma, Pradeep, Chertkow, Howard, Vaitekunis, Susan, Hosein, Chris, Black, Sandra, Stefanovic, Bojana, Heyn, Chris (Chinthaka), Robin Hsiung, Ging-Yuek, Kim, Ellen, Mudge, Benita, Sossi, Vesna, Feldman, Howard, Assaly, Michele, Finger, Elizabeth, Pasternak, Stephen, Rachinsky, Irina, Kertesz, Andrew, Drost, Dick, Rogers, John, Grant, Ian, Muse, Brittanie, Rogalski, Emily, Robson, Jordan, Mesulam, M.-Marsel, Kerwin, Diana, Wu, Chuang-Kuo, Johnson, Nancy, Lipowski, Kristine, Weintraub, Sandra, Bonakdarpour, Borna, Pomara, Nunzio, Hernando, Raymundo, Sarrael, Antero, Rosen, Howard J., Miller, Bruce L., Perry, David, Turner, Raymond Scott, Johnson, Kathleen, Reynolds, Brigid, MCCann, Kelly, Poe, Jessica, Sperling, Reisa A., Johnson, Keith A., Marshall, Gad A., Yesavage, Jerome, Taylor, Joy L., Chao, Steven, Coleman, Jaila, White, Jessica D., Lane, Barton, Rosen, Allyson, Tinklenberg, Jared, Belden, Christine M., Atri, Alireza, Clark, Kelly A., Zamrini, Edward, Sabbagh, Marwan, Killiany, Ronald, Stern, Robert, Mez, Jesse, Kowall, Neil, Budson, Andrew E., Obisesan, Thomas O., Ntekim, Oyonumo E., Wolday, Saba, Khan, Javed I., Nwulia, Evaristus, Nadarajah, Sheeba, Lerner, Alan, Ogrocki, Paula, Tatsuoka, Curtis, Fatica, Parianne, Maillard, Pauline, Olichney, John, Carmichael, Owen, Bates, Vernice, Capote, Horacio, Rainka, Michelle, Borrie, Michael, Lee, T.-Y., Bartha, Dr Rob, Johnson, Sterling, Asthana, Sanjay, Carlsson, Cynthia M., Perrin, Allison, Burke, Anna, Scharre, Douglas W., Kataki, Maria, Tarawneh, Rawan, Hart, David, Zimmerman, Earl A., Celmins, Dzintra, Miller, Delwyn D., BolesPonto, Laura L., Smith, Karen Ekstam, Koleva, Hristina, Shim, Hyungsub, Nam, Ki Won, Schultz, Susan K., Williamson, Jeff D., Craft, Suzanne, Cleveland, Jo, Yang, Mia, Sink, Kaycee M., Ott, Brian R., Drake, Jonathan, Tremont, Geoffrey, Daiello, Lori A., Drake, Jonathan D., Ritter, Aaron, Bernick, Charles, Munic, Donna, O'Connelll, Abigail, Mintzer, Jacobo, Wiliams, Arthur, Masdeu, Joseph, Shi, Jiong, Garcia, Angelica, Newhouse, Paul, Potkin, Steven, Salloway, Stephen, Malloy, Paul, Correia, Stephen, Kittur, Smita, Pearlson, Godfrey D., Blank, Karen, Anderson, Karen, Flashman, Laura A., Seltzer, Marc, Hynes, Mary L., Santulli, Robert B., Relkin, Norman, Chiang, Gloria, Lee, Athena, Lin, Michael, Ravdin, Lisa, Petersen, Ron, Neylan, Thomas, Grafman, Jordan, Danowski, Sarah, Nguyen-Barrera, Catherine, Hayes, Jacqueline, Finley, Shannon, Bernstein, Matthew, Senjem, Matt, Foster, Norm, Kim, Sungeun, Sood, Ajay, Blanchard, Kimberly S., Fleischman, Debra, Arfanakis, Konstantinos, Varon, Daniel, Greig, Maria T., Petrella, Jeffrey R., Goldstein, Bonnie, Martin, Kimberly S., Reist, Christopher, Sadowsky, Carl, Martinez, Walter, Villena, Teresa, Rosen, Howard, Marshall, Gad, Peskind, Elaine R., Petrie, Eric C., Li, Gail, Mackin, Scott, Jimenez-Maggiora, Gustavo, Drake, Erin, Donohue, Mike, Nelson, Craig, Bickford, David, Butters, Meryl, Zmuda, Michelle, Reyes, Denise, Faber, Kelley M., Nudelman, Kelly N., Au, Yiu Ho, Scherer, Kelly, Catalinotto, Daniel, Stark, Samuel, Ong, Elise, Fernandez, Dariella, Jo, Taeho, Kim, Junpyo, Bice, Paula, Huynh, Kevin, Wang, Tingting, Meikle, Peter J., and Giles, Corey

