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1. Non-invasive quantification of 18F-florbetaben with total-body EXPLORER PET

Author

Emily Nicole Holy, Elizabeth Li, Anjan Bhattarai, Evan Fletcher, Evelyn R. Alfaro, Danielle J. Harvey, Benjamin A. Spencer, Simon R. Cherry, Charles S. DeCarli, and Audrey P. Fan

Subjects
18F-florbetaben, Alzheimer disease, β-Amyloid, Total body EXPLORER PET, Kinetic modeling, image derived input function, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Abstract Background Kinetic modeling of 18F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort. Methods 18F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer’s disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 s after injection. Dynamic time-activity curves (TACs) for 110 min were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (VT, Vs) in key brain regions with early amyloid accumulation. Non-displaceable binding potential ( $$ {BP}_{ND})$$ was also calculated from the multi-reference tissue model (MRTM). Results Amyloid-positive (AD) patients showed the highest VT and VS in anterior cingulate, posterior cingulate, and precuneus, consistent with $$ {BP}_{ND}$$ analysis. $$ {BP}_{ND} $$ and VT from kinetic models were correlated (r² = 0.46, P

Published
2024
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2. Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging

Author

Khush Patel, Ziqian Xie, Hao Yuan, Sheikh Muhammad Saiful Islam, Yaochen Xie, Wei He, Wanheng Zhang, Assaf Gottlieb, Han Chen, Luca Giancardo, Alexander Knaack, Evan Fletcher, Myriam Fornage, Shuiwang Ji, and Degui Zhi

Subjects
Biology (General), QH301-705.5
Abstract

Abstract Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants’ T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.

Published
2024
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3. Sex Modulates the Pathological Aging Effect on Caudate Functional Connectivity in Mild Cognitive Impairment

Author

Zhengshi Yang, Jessica Z. K. Caldwell, Jeffrey L. Cummings, Aaron Ritter, Jefferson W. Kinney, Dietmar Cordes, The Alzheimer's Disease Neuroimaging Initiative (ADNI), Michael Weiner, Paul Aisen, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew J. Saykin, John Morris, Enchi Liu, Tom Montine, Anthony Gamst, Ronald G. Thomas, Michael Donohue, Sarah Walter, Devon Gessert, Tamie Sather, Danielle Harvey, John Kornak, Anders Dale, Matthew Bernstein, Joel Felmlee, Nick Fox, Paul Thompson, Norbert Schuff, Gene Alexander, Charles DeCArli, Dan Bandy, Robert A. Koeppe, Norm Foster, Eric M. Reiman, Kewei Chen, Chet Mathis, Nigel J. Cairns, Lisa Taylor-Reinwald, J. Q. Trojanowki, Les Shaw, Virginia M.Y. Lee, Magdalena Korecka, Karen Crawford, Scott Neu, Tatiana M. Foroud, Steven Potkin, Li Shen, Zaven Khachaturian, Richard Frank, Peter J. Snyder, Susan Molchan, Jeffrey Kaye, Joseph Quinn, Betty Lind, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Bryan M. Spann, James Brewer, Helen Vanderswag, Judith L. Heidebrink, Joanne L. Lord, Kris Johnson, Rachelle S. Doody, Javier Villanueva-Meyer, Munir Chowdhury, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, John C. Morris, Beau Ances, Maria Carroll, Sue Leon, Mark A. Mintun, Stacy Schneider, Daniel Marson, Randall Griffith, David Clark, Hillel Grossman, Effie Mitsis, Aliza Romirowsky, Leyla deToledo-Morrell, Raj C. Shah, Ranjan Duara, Daniel Varon, Peggy Roberts, Marilyn Albert, Chiadi Onyike, Stephanie Kielb, Henry Rusinek, Mony J de Leon, Lidia Glodzik, P. Murali Doraiswamy, Jeffrey R. Petrella, Edward R. Coleman, Steven E. Arnold, Jason H. Karlawish, David Wolk, Charles D. Smith, Greg Jicha, Peter Hardy, Oscar L. Lopez, MaryAnn Oakley, Donna M. Simpson, Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Ruth A. Mulnard, Gaby Thai, Catherine Mc-Adams-Ortiz, Ramon Diaz-Arrastia, Kristen Martin-Cook, Michael DeVous, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Heather S Anderson, Russell H. Swerdlow, Liana Apostolova, Po H. Lu, George Bartzokis, Daniel H.S. Silverman, Neill R Graff-Radford, Francine Parfitt, Heather Johnson, Martin Farlow, Scott Herring, Ann M. Hake, Christopher H. van Dyck, Richard E. Carson, Martha G. MacAvoy, Howard Chertkow, Howard Bergman, Chris Hosein, Sandra Black, Bojana Stefanovic, Curtis Caldwell, Ging-Yuek Robin Hsiung, Howard Feldman, Michele Assaly, Andrew KertesZ, John Rogers, Dick Trost, Charles Bernick, Donna Munic, Diana Kerwin, Marek-Marsel Mesulam, Kristine Lipowski, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Walter Martinez, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad Marshall, Meghan Frey, Allyson Rosen, Jared Tinklenberg, Marwan Sabbagh, Christine Belden, Sandra Jacobson, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Thomas O. Obisesan, Saba Wolday, Salome K. Bwayo, Alan Lerner, Leon Hudson, Paula Ogrocki, Evan Fletcher, Owen Carmichael, John Olichney, Charles DeCarli, Smita Kittur, Michael Borrie, T-Y Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Steven G. Potkin, Adrian Preda, Dana Nguyen, Pierre Tariot, Adam Fleisher, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Barry A. Hendin, Douglas W. Scharre, Maria Kataki, Earl A. Zimmerman, Dzintra Celmins, Alice D. Brown, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Robert B. Santulli, Eben S. Schwartz, Kaycee M. Sink, Jeff D. Williamson, Pradeep Garg, Franklin Watkins, Brian R. Ott, Henry Querfurth, Geoffrey Tremont, Stephen Salloway, Paul Malloy, Stephen Correia, Howard J. Rosen, Bruce L. Miller, Jacobo Mintzer, Crystal Flynn Longmire, and Kenneth Spicer

Subjects
aging effect, caudate, mild cognitive impairment, functional connectivity, Alzheimer's Disease, sex difference, Psychiatry, RC435-571
Abstract

PurposeTo assess the pathological aging effect on caudate functional connectivity among mild cognitive impairment (MCI) participants and examine whether and how sex and amyloid contribute to this process.Materials and MethodsTwo hundred and seventy-seven functional magnetic resonance imaging (fMRI) sessions from 163 cognitive normal (CN) older adults and 309 sessions from 139 participants with MCI were included as the main sample in our analysis. Pearson's correlation was used to characterize the functional connectivity (FC) between caudate nuclei and each brain region, then caudate nodal strength was computed to quantify the overall caudate FC strength. Association analysis between caudate nodal strength and age was carried out in MCI and CN separately using linear mixed effect (LME) model with covariates (education, handedness, sex, Apolipoprotein E4, and intra-subject effect). Analysis of covariance was conducted to investigate sex, amyloid status, and their interaction effects on aging with the fMRI data subset having amyloid status available. LME model was applied to women and men separately within MCI group to evaluate aging effects on caudate nodal strength and each region's connectivity with caudate nuclei. We then evaluated the roles of sex and amyloid status in the associations of neuropsychological scores with age or caudate nodal strength. An independent cohort was used to validate the sex-dependent aging effects in MCI.ResultsThe MCI group had significantly stronger age-related increase of caudate nodal strength compared to the CN group. Analyzing women and men separately revealed that the aging effect on caudate nodal strength among MCI participants was significant only for women (left: P = 6.23 × 10−7, right: P = 3.37 × 10−8), but not for men (P > 0.3 for bilateral caudate nuclei). The aging effects on caudate nodal strength were not significantly mediated by brain amyloid burden. Caudate connectivity with ventral prefrontal cortex substantially contributed to the aging effect on caudate nodal strength in women with MCI. Higher caudate nodal strength is significantly related to worse cognitive performance in women but not in men with MCI.ConclusionSex modulates the pathological aging effects on caudate nodal strength in MCI regardless of amyloid status. Caudate nodal strength may be a sensitive biomarker of pathological aging in women with MCI.

Published
2022
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4. Kidney Function Is Not Related to Brain Amyloid Burden on PET Imaging in The 90+ Study Cohort

Author

Wei Ling Lau, Mark Fisher, Evan Fletcher, Charles DeCarli, Hayden Troutt, María M. Corrada, Claudia Kawas, and Annlia Paganini-Hill

Subjects
chronic kidney disease, cystatin C, amyloid PET, aging, cognitive decline and dementia, Medicine (General), R5-920
Abstract

Cognitive decline is common in chronic kidney disease (CKD). While the evidence of vascular cognitive impairment in this population is robust, the role of Alzheimer's pathology is unknown. We evaluated serum cystatin C-estimated glomerular filtration rate (eGFR), brain amyloid-β positron emission tomography (PET) imaging, and cognitive function in 166 participants from The 90+ Study. Mean age was 93 years (range 90-107) and 101 (61%) were women; 107 participants had normal cognitive status while 59 participants had cognitive impairment no dementia (CIND) or dementia. Mean ± standard deviation cystatin C was 1.59 ± 0.54 mg/L with eGFR 40.7 ± 18.7 ml/min/1.73m2. Higher amyloid-β burden was associated with dementia, but not with age, diabetes, hypertension, or cardiovascular disease. We found no association between brain amyloid-β burden and cystatin C eGFR. We previously reported that kidney function was associated with cognition and cerebral microbleeds in the same cohort of oldest-old adults (90+ years old). Collectively, these findings suggest that microvascular rather than Alzheimer's pathology drives CKD-associated cognitive dysfunction in this population.

Published
2021
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5. Convolutional Neural Net Learning Can Achieve Production-Level Brain Segmentation in Structural Magnetic Resonance Imaging

Author

Evan Fletcher, Charles DeCarli, Audrey P. Fan, and Alexander Knaack

Subjects
magnetic resonance imaging, brain segmentation, deep learning, convolutional neural network, medical image processing, medical imaging data ground truth, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as “production-level” because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability.

Published
2021
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6. Amyloid-PET imaging offers small improvements in predictions of future cognitive trajectories

Author

Sarah F. Ackley, Eleanor Hayes-Larson, Willa D. Brenowitz, Kaitlin Swinnerton, Dan Mungas, Evan Fletcher, Baljeet Singh, Rachel A. Whitmer, Charles DeCarli, and M. Maria Glymour

Subjects
Alzheimer’s disease, amyloid-PET, Prediction, Cognitive change, Longitudinal studies, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

Background: Amyloid β (Aβ) is thought to initiate a cascade of pathology culminating in Alzheimer’s disease-related cognitive decline. Aβ accumulation in brain tissues may begin one to two decades prior to clinical diagnosis of Alzheimer’s disease. Prior studies have demonstrated that Aβ detected in vivo with positron emission tomography with amyloid ligands (amyloid-PET) predicts contemporaneously measured cognition and future cognitive trajectories. Prior studies have not evaluated the added value of Aβ measures in predicting future cognition when repeated past cognitive measures are available. We evaluated the extent to which amyloid-PET improves prediction of future cognitive changes over and above predictions based only on sociodemographics and past cognitive measures. Methods: We used data from participants in the University of California Davis Alzheimer’s Disease Research cohort who were cognitively normal at baseline, participated in amyloid-PET imaging, and completed at least three cognitive assessments prior to amyloid-PET imaging (N = 132 for memory andN = 135 for executive function). We used sociodemographic and cognitive measures taken prior to amyloid-PET imaging to predict cognitive trajectory after amyloid-PET imaging and assessed whether measures of amyloid burden improved predictions of subsequent cognitive change. Improvements in prediction were characterized as percent reduction in the mean squared error (MSE) in predicted cognition post amyloid-PET and increase in percent variance explained. Results: The base model using only sociodemographics and past cognitive performance explained the majority of variance in both predicted memory measures (55.6%) and executive function measures (74.5%) following amyloid-PET. Adding amyloid positivity to the model reduced the MSE for memory by 0.2%, 95% CI: (0%, 2.6%), p = 0.48 and for executive function by 3.4%, 95% CI: (0.6%, 10.2%), p = 0.002. This corresponded to an increase in the percent variance explained of 0.1%, 95% CI: (0%, 1.2%) for memory and 0.9%, 95% CI: (0.1%, 2.8%) for executive function. Similar results were obtained using a continuous measure of amyloid burden. Conclusion: In this cohort, the addition of amyloid burden slightly improved predictions of executive function compared to models based only on past cognitive assessments and sociodemographics. When repeated cognitive assessments are available, the additional utility of amyloid-PET in predicting future cognitive impairment may be limited.

