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279 results on '"Vlassenko, Andrei G"'

2. Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning

Author

Rahmani, Maryam, Dierker, Donna, Yaeger, Lauren, Saykin, Andrew, Luckett, Patrick H., Vlassenko, Andrei G., Owens, Christopher, Jafri, Hussain, Womack, Kyle, Fripp, Jurgen, Xia, Ying, Tosun, Duygu, Benzinger, Tammie L. S., Masters, Colin L., Lee, Jin-Moo, Morris, John C., Goyal, Manu S., Strain, Jeremy F., Kukull, Walter, Weiner, Michael, Burnham, Samantha, CoxDoecke, Tim James, Fedyashov, Victor, Fripp, Jurgen, Shishegar, Rosita, Xiong, Chengjie, Marcus, Daniel, Raniga, Parnesh, Li, Shenpeng, Aschenbrenner, Andrew, Hassenstab, Jason, Lim, Yen Ying, Maruff, Paul, Sohrabi, Hamid, Robertson, Jo, Markovic, Shaun, Bourgeat, Pierrick, Doré, Vincent, Mayo, Clifford Jack, Mussoumzadeh, Parinaz, Rowe, Chris, Villemagne, Victor, Bateman, Randy, Fowler, Chris, Li, Qiao-Xin, Martins, Ralph, Schindler, Suzanne, Shaw, Les, Cruchaga, Carlos, Harari, Oscar, Laws, Simon, Porter, Tenielle, O’Brien, Eleanor, Perrin, Richard, Kukull, Walter, Bateman, Randy, McDade, Eric, Jack, Clifford, Morris, John, Yassi, Nawaf, Bourgeat, Pierrick, Perrin, Richard, Roberts, Blaine, Villemagne, Victor, Fedyashov, Victor, and Goudey, Benjamin

Published
2024
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6. Comparison of Pittsburgh compound B and florbetapir in cross‐sectional and longitudinal studies

Author

Su, Yi, Flores, Shaney, Wang, Guoqiao, Hornbeck, Russ C, Speidel, Benjamin, Joseph‐Mathurin, Nelly, Vlassenko, Andrei G, Gordon, Brian A, Koeppe, Robert A, Klunk, William E, Jack, Clifford R, Farlow, Martin R, Salloway, Stephen, Snider, Barbara J, Berman, Sarah B, Roberson, Erik D, Brosch, Jared, Jimenez‐Velazques, Ivonne, Dyck, Christopher H, Galasko, Douglas, Yuan, Shauna H, Jayadev, Suman, Honig, Lawrence S, Gauthier, Serge, Hsiung, Ging‐Yuek R, Masellis, Mario, Brooks, William S, Fulham, Michael, Clarnette, Roger, Masters, Colin L, Wallon, David, Hannequin, Didier, Dubois, Bruno, Pariente, Jeremie, Sanchez‐Valle, Raquel, Mummery, Catherine, Ringman, John M, Bottlaender, Michel, Klein, Gregory, Milosavljevic‐Ristic, Smiljana, McDade, Eric, Xiong, Chengjie, Morris, John C, Bateman, Randall J, and Benzinger, Tammie LS

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Bioengineering, Aging, Alzheimer's Disease, Clinical Research, Brain Disorders, Neurodegenerative, Dementia, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biomedical Imaging, Neurosciences, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, PiB, Florbetapir, Amyloid imaging, Centiloid, Positron emission tomography, Genetics, Biological psychology
Abstract

IntroductionQuantitative in vivo measurement of brain amyloid burden is important for both research and clinical purposes. However, the existence of multiple imaging tracers presents challenges to the interpretation of such measurements. This study presents a direct comparison of Pittsburgh compound B-based and florbetapir-based amyloid imaging in the same participants from two independent cohorts using a crossover design.MethodsPittsburgh compound B and florbetapir amyloid PET imaging data from three different cohorts were analyzed using previously established pipelines to obtain global amyloid burden measurements. These measurements were converted to the Centiloid scale to allow fair comparison between the two tracers. The mean and inter-individual variability of the two tracers were compared using multivariate linear models both cross-sectionally and longitudinally.ResultsGlobal amyloid burden measured using the two tracers were strongly correlated in both cohorts. However, higher variability was observed when florbetapir was used as the imaging tracer. The variability may be partially caused by white matter signal as partial volume correction reduces the variability and improves the correlations between the two tracers. Amyloid burden measured using both tracers was found to be in association with clinical and psychometric measurements. Longitudinal comparison of the two tracers was also performed in similar but separate cohorts whose baseline amyloid load was considered elevated (i.e., amyloid positive). No significant difference was detected in the average annualized rate of change measurements made with these two tracers.DiscussionAlthough the amyloid burden measurements were quite similar using these two tracers as expected, difference was observable even after conversion into the Centiloid scale. Further investigation is warranted to identify optimal strategies to harmonize amyloid imaging data acquired using different tracers.

Published
2019

8. The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Author

Volpi, Tommaso, Silvestri, Erica, Aiello, Marco, Lee, John J, Vlassenko, Andrei G, Goyal, Manu S, Corbetta, Maurizio, and Bertoldo, Alessandra

Abstract

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Persistent metabolic youth in the aging female brain

Author

Goyal, Manu S., Blazey, Tyler M., Su, Yi, Couture, Lars E., Durbin, Tony J., Bateman, Randall J., Benzinger, Tammie L.-S., Morris, John C., Raichle, Marcus E., and Vlassenko, Andrei G.