Published
2023
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17. CSF proteome profiling across the Alzheimer’s disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels

Author

del Campo, Marta, Peeters, Carel F. W., Johnson, Erik C. B., Vermunt, Lisa, Hok-A-Hin, Yanaika S., van Nee, Mirrelijn, Chen-Plotkin, Alice, Irwin, David J., Hu, William T., Lah, James J., Seyfried, Nicholas T., Dammer, Eric B., Herradon, Gonzalo, Meeter, Lieke H., van Swieten, John, Alcolea, Daniel, Lleó, Alberto, Levey, Allan I., Lemstra, Afina W., Pijnenburg, Yolande A. L., Visser, Pieter J., Tijms, Betty M., van der Flier, Wiesje M., and Teunissen, Charlotte E.

Published
2022
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18. Identification of Conserved Proteomic Networks in Neurodegenerative Dementia

Author

Swarup, Vivek, Chang, Timothy S, Duong, Duc M, Dammer, Eric B, Dai, Jingting, Lah, James J, Johnson, Erik CB, Seyfried, Nicholas T, Levey, Allan I, and Geschwind, Daniel H

Subjects
Acquired Cognitive Impairment, Human Genome, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Genetics, Biotechnology, Aging, Brain Disorders, Alzheimer's Disease, Neurosciences, Dementia, Aetiology, 2.1 Biological and endogenous factors, Neurological, Good Health and Well Being, Alzheimer Disease, Genetic Predisposition to Disease, Humans, Nerve Degeneration, Proteome, Proteomics, Risk Factors, Transcriptome, Alzheimer’s disease, gene networks, genetics, genomics, neurodegenerative diseases, progressive supranuclear palsy, proteomics, systems biology, transcriptomics, Biochemistry and Cell Biology, Medical Physiology
Abstract

Data-driven analyses are increasingly valued in modern medicine. We integrate quantitative proteomics and transcriptomics from over 1,000 post-mortem brains from six cohorts representing Alzheimer's disease (AD), asymptomatic AD, progressive supranuclear palsy (PSP), and control patients from the Accelerating Medicines Partnership - Alzheimer's Disease consortium. We define robust co-expression trajectories related to disease progression, including early neuronal, microglial, astrocyte, and immune response modules, and later mRNA splicing and mitochondrial modules. The majority of, but not all, modules are conserved at the transcriptomic level, including module C3, which is only observed in proteome networks and enriched in mitogen-activated protein kinase (MAPK) signaling. Genetic risk enriches in modules changing early in disease and indicates that AD and PSP have distinct causal biological drivers at the pathway level, despite aspects of similar pathology, including synaptic loss and glial inflammatory changes. The conserved, high-confidence proteomic changes enriched in genetic risk represent targets for drug discovery.

Published
2020

19. Proteomic changes in the human cerebrovasculature in Alzheimer's disease and related tauopathies linked to peripheral biomarkers in plasma and cerebrospinal fluid

Author

Wojtas, Aleksandra M., primary, Dammer, Eric B., additional, Guo, Qi, additional, Ping, Lingyan, additional, Shantaraman, Ananth, additional, Duong, Duc M., additional, Yin, Luming, additional, Fox, Edward J., additional, Seifar, Fatemeh, additional, Lee, Edward B., additional, Johnson, Erik C. B., additional, Lah, James J., additional, Levey, Allan I., additional, Levites, Yona, additional, Rangaraju, Srikant, additional, Golde, Todd E., additional, and Seyfried, Nicholas T., additional

Published
2024
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20. Contribution of Peripheral Microvascular Dysfunction to Mild Cognitive Impairment

Author

Gold, Matthew E., primary, Rogers, Steven C., additional, Kulshreshtha, Ambar, additional, Chen, Yuxuan, additional, Ko, Yi-An, additional, Cheng, Michael L., additional, Gold, Daniel A., additional, Vatsa, Nishant, additional, Jain, Vardhmaan, additional, Desai, Shivang, additional, Moazzami, Kasra, additional, Thomas, Tiffany, additional, Okafor, Maureen, additional, Goldstein, Felicia, additional, Lah, James, additional, Quyyumi, Arshed A., additional, and Hajjar, Ihab, additional

Published
2024
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21. Large- Scale Deep Proteomic Analysis in Alzheimer's Disease Brain Regions Across Race and Ethnicity