Published
2021
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7. Prediction and Classification of Alzheimer’s Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers

Author

Yubraj Gupta, Ramesh Kumar Lama, Goo-Rak Kwon, Alzheimer's Disease Neuroimaging Initiative, Michael W. Weiner, Paul Aisen, Michael Weiner, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew J. Saykin, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Maria Carrillo, Lew Kuller, Marc Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, M. Marcel Mesulam, William Potter, Peter Snyder, Adam Schwartz, Tom Montine, Ronald G. Thomas, Michael Donohue, Sarah Walter, Devon Gessert, Tamie Sather, Gus Jiminez, Archana B. Balasubramanian, Jennifer Mason, Iris Sim, Danielle Harvey, Matthew Bernstein, Nick Fox, Paul Thompson, Norbert Schuff, Charles DeCArli, Bret Borowski, Jeff Gunter, Matt Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarci, Chad Ward, Robert A. Koeppe, Norm Foster, Eric M. Reiman, Kewei Chen, Chet Mathis, Susan Landau, John C. Morris, Nigel J. Cairns, Erin Franklin, Lisa Taylor-Reinwald, Virginia Lee, Magdalena Korecka, Michal Figurski, Karen Crawford, Scott Neu, Tatiana M. Foroud, Steven Potkin, Li Shen, Kelley Faber, Sungeun Kim, Kwangsik Nho, Lean Thal, Leon Thal, Neil Buckholtz, Peter J. Snyder, Marilyn Albert, Richard Frank, John Hsiao, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Becerra, Liberty Teodoro, Bryan M. Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Judith L. Heidebrink, Joanne L. Lord, Sara S. Mason, Colleen S. Albers, David Knopman, Kris Johnson, Rachelle S. Doody, Javier Villanueva-Meyer, Valory Pavlik, Victoria Shibley, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, Beau Ances, Maria Carroll, Mary L. Creech, Mark A. Mintun, Stacy Schneider, Angela Oliver, Daniel Marson, David Geldmacher, Marissa Natelson Love, Randall Griffith, David Clark, John Brockington, Erik Roberson, Hillel Grossman, Effie Mitsis, Raj C. Shah, Leyla deToledo-Morrell, Ranjan Duara, Maria T. Greig-Custo, Warren Barker, Chiadi Onyike, Daniel D'Agostino, Stephanie Kielb, Martin Sadowski, Mohammed O. Sheikh, Ulysse Anaztasia, Gaikwad Mrunalini, P. Murali Doraiswamy, Jeffrey R. Petrella, Salvador Borges-Neto, Terence Z. Wong, Edward Coleman, Steven E. Arnold, Jason H. Karlawish, David A. Wolk, Christopher M. Clark, Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad, Oscar L. Lopez, MaryAnn Oakley, Donna M. Simpson, Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Steven G. Potkin, Adrian Preda, Dana Nguyen, Kyle Womack, Dana Mathews, Mary Quiceno, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Russell H. Swerdlow, William M. Brooks, Liana Apostolova, Kathleen Tingus, Ellen Woo, Daniel H.S. Silverman, Po H. Lu, George Bartzokis, Neill R Graff-Radford, Francine Parfitt, Kim Poki-Walker, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Jared R. Brosch, Scott Herring, Christopher H. van Dyck, Richard E. Carson, Martha G. MacAvoy, Pradeep Varma, Howard Chertkow, Howard Bergman, Chris Hosein, Sandra Black, Bojana Stefanovic, Curtis Caldwell, Ging-Yuek Robin Hsiung, Benita Mudge, Vesna Sossi, Howard Feldman, Michele Assaly, Elizabeth Finger, Stephen Pasternack, Irina Rachisky, Dick Trost, Andrew Kertesz, Charles Bernick, Donna Munic, Marek-Marsel Mesulam, Emily Rogalski, Kristine Lipowski, Sandra Weintraub, Borna Bonakdarpour, Diana Kerwin, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad Marshall, Jerome Yesavage, Joy L. Taylor, Barton Lane, Allyson Rosen, Jared Tinklenberg, Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson, Sherye A. Sirrel, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Thomas O. Obisesan, Saba Wolday, Joanne Allard, Alan Lerner, Paula Ogrocki, Curtis Tatsuoka, Parianne Fatica, Evan Fletcher, Pauline Maillard, John Olichney, Charles DeCarli, Owen Carmichael, Smita Kittur, Michael Borrie, T-Y Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Pierre Tariot, Anna Burke, Ann Marie Milliken, Nadira Trncic, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Douglas W. Scharre, Maria Kataki, Brendan Kelley, Earl A. Zimmerman, Dzintra Celmins, Alice D. Brown, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Laura A. Flashman, Marc Seltzer, Mary L. Hynes, Robert B. Santulli, Kaycee M. Sink, Gordineer Leslie, Jeff D. Williamson, Pradeep Garg, Franklin Watkins, Brian R. Ott, Geoffrey Tremont, Lori A. Daiello, Stephen Salloway, Paul Malloy, Stephen Correia, Howard J. Rosen, Bruce L. Miller, David Perry, Jacobo Mintzer, Kenneth Spicer, David Bachman, Stephen Pasternak, Irina Rachinsky, John Rogers, Dick Drost, Nunzio Pomara, Raymundo Hernando, Antero Sarrael, Susan K. Schultz, Karen Ekstam Smith, Hristina Koleva, Ki Won Nam, Hyungsub Shim, Norman Relkin, Gloria Chiang, Michael Lin, Lisa Ravdin, Amanda Smith, Balebail Ashok Raj, Kristin Fargher, Thomas Neylan, Jordan Grafman, Gessert Devon, Davis Melissa, Rosemary Morrison, Hayes Jacqueline, Finley Shannon, Kantarci Kejal, Ward Chad, Erin Householder, Crawford Karen, Neu Scott, Friedl Karl, Becerra Mauricio, Debra Fleischman, Konstantinos Arfanakis, Daniel Varon, Maria T Greig, Olga James, Bonnie Goldstein, Kimberly S. Martin, Dino Massoglia, Olga Brawman-Mintzer, Walter Martinez, Howard Rosen, Kelly Behan, Sterling C. Johnson, J. Jay Fruehling, Sandra Harding, Elaine R. Peskind, Eric C. Petrie, Gail Li, Jerome A. Yesavage, Ansgar J. Furst, Steven Chao, Scott Mackin, Rema Raman, Erin Drake, Mike Donohue, Gustavo Jimenez, Kelly Harless, Jennifer Salazar, Yuliana Cabrera, Lindsey Hergesheimer, Elizabeth Shaffer, Craig Nelson, David Bickford, Meryl Butters, Michelle Zmuda, Denise Reyes, Kelley M. Faber, Kelly N. Nudelman, Yiu Ho Au, Kelly Scherer, Daniel Catalinotto, Samuel Stark, Elise Ong, and Dariella Fernandez

Subjects
Alzheimer's disease, MCIs (MCI stable), MCIc (MCI converted), sMRI, FDG-PET, CSF, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.

Published
2019
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8. Staging of amyloid β, t‐tau, regional atrophy rates, and cognitive change in a nondemented cohort: Results of serial mediation analyses

Author

Evan Fletcher, Teresa Jenica Filshtein, Danielle Harvey, Alice Renaud, Dan Mungas, Charles DeCarli, and Alzheimer's Disease Neuroimaging Initiative

Subjects
Alzheimer's disease, MCI, Amyloid β, Tau, Cognitive aging, Longitudinal atrophy, Neurology. Diseases of the nervous system, RC346-429, Geriatrics, RC952-954.6
Abstract

Abstract Introduction Current models posit a sequence of amyloid β (Aβ), tau, atrophy, and cognitive change leading to Alzheimer's disease, but ambiguities remain. We examined these sequences via serial mediations. Methods We studied normal controls, early mild cognitive impairment, and late mild cognitive impairment individuals from the Alzheimer's Disease Neuroimaging Initiative 2 database for the mediation of baseline cerebrospinal fluid Aβ effects on 2‐year cognitive change via regional longitudinal atrophy rate (AR) alone or AR and tau. Results In normal controls, Aβ correlated directly with regional ARs and memory loss, with no mediations. In early mild cognitive impairment, tau and lateral temporal ARs serially mediated the influence of Aβ on memory while Aβ affected memory via hippocampal AR. Late mild cognitive impairment consistently showed serial mediations of tau followed by atrophy. However, Aβ effects on memory also continued to be specifically mediated by medial temporal ARs without intermediate tau. Discussion Biomarker sequences vary by region and disease state, suggesting the need to refine current cascade models.

Published
2018
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9. The Influence of Cerebrospinal Fluid Abnormalities and APOE 4 on PHF-Tau Protein: Evidence From Voxel Analysis and Graph Theory

Author

Yuan Li, Zhijun Yao, Yue Yu, Yu Fu, Ying Zou, Bin Hu, for the Alzheimer’s Disease Neuroimaging Initiative, Michael W. Weiner, Paul Aisen, Michael Weiner, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew J. Saykin, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Maria Carrillo, Lew Kuller, Marc Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, M. Marcel Mesulam, William Potter, Peter Snyder, Tom Montine, Gustavo Jimenez, Michael Donohue, Devon Gessert, Kelly Harless, Jennifer Salazar, Yuliana Cabrera, Sarah Walter, Lindsey Hergesheimer, Danielle Harvey, Matthew Bernstein, Nick Fox, Paul Thompson, Norbert Schuff, Charles DeCArli, Bret Borowski, Jeff Gunter, Matt Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarci, Chad Ward, Robert A. Koeppe, Norm Foster, Eric M. Reiman, Kewei Chen, Chet Mathis, John C. Morris, Nigel J. Cairns, Erin Franklin, Lisa Taylor-Reinwald, Virginia Lee, Magdalena Korecka, Michal Figurski, Karen Crawford, Scott Neu, Tatiana M. Foroud, Steven Potkin, Li Shen, Kelley Faber, Sungeun Kim, Kwangsik Nho, Lean Thal, Leon Thal, Neil Buckholtz, Peter J. Snyder, Marilyn Albert, Richard Frank, John Hsiao, Joseph Quinn, Lisa C. Silbert, Betty Lind, Jeffrey A. Kaye, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Becerra, Liberty Teodoro, Bryan M. Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Jaimie Ziolkowski, Judith L. Heidebrink, Joanne L. Lord, Sara S. Mason, Colleen S. Albers, David Knopman, Kris Johnson, Javier Villanueva-Meyer, Valory Pavlik, Nathaniel Pacini, Ashley Lamb, Joseph S. Kass, Rachelle S. Doody, Victoria Shibley, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, Randy Yeh, Beau Ances, David Winkfield, Maria Carroll, Angela Oliver, Mary L. Creech, Mark A. Mintun, Stacy Schneider, Daniel Marson, David Geldmacher, Marissa Natelson Love, Randall Griffith, David Clark, John Brockington, Hillel Grossman, Effie Mitsis, Raj C. Shah, Melissa Lamar, Patricia Samuels, Ranjan Duara, Maria T. Greig-Custo, Rosemarie Rodriguez, Chiadi Onyike, Daniel D’Agostino, Stephanie Kielb, Martin Sadowski, Mohammed O. Sheikh, Jamika Singleton-Garvin, Anaztasia Ulysse, Mrunalini Gaikwad, P. Murali Doraiswamy, Jeffrey R. Petrella, Olga James, Salvador Borges-Neto, Terence Z. Wong, Edward Coleman, Jason H. Karlawish, David A. Wolk, Sanjeev Vaishnavi, Christopher M. Clark, Steven E. Arnold, Charles D. Smith, Greg Jicha, Peter Hardy, Riham El Khouli, Elizabeth Oates, Gary Conrad, Oscar L. Lopez, MaryAnn Oakley, Donna M. Simpson, Anton P. Porsteinsson, Kim Martin, Nancy Kowalksi, Melanie Keltz, Bonnie S. Goldstein, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Gaby Thai, Aimee Pierce, Beatriz Yanez, Elizabeth Sosa, Megan Witbracht, Kyle Womack, Dana Mathews, Mary Quiceno, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Russell H. Swerdlow, William M. Brooks, Ellen Woo, Daniel H.S. Silverman, Edmond Teng, Sarah Kremen, Liana Apostolova, Kathleen Tingus, Po H. Lu, George Bartzokis, Neill R Graff-Radford, Francine Parfitt, Kim Poki-Walker, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Jared R. Brosch, Scott Herring, Christopher H. van Dyck, Richard E. Carson, Pradeep Varma, Howard Chertkow, Howard Bergman, Chris Hosein, Sandra Black, Bojana Stefanovic, Chris (Chinthaka) Heyn, Ging-Yuek Robin Hsiung, Benita Mudge, Vesna Sossi, Howard Feldman, Michele Assaly, Elizabeth Finger, Stephen Pasternack, William Pavlosky, Irina Rachinsky, Dick Drost, Andrew Kertesz, Charles Bernick, Donna Munic, Marek-Marsel Mesulam, Emily Rogalski, Kristine Lipowski, Sandra Weintraub, Borna Bonakdarpour, Diana Kerwin, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad A. Marshall, Jerome Yesavage, Joy L. Taylor, Steven Chao, Barton Lane, Allyson Rosen, Jared Tinklenberg, Edward Zamrini, Christine M. Belden, Sherye A. Sirrel, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Thomas O. Obisesan, Ntekim E. Oyonumo, Joanne Allard, Olu Ogunlana, Alan Lerner, Paula Ogrocki, Curtis Tatsuoka, Parianne Fatica, Evan Fletcher, Pauline Maillard, John Olichney, Charles DeCarli, Owen Carmichael, Smita Kittur, Michael Borrie, T-Y Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Pierre Tariot, Anna Burke, Joel Hetelle, Kathryn DeMarco, Nadira Trncic, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Douglas W. Scharre, Maria Kataki, Rawan Tarawneh, Earl A. Zimmerman, Dzintra Celmins, David Hart, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Laura A. Flashman, Marc Seltzer, Mary L. Hynes, Robert B. Santulli, Kaycee M. Sink, Mia Yang, Akiva Mintz, Rhode Island, Brian R. Ott, Geoffrey Tremont, Lori A. Daiello, Courtney Bodge, Stephen Salloway, Paul Malloy, Stephen Correia, Athena Lee, Howard J. Rosen, Bruce L. Miller, David Perry, Jacobo Mintzer, Kenneth Spicer, David Bachman, Stephen Pasternak, John Rogers, Nunzio Pomara, Raymundo Hernando, Antero Sarrael, Delwyn D. Miller, Karen Ekstam Smith, Hristina Koleva, Ki Won Nam, Hyungsub Shim, Susan K. Schultz, Norman Relkin, Gloria Chiang, Michael Lin, Lisa Ravdin, Amanda Smith, Christi Leach, Balebail Ashok Raj, Kristin Fargher, Thomas Neylan, Jordan Grafman, Lindsey Hergesheimen, Jacqueline Hayes, Shannon Finley, Susan Landau, Erin Householder, Karl Friedl, Debra Fleischman, Konstantinos Arfanakis, Daniel Varon, Maria T Greig, Bonnie Goldstein, Kimberly S. Martin, Steven G. Potkin, Adrian Preda, Dana Nguyen, Dino Massoglia, Olga Brawman-Mintzer, Walter Martinez, Howard Rosen, Kelly Behan, Gad Marshall, Marwan N. Sabbagh, Sandra A. Jacobson, Saba Wolday, Sterling C. Johnson, J. Jay Fruehling, Sandra Harding, Elaine R. Peskind, Eric C. Petrie, Gail Li, Jerome A. Yesavage, Ansgar J. Furst, Scott Mackin, Rema Raman, Erin Drake, Mike Donohue, Elizabeth Shaffer, Craig Nelson, David Bickford, Meryl Butters, Michelle Zmuda, Denise Reyes, Kelley M. Faber, Kelly N. Nudelman, Yiu Ho Au, Kelly Scherer, Daniel Catalinotto, Samuel Stark, Elise Ong, and Dariella Fernandez