Published
2019

12. Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: an independent validation study

Author

Strain, Jeremy F., primary, Rahmani, Maryam, additional, Dierker, Donna, additional, Owens, Christopher, additional, Jafri, Hussain, additional, Vlassenko, Andrei G., additional, Womack, Kyle, additional, Fripp, Jurgen, additional, Tosun, Duygu, additional, Benzinger, Tammie L.S., additional, Wiener, Michael W., additional, Masters, Colin, additional, Lee, Jin-Moo, additional, Morris, John C., additional, and Goyal, Manu S., additional

Published
2023
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22. A new framework for metabolic connectivity mapping using bolus [ 18 F]FDG PET and kinetic modeling.

Author

Volpi, Tommaso, Vallini, Giulia, Silvestri, Erica, Francisci, Mattia De, Durbin, Tony, Corbetta, Maurizio, Lee, John J, Vlassenko, Andrei G, Goyal, Manu S, and Bertoldo, Alessandra

Abstract

Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., K i, K 1, k 3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, K i, K 1, k 3 produced different networks depending on the chosen [18F]FDG parameter (k 3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47–0.63) than for ai-MC (0.24–0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Brain aerobic glycolysis and resilience in Alzheimer disease

Author

Goyal, Manu S., primary, Blazey, Tyler, additional, Metcalf, Nicholas V., additional, McAvoy, Mark P., additional, Strain, Jeremy F., additional, Rahmani, Maryam, additional, Durbin, Tony J., additional, Xiong, Chengjie, additional, Benzinger, Tammie L.-S., additional, Morris, John C., additional, Raichle, Marcus E., additional, and Vlassenko, Andrei G., additional

Published
2023
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24. sj-pdf-1-jcb-10.1177_0271678X231184365 - Supplemental material for A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling

Author

Volpi, Tommaso, Vallini, Giulia, Silvestri, Erica, Francisci, Mattia De, Durbin, Tony, Corbetta, Maurizio, Lee, John J, Vlassenko, Andrei G, Goyal, Manu S, and Bertoldo, Alessandra

Subjects
Endocrinology, Neurology and Neuromuscular Diseases, FOS: Clinical medicine, Emergency Medicine, Medicine, Cell Biology, Biochemistry, Radiology and Organ Imaging, Biological Sciences not elsewhere classified, Neuroscience
Abstract

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X231184365 for A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling by Tommaso Volpi, Giulia Vallini, Erica Silvestri, Mattia De Francisci, Tony Durbin, Maurizio Corbetta, John J Lee, Andrei G Vlassenko, Manu S Goyal and Alessandra Bertoldo in Journal of Cerebral Blood Flow & Metabolism

Published
2023
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28. Characteristics and quantitative impact of off‐target skull binding in tau PET studies of Alzheimer disease

Author

Flores, Shaney, primary, Chen, Charles D., additional, Su, Yi, additional, Dincer, Aylin, additional, Keefe, Sarah J., additional, Perez‐Carrillo, Gloria Guzman, additional, Hornbeck, Russ C., additional, Goyal, Manu S., additional, Vlassenko, Andrei G., additional, Schwarz, Sally, additional, Nickels, Michael L., additional, Wong, Dean F, additional, Tu, Zhude, additional, McConathy, Jonathan, additional, Morris, John C., additional, Benzinger, Tammie L.S., additional, and Gordon, Brian A., additional

Published
2022
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33. Brain aerobic glycolysis and resilience in Alzheimer disease

Author

Goyal, Manu S., primary, Blazey, Tyler, additional, Metcalf, Nicholas V., additional, McAvoy, Mark P., additional, Strain, Jeremy, additional, Rahmani, Maryam, additional, Durbin, Tony J., additional, Xiong, Chengjie, additional, Benzinger, Tammie L.-S., additional, Morris, John C., additional, Raichle, Marcus E., additional, and Vlassenko, Andrei G., additional

Published
2022
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40. Spatial correlation between brain aerobic glycolysis and amyloid-[beta] (A[beta]) deposition

Author

Vlassenko, Andrei G., Vaishnavi, S. Neil, Couture, Lars, Sacco, Dana, Shannon, Benjamin J., Mach, Robert H., Morris, John C., Raichle, Marcus E., and Mintun, Mark A.

Subjects
Glucose metabolism -- Health aspects, Glucose metabolism -- Genetic aspects, Alzheimer's disease -- Genetic aspects, Alzheimer's disease -- Health aspects, Glycolysis -- Research, Glycoproteins -- Health aspects, PET imaging -- Methods, Science and technology
Abstract

Amyloid-[beta] (A[beta]) plaque deposition can precede the clinical manifestations of dementia of the Alzheimer type (DAT) by many years and can be associated with changes in brain metabolism. Both the All plaque deposition and the changes in metabolism appear to be concentrated in the brain's default-mode network. In contrast to prior studies of brain metabolism which viewed brain metabolism from a unitary perspective that equated glucose utilization with oxygen consumption, we here report on regional glucose use apart from that entering oxidative phosphorylation (so-called 'aerobic glycolysis'). Using PET, we found that the spatial distribution of aerobic glycolysis in normal young adults correlates spatially with A[beta] deposition in individuals with DAT and cognitively normal participants with elevated A[beta], suggesting a possible link between regional aerobic glycolysis in young adulthood and later development of Alzheimer pathology. Alzheimer's disease | default mode network | positron emission tomography doi/ 10.1073/pnas.1010461107

Published
2010

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