Author

Seifar, Fatemeh, primary, Fox, Edward J., additional, Shantaraman, Ananth, additional, Liu, Yue, additional, Dammer, Eric B., additional, Modeste, Erica, additional, Duong, Duc M., additional, Yin, Luming, additional, Trautwig, Adam N., additional, Guo, Qi, additional, Xu, Kaiming, additional, Ping, Lingyan, additional, Reddy, Joseph S., additional, Allen, Mariet, additional, Quicksall, Zachary, additional, Heath, Laura, additional, Scanlan, Jo, additional, Wang, Erming, additional, Wang, Minghui, additional, Vander Linden, Abby, additional, Poehlman, William, additional, Chen, Xianfeng, additional, Baheti, Saurabh, additional, Ho, Charlotte, additional, Nguyen, Thuy, additional, Yepez, Geovanna, additional, Mitchell, Adriana O., additional, Oatman, Stephanie R., additional, Wang, Xue, additional, Carrasquillo, Minerva M., additional, Runnels, Alexi, additional, Beach, Thomas, additional, Serrano, Geidy E., additional, Dickson, Dennis W., additional, Lee, Edward B., additional, Golde, Todd E., additional, Prokop, Stefan, additional, Barnes, Lisa L., additional, Zhang, Bin, additional, Haroutunian, Varham, additional, Gearing, Marla, additional, Lah, James J., additional, De Jager, Philip, additional, Bennett, David A., additional, Greenwood, Anna, additional, Ertekin-Taner, Nil fer, additional, Levey, Allan I., additional, Wingo, Aliza, additional, Wingo, Thomas, additional, and Seyfried, Nicholas T., additional

Published
2024
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22. The association between neighborhood deprivation and DNA methylation in an autopsy cohort

Author

Pett, Lindsay, primary, Li, Zhenjiang, additional, Abrishamcar, Sarina, additional, Hodge, Kenyaita, additional, Everson, Todd, additional, Christensen, Grace, additional, Gearing, Marla, additional, Kobor, Michael S., additional, Konwar, Chaini, additional, MacIsaac, Julia L., additional, Dever, Kristy, additional, Wingo, Aliza P., additional, Levey, Allan, additional, Lah, James J., additional, Wingo, Thomas S., additional, and Hüls, Anke, additional

Published
2024
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26. Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia

Author

Swarup, Vivek, Hinz, Flora I, Rexach, Jessica E, Noguchi, Ken-ichi, Toyoshiba, Hiroyoshi, Oda, Akira, Hirai, Keisuke, Sarkar, Arjun, Seyfried, Nicholas T, Cheng, Chialin, Haggarty, Stephen J, Grossman, Murray, Van Deerlin, Vivianna M, Trojanowski, John Q, Lah, James J, Levey, Allan I, Kondou, Shinichi, and Geschwind, Daniel H

Subjects
Biomedical and Clinical Sciences, Health Sciences, Frontotemporal Dementia (FTD), Neurodegenerative, Genetics, Dementia, Alzheimer's Disease, Rare Diseases, Aging, Brain Disorders, Neurosciences, Orphan Drug, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Human Genome, Acquired Cognitive Impairment, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Neurological, Animals, Cell Death, Disease Models, Animal, Evolution, Molecular, Frontotemporal Dementia, Gene Expression Regulation, Gene Regulatory Networks, Genetic Predisposition to Disease, Genetic Vectors, Humans, Mice, Inbred C57BL, Mice, Transgenic, MicroRNAs, Neurodegenerative Diseases, Proteomics, RNA, Messenger, Reproducibility of Results, Transcriptome, tau Proteins, International Frontotemporal Dementia Genomics Consortium, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Identifying the mechanisms through which genetic risk causes dementia is an imperative for new therapeutic development. Here, we apply a multistage, systems biology approach to elucidate the disease mechanisms in frontotemporal dementia. We identify two gene coexpression modules that are preserved in mice harboring mutations in MAPT, GRN and other dementia mutations on diverse genetic backgrounds. We bridge the species divide via integration with proteomic and transcriptomic data from the human brain to identify evolutionarily conserved, disease-relevant networks. We find that overexpression of miR-203, a hub of a putative regulatory microRNA (miRNA) module, recapitulates mRNA coexpression patterns associated with disease state and induces neuronal cell death, establishing this miRNA as a regulator of neurodegeneration. Using a database of drug-mediated gene expression changes, we identify small molecules that can normalize the disease-associated modules and validate this experimentally. Our results highlight the utility of an integrative, cross-species network approach to drug discovery.