Subjects
PHF-Tau, graph theory, network properties, APOE 4, CSF-Tau, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Mild cognitive impairment (MCI) is a transitional state between the cognitive changes in normal aging and Alzheimer’s disease (AD), which induces abnormalities in specific brain regions. Previous studies showed that paired helical filaments Tau (PHF-Tau) protein is a potential pathogenic protein which may cause abnormal brain function and structure in MCI and AD patients. However, the understanding of the PHF-Tau protein network in MCI patients is limited. In this study, 225 subjects with PHF-Tau Positron Emission Tomography (PET) images were divided into four groups based on whether they carried Apolipoprotein E ε4 (APOE 4) or abnormal cerebrospinal fluid Total-Tau (CSF T-Tau). They are two important pathogenic factors that might cause cognitive function impairment. The four groups were: individuals harboring CSF T-Tau pathology but no APOE 4 (APOE 4−T+); APOE 4 carriers with normal CSF T-Tau (APOE 4+T−); APOE 4 carriers with abnormal CSF T-Tau (APOE 4+T+); and APOE 4 noncarriers with abnormal CSF T-Tau (APOE 4−T−). We explored the topological organization of PHF-Tau networks in these four groups and calculated five kinds of network properties: clustering coefficient, shortest path length, Q value of modularity, nodal centrality and degree. Our findings showed that compared with APOE 4−T− group, the other three groups showed different alterations in the clustering coefficient, shortest path length, Q value of modularity, nodal centrality and degree. Simultaneously, voxel-level analysis was conducted and the results showed that compared with APOE 4−T− group, the other three groups were found increased PHF-Tau distribution in some brain regions. For APOE 4+T+ group, positive correlation was found between the value of PHF-Tau distribution in altered regions and Functional Assessment Questionnaire (FAQ) score. Our results indicated that the effects of APOE 4 and abnormal CSF T-Tau may induce abnormalities of PHF-Tau protein and APOE 4 has a greater impact on PHF-Tau than abnormal CSF T-Tau. Our results may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI patients.

Published
2019
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10. New Perspective for Non-invasive Brain Stimulation Site Selection in Mild Cognitive Impairment: Based on Meta- and Functional Connectivity Analyses

Author

Jiao Liu, Binlong Zhang, Georgia Wilson, Jian Kong, the Alzheimer’s Disease Neuroimaging Initiative, Michael W. Weiner, Paul Aisen, Michael Weiner, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew J. Saykin, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Maria Carrillo, Lew Kuller, Marc Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, M. Marcel Mesulam, William Potter, Peter Snyder, Adam Schwartz, Tom Montine, Ronald G. Thomas, Michael Donohue, Sarah Walter, Devon Gessert, Tamie Sather, Gus Jiminez, Archana B. Balasubramanian, Jennifer Mason, Iris Sim, Danielle Harvey, Matthew Bernstein, Nick Fox, Paul Thompson, Norbert Schuff, Charles DeCArli, Bret Borowski, Jeff Gunter, Matt Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarci, Chad Ward, Robert A. Koeppe, Norm Foster, Eric M. Reiman, Kewei Chen, Chet Mathis, Susan Landau, John C. Morris, Nigel J. Cairns, Erin Franklin, Lisa Taylor-Reinwald, Virginia Lee, Magdalena Korecka, Michal Figurski, Karen Crawford, Scott Neu, Tatiana M. Foroud, Steven Potkin, Li Shen, Kelley Faber, Sungeun Kim, Kwangsik Nho, Lean Thal, Leon Thal, Neil Buckholtz, Peter J. Snyder, Marilyn Albert, Richard Frank, John Hsiao, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Becerra, Liberty Teodoro, Bryan M. Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Judith L. Heidebrink, Joanne L. Lord, Sara S. Mason, Colleen S. Albers, David Knopman, Kris Johnson, Rachelle S. Doody, Javier Villanueva-Meyer, Valory Pavlik, Victoria Shibley, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, Beau Ances, Maria Carroll, Mary L. Creech, Mark A. Mintun, Stacy Schneider, Angela Oliver, Daniel Marson, David Geldmacher, Marissa Natelson Love, Randall Griffith, David Clark, John Brockington, Erik Roberson, Hillel Grossman, Effie Mitsis, Raj C. Shah, Leyla deToledo-Morrell, Ranjan Duara, Maria T. Greig-Custo, Warren Barker, Chiadi Onyike, Daniel D'Agostino, Stephanie Kielb, Martin Sadowski, Mohammed O. Sheikh, Ulysse Anaztasia, Gaikwad Mrunalini, P. Murali Doraiswamy, Jeffrey R. Petrella, Salvador Borges-Neto, Terence Z. Wong, Edward Coleman, Steven E. Arnold, Jason H. Karlawish, David A. Wolk, Christopher M. Clark, Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad, Oscar L. Lopez, MaryAnn Oakley, Donna M. Simpson, Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Steven G. Potkin, Adrian Preda, Dana Nguyen, Kyle Womack, Dana Mathews, Mary Quiceno, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Russell H. Swerdlow, William M. Brooks, Liana Apostolova, Kathleen Tingus, Ellen Woo, Daniel H.S. Silverman, Po H. Lu, George Bartzokis, Neill R Graff-Radford, Francine Parfitt, Kim Poki-Walker, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Jared R. Brosch, Scott Herring, Christopher H. van Dyck, Richard E. Carson, Martha G. MacAvoy, Pradeep Varma, Howard Chertkow, Howard Bergman, Chris Hosein, Sandra Black, Bojana Stefanovic, Curtis Caldwell, Ging-Yuek Robin Hsiung, Benita Mudge, Vesna Sossi, Howard Feldman, Michele Assaly, Elizabeth Finger, Stephen Pasternack, Irina Rachisky, Dick Trost, Andrew Kertesz, Charles Bernick, Donna Munic, Marek-Marsel Mesulam, Emily Rogalski, Kristine Lipowski, Sandra Weintraub, Borna Bonakdarpour, Diana Kerwin, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad Marshall, Jerome Yesavage, Joy L. Taylor, Barton Lane, Allyson Rosen, Jared Tinklenberg, Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson, Sherye A. Sirrel, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Thomas O. Obisesan, Saba Wolday, Joanne Allard, Alan Lerner, Paula Ogrocki, Curtis Tatsuoka, Parianne Fatica, Evan Fletcher, Pauline Maillard, John Olichney, Charles DeCarli, Owen Carmichael, Smita Kittur, Michael Borrie, T-Y Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Pierre Tariot, Anna Burke, Ann Marie Milliken, Nadira Trncic, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Douglas W. Scharre, Maria Kataki, Brendan Kelley, Earl A. Zimmerman, Dzintra Celmins, Alice D. Brown, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Laura A. Flashman, Marc Seltzer, Mary L. Hynes, Robert B. Santulli, Kaycee M. Sink, Gordineer Leslie, Jeff D. Williamson, Pradeep Garg, Franklin Watkins, Brian R. Ott, Geoffrey Tremont, Lori A. Daiello, Stephen Salloway, Paul Malloy, Stephen Correia, Howard J. Rosen, Bruce L. Miller, David Perry, Jacobo Mintzer, Kenneth Spicer, David Bachman, Stephen Pasternak, Irina Rachinsky, John Rogers, Dick Drost, Nunzio Pomara, Raymundo Hernando, Antero Sarrael, Susan K. Schultz, Karen Ekstam Smith, Hristina Koleva, Ki Won Nam, Hyungsub Shim, Norman Relkin, Gloria Chiang, Michael Lin, Lisa Ravdin, Amanda Smith, Balebail Ashok Raj, Kristin Fargher, Thomas Neylan, Jordan Grafman, Gessert Devon, Davis Melissa, Rosemary Morrison, Hayes Jacqueline, Finley Shannon, Kantarci Kejal, Ward Chad, Erin Householder, Crawford Karen, Neu Scott, Friedl Karl, Becerra Mauricio, Debra Fleischman, Konstantinos Arfanakis, Daniel Varon, Maria T Greig, Olga James, Bonnie Goldstein, Kimberly S. Martin, Dino Massoglia, Olga Brawman-Mintzer, Walter Martinez, Howard Rosen, Kelly Behan, Sterling C. Johnson, J. Jay Fruehling, Sandra Harding, Elaine R. Peskind, Eric C. Petrie, Gail Li, Jerome A. Yesavage, Ansgar J. Furst, and Steven Chao

Subjects
mild cognitive impairment, non-invasive brain stimulation, stimulation site, meta-analysis, resting state functional connectivity, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

BackgroundNon-invasive brain stimulation (NIBS) has been widely used to treat mild cognitive impairment (MCI). However, there exists no consensus on the best stimulation sites.ObjectiveTo explore potential stimulation locations for NIBS treatment in patients with MCI, combining meta- and resting state functional connectivity (rsFC) analyses.MethodsThe meta-analysis was conducted to identify brain regions associated with MCI. Regions of interest (ROIs) were extracted based on this meta-analysis. The rsFC analysis was applied to 45 MCI patients to determine brain surface regions that are functionally connected with the above ROIs.ResultsWe found that the dorsolateral prefrontal cortex (DLPFC) and inferior frontal gyrus (IFG) were the overlapping brain regions between our results and those of previous studies. In addition, we recommend that the temporoparietal junction (including the angular gyrus), which was found in both the meta- and rsFC analysis, should be considered in NIBS treatment of MCI. Furthermore, the bilateral orbital prefrontal gyrus, inferior temporal gyrus, medial superior frontal gyrus, and right inferior occipital gyrus may be potential brain stimulation sites for NIBS treatment of MCI.ConclusionOur results provide several potential sites for NIBS, such as the DLFPC and IFG, and may shed light on the locations of NIBS sites in the treatment of patients with MCI.

Published
2019
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11. Performing Sparse Regularization and Dimension Reduction Simultaneously in Multimodal Data Fusion

Author

Zhengshi Yang, Xiaowei Zhuang, Christopher Bird, Karthik Sreenivasan, Virendra Mishra, Sarah Banks, Dietmar Cordes, the Alzheimer's Disease Neuroimaging Initiative, Michael W. Weiner, Paul Aisen, Michael Weiner, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew J. Saykin, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Maria Carrillo, Lew Kuller, Marc Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, M. Marcel Mesulam, William Potter, Peter Snyder, Adam Schwartz, Tom Montine, Ronald G. Thomas, Michael Donohue, Sarah Walter, Devon Gessert, Tamie Sather, Gus Jiminez, Archana B. Balasubramanian, Jennifer Mason, Iris Sim, Danielle Harvey, Matthew Bernstein, Nick Fox, Paul Thompson, Norbert Schuff, Charles DeCArli, Bret Borowski, Jeff Gunter, Matt Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarci, Chad Ward, Robert A. Koeppe, Norm Foster, Eric M. Reiman, Kewei Chen, Chet Mathis, Susan Landau, John C. Morris, Nigel J. Cairns, Erin Franklin, Lisa Taylor-Reinwald, Virginia Lee, Magdalena Korecka, Michal Figurski, Karen Crawford, Scott Neu, Tatiana M. Foroud, Steven Potkin, Li Shen, Kelley Faber, Sungeun Kim, Kwangsik Nho, Lean Thal, Leon Thal, Neil Buckholtz, Peter J. Snyder, Marilyn Albert, Richard Frank, John Hsiao, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Becerra, Liberty Teodoro, Bryan M. Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Judith L. Heidebrink, Joanne L. Lord, Sara S. Mason, Colleen S. Albers, David Knopman, Kris Johnson, Rachelle S. Doody, Javier Villanueva-Meyer, Valory Pavlik, Victoria Shibley, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, Beau Ances, Maria Carroll, Mary L. Creech, Mark A. Mintun, Stacy Schneider, Angela Oliver, Daniel Marson, David Geldmacher, Marissa Natelson Love, Randall Griffith, David Clark, John Brockington, Erik Roberson, Hillel Grossman, Effie Mitsis, Raj C. Shah, Leyla deToledo-Morrell, Ranjan Duara, Maria T. Greig-Custo, Warren Barker, Chiadi Onyike, Daniel D'Agostino, Stephanie Kielb, Martin Sadowski, Mohammed O. Sheikh, Ulysse Anaztasia, Gaikwad Mrunalini, P. Murali Doraiswamy, Jeffrey R. Petrella, Salvador Borges-Neto, Terence Z. Wong, Edward Coleman, Steven E. Arnold, Jason H. Karlawish, David A. Wolk, Christopher M. Clark, Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad, Oscar L. Lopez, MaryAnn Oakley, Donna M. Simpson, Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Steven G. Potkin, Adrian Preda, Dana Nguyen, Kyle Womack, Dana Mathews, Mary Quiceno, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Russell H. Swerdlow, William M. Brooks, Liana Apostolova, Kathleen Tingus, Ellen Woo, Daniel H.S. Silverman, Po H. Lu, George Bartzokis, Neill R Graff-Radford, Francine Parfitt, Kim Poki-Walker, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Jared R. Brosch, Scott Herring, Christopher H. van Dyck, Richard E. Carson, Martha G. MacAvoy, Pradeep Varma, Howard Chertkow, Howard Bergman, Chris Hosein, Sandra Black, Bojana Stefanovic, Curtis Caldwell, Ging-Yuek Robin Hsiung, Benita Mudge, Vesna Sossi, Howard Feldman, Michele Assaly, Elizabeth Finger, Stephen Pasternack, Irina Rachisky, Dick Trost, Andrew Kertesz, Charles Bernick, Donna Munic, Marek-Marsel Mesulam, Emily Rogalski, Kristine Lipowski, Sandra Weintraub, Borna Bonakdarpour, Diana Kerwin, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad Marshall, Jerome Yesavage, Joy L. Taylor, Barton Lane, Allyson Rosen, Jared Tinklenberg, Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson, Sherye A. Sirrel, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Thomas O. Obisesan, Saba Wolday, Joanne Allard, Alan Lerner, Paula Ogrocki, Curtis Tatsuoka, Parianne Fatica, Evan Fletcher, Pauline Maillard, John Olichney, Charles DeCarli, Owen Carmichael, Smita Kittur, Michael Borrie, T-Y Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Pierre Tariot, Anna Burke, Ann Marie Milliken, Nadira Trncic, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Douglas W. Scharre, Maria Kataki, Brendan Kelley, Earl A. Zimmerman, Dzintra Celmins, Alice D. Brown, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Laura A. Flashman, Marc Seltzer, Mary L. Hynes, Robert B. Santulli, Kaycee M. Sink, Gordineer Leslie, Jeff D. Williamson, Pradeep Garg, Franklin Watkins, Brian R. Ott, Geoffrey Tremont, Lori A. Daiello, Stephen Salloway, Paul Malloy, Stephen Correia, Howard J. Rosen, Bruce L. Miller, David Perry, Jacobo Mintzer, Kenneth Spicer, David Bachman, Stephen Pasternak, Irina Rachinsky, John Rogers, Dick Drost, Nunzio Pomara, Raymundo Hernando, Antero Sarrael, Susan K. Schultz, Karen Ekstam Smith, Hristina Koleva, Ki Won Nam, Hyungsub Shim, Norman Relkin, Gloria Chiang, Michael Lin, Lisa Ravdin, Amanda Smith, Balebail Ashok Raj, Kristin Fargher, Thomas Neylan, Jordan Grafman, Gessert Devon, Davis Melissa, Rosemary Morrison, Hayes Jacqueline, Finley Shannon, Kantarci Kejal, Ward Chad, Erin Householder, Crawford Karen, Neu Scott, Friedl Karl, Becerra Mauricio, Debra Fleischman, Konstantinos Arfanakis, Daniel Varon, Maria T Greig, Olga James, Bonnie Goldstein, Kimberly S. Martin, Dino Massoglia, Olga Brawman-Mintzer, Walter Martinez, Howard Rosen, Kelly Behan, Sterling C. Johnson, J. Jay Fruehling, Sandra Harding, Elaine R. Peskind, Eric C. Petrie, Gail Li, Jerome A. Yesavage, Ansgar J. Furst, Steven Chao, Scott Mackin, Rema Raman, Erin Drake, Mike Donohue, Gustavo Jimenez, Kelly Harless, Jennifer Salazar, Yuliana Cabrera, Lindsey Hergesheimer, Elizabeth Shaffer, Craig Nelson, David Bickford, Meryl Butters, Michelle Zmuda, Denise Reyes, Kelley M. Faber, Kelly N. Nudelman, Yiu Ho Au, Kelly Scherer, Daniel Catalinotto, Samuel Stark, Elise Ong, and Dariella Fernandez

Subjects
sparse principal component analysis, PCA, canonical correlation analysis, CCA, data fusion, mild cognitive impairment, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Collecting multiple modalities of neuroimaging data on the same subject is increasingly becoming the norm in clinical practice and research. Fusing multiple modalities to find related patterns is a challenge in neuroimaging analysis. Canonical correlation analysis (CCA) is commonly used as a symmetric data fusion technique to find related patterns among multiple modalities. In CCA-based data fusion, principal component analysis (PCA) is frequently applied as a preprocessing step to reduce data dimension followed by CCA on dimension-reduced data. PCA, however, does not differentiate between informative voxels from non-informative voxels in the dimension reduction step. Sparse PCA (sPCA) extends traditional PCA by adding sparse regularization that assigns zero weights to non-informative voxels. In this study, sPCA is incorporated into CCA-based fusion analysis and applied on neuroimaging data. A cross-validation method is developed and validated to optimize the parameters in sPCA. Different simulations are carried out to evaluate the improvement by introducing sparsity constraint to PCA. Four fusion methods including sPCA+CCA, PCA+CCA, parallel ICA and sparse CCA were applied on structural and functional magnetic resonance imaging data of mild cognitive impairment subjects and normal controls. Our results indicate that sPCA significantly can reduce the impact of non-informative voxels and lead to improved statistical power in uncovering disease-related patterns by a fusion analysis.

Published
2019
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12. Quantitative 18F-AV1451 Brain Tau PET Imaging in Cognitively Normal Older Adults, Mild Cognitive Impairment, and Alzheimer's Disease Patients

Author

Qian Zhao, Min Liu, Lingxia Ha, Yun Zhou, Alzheimer's Disease Neuroimaging Initiative, Michael W. Weiner, Paul Aisen, Michael Weiner, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew J. Saykin, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Maria Carrillo, Lew Kuller, Marc Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, M. Marcel Mesulam, William Potter, Peter Snyder, Adam Schwartz, Tom Montine, Ronald G. Thomas, Michael Donohue, Sarah Walter, Devon Gessert, Tamie Sather, Gus Jiminez, Archana B. Balasubramanian, Jennifer Mason, Iris Sim, Danielle Harvey, Matthew Bernstein, Nick Fox, Paul Thompson, Norbert Schuff, Charles DeCArli, Bret Borowski, Jeff Gunter, Matt Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarci, Chad Ward, Robert A. Koeppe, Norm Foster, Eric M. Reiman, Kewei Chen, Chet Mathis, Susan Landau, John C. Morris, Nigel J. Cairns, Erin Franklin, Lisa Taylor-Reinwald, Virginia Lee, Magdalena Korecka, Michal Figurski, Karen Crawford, Scott Neu, Tatiana M. Foroud, Steven Potkin, Li Shen, Kelley Faber, Sungeun Kim, Kwangsik Nho, Lean Thal, Leon Thal, Neil Buckholtz, Peter J. Snyder, Marilyn Albert, Richard Frank, John Hsiao, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Becerra, Liberty Teodoro, Bryan M. Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Judith L. Heidebrink, Joanne L. Lord, Sara S. Mason, Colleen S. Albers, David Knopman, Kris Johnson, Rachelle S. Doody, Javier Villanueva-Meyer, Valory Pavlik, Victoria Shibley, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, Beau Ances, Maria Carroll, Mary L. Creech, Mark A. Mintun, Stacy Schneider, Angela Oliver, Daniel Marson, David Geldmacher, Marissa Natelson Love, Randall Griffith, David Clark, John Brockington, Erik Roberson, Hillel Grossman, Effie Mitsis, Raj C. Shah, Leyla deToledo-Morrell, Ranjan Duara, Maria T. Greig-Custo, Warren Barker, Chiadi Onyike, Daniel D'Agostino, Stephanie Kielb, Martin Sadowski, Mohammed O. Sheikh, Ulysse Anaztasia, Gaikwad Mrunalini, P. Murali Doraiswamy, Jeffrey R. Petrella, Salvador Borges-Neto, Terence Z. Wong, Edward Coleman, Steven E. Arnold, Jason H. Karlawish, David A. Wolk, Christopher M. Clark, Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad, Oscar L. Lopez, MaryAnn Oakley, Donna M. Simpson, Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Steven G. Potkin, Adrian Preda, Dana Nguyen, Kyle Womack, Dana Mathews, Mary Quiceno, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Russell H. Swerdlow, William M. Brooks, Liana Apostolova, Kathleen Tingus, Ellen Woo, Daniel H.S. Silverman, Po H. Lu, George Bartzokis, Neill R Graff-Radford, Francine Parfitt, Kim Poki-Walker, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Jared R. Brosch, Scott Herring, Christopher H. van Dyck, Richard E. Carson, Martha G. MacAvoy, Pradeep Varma, Howard Chertkow, Howard Bergman, Chris Hosein, Sandra Black, Bojana Stefanovic, Curtis Caldwell, Ging-Yuek Robin Hsiung, Benita Mudge, Vesna Sossi, Howard Feldman, Michele Assaly, Elizabeth Finger, Stephen Pasternack, Irina Rachisky, Dick Trost, Andrew Kertesz, Charles Bernick, Donna Munic, Marek-Marsel Mesulam, Emily Rogalski, Kristine Lipowski, Sandra Weintraub, Borna Bonakdarpour, Diana Kerwin, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad Marshall, Jerome Yesavage, Joy L. Taylor, Barton Lane, Allyson Rosen, Jared Tinklenberg, Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson, Sherye A. Sirrel, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Thomas O. Obisesan, Saba Wolday, Joanne Allard, Alan Lerner, Paula Ogrocki, Curtis Tatsuoka, Parianne Fatica, Evan Fletcher, Pauline Maillard, John Olichney, Charles DeCarli, Owen Carmichael, Smita Kittur, Michael Borrie, T-Y Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Pierre Tariot, Anna Burke, Ann Marie Milliken, Nadira Trncic, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Douglas W. Scharre, Maria Kataki, Brendan Kelley, Earl A. Zimmerman, Dzintra Celmins, Alice D. Brown, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Laura A. Flashman, Marc Seltzer, Mary L. Hynes, Robert B. Santulli, Kaycee M. Sink, Gordineer Leslie, Jeff D. Williamson, Pradeep Garg, Franklin Watkins, Brian R. Ott, Geoffrey Tremont, Lori A. Daiello, Stephen Salloway, Paul Malloy, Stephen Correia, Howard J. Rosen, Bruce L. Miller, David Perry, Jacobo Mintzer, Kenneth Spicer, David Bachman, Stephen Pasternak, Irina Rachinsky, John Rogers, Dick Drost, Nunzio Pomara, Raymundo Hernando, Antero Sarrael, Susan K. Schultz, Karen Ekstam Smith, Hristina Koleva, Ki Won Nam, Hyungsub Shim, Norman Relkin, Gloria Chiang, Michael Lin, Lisa Ravdin, Amanda Smith, Balebail Ashok Raj, Kristin Fargher, Thomas Neylan, Jordan Grafman, Gessert Devon, Davis Melissa, Rosemary Morrison, Hayes Jacqueline, Finley Shannon, Kantarci Kejal, Ward Chad, Erin Householder, Crawford Karen, Neu Scott, Friedl Karl, Becerra Mauricio, Debra Fleischman, Konstantinos Arfanakis, Daniel Varon, Maria T Greig, Olga James, Bonnie Goldstein, Kimberly S. Martin, Dino Massoglia, Olga Brawman-Mintzer, Walter Martinez, Howard Rosen, Kelly Behan, Sterling C. Johnson, J. Jay Fruehling, Sandra Harding, Elaine R. Peskind, Eric C. Petrie, Gail Li, Jerome A. Yesavage, Ansgar J. Furst, Steven Chao, Scott Mackin, Rema Raman, Erin Drake, Mike Donohue, Gustavo Jimenez, Kelly Harless, Jennifer Salazar, Yuliana Cabrera, Lindsey Hergesheimer, Elizabeth Shaffer, Craig Nelson, David Bickford, Meryl Butters, Michelle Zmuda, Denise Reyes, Kelley M. Faber, Kelly N. Nudelman, Yiu Ho Au, Kelly Scherer, Daniel Catalinotto, Samuel Stark, Elise Ong, and Dariella Fernandez

Subjects
18F-AV1451, Tau PET, cognitively normal, mild cognition impairment, Alzheimer's disease, Neurology. Diseases of the nervous system, RC346-429
Abstract

Recent developments of tau Positron Emission Tomography (PET) allows assessment of regional neurofibrillary tangles (NFTs) deposition in human brain. Among the tau PET molecular probes, 18F-AV1451 is characterized by high selectivity for pathologic tau aggregates over amyloid plaques, limited non-specific binding in white and gray matter, and confined off-target binding. The objectives of the study are (1) to quantitatively characterize regional brain tau deposition measured by 18F-AV1451 PET in cognitively normal older adults (CN), mild cognitive impairment (MCI), and AD participants; (2) to evaluate the correlations between cerebrospinal fluid (CSF) biomarkers or Mini-Mental State Examination (MMSE) and 18F-AV1451 PET standardized uptake value ratio (SUVR); and (3) to evaluate the partial volume effects on 18F-AV1451 brain uptake.Methods: The study included total 115 participants (CN = 49, MCI = 58, and AD = 8) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed 18F-AV1451 PET images, structural MRIs, and demographic and clinical assessments were downloaded from the ADNI database. A reblurred Van Cittertiteration method was used for voxelwise partial volume correction (PVC) on PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. The parametric images of 18F-AV1451 SUVR relative to cerebellum were calculated. The ROI SUVR measurements from PVC and non-PVC SUVR images were compared. The correlation between ROI 18F-AV1451 SUVR and the measurements of MMSE, CSF total tau (t-tau), and phosphorylated tau (p-tau) were also assessed.Results:18F-AV1451 prominently specific binding was found in the amygdala, entorhinal cortex, parahippocampus, fusiform, posterior cingulate, temporal, parietal, and frontal brain regions. Most regional SUVRs showed significantly higher uptake of 18F-AV1451 in AD than MCI and CN participants. SUVRs of small regions like amygdala, entorhinal cortex and parahippocampus were statistically improved by PVC in all groups (p < 0.01). Although there was an increasing tendency of 18F-AV-1451 SUVRs in MCI group compared with CN group, no significant difference of 18F-AV1451 deposition was found between CN and MCI brains with or without PVC (p > 0.05). Declined MMSE score was observed with increasing 18F-AV1451 binding in amygdala, entorhinal cortex, parahippocampus, and fusiform. CSF p-tau was positively correlated with 18F-AV1451 deposition. PVC improved the results of 18F-AV-1451 tau deposition and correlation studies in small brain regions.Conclusion: The typical deposition of 18F-AV1451 tau PET imaging in AD brain was found in amygdala, entorhinal cortex, fusiform and parahippocampus, and these regions were strongly associated with cognitive impairment and CSF biomarkers. Although more deposition was observed in MCI group, the 18F-AV-1451 PET imaging could not differentiate the MCI patients from CN population. More tau deposition related to decreased MMSE score and increased level of CSF p-tau, especially in ROIs of amygdala, entorhinal cortex and parahippocampus. PVC did improve the results of tau deposition and correlation studies in small brain regions and suggest to be routinely used in 18F-AV1451 tau PET quantification.