Published
2019

28. Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level

Author

Johnson, Erik C. B., Carter, E. Kathleen, Dammer, Eric B., Duong, Duc M., Gerasimov, Ekaterina S., Liu, Yue, Liu, Jiaqi, Betarbet, Ranjita, Ping, Lingyan, Yin, Luming, Serrano, Geidy E., Beach, Thomas G., Peng, Junmin, De Jager, Philip L., Haroutunian, Vahram, Zhang, Bin, Gaiteri, Chris, Bennett, David A., Gearing, Marla, Wingo, Thomas S., Wingo, Aliza P., Lah, James J., Levey, Allan I., and Seyfried, Nicholas T.

Published
2022
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32. Cerebral Aβ deposition in an Aβ-precursor protein-transgenic rhesus monkey

Author

Chan, Anthony W.S., Cho, In Ki, Li, Chun-Xia, Zhang, Xiaodong, Patel, Sudeep, Rusnak, Rebecca, Raper, Jessica, Bachevalier, Jocelyne, Moran, Sean P., Chi, Tim, Cannon, Katherine H., Hunter, Carissa E., Martin, Ryan C., Xiao, Hailian, Yang, Shang-Hsun, Gumber, Sanjeev, Herndon, James G., Rosen, Rebecca F., Hu, William T., Lah, James J., Levey, Allan I., Smith, Yoland, and Walker, Lary C.

Published
2022
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33. Lower Prevalence of Asymptomatic Alzheimer's Disease Among Healthy African Americans.

Author

Lah, James J., Tian, Ganzhong, Risk, Benjamin B., Hanfelt, John J., Wang, Liangkang, Zhao, Liping, Hales, Chadwick M., Johnson, Erik C.B., Elmor, Morgan B., Malakauskas, Sarah J., Heilman, Craig, Wingo, Thomas S., Dorbin, Cornelya D., Davis, Crystal P., Thomas, Tiffany I., Hajjar, Ihab M., Levey, Allan I., and Parker, Monica W.

Subjects
*ALZHEIMER'S disease, *GAUSSIAN mixture models, *CEREBROSPINAL fluid, *DISEASE risk factors, *AFRICAN Americans
Abstract

Objective: Alzheimer's disease (AD) is believed to be more common in African Americans (AA), but biomarker studies in AA populations are limited. This report represents the largest study to date examining cerebrospinal fluid AD biomarkers in AA individuals. Methods: We analyzed 3,006 cerebrospinal fluid samples from controls, AD cases, and non‐AD cases, including 495 (16.5%) self‐identified black/AA and 2,456 (81.7%) white/European individuals using cutoffs derived from the Alzheimer's Disease Neuroimaging Initiative, and using a data‐driven multivariate Gaussian mixture of regressions. Results: Distinct effects of race were found in different groups. Total Tauand phospho181‐Tau were lower among AA individuals in all groups (p < 0.0001), and Aβ42 was markedly lower in AA controls compared with white controls (p < 0.0001). Gaussian mixture of regressions modeling of cerebrospinal fluid distributions incorporating adjustments for covariates revealed coefficient estimates for AA race comparable with 2‐decade change in age. Using Alzheimer's Disease Neuroimaging Initiative cutoffs, fewer AA controls were classified as biomarker‐positive asymptomatic AD (8.0% vs 13.4%). After adjusting for covariates, our Gaussian mixture of regressions model reduced this difference, but continued to predict lower prevalence of asymptomatic AD among AA controls (9.3% vs 13.5%). Interpretation: Although the risk of dementia is higher, data‐driven modeling indicates lower frequency of asymptomatic AD in AA controls, suggesting that dementia among AA populations may not be driven by higher rates of AD. ANN NEUROL 2024;96:463–475 [ABSTRACT FROM AUTHOR]

Published
2024
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34. Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases

Author

Allen, Mariet, Wang, Xue, Burgess, Jeremy D, Watzlawik, Jens, Serie, Daniel J, Younkin, Curtis S, Nguyen, Thuy, Malphrus, Kimberly G, Lincoln, Sarah, Carrasquillo, Minerva M, Ho, Charlotte, Chakrabarty, Paramita, Strickland, Samantha, Murray, Melissa E, Swarup, Vivek, Geschwind, Daniel H, Seyfried, Nicholas T, Dammer, Eric B, Lah, James J, Levey, Allan I, Golde, Todd E, Funk, Cory, Li, Hongdong, Price, Nathan D, Petersen, Ronald C, Graff‐Radford, Neill R, Younkin, Steven G, Dickson, Dennis W, Crook, Julia R, Asmann, Yan W, and Ertekin‐Taner, Nilüfer