Published
2019
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13. Blood Pressure Circadian Variation, Cognition and Brain Imaging in 90+ Year-Olds

Author

Annlia Paganini-Hill, Natalie Bryant, Maria M. Corrada, Dana E. Greenia, Evan Fletcher, Baljeet Singh, David Floriolli, Claudia H. Kawas, and Mark J. Fisher

Subjects
ambulatory blood pressure monitoring, cognition, cognitive neurological examination, neuropsychological tests, magnetic resonance imaging, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Purpose: To analyze the relationship between blood pressure (BP) variables, including circadian pattern, and cognition in 90+ year-olds.Methods: Twenty-four hour ambulatory BP monitoring was completed on 121 participants drawn from a longitudinal study of aging and dementia in the oldest-old. Various measures of BP and its variability, including nocturnal dipping, were calculated. Each person was given both a neuropsychological test battery covering different cognitive domains and a neurological examination to determine cognitive status. Seventy-one participants had a brain magnetic resonance imaging (MRI) scan.Results: Participants ranged in age from 90 to 102 years (mean = 93), about two-thirds were female, and nearly 80% had at least some college education. Mean nocturnal dips differed significantly between cognitively normal (n = 97) and impaired individuals (n = 24), with cognitively normal participants having on average greater nocturnal dips [6.6% vs. 1.3%, p = 0.006 for systolic BP (SBP); 11% vs. 4.4%, p = 0.002 for diastolic BP (DBP)]. Nocturnal dips were also related to performance on select cognitive test scores (especially those related to language, recent memory and visual-spatial ability), with individuals who performed below previously established median norms having significantly smaller nocturnal dips (both SBP and DBP) than those above the median. DBP reverse dippers had larger mean white matter hyperintensities (WMH as percent of total brain volume; 1.7% vs. 1.2%, 1.1% and 1.0% in extreme dippers, dippers, non-dippers) and a greater proportion had lobar cerebral microbleeds (CMBs; 44% vs. 0%, 7%, 16%, p < 0.05). Impaired participants had higher mean WMH than those with normal cognition (1.6% vs. 1.0% p = 0.03) and more tended to have CMB (31% vs. 20%, p = n.s.).Conclusion: These findings suggest that cognitive dysfunction is associated with dysregulation in the normal circadian BP pattern. Further study is warranted of the potential role of WHM and CMB as mediators of this association.

Published
2019
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14. A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants

Author

Mohamed Salah Khlif, Natalia Egorova, Emilio Werden, Alberto Redolfi, Marina Boccardi, Charles S. DeCarli, Evan Fletcher, Baljeet Singh, Qi Li, Laura Bird, and Amy Brodtmann

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

Manual quantification of the hippocampal atrophy state and rate is time consuming and prone to poor reproducibility, even when performed by neuroanatomical experts. The automation of hippocampal segmentation has been investigated in normal aging, epilepsy, and in Alzheimer's disease. Our first goal was to compare manual and automated hippocampal segmentation in ischemic stroke and to, secondly, study the impact of stroke lesion presence on hippocampal volume estimation. We used eight automated methods to segment T1-weighted MR images from 105 ischemic stroke patients and 39 age-matched controls sampled from the Cognition And Neocortical Volume After Stroke (CANVAS) study. The methods were: AdaBoost, Atlas-based Hippocampal Segmentation (ABHS) from the IDeALab, Computational Anatomy Toolbox (CAT) using 3 atlas variants (Hammers, LPBA40 and Neuromorphometics), FIRST, FreeSurfer v5.3, and FreeSurfer v6.0-Subfields. A number of these methods were employed to re-segment the T1 images for the stroke group after the stroke lesions were masked (i.e., removed). The automated methods were assessed on eight measures: process yield (i.e. segmentation success rate), correlation (Pearson's R and Shrout's ICC), concordance (Lin's RC and Kandall's W), slope ‘a’ of best-fit line from correlation plots, percentage of outliers from Bland-Altman plots, and significance of control−stroke difference. We eliminated the redundant measures after analysing between-measure correlations using Spearman's rank correlation. We ranked the automated methods based on the sum of the remaining non-redundant measures where each measure ranged between 0 and 1. Subfields attained an overall score of 96.3%, followed by AdaBoost (95.0%) and FIRST (94.7%). CAT using the LPBA40 atlas inflated hippocampal volumes the most, while the Hammers atlas returned the smallest volumes overall. FIRST (p = 0.014), FreeSurfer v5.3 (p = 0.007), manual tracing (p = 0.049), and CAT using the Neuromorphometics atlas (p = 0.017) all showed a significantly reduced hippocampal volume mean for the stroke group compared to control at three months. Moreover, masking of the stroke lesions prior to segmentation resulted in hippocampal volumes which agreed less with manual tracing. These findings recommend an automated segmentation without lesion masking as a more reliable procedure for the estimation of hippocampal volume in ischemic stroke. Keywords: Hippocampus, Magnetic resonance imaging, Automatic segmentation, Manual segmentation, Stroke

Published
2019
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15. 'Liquid Biopsy' of White Matter Hyperintensity in Functionally Normal Elders

Author

Fanny M. Elahi, Kaitlin B. Casaletto, Marie Altendahl, Adam M. Staffaroni, Evan Fletcher, Teresa J. Filshtein, Maria M. Glymour, Bruce L. Miller, Jason D. Hinman, Charles DeCarli, Edward J. Goetzl, and Joel H. Kramer

Subjects
cerebral small vessel disease, exosomes, extracellular vesicles, biomarkers, white matter, inflammation, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Background and Objective: In the aging brain, increased blood-brain barrier (BBB) leakage and white matter hyperintensity (WMH) on MRI are frequently presumed secondary to cerebral small vessel disease (cSVD) or endotheliopathy. We investigate this association in vivo by quantifying protein cargo from endothelial-derived exosomes (EDE), and comparing levels between two groups of functionally normal elders with and without WMH. In addition, we study associations of EDE proteins with upstream and downstream factors, such as inflammation and neurodegenerative changes, respectively.Methods: Twenty six neurologically normal older adults completed general health questionnaires, neuropsychological and physical examinations, and brain MRI. WMH was visually graded with modified Fazekas score of 2 or greater used to classify 11 subjects as cases, and 15 without WMH as controls. Plasma total exosomes were precipitated and EDEs enriched by sequential immuno-precipitations. In addition, we quantified three inflammatory cytokines from plasma and imaging variables on MRI. Group means were compared, the discriminant functions of biomarkers calculated, and the association of EDE biomarkers with plasma inflammatory markers, cognition, and imaging outcomes assessed via regression modeling.Results: Plasma levels of EDE cargo proteins GLUT1, LAT1, P-GP, and NOSTRIN were significantly higher in subjects with WMH in comparison to those without. In contrast, EDE levels of the marker with low expression in brain (VCAM1) were equal between groups. The effect sizes for each of the brain-expressed cargo proteins (GLUT1, LAT1, and P-GP) were such that age-adjusted logistic regressions revealed areas under the curve (AUC) with range of 0.82–0.89, differentiating subjects with WMH from those without. VCAM1 poorly discriminated between groups (AUC:0.55). Higher levels of all brain-expressed EDE proteins were also associated with lower cognitive function, unrelated to burden of WMH. Levels of LAT1 and P-GP were significantly inversely associated with global gray matter volumes, and EDE GLUT1, LAT-1, and P-GP concentrations were significantly associated with systemic IL-6 levels.Conclusion: In a case control study of clinically normal adults with and without WMH, concentrations of EDE proteins were significantly higher in subjects with WMH in comparison to controls. This work is a first step toward in vivo dissection of molecular changes in endothelia of functionally normal subjects with radiographic evidence of age-associated white matter disease.

Published
2018
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16. Corrigendum: Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Author

Shengwen Guo, Chunren Lai, Congling Wu, Guiyin Cen, The Alzheimer's Disease Neuroimaging Initiative, Michael W. Weiner, Paul Aisen, Michael Weiner, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew J. Saykin, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Maria Carrillo, Lew Kuller, Marc Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, M. Marcel Mesulam, William Potter, Peter Snyder, Adam Schwartz, Tom Montine, Ronald G. Thomas, Michael Donohue, Sarah Walter, Devon Gessert, Tamie Sather, Gus Jiminez, Archana B. Balasubramanian, Jennifer Mason, Iris Sim, Danielle Harvey, Matthew Bernstein, Nick Fox, Paul Thompson, Norbert Schuff, Charles DeCArli, Bret Borowski, Jeff Gunter, Matt Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarci, Chad Ward, Robert A. Koeppe, Norm Foster, Eric M. Reiman, Kewei Chen, Chet Mathis, Susan Landau, John C. Morris, Nigel J. Cairns, Erin Franklin, Lisa Taylor-Reinwald, Virginia Lee, Magdalena Korecka, Michal Figurski, Karen Crawford, Scott Neu, Tatiana M. Foroud, Steven Potkin, Li Shen, Kelley Faber, Sungeun Kim, Kwangsik Nho, Lean Thal, Leon Thal, Neil Buckholtz, Peter J. Snyder, Marilyn Albert, Richard Frank, John Hsiao, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Becerra, Liberty Teodoro, Bryan M. Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Judith L. Heidebrink, Joanne L. Lord, Sara S. Mason, Colleen S. Albers, David Knopman, Kris Johnson, Rachelle S. Doody, Javier Villanueva-Meyer, Valory Pavlik, Victoria Shibley, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, Beau Ances, Maria Carroll, Mary L. Creech, Mark A. Mintun, Stacy Schneider, Angela Oliver, Daniel Marson, David Geldmacher, Marissa Natelson Love, Randall Griffith, David Clark, John Brockington, Erik Roberson, Hillel Grossman, Effie Mitsis, Raj C. Shah, Leyla deToledo-Morrell, Ranjan Duara, Maria T. Greig-Custo, Warren Barker, Chiadi Onyike, Daniel D'Agostino, Stephanie Kielb, Martin Sadowski, Mohammed O. Sheikh, Ulysse Anaztasia, Gaikwad Mrunalini, P. Murali Doraiswamy, Jeffrey R. Petrella, Salvador Borges-Neto, Terence Z. Wong, Edward Coleman, Steven E. Arnold, Jason H. Karlawish, David A. Wolk, Christopher M. Clark, Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad, Oscar L. Lopez, MaryAnn Oakley, Donna M. Simpson, Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Steven G. Potkin, Adrian Preda, Dana Nguyen, Kyle Womack, Dana Mathews, Mary Quiceno, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Russell H. Swerdlow, William M. Brooks, Liana Apostolova, Kathleen Tingus, Ellen Woo, Daniel H.S. Silverman, Po H. Lu, George Bartzokis, Neill R Graff-Radford, Francine Parfitt, Kim Poki-Walker, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Jared R. Brosch, Scott Herring, Christopher H. van Dyck, Richard E. Carson, Martha G. MacAvoy, Pradeep Varma, Howard Chertkow, Howard Bergman, Chris Hosein, Sandra Black, Bojana Stefanovic, Curtis Caldwell, Ging-Yuek Robin Hsiung, Benita Mudge, Vesna Sossi, Howard Feldman, Michele Assaly, Elizabeth Finger, Stephen Pasternack, Irina Rachisky, Dick Trost, Andrew Kertesz, Charles Bernick, Donna Munic, Marek-Marsel Mesulam, Emily Rogalski, Kristine Lipowski, Sandra Weintraub, Borna Bonakdarpour, Diana Kerwin, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad Marshall, Jerome Yesavage, Joy L. Taylor, Barton Lane, Allyson Rosen, Jared Tinklenberg, Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson, Sherye A. Sirrel, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Thomas O. Obisesan, Saba Wolday, Joanne Allard, Alan Lerner, Paula Ogrocki, Curtis Tatsuoka, Parianne Fatica, Evan Fletcher, Pauline Maillard, John Olichney, Charles DeCarli, Owen Carmichael, Smita Kittur, Michael Borrie, T-Y Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Pierre Tariot, Anna Burke, Ann Marie Milliken, Nadira Trncic, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Douglas W. Scharre, Maria Kataki, Brendan Kelley, Earl A. Zimmerman, Dzintra Celmins, Alice D. Brown, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Laura A. Flashman, Marc Seltzer, Mary L. Hynes, Robert B. Santulli, Kaycee M. Sink, Gordineer Leslie, Jeff D. Williamson, Pradeep Garg, Franklin Watkins, Brian R. Ott, Geoffrey Tremont, Lori A. Daiello, Stephen Salloway, Paul Malloy, Stephen Correia, Howard J. Rosen, Bruce L. Miller, David Perry, Jacobo Mintzer, Kenneth Spicer, David Bachman, Stephen Pasternak, Irina Rachinsky, John Rogers, Dick Drost, Nunzio Pomara, Raymundo Hernando, Antero Sarrael, Susan K. Schultz, Karen Ekstam Smith, Hristina Koleva, Ki Won Nam, Hyungsub Shim, Norman Relkin, Gloria Chiang, Michael Lin, Lisa Ravdin, Amanda Smith, Balebail Ashok Raj, Kristin Fargher, Thomas Neylan, Jordan Grafman, Gessert Devon, Davis Melissa, Rosemary Morrison, Hayes Jacqueline, Finley Shannon, Kantarci Kejal, Ward Chad, Erin Householder, Crawford Karen, Neu Scott, Friedl Karl, Becerra Mauricio, Debra Fleischman, Konstantinos Arfanakis, Daniel Varon, Maria T Greig, Olga James, Bonnie Goldstein, Kimberly S. Martin, Dino Massoglia, Olga Brawman-Mintzer, Walter Martinez, Howard Rosen, Kelly Behan, Sterling C. Johnson, J. Jay Fruehling, Sandra Harding, Elaine R. Peskind, Eric C. Petrie, Gail Li, Jerome A. Yesavage, Ansgar J. Furst, and Steven Chao

Subjects
mild cognitive impairment, conversion, cortical feature, sparse-constrained regression, feature reduction, classification, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Published
2017
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17. Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Author

Shengwen Guo, Chunren Lai, Congling Wu, Guiyin Cen, The Alzheimer's Disease Neuroimaging Initiative, Michael W. Weiner, Paul Aisen, Michael Weiner, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew J. Saykin, John Morris, Leslie M. Shaw, Zaven Khachaturian, Greg Sorensen, Maria Carrillo, Lew Kuller, Marc Raichle, Steven Paul, Peter Davies, Howard Fillit, Franz Hefti, David Holtzman, M. Marcel Mesulam, William Potter, Peter Snyder, Adam Schwartz, Tom Montine, Ronald G. Thomas, Michael Donohue, Sarah Walter, Devon Gessert, Tamie Sather, Gus Jiminez, Archana B. Balasubramanian, Jennifer Mason, Iris Sim, Danielle Harvey, Matthew Bernstein, Nick Fox, Paul Thompson, Norbert Schuff, Charles DeCArli, Bret Borowski, Jeff Gunter, Matt Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarci, Chad Ward, Robert A. Koeppe, Norm Foster, Eric M. Reiman, Kewei Chen, Chet Mathis, Susan Landau, John C. Morris, Nigel J. Cairns, Erin Franklin, Lisa Taylor-Reinwald, Virginia Lee, Magdalena Korecka, Michal Figurski, Karen Crawford, Scott Neu, Tatiana M. Foroud, Steven Potkin, Li Shen, Kelley Faber, Sungeun Kim, Kwangsik Nho, Lean Thal, Leon Thal, Neil Buckholtz, Peter J. Snyder, Marilyn Albert, Richard Frank, John Hsiao, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Becerra, Liberty Teodoro, Bryan M. Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Judith L. Heidebrink, Joanne L. Lord, Sara S. Mason, Colleen S. Albers, David Knopman, Kris Johnson, Rachelle S. Doody, Javier Villanueva-Meyer, Valory Pavlik, Victoria Shibley, Munir Chowdhury, Susan Rountree, Mimi Dang, Yaakov Stern, Lawrence S. Honig, Karen L. Bell, Beau Ances, Maria Carroll, Mary L. Creech, Mark A. Mintun, Stacy Schneider, Angela Oliver, Daniel Marson, David Geldmacher, Marissa Natelson Love, Randall Griffith, David Clark, John Brockington, Erik Roberson, Hillel Grossman, Effie Mitsis, Raj C. Shah, Leyla deToledo-Morrell, Ranjan Duara, Maria T. Greig-Custo, Warren Barker, Chiadi Onyike, Daniel D'Agostino, Stephanie Kielb, Martin Sadowski, Mohammed O. Sheikh, Ulysse Anaztasia, Gaikwad Mrunalini, P. Murali Doraiswamy, Jeffrey R. Petrella, Salvador Borges-Neto, Terence Z. Wong, Edward Coleman, Steven E. Arnold, Jason H. Karlawish, David A. Wolk, Christopher M. Clark, Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad, Oscar L. Lopez, MaryAnn Oakley, Donna M. Simpson, Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail, Connie Brand, Steven G. Potkin, Adrian Preda, Dana Nguyen, Kyle Womack, Dana Mathews, Mary Quiceno, Allan I. Levey, James J. Lah, Janet S. Cellar, Jeffrey M. Burns, Russell H. Swerdlow, William M. Brooks, Liana Apostolova, Kathleen Tingus, Ellen Woo, Daniel H.S. Silverman, Po H. Lu, George Bartzokis, Neill R Graff-Radford, Francine Parfitt, Kim Poki-Walker, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Jared R. Brosch, Scott Herring, Christopher H. van Dyck, Richard E. Carson, Martha G. MacAvoy, Pradeep Varma, Howard Chertkow, Howard Bergman, Chris Hosein, Sandra Black, Bojana Stefanovic, Curtis Caldwell, Ging-Yuek Robin Hsiung, Benita Mudge, Vesna Sossi, Howard Feldman, Michele Assaly, Elizabeth Finger, Stephen Pasternack, Irina Rachisky, Dick Trost, Andrew Kertesz, Charles Bernick, Donna Munic, Marek-Marsel Mesulam, Emily Rogalski, Kristine Lipowski, Sandra Weintraub, Borna Bonakdarpour, Diana Kerwin, Chuang-Kuo Wu, Nancy Johnson, Carl Sadowsky, Teresa Villena, Raymond Scott Turner, Kathleen Johnson, Brigid Reynolds, Reisa A. Sperling, Keith A. Johnson, Gad Marshall, Jerome Yesavage, Joy L. Taylor, Barton Lane, Allyson Rosen, Jared Tinklenberg, Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson, Sherye A. Sirrel, Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash, Patricia Lynn Johnson, Thomas O. Obisesan, Saba Wolday, Joanne Allard, Alan Lerner, Paula Ogrocki, Curtis Tatsuoka, Parianne Fatica, Evan Fletcher, Pauline Maillard, John Olichney, Charles DeCarli, Owen Carmichael, Smita Kittur, Michael Borrie, T-Y Lee, Rob Bartha, Sterling Johnson, Sanjay Asthana, Cynthia M. Carlsson, Pierre Tariot, Anna Burke, Ann Marie Milliken, Nadira Trncic, Stephanie Reeder, Vernice Bates, Horacio Capote, Michelle Rainka, Douglas W. Scharre, Maria Kataki, Brendan Kelley, Earl A. Zimmerman, Dzintra Celmins, Alice D. Brown, Godfrey D. Pearlson, Karen Blank, Karen Anderson, Laura A. Flashman, Marc Seltzer, Mary L. Hynes, Robert B. Santulli, Kaycee M. Sink, Gordineer Leslie, Jeff D. Williamson, Pradeep Garg, Franklin Watkins, Brian R. Ott, Geoffrey Tremont, Lori A. Daiello, Stephen Salloway, Paul Malloy, Stephen Correia, Howard J. Rosen, Bruce L. Miller, David Perry, Jacobo Mintzer, Kenneth Spicer, David Bachman, Stephen Pasternak, Irina Rachinsky, John Rogers, Dick Drost, Nunzio Pomara, Raymundo Hernando, Antero Sarrael, Susan K. Schultz, Karen Ekstam Smith, Hristina Koleva, Ki Won Nam, Hyungsub Shim, Norman Relkin, Gloria Chiang, Michael Lin, Lisa Ravdin, Amanda Smith, Balebail Ashok Raj, Kristin Fargher, Thomas Neylan, Jordan Grafman, Gessert Devon, Davis Melissa, Rosemary Morrison, Hayes Jacqueline, Finley Shannon, Kantarci Kejal, Ward Chad, Erin Householder, Crawford Karen, Neu Scott, Friedl Karl, Becerra Mauricio, Debra Fleischman, Konstantinos Arfanakis, Daniel Varon, Maria T Greig, Olga James, Bonnie Goldstein, Kimberly S. Martin, Dino Massoglia, Olga Brawman-Mintzer, Walter Martinez, Howard Rosen, Kelly Behan, Sterling C. Johnson, J. Jay Fruehling, Sandra Harding, Elaine R. Peskind, Eric C. Petrie, Gail Li, Jerome A. Yesavage, Ansgar J. Furst, and Steven Chao

Subjects
mild cognitive impairment, conversion, cortical feature, sparse-constrained regression, feature reduction, classification, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.

Published
2017
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18. Multifactorial Modeling of Cognitive Trajectories Using an Advanced Regression Technique: Improving Our Understanding of Biomarkers and Modifiable Variables that Support Cognition

Author

Evan Fletcher

Subjects
Psychiatry and Mental health, Clinical Psychology, General Neuroscience, General Medicine, Geriatrics and Gerontology
Abstract

Current research trends emphasize complex models of cognitive outcomes, with multiple, interacting predictors, including factors amenable to interventions toward sustaining healthy cognitive aging. Such models often require advanced analysis techniques. The article by Stark et al., ‘Partial least squares regression analysis of Alzheimer’s disease biomarkers, modifiable health variables, and cognitive change in older adults with mild cognitive impairment’, uses partial least squares regression to examine the associations to memory and executive change of 29 biomarker and demographic variables. This commentary discusses the significance of their results and methods within the context of current research foci.

Published
2023

19. Genomic Studies Across the Lifespan Point to Early Mechanisms Determining Subcortical Volumes

Author

Aniket Mishra, Tomáš Paus, Alexa S. Beiser, Sudha Seshadri, Ami Tsuchida, Christophe Tzourio, Hieab H.H. Adams, Stéphanie Debette, Fabrice Crivello, Bernard Mazoyer, Charles DeCarli, Quentin Le Grand, Joshua C. Bis, Melissa Macalli, Alexandre Laurent, Aicha Soumare, Baljeet Singh, Evan Fletcher, Muralidharan Sargurupremraj, Jean Shin, Claudia L. Satizabal, Mark Lathrop, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Clinical Genetics, and Radiology & Nuclear Medicine

Subjects
Aging, Epidemiology, Caudate nucleus, Disease, Neurodegenerative, 0302 clinical medicine, 80 and over, 2.1 Biological and endogenous factors, Aetiology, Young adult, Aged, 80 and over, 0303 health sciences, Putamen, Brain, Organ Size, Genomics, Magnetic Resonance Imaging, Neurological, Mental health, Adult, 1.1 Normal biological development and functioning, Cognitive Neuroscience, Longevity, Life course approach, Biology, Genetic correlation, Article, 03 medical and health sciences, Neuroimaging, Underpinning research, Genetics, medicine, Humans, Dementia, Radiology, Nuclear Medicine and imaging, Biological Psychiatry, Aged, 030304 developmental biology, Genetic association, Prevention, Human Genome, Neurosciences, medicine.disease, Subcortical volumes, Lifecourse approach, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Neurology (clinical), Neuroscience, 030217 neurology & neurosurgery
Abstract

Background Subcortical brain structures play a key role in pathological processes of age-related neurodegenerative disorders. Mounting evidence also suggests that early-life factors may have an impact on the development of common late-life neurological diseases, including genetic factors that can influence both brain maturation and neurodegeneration. Methods Using large population-based brain imaging datasets across the lifespan (N 40,628) we aimed to: (i) estimate the heritability of subcortical volumes in young ( 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ), middle (35-65), and older age (65+), and their genetic correlation across age groups; (ii) identify whether genetic loci associated with subcortical volumes in older persons also show associations in early adulthood, and explore underlying genes using transcriptome-wide association studies; (iii) explore their association with neurological phenotypes. Results Heritability of subcortical volumes consistently decreased with increasing age. Genetic risk scores for smaller caudate nucleus, putamen and hippocampus volume in older adults were associated with smaller volumes in young adults. Individually, ten loci associated with subcortical volumes in older adults also showed associations in young adults. Within these loci, transcriptome-wide association studies showed that expression of several genes in brain tissues (especially MYLK2 and TUFM) was associated with subcortical volumes in both age-groups. One risk variant for smaller caudate nucleus volume (TUFM locus) was associated with lower cognitive performance. Genetically-predicted Alzheimer’s disease was associated with smaller subcortical volumes in middle and older age. Conclusions Our findings provide novel insights into the genetic determinants of subcortical volumes across the lifespan. More studies are needed to decipher the underlying biology and clinical impact.