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Aging, Acquired Cognitive Impairment, Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Neurodegenerative, Alzheimer's Disease, 2.1 Biological and endogenous factors, Aetiology, 1.1 Normal biological development and functioning, Underpinning research, Neurological, Alzheimer Disease, Brain, Cohort Studies, Computational Biology, Female, Gene Expression Regulation, Genetic Predisposition to Disease, Humans, Male, Myelin Sheath, Neurons, Supranuclear Palsy, Progressive, Transcriptome, Proteinopathies, Alzheimer's disease, Progressive supranuclear palsy, Myelination, Coexpression networks, Temporal cortex, Cerebellum, Geriatrics, Clinical sciences, Biological psychology
Abstract

IntroductionComparative transcriptome analyses in Alzheimer's disease (AD) and other neurodegenerative proteinopathies can uncover both shared and distinct disease pathways.MethodsWe analyzed 940 brain transcriptomes including patients with AD, progressive supranuclear palsy (PSP; a primary tauopathy), and control subjects.ResultsWe identified transcriptional coexpression networks implicated in myelination, which were lower in PSP temporal cortex (TCX) compared with AD. Some of these associations were retained even after adjustments for brain cell population changes. These TCX myelination network structures were preserved in cerebellum but they were not differentially expressed in cerebellum between AD and PSP. Myelination networks were downregulated in both AD and PSP, when compared with control TCX samples.DiscussionDownregulation of myelination networks may underlie both PSP and AD pathophysiology, but may be more pronounced in PSP. These data also highlight conservation of transcriptional networks across brain regions and the influence of cell type changes on these networks.

Published
2018

37. Network Analysis of the Cerebrospinal Fluid Proteome Reveals Shared and Unique Differences Between Sporadic and Familial Forms of Amyotrophic Lateral Sclerosis

Author

Trautwig, Adam N, primary, Fox, Edward J., additional, Dammer, Eric B., additional, Shantaraman, Ananth, additional, Ping, Lingyan, additional, Duong, Duc M., additional, Levey, Allan I., additional, Lah, James J., additional, Fournier, Christina N., additional, McEachin, Zachary T., additional, Glass, Jonathan D., additional, and Seyfried, Nicholas T., additional

Published
2024
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38. Heparin-enriched plasma proteome is significantly altered in Alzheimer’s Disease

Author

Guo, Qi, primary, Ping, Lingyan, additional, Dammer, Eric B, additional, Yin, Luming, additional, Xu, Kaiming, additional, Shantaraman, Anantharaman, additional, Fox, Edward J., additional, Golde, Todd E, additional, Johnson, Erik C.B., additional, Roberts, Blaine R., additional, Lah, James J., additional, Levey, Allan I., additional, and Seyfried, Nicholas T., additional

Published
2024
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39. Network Proteomics of the Lewy Body Dementia Brain Reveals Presynaptic Signatures Distinct from Alzheimer's Disease

Author

Shantaraman, Anantharaman, primary, Dammer, Eric B., additional, Ugochukwu, Obiadada, additional, Duong, Duc M., additional, Yin, Luming, additional, Carter, E. Kathleen, additional, Gearing, Marla, additional, Chen-Plotkin, Alice, additional, Lee, Edward B., additional, Trojanowski, John Q., additional, Bennett, David A., additional, Lah, James J., additional, Levey, Allan I., additional, Seyfried, Nicholas T., additional, and Higginbotham, Lenora, additional

Published
2024
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40. A trial of gantenerumab or solanezumab in dominantly inherited Alzheimer’s disease

Author

Salloway, Stephen, Farlow, Martin, McDade, Eric, Clifford, David B., Wang, Guoqiao, Llibre-Guerra, Jorge J., Hitchcock, Janice M., Mills, Susan L., Santacruz, Anna M., Aschenbrenner, Andrew J., Hassenstab, Jason, Benzinger, Tammie L. S., Gordon, Brian A., Fagan, Anne M., Coalier, Kelley A., Cruchaga, Carlos, Goate, Alison A., Perrin, Richard J., Xiong, Chengjie, Li, Yan, Morris, John C., Snider, B. Joy, Mummery, Catherine, Surti, G. Mustafa, Hannequin, Didier, Wallon, David, Berman, Sarah B., Lah, James J., Jimenez-Velazquez, Ivonne Z., Roberson, Erik D., van Dyck, Christopher H., Honig, Lawrence S., Sánchez-Valle, Raquel, Brooks, William S., Gauthier, Serge, Galasko, Douglas R., Masters, Colin L., Brosch, Jared R., Hsiung, Ging-Yuek Robin, Jayadev, Suman, Formaglio, Maité, Masellis, Mario, Clarnette, Roger, Pariente, Jérémie, Dubois, Bruno, Pasquier, Florence, Jack, Jr, Clifford R., Koeppe, Robert, Snyder, Peter J., Aisen, Paul S., Thomas, Ronald G., Berry, Scott M., Wendelberger, Barbara A., Andersen, Scott W., Holdridge, Karen C., Mintun, Mark A., Yaari, Roy, Sims, John R., Baudler, Monika, Delmar, Paul, Doody, Rachelle S., Fontoura, Paulo, Giacobino, Caroline, Kerchner, Geoffrey A., and Bateman, Randall J.