Published
2022

20. Cardiovascular health and resilience to cognitive decline and impairment among diverse Hispanics/Latinos: Results from the Study of Latinos‐ Investigation of Neurocognitive Aging (SOL‐INCA)

Author

Wassim Tarraf, Ariana M Stickel, Benson Wu, James B. Brewer, Linda C Gallo, Gregory A Talavera, Carmen R Isasi, Robert Kaplan, Richard B. Lipton, Martha L Daviglus, Donglin Zeng, James R. Pike, Neil Schneiderman, Tatjana Rundek, Tamar Sofer, Myriam Fornage, Charles S. DeCarli, Evan Fletcher, Craig Branch, Xiaohong Zhou, and Hector Leonardo Gonzalez

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2022

21. Reducing MRI Inter‐Scanner Variability Using 3D Superpixel ComBat

Author

Chang‐Le Chen, Mahbaneh Eshaghzadeh Torbati, James D Wilson, Davneet S Minhas, Charles M Laymon, Seong Jae Hwang, Pauline Maillard, Evan Fletcher, Charles S. DeCarli, and Dana L Tudorascu

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2022

22. Associations of Amyloid, White Matter Hyperintensities, and Hippocampal Volume with Cognitive Trajectories in the Oldest‐Old: The 90+ Study

Author

Jingxuan Wang, Sarah F Ackley, Davis C. Woodworth, S. Ahmad Sajjadi, Evan Fletcher, Charles S. DeCarli, M. Maria Glymour, Claudia H. Kawas, and Maria M. Corrada

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2022

28. The latent factor structure underlying regional brain volume change and its relation to cognitive change in older adults

Author

Brandon E. Gavett, Evan Fletcher, Keith F. Widaman, Charles DeCarli, Sarah E Tomaszewski Farias, and Dan M Mungas

Subjects
Aging, factor analysis, Latent variable, Neurodegenerative, Article, Cognition, Atrophy, Clinical Research, Acquired Cognitive Impairment, medicine, Humans, magnetic resonance imaging, longitudinal studies, Psychology, Cognitive Dysfunction, Gray Matter, Cognitive decline, skin and connective tissue diseases, Latent variable model, Association (psychology), Aged, medicine.diagnostic_test, cognitive aging, Neurosciences, Brain, Neurodegenerative Diseases, Experimental Psychology, Magnetic resonance imaging, medicine.disease, Magnetic Resonance Imaging, Brain Disorders, Neuropsychology and Physiological Psychology, Neurological, Brain size, Biomedical Imaging, brain mapping, Dementia, Mental health, Cognitive Sciences, sense organs, Neuroscience
Abstract

OBJECTIVE Late-life changes in cognition and brain integrity are both highly multivariate, time-dependent processes that are essential for understanding cognitive aging and neurodegenerative disease outcomes. The present study seeks to identify a latent variable model capable of efficiently reducing a multitude of structural brain change magnetic resonance imaging (MRI) measurements into a smaller number of dimensions. We further seek to demonstrate the validity of this model by evaluating its ability to reproduce patterns of coordinated brain volume change and to explain the rate of cognitive decline over time. METHOD We used longitudinal cognitive data and structural MRI scans, obtained from a diverse sample of 358 participants (Mage = 74.81, SD = 7.17), to implement latent variable models for measuring brain change and to estimate the effects of these brain change factors on cognitive decline. RESULTS Results supported a bifactor model for brain change with four group factors (prefrontal, temporolimbic, medial temporal, and posterior association) and one general change factor (global atrophy). Atrophy in the global (β = 0.434, SE = 0.070), temporolimbic (β = 0.275, SE = 0.085), and medial temporal (β = 0.240, SE = 0.085) factors were the strongest predictors of global cognitive decline. Overall, the brain change model explained 59% of the variance in global cognitive slope. CONCLUSIONS The current results suggest that brain change across 27 bilateral regions of interest can be grouped into five change factors, three of which (global gray matter, temporolimbic, and medial temporal lobe atrophy) are strongly associated with cognitive decline. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Published
2021

32. A robust brain signature region approach for episodic memory performance in older adults

Author

Dan M Mungas, Charles DeCarli, Laura B. Zahodne, Keith F. Widaman, Miguel Arce Rentería, Brandon E. Gavett, Alzheimer’s Disease Neuroimaging Initiative, Anja Soldan, Paul K. Crane, Colin Groot, Evan Fletcher, Timothy J. Hohman, and Sarah E Tomaszewski Farias

Subjects
Male, Aging, Memory, Episodic, Models, Neurological, MEDLINE, Neuroimaging, computer.software_genre, Cognition, Text mining, Image Interpretation, Computer-Assisted, medicine, Humans, Dementia, Cognitive Dysfunction, Computer Simulation, Neuropsychological assessment, Association (psychology), Episodic memory, Aged, Aged, 80 and over, medicine.diagnostic_test, business.industry, Brain, Original Articles, Middle Aged, medicine.disease, Magnetic Resonance Imaging, Signature (logic), Cohort, Multiple comparisons problem, Female, Artificial intelligence, Neurology (clinical), business, Psychology, computer, Natural language processing, Cognitive psychology
Abstract

The brain signature concept aims to characterize brain regions most strongly associated with an outcome of interest. Brain signatures derive their power from data-driven searches that select features based solely on performance metrics of prediction or classification. This approach has important potential to delineate biologically relevant brain substrates for prediction or classification of future trajectories. Recent work has used exploratory voxel-wise or atlas-based searches, with some using machine learning techniques to define salient features. These have shown undoubted usefulness, but two issues remain. The preponderance of recent work has been aimed at categorical rather than continuous outcomes, and it is rare for non-atlas reliant voxel-based signatures to be reported that would be useful for modelling and hypothesis testing. We describe a cross-validated signature region model for structural brain components associated with baseline and longitudinal episodic memory across cognitively heterogeneous populations including normal, mild impairment and dementia. We used three non-overlapping cohorts of older participants: from the UC Davis Aging and Diversity cohort (n = 255; mean age 75.3 ± 7.1 years; 128 cognitively normal, 97 mild cognitive impairment, 30 demented and seven unclassified); from Alzheimer’s Disease Neuroimaging Initiative (ADNI) 1 (n = 379; mean age 75.1 ± 7.2; 82 cognitively normal, 176 mild cognitive impairment, 121 Alzheimer’s dementia); and from ADNI2/GO (n = 680; mean age 72.5 ± 7.1; 220 cognitively normal, 381 mild cognitive impairment and 79 Alzheimer’s dementia). We used voxel-wise regression analysis, correcting for multiple comparisons, to generate an array of regional masks corresponding to different association strength levels of cortical grey matter with baseline memory and brain atrophy with memory change. Cognitive measures were episodic memory using Spanish and English Neuropsychological Assessment Scales instruments for UC Davis and ADNI-Mem for ADNI 1 and ADNI2/GO. Performance metric was the adjusted R2 coefficient of determination of each model explaining outcomes in two cohorts other than where it was computed. We compared within-cohort performances of signature models against each other and against other recent signature models of episodic memory. Findings were: (i) two independently generated signature region of interest models performed similarly in a third separate cohort; (ii) a signature region of interest generated in one imaging cohort replicated its performance level when explaining cognitive outcomes in each of other, separate cohorts; and (iii) this approach better explained baseline and longitudinal memory than other recent theory-driven and data-driven models. This suggests our approach can generate signatures that may be easily and robustly applied for modelling and hypothesis testing in mixed cognition cohorts.

Published
2021

35. Brain signatures for neuropsychological and everyday memory achieve high replicability and explanatory power in two data cohorts

Author

Evan Fletcher, Sarah Farias, Charles DeCarli, Brandon Gavett, Keith Widaman, Fransia De Leon, and Dan Mungas

Abstract

BackgroundThe “brain signature of cognition” concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of clinical outcomes. However, to be a robust brain phenotype, the signature approach requires a statistical foundation showing that model performance replicates across a variety of cohorts. Here, we outline a procedure that provides this foundation for a signature models of two memory-related behavioral domains.MethodIn each of two independent data cohorts, we derived regional brain gray matter thickness associations for neuropsychological and everyday cognition memory, testing for replicability. In each cohort we computed regional association to outcome in 40 randomly selected “discovery subsets” of size N = 400; we generated spatial overlap frequency maps and selected high-frequency regions as “consensus” signature masks for each cohort. We tested replicability by comparing cohort-based consensus model fits in all discovery sets. We tested explanatory power in each full cohort, compare signature model fits with competing “standard” models of each outcome.ResultSpatial replications produced strongly convergent consensus signature regions derived from UCD and ADNI. Consensus model fits were highly correlated in 40 random subsets of each cohort indicating high replicability. In comparisons over each full cohort, signature models outperformed other models with one exception.ConclusionMultiple random model generations, followed by consensus selection of regional brain substrates, produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Robust biomarkers of cognition and everyday function may be achievable by this method.FundingThis project was funded by R01 AG052132 (NIH/NIA)

Published
2022

36. Cystatin C, cognition, and brain MRI findings in 90+-year-olds

Author

Dana Greenia, Christina Whittle, Evan Fletcher, Wei Ling Lau, Seyed Ahmad Sajjadi, Maria M. Corrada, Annlia Paganini-Hill, David Floriolli, Claudia H. Kawas, Mark Fisher, and Baljeet Singh

Subjects
Male, 0301 basic medicine, Aging, medicine.medical_specialty, Renal function, urologic and male genital diseases, Article, 03 medical and health sciences, chemistry.chemical_compound, Cognition, 0302 clinical medicine, Internal medicine, medicine, Humans, Dementia, Cystatin C, Renal Insufficiency, Chronic, Aged, 80 and over, Creatinine, biology, business.industry, General Neuroscience, Hazard ratio, Age Factors, Brain, medicine.disease, Magnetic Resonance Imaging, female genital diseases and pregnancy complications, Confidence interval, 030104 developmental biology, chemistry, Cohort, Cardiology, biology.protein, Female, Neurology (clinical), Geriatrics and Gerontology, business, Biomarkers, 030217 neurology & neurosurgery, Developmental Biology, Kidney disease
Abstract

Chronic kidney disease is emerging as a novel risk factor for cerebrovascular disease, but this association remains largely unexplored in older adults. Cystatin C is a more accurate measure than creatinine of kidney function in the elderly. We evaluated cystatin C, cognitive function, and brain imaging in 193 participants from The 90+ Study neuroimaging component. The mean age was 93.9 years; 61% were women. Mean cystatin C was 1.62 mg/L with estimated glomerular filtration rate 39.2 mL/min/1.73 m2. Performance on measures of global cognition, executive function, and visual-spatial ability declined at higher tertiles of cystatin C (lower kidney function). Higher cystatin C was significantly associated with infratentorial microbleeds and lower gray matter volume. Adjusted risk of incident dementia was increased in the middle and high cystatin C tertile groups compared with the low group (hazard ratio in highest tertile 3.81 [95% confidence interval 1.14–12.7]), which appeared to be explained in part by the presence of cerebral microbleeds. Overall, cystatin C was associated with cognitive performance, brain imaging pathology, and decline to dementia in this oldest-old cohort.

Published
2020

37. Differential Item Functioning of the Everyday Cognition (ECog) Scales in Relation to Racial/Ethnic Groups

Author

Evan Fletcher, Rachel A. Whitmer, Charles DeCarli, Sarah E Tomaszewski Farias, Michelle L. Chan, Teresa J. Filshtein, and Dan M Mungas

Subjects
Male, Activities of daily living, Psychometrics, Ethnic group, Black People, White People, 050105 experimental psychology, Cohort Studies, 03 medical and health sciences, Cognition, 0302 clinical medicine, Surveys and Questionnaires, Activities of Daily Living, Ethnicity, medicine, Humans, Dementia, 0501 psychology and cognitive sciences, Measurement invariance, Aged, Aged, 80 and over, Psychiatric Status Rating Scales, General Neuroscience, 05 social sciences, Hispanic or Latino, Middle Aged, medicine.disease, Differential item functioning, Confirmatory factor analysis, Psychiatry and Mental health, Clinical Psychology, Cohort, Female, Neurology (clinical), Factor Analysis, Statistical, Psychology, 030217 neurology & neurosurgery, Clinical psychology
Abstract

Objective:The Everyday Cognition (ECog) scales measure cognitively based across domains of everyday abilities that are affected early in the course of neurodegenerative disorders such as Alzheimer’s disease. However, the degree to which the ECog may be differentially influenced by ethnic/racial background is unknown. This study evaluates measurement invariance of the ECog across non-Hispanic White (NHW), Black, and Hispanic individuals.Methods:Participants included 1177 NHW, 243 Black, and 216 Hispanic older adults from the UC Davis Alzheimer’s Disease Center Cohort who had an ECog. Differential item functioning (DIF) for each ECog domain was evaluated separately for Black and Hispanic participants compared to NHW participants. An iterative multiple group confirmatory factor analysis approach for ordinal scores was used to identify items whose measurement properties differed across groups and to adjust scores for DIF. Adjusted scores were then evaluated to test whether they were more strongly associated with cognitive function (concurrent and longitudinal change in cognition) and brain volumes (measured by brain imaging).Results:Varying levels, patterns, and impacts of DIF were found across domains and groups. However, the impact of DIF was relatively small, and DIF effects on scores generally were less than one-half standard error of measurement. There were no meaningful differences in associations with cognition and brain injury between DIF adjusted and unadjusted scores.Conclusions:Varying patterns of DIF were observed across the Black and Hispanic participants across select ECog domains. Overall, DIF effects were relatively small and did not change the relationship between the ECog and other indicators of disease.

Published
2020

38. White matter hyperintensities and CSF Alzheimer disease biomarkers in preclinical Alzheimer disease

Author

Charles DeCarli, Rebecca F. Gottesman, Oliver Martinez, Abhay Moghekar, Corinne Pettigrew, Baljeet Singh, Marilyn S. Albert, Anja Soldan, Evan Fletcher, Mei Cheng Wang, and Yuxin Zhu

Subjects
Adult, Male, 0301 basic medicine, Oncology, medicine.medical_specialty, Prodromal Symptoms, Neuroimaging, tau Proteins, Neuropsychological Tests, behavioral disciplines and activities, Article, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Alzheimer Disease, Internal medicine, mental disorders, medicine, Humans, Cognitive Dysfunction, Longitudinal Studies, Age of Onset, Phosphorylation, Young adult, Aged, Aged, 80 and over, Amyloid beta-Peptides, medicine.diagnostic_test, Proportional hazards model, business.industry, Hazard ratio, Brain, Magnetic resonance imaging, Middle Aged, medicine.disease, Magnetic Resonance Imaging, White Matter, Peptide Fragments, Hyperintensity, 030104 developmental biology, Disease Progression, Female, Neurology (clinical), Alzheimer's disease, Age of onset, business, Biomarkers, 030217 neurology & neurosurgery
Abstract

ObjectiveRecent studies suggest that white matter hyperintensities (WMH) on MRI, which primarily reflect small vessel cerebrovascular disease, may play a role in the evolution of Alzheimer disease (AD). In a longitudinal study, we investigated whether WMH promote the progression of AD pathology, or alter the association between AD pathology and risk of progression from normal cognition to mild cognitive impairment (MCI).MethodsTwo sets of analyses were conducted. The relationship between whole brain WMH load, based on fluid-attenuated inversion recovery MRI, obtained in initially cognitively normal participants (n = 274) and time to onset of symptoms of MCI (n = 60) was examined using Cox regression models. In a subset of the participants with both MRI and CSF data (n = 204), the interaction of WMH load and CSF AD biomarkers was also evaluated.ResultsBaseline WMH load interacted with CSF total tau (t-tau) with respect to symptom onset, but not with CSF β-amyloid 1–42 or phosphorylated tau (p-tau) 181. WMH volume was associated with time to symptom onset of MCI among individuals with low t-tau (hazard ratio [HR] 1.35, confidence interval [CI] 1.06–1.73, p = 0.013), but not those with high t-tau (HR 0.86, CI 0.56–1.32, p = 0.47). The rate of change in the CSF biomarkers over time was not associated with the rate of change in WMH volumes.ConclusionThese results suggest that WMH primarily affect the risk of progression when CSF measures of neurodegeneration or neuronal injury (as reflected by t-tau) are low. However, CSF biomarkers of amyloid and p-tau and WMH appear to have largely independent and nonsynergistic effects on the risk of progression to MCI.