Published
2021
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41. A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer’s Disease

Author

Seyfried, Nicholas T, Dammer, Eric B, Swarup, Vivek, Nandakumar, Divya, Duong, Duc M, Yin, Luming, Deng, Qiudong, Nguyen, Tram, Hales, Chadwick M, Wingo, Thomas, Glass, Jonathan, Gearing, Marla, Thambisetty, Madhav, Troncoso, Juan C, Geschwind, Daniel H, Lah, James J, and Levey, Allan I

Subjects
Genetics, Acquired Cognitive Impairment, Alzheimer's Disease, Brain Disorders, Dementia, Biotechnology, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Neurodegenerative, Neurosciences, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Neurological, Aged, Aged, 80 and over, Alzheimer Disease, Autopsy, Brain, Cognition, Cohort Studies, Disease Progression, Female, Gene Regulatory Networks, Genetic Predisposition to Disease, Humans, Male, Neuroglia, Neurons, Protein Processing, Post-Translational, Proteome, Proteomics, Risk Factors, Transcriptome, Biochemistry and Cell Biology
Abstract

Here, we report proteomic analyses of 129 human cortical tissues to define changes associated with the asymptomatic and symptomatic stages of Alzheimer's disease (AD). Network analysis revealed 16 modules of co-expressed proteins, 10 of which correlated with AD phenotypes. A subset of modules overlapped with RNA co-expression networks, including those associated with neurons and astroglial cell types, showing altered expression in AD, even in the asymptomatic stages. Overlap of RNA and protein networks was otherwise modest, with many modules specific to the proteome, including those linked to microtubule function and inflammation. Proteomic modules were validated in an independent cohort, demonstrating some module expression changes unique to AD and several observed in other neurodegenerative diseases. AD genetic risk loci were concentrated in glial-related modules in the proteome and transcriptome, consistent with their causal role in AD. This multi-network analysis reveals protein- and disease-specific pathways involved in the etiology, initiation, and progression of AD.

Published
2017

43. VisMET: A Passive, Efficient, and Sensitive Assessment of Visuospatial Memory in Healthy Aging, Mild Cognitive Impairment, and Alzheimer's Disease

Author

Haque, Rafi U., Manzanares, Cecelia M., Brown, Lavonda N., Pongos, Alvince L., Lah, James J., Clifford, Gari D., and Levey, Allan I.

Abstract

The entorhinal-hippocampal circuit is one of the earliest sites of cortical pathology in Alzheimer's disease (AD). Visuospatial memory paradigms that are mediated by the entorhinal-hippocampal circuit may offer a means to detect memory impairment during the early stages of AD. In this study, we developed a 4-min visuospatial memory paradigm called VisMET (Visuospatial Memory Eye-Tracking Task) that passively assesses memory using eye movements rather than explicit memory judgements. We had 296 control or memory-impaired participants view a set of images followed by a modified version of the images with either an object removed, or a new object added. Healthy controls spent significantly more time viewing these manipulations compared to subjects with mild cognitive impairment and AD. Using a logistic regression model, the amount of time that individuals viewed these manipulations could predict cognitive impairment and disease status with an out of sample area under the receiver-operator characteristic curve of 0.85. Based on these results, VisMET offers a passive, sensitive, and efficient memory paradigm capable of detecting objective memory impairment and predicting cognitive and disease status.

Published
2019
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45. Proteomic analysis of Alzheimer's disease cerebrospinal fluid reveals alterations associated with APOE ε4 and atomoxetine treatment.

Author

Dammer, Eric B., Shantaraman, Anantharaman, Ping, Lingyan, Duong, Duc M., Gerasimov, Ekaterina S., Ravindran, Suda Parimala, Gudmundsdottir, Valborg, Frick, Elisabet A., Gomez, Gabriela T., Walker, Keenan A., Emilsson, Valur, Jennings, Lori L., Gudnason, Vilmundur, Western, Daniel, Cruchaga, Carlos, Lah, James J., Wingo, Thomas S., Wingo, Aliza P., Seyfried, Nicholas T., and Levey, Allan I.

Subjects
ALZHEIMER'S disease, APOLIPOPROTEIN E, CEREBROSPINAL fluid, PROTEOMICS, CEREBRAL amyloid angiopathy, TAU proteins, PATHOLOGICAL physiology
Abstract

Alzheimer's disease (AD) is currently defined by the aggregation of amyloid-β (Aβ) and tau proteins in the brain. Although biofluid biomarkers are available to measure Aβ and tau pathology, few markers are available to measure the complex pathophysiology that is associated with these two cardinal neuropathologies. Here, we characterized the proteomic landscape of cerebrospinal fluid (CSF) changes associated with Aβ and tau pathology in 300 individuals using two different proteomic technologies—tandem mass tag mass spectrometry and SomaScan. Integration of both data types allowed for generation of a robust protein coexpression network consisting of 34 modules derived from 5242 protein measurements, including disease-relevant modules associated with autophagy, ubiquitination, endocytosis, and glycolysis. Three modules strongly associated with the apolipoprotein E ε4 (APOE ε4) AD risk genotype mapped to oxidant detoxification, mitogen-associated protein kinase signaling, neddylation, and mitochondrial biology and overlapped with a previously described lipoprotein module in serum. Alterations of all three modules in blood were associated with dementia more than 20 years before diagnosis. Analysis of CSF samples from an AD phase 2 clinical trial of atomoxetine (ATX) demonstrated that abnormal elevations in the glycolysis CSF module—the network module most strongly correlated to cognitive function—were reduced by ATX treatment. Clustering of individuals based on their CSF proteomic profiles revealed heterogeneity of pathological changes not fully reflected by Aβ and tau. Editor's summary: The use of fluid biomarkers to stratify patients and monitor treatment response can facilitate the development of therapeutics for Alzheimer's disease (AD). To extend the AD marker toolkit, Dammer et al. established an integrated cerebrospinal fluid (CSF) proteomic network and showed that interconnected groups of proteins (modules) were associated with distinct disease aspects such as Aβ and tau pathology, cognitive dysfunction, or APOE E4 status. The M20 glycolysis/redox homeostasis module was elevated in AD and correlated with cognitive dysfunction. M20 was reduced after atomoxetine treatment in CSF from patients participating in a clinical trial. Together, these results could provide a useful resource for AD biomarker development. —Daniela Neuhofer [ABSTRACT FROM AUTHOR]

Published
2024
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46. Examining amyloid reduction as a surrogate endpoint through latent class analysis using clinical trial data for dominantly inherited Alzheimer's disease.

Author

Wang, Guoqiao, Li, Yan, Xiong, Chengjie, Benzinger, Tammie L. S., Gordon, Brian A., Hassenstab, Jason, Aschenbrenner, Andrew J., McDade, Eric, Clifford, David B., Libre‐Guerra, Jorge J., Shi, Xinyu, Mummery, Catherine J., van Dyck, Christopher H., Lah, James J., Honig, Lawrence S., Day, Gregg, Ringman, John M., Brooks, William S., Fox, Nick C., and Suzuki, Kazushi

Abstract

INTRODUCTION: Increasing evidence suggests that amyloid reduction could serve as a plausible surrogate endpoint for clinical and cognitive efficacy. The double‐blind phase 3 DIAN‐TU‐001 trial tested clinical and cognitive declines with increasing doses of solanezumab or gantenerumab. METHODS: We used latent class (LC) analysis on data from the Dominantly Inherited Alzheimer Network Trials Unit 001 trial to test amyloid positron emission tomography (PET) reduction as a potential surrogate biomarker. RESULTS: LC analysis categorized participants into three classes: amyloid no change, amyloid reduction, and amyloid growth, based on longitudinal amyloid Pittsburgh compound B PET standardized uptake value ratio data. The amyloid‐no‐change class was at an earlier disease stage for amyloid amounts and dementia. Despite similar baseline characteristics, the amyloid‐reduction class exhibited reductions in the annual decline rates compared to the amyloid‐growth class across multiple biomarker, clinical, and cognitive outcomes. DISCUSSION: LC analysis indicates that amyloid reduction is associated with improved clinical outcomes and supports its use as a surrogate biomarker in clinical trials. Highlights: We used latent class (LC) analysis to test amyloid reduction as a surrogate biomarker.Despite similar baseline characteristics, the amyloid‐reduction class exhibited remarkably better outcomes compared to the amyloid‐growth class across multiple measures.LC analysis proves valuable in testing amyloid reduction as a surrogate biomarker in clinical trials lacking significant treatment effects. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Differential DNA methylation in the brain as potential mediator of the association between traffic‐related PM2.5 and neuropathology markers of Alzheimer's disease.