Published
2019

40. Comparison of Education and Episodic Memory as Modifiers of Brain Atrophy Effects on Cognitive Decline: Implications for Measuring Cognitive Reserve

Author

Dan M Mungas, David K. Johnson, Sarah E Tomaszewski Farias, Keith F. Widaman, Laura B. Zahodne, N. Maritza Dowling, Timothy J. Hohman, Elizabeth Rose Mayeda, Brandon E. Gavett, and Evan Fletcher

Subjects
Aging, Brain atrophy, Cognitive decline, Neurodegenerative, Alzheimer's Disease, Medical and Health Sciences, 0302 clinical medicine, Cognitive Reserve, Episodic memory, Cognitive reserve, General Neuroscience, 05 social sciences, Brain, Cognition, Experimental Psychology, Psychiatry and Mental health, Clinical Psychology, Gray matter change, Neurological, Educational Status, Mental health, Psychology, Episodic, Clinical psychology, MRI, Memory, Episodic, Gray (unit), Article, 050105 experimental psychology, Education, 03 medical and health sciences, Atrophy, Cognitive change, Memory, Clinical Research, Behavioral and Social Science, medicine, Acquired Cognitive Impairment, Humans, 0501 psychology and cognitive sciences, Cognitive Dysfunction, Aged, Psychology and Cognitive Sciences, Neurosciences, Construct validity, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), medicine.disease, Brain Disorders, Quality Education, cognitive change, Dementia, Neurology (clinical), 030217 neurology & neurosurgery
Abstract

Objective:This study compared the level of education and tests from multiple cognitive domains as proxies for cognitive reserve.Method:The participants were educationally, ethnically, and cognitively diverse older adults enrolled in a longitudinal aging study. We examined independent and interactive effects of education, baseline cognitive scores, and MRI measures of cortical gray matter change on longitudinal cognitive change.Results:Baseline episodic memory was related to cognitive decline independent of brain and demographic variables and moderated (weakened) the impact of gray matter change. Education moderated (strengthened) the gray matter change effect. Non-memory cognitive measures did not incrementally explain cognitive decline or moderate gray matter change effects.Conclusions:Episodic memory showed strong construct validity as a measure of cognitive reserve. Education effects on cognitive decline were dependent upon the rate of atrophy, indicating education effectively measures cognitive reserve only when atrophy rate is low. Results indicate that episodic memory has clinical utility as a predictor of future cognitive decline and better represents the neural basis of cognitive reserve than other cognitive abilities or static proxies like education.

Published
2021

41. Functional reserve: The residual variance in instrumental activities of daily living not explained by brain structure, cognition, and demographics

Author

Dan M Mungas, Sarah Tomazewski Farias, Brandon E. Gavett, Evan Fletcher, Lauren Massimo, Carmen I Carrión, A. Zarina Kraal, and Luis D. Medina

Subjects
Male, Aging, Activities of daily living, Neurodegenerative, Neuropsychological Tests, Alzheimer's Disease, Cognition, Cognitive Reserve, Activities of Daily Living, 80 and over, 2.1 Biological and endogenous factors, Psychology, Apathy, Longitudinal Studies, Cognitive decline, Aetiology, older adults, Cognitive reserve, Aged, 80 and over, 05 social sciences, Rehabilitation, Brain, Experimental Psychology, Middle Aged, Magnetic Resonance Imaging, Neuropsychology and Physiological Psychology, Neurological, Disease Progression, Cognitive Sciences, Female, medicine.symptom, Clinical psychology, functional abilities, Context (language use), 050105 experimental psychology, Article, instrumental activities of daily living, Clinical Research, Behavioral and Social Science, medicine, Acquired Cognitive Impairment, Dementia, Humans, 0501 psychology and cognitive sciences, Cognitive Dysfunction, Effects of sleep deprivation on cognitive performance, Aged, Demography, Psychiatric Status Rating Scales, Prevention, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), medicine.disease, Brain Disorders, Nervous System Diseases
Abstract

Objective Cognitive reserve is a concept that explains individual differences in resilience to brain pathology and susceptibility to poor late-life cognitive outcomes. We evaluate the analogous concept of "Functional Reserve," defined as the difference between observed functional abilities and those predicted by brain structure, cognitive performance, and demographics. This study aims to validate the construct of functional reserve by testing its utility in predicting clinical outcomes and exploring its predictors. Method Longitudinal data collected annually for up to 7 years from 1,084 older adults (ndementia = 163; nMCI = 333; nCN = 523) were analyzed. Functional reserve was operationalized as the residual variance in the Lawton-Brody Instrumental Activities of Daily Living (IADL) Scale after accounting for demographics (sex/gender, race, ethnicity, education), neuropathology (gray matter, hippocampal, and white matter hyperintensity volumes), and cognition (executive function, verbal episodic memory, semantic memory, and spatial function). Structural equation models estimated (a) functional reserve's associations with 7-year changes in clinical diagnosis and disease severity and (b) predictors of functional reserve. Results Functional reserve was lower in dementia versus cognitively normal individuals. Higher baseline functional reserve was associated with lower concurrent dementia severity and slower clinical progression and attenuated the association of cognition with concurrent dementia severity. Physical function and apathy were the strongest predictors of functional reserve. Conclusions Results provide preliminary validation of functional reserve for explaining individual differences in susceptibility to IADL dysfunction independent of neuropathology, cognition, and demographics. Physical functioning and apathy are promising modifiable intervention targets to enhance functional reserve in the context of brain atrophy and cognitive decline. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Published
2021

43. Association of plasma abeta and tau levels in relation to cognition and brain structure in a diverse community

Author

Brittany N. Dugger, Kelsey Erickson, David K. Johnson, Charles DeCarli, John M Olichney, Pauline Maillard, Evan Fletcher, Izumi Maezawa, Sarah E Tomaszewski Farias, Lee-Way Jin, and Dan M Mungas

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Cognition, Neurology (clinical), Geriatrics and Gerontology, Relation (history of concept), Association (psychology), Psychology, Neuroscience
Published
2020

45. Self‐reported sleep duration is not associated with pet amyloid deposition in the oldest‐old: The 90+ Study

Author

Charles DeCarli, Maria M. Corrada, Bryce A. Mander, Zarui A. Melikyan, Dana Greenia, Evan Fletcher, Claudia H. Kawas, and Michael J. Phelan

Subjects
medicine.medical_specialty, Epidemiology, Behavioral neurology, business.industry, Health Policy, Neuropsychiatry, Oldest old, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Amyloid deposition, Developmental Neuroscience, medicine, Neurology (clinical), Geriatrics and Gerontology, Psychiatry, business, Sleep duration
Published
2020

46. Structural brain network efficiency and cognitive processing speed in healthy aging

Author

Alexa S. Beiser, Jayandra J. Himali, Evan Fletcher, Claudia L. Satizabal, Charles DeCarli, Sudha Seshadri, Pauline Maillard, and Stephan Seiler

Subjects
Brain network, Epidemiology, business.industry, Health Policy, Cognition, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Neuroimaging, Medicine, Neurology (clinical), Geriatrics and Gerontology, Healthy aging, business, Neuroscience, Brain aging
Published
2020

47. Towards a generalized deep learning framework for production scale segmentation of brain structures

Author

Francisco Sandoval, Alexander Knaack, Jeremy Delahanty, Charles DeCarli, Sean G McDougall, and Evan Fletcher

Subjects
Scale (ratio), Epidemiology, Computer science, business.industry, Health Policy, Deep learning, Machine learning, computer.software_genre, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Neuroimaging, Production (economics), Segmentation, Neurology (clinical), Artificial intelligence, Geriatrics and Gerontology, business, computer
Published
2020

48. Reliable change in neuropsychological test scores is associated with brain atrophy in older adults

Author

Evan Fletcher, Brandon E. Gavett, and Shayne S.-H. Lin

Subjects
medicine.medical_specialty, Cognitive Neuroscience, Audiology, Neuropsychological Tests, 050105 experimental psychology, Temporal lobe, 03 medical and health sciences, Behavioral Neuroscience, 0302 clinical medicine, Atrophy, Alzheimer Disease, mental disorders, medicine, Humans, 0501 psychology and cognitive sciences, Cognitive Dysfunction, Cognitive decline, skin and connective tissue diseases, Aged, Temporal cortex, medicine.diagnostic_test, 05 social sciences, Neuropsychology, Parietal lobe, Brain, Neuropsychological test, medicine.disease, Magnetic Resonance Imaging, Neuropsychology and Physiological Psychology, Frontal lobe, sense organs, Psychology, 030217 neurology & neurosurgery
Abstract

The reliable change index (RCI) is a commonly used method for interpreting change in neuropsychological test scores over time. However, the RCI is a psychometric method that, to date, has not been validated against neuroanatomical changes. Longitudinal neuroimaging and neuropsychological data from baseline and one-year follow-up visits were retrieved from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The RCI was used to identify participants showing reliable decline on memory (ADNI-Mem; N = 450) and executive functioning (ADNI-EF; N = 456) factor scores. For each factor score, two groups (reliable change vs. no reliable change) were matched on potential baseline confounding variables. Longitudinal neuroanatomical data were analysed using tensor-based morphometry. Analysis revealed that reliable change on ADNI-Mem was associated with atrophy in the medial temporal cortex, limbic cortex, temporal lobe and some regions of the parietal lobe. Similar atrophy patterns were found for reliable change on ADNI-EF, except that atrophy extended to the frontal lobe and the atrophy was more extensive and of higher magnitude. The current study not only validates clinical usage of the RCI with neuroanatomical evidence of associated underlying brain change but also suggests patterns of likely brain atrophy when reliable cognitive decline is detected.

Published
2020

50. Association of vascular brain injury, neurodegeneration, amyloid, and cognitive trajectory

Author

Charles DeCarli, Oliver Martinez, Pauline Maillard, Sylvia Villeneuve, Danielle J Harvey, John M Olichney, Dan M Mungas, Sarah E Tomaszewski Farias, William J. Jagust, Ji Won Han, David K. Johnson, and Evan Fletcher

Subjects
Male, medicine.medical_specialty, Memory, Episodic, Plaque, Amyloid, Disease, Neuropsychological Tests, Hippocampus, 050105 experimental psychology, Article, 03 medical and health sciences, Executive Function, 0302 clinical medicine, Cognition, Internal medicine, Medicine, Dementia, Humans, 0501 psychology and cognitive sciences, Cognitive Dysfunction, Effects of sleep deprivation on cognitive performance, Episodic memory, Aged, Aged, 80 and over, Memory Disorders, Amyloid beta-Peptides, Aniline Compounds, medicine.diagnostic_test, business.industry, 05 social sciences, Brain, Magnetic resonance imaging, Organ Size, medicine.disease, Magnetic Resonance Imaging, White Matter, Confidence interval, Cerebrovascular Disorders, Thiazoles, Positron-Emission Tomography, Cohort, Cardiology, Disease Progression, Female, Neurology (clinical), Independent Living, business, 030217 neurology & neurosurgery
Abstract

ObjectiveTo determine whether vascular and neurodegenerative factors influence cognition before clinically relevant Alzheimer disease pathology, we analyzed MRI measures and amyloid imaging in an ethnoracially diverse cohort of cognitively normal individuals older than 60 years.MethodsParticipants (n = 154; mean age 74.15 ± 6.94; 50% female; 54% Caucasian, 22.1% Hispanic, 14.9% African American) were recruited from the University of California, Davis Alzheimer's Disease Research Center, who were cognitively normal at baseline, time of PET, and MRI, and received yearly cognitive assessment for 6.23 ± 4.16 years. Mixed model regression with random slope and intercept was calculated for episodic memory and executive function, adjusting for age, sex, education, and ethnicity.ResultsVascular burden score was associated with total white matter hyperintensity (WMH) volume (β, 0.171; 95% confidence interval [CI], 0.024–0.318). WMH volume was associated with low baseline executive function (−0.115; −0.226 to −0.003) and rate of change in memory (−0.029; −0.045 to −0.012). Hippocampal volume was associated with the rate of change in memory (0.040; 0.021–0.059) and executive function (0.024; 0.008–0.039). Continuous measures of amyloid status influenced change in memory (−0.026; −0.044 to −0.008) and executive function (−0.033; −0.046 to −0.021) independently of MRI measures.ConclusionVascular brain injury and neurodegeneration are associated with baseline cognitive performance and the rate of longitudinal change independent of amyloid status among community-dwelling, ethnicity diverse cognitively normal individuals, supporting the role of vascular diseases as risk factors for later-life dementia.

Published
2020

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