Author

Li, Zhenjiang, Liang, Donghai, Ebelt, Stefanie, Gearing, Marla, Kobor, Michael S., Konwar, Chaini, Maclsaac, Julie L., Dever, Kristy, Wingo, Aliza P., Levey, Allan I., Lah, James J., Wingo, Thomas S., and Hüls, Anke

Abstract

INTRODUCTION: Growing evidence indicates that fine particulate matter (PM2.5) is a risk factor for Alzheimer's disease (AD), but the underlying mechanisms have been insufficiently investigated. We hypothesized differential DNA methylation (DNAm) in brain tissue as a potential mediator of this association. METHODS: We assessed genome‐wide DNAm (Illumina EPIC BeadChips) in prefrontal cortex tissue and three AD‐related neuropathological markers (Braak stage, CERAD, ABC score) for 159 donors, and estimated donors' residential traffic‐related PM2.5 exposure 1, 3, and 5 years prior to death. We used a combination of the Meet‐in‐the‐Middle approach, high‐dimensional mediation analysis, and causal mediation analysis to identify potential mediating CpGs. RESULTS: PM2.5 was significantly associated with differential DNAm at cg25433380 and cg10495669. Twenty‐four CpG sites were identified as mediators of the association between PM2.5 exposure and neuropathology markers, several located in genes related to neuroinflammation. DISCUSSION: Our findings suggest differential DNAm related to neuroinflammation mediates the association between traffic‐related PM2.5 and AD. Highlights: First study to evaluate the potential mediation effect of DNA methylation for the association between PM2.5 exposure and neuropathological changes of Alzheimer's disease.Study was based on brain tissues rarely investigated in previous air pollution research.Cg10495669, assigned to RBCK1 gene playing a role in inflammation, was associated consistently with 1‐year, 3‐year, and 5‐year traffic‐related PM2.5 exposures prior to death.Meet‐in‐the‐middle approach and high‐dimensional mediation analysis were used simultaneously to increase the potential of identifying the differentially methylated CpGs.Differential DNAm related to neuroinflammation was found to mediate the association between traffic‐related PM2.5 and Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Feasibility Study for Longitudinal Retinal Imaging in the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) Trial

Author

Rissman, Robert A, primary, Ngolab, Jennifer, additional, Donohue, Michael C., additional, Belsha, Alison, additional, Salazar, Jennifer, additional, Cohen, Paula, additional, Jaiswal, Sandhya, additional, Tan, Veasna, additional, Aggarwal, Neelum T., additional, Alber, Jessica, additional, Johnson, Ken, additional, Jicha, Gregory A, additional, van Dyck, Christopher H, additional, Lah, James J., additional, Salloway, Stephen, additional, Rafii, Michael S, additional, Aisen, Paul S. S, additional, and Sperling, Reisa A., additional

Published
2023
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50. Differential A/T/N Networks of Cerebrospinal Fluid and Neuroimaging Biomarkers and their Prediction of Cognition between Self‐reported Black/African American and Non‐Hispanic White Individuals

Author

Xiong, Chengjie, primary, Lu, Ruijin, additional, Lah, James J., additional, Wolk, David A., additional, Gleason, Carey E., additional, Roberson, Erik D, additional, Schindler, Suzanne E., additional, Benzinger, Tammie L.S., additional, Shaw, Les M, additional, Manzanares, Cecelia, additional, Qiu, Deqiang, additional, Hassenstab, Jason J., additional, Moulder, Krista L., additional, Balls‐Berry, Joyce E, additional, Sperling, Reisa A., additional, Johnson, Keith A., additional, Levey, Allan I., additional, Johnson, Sterling C, additional, Gremminger, Emily, additional, Agboola, Folasade, additional, Grant, Elizabeth A, additional, Hornbeck, Russ C., additional, Massoumzadeh, Parinaz, additional, Keefe, Sarah J., additional, Dierker, Donna, additional, Gray, Julia D, additional, Henson, Rachel L., additional, Streitz, Marissa, additional, Mechanic‐Hamilton, Dawn, additional, Stites, Shana D., additional, and Morris, John C, additional

Published
2023
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