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61 Network Segregation Predicts Processing Speed in the Cognitively Healthy Oldest-old
- Sara A Nolin, Mary E Faulkner, Paul Stewart, Leland Fleming, Stacy Merritt, Roxanne F Rezaei, Pradyumna K Bharadwaj, Mary Kathryn Franchetti, Daniel A Raichlen, Courtney J Jessup, Lloyd Edwards, G Alex Hishaw, Emily J Van Etten, Theodore P Trouard, David S Geldmacher, Virginia G Wadley, Noam Alperin, Eric C Porges, Adam J Woods, Ronald A Cohen, Bonnie E Levin, Tatjana Rundek, Gene E Alexander, Kristina M Visscher
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 367-368
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Objective:
Understanding the factors contributing to optimal cognitive function throughout the aging process is essential to better understand successful cognitive aging. Processing speed is an age sensitive cognitive domain that usually declines early in the aging process; however, this cognitive skill is essential for other cognitive tasks and everyday functioning. Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brain’s organization, and the cognitive effects of aging may relate to this large-scale organization. To follow-up on our prior work, we sought to replicate our findings regarding network segregation’s relationship with processing speed. In order to address possible influences of node location or network membership we replicated the analysis across 4 different node sets.
Participants and Methods:Data were acquired as part of a multi-center study of 85+ cognitively normal individuals, the McKnight Brain Aging Registry (MBAR). For this analysis, we included 146 community-dwelling, cognitively unimpaired older adults, ages 85-99, who had undergone structural and BOLD resting state MRI scans and a battery of neuropsychological tests. Exploratory factor analysis identified the processing speed factor of interest. We preprocessed BOLD scans using fmriprep, Ciftify, and XCPEngine algorithms. We used 4 different sets of connectivity-based parcellation: 1)MBAR data used to define nodes and Power (2011) atlas used to determine node network membership, 2) Younger adults data used to define nodes (Chan 2014) and Power (2011) atlas used to determine node network membership, 3) Older adults data from a different study (Han 2018) used to define nodes and Power (2011) atlas used to determine node network membership, and 4) MBAR data used to define nodes and MBAR data based community detection used to determine node network membership.
Segregation (balance of within-network and between-network connections) was measured within the association system and three wellcharacterized networks: Default Mode Network (DMN), Cingulo-Opercular Network (CON), and Fronto-Parietal Network (FPN). Correlation between processing speed and association system and networks was performed for all 4 node sets.
Results:We replicated prior work and found the segregation of both the cortical association system, the segregation of FPN and DMN had a consistent relationship with processing speed across all node sets (association system range of correlations: r=.294 to .342, FPN: r=.254 to .272, DMN: r=.263 to .273). Additionally, compared to parcellations created with older adults, the parcellation created based on younger individuals showed attenuated and less robust findings as those with older adults (association system r=.263, FPN r=.255, DMN r=.263).
Conclusions:This study shows that network segregation of the oldest-old brain is closely linked with processing speed and this relationship is replicable across different node sets created with varied datasets. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating replicability and consistency of the finding that as essential cognitive skill, processing speed, is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging.
Brain Arterial Diameters and Cognitive Performance: The Northern Manhattan Study
- Jose Gutierrez, Erin Kulick, Yeseon Park Moon, Chuanhui Dong, Ken Cheung, Bagci Ahmet, Yaakov Stern, Noam Alperin, Tatjana Rundek, Ralph L. Sacco, Clinton B. Wright, Mitchell S.V. Elkind
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- Journal:
- Journal of the International Neuropsychological Society / Volume 24 / Issue 4 / April 2018
- Published online by Cambridge University Press:
- 23 November 2017, pp. 335-346
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Objectives: To test the hypothesis that brain arterial diameters are associated with cognitive performance, particularly in arteries supplying domain-specific territories. Methods: Stroke-free participants in the Northern Manhattan Study were invited to have a brain MRI from 2003–2008. The luminal diameters of 13 intracranial arterial segments were obtained using time-of-flight magnetic resonance angiogram (MRA), and then averaged and normalized into a global score and region-specific arterial diameters. Z-Scores for executive function, semantic memory, episodic memory and processing speed were obtained at MRI and during follow-up. Adjusted generalized additive models were used to assess for associations. Results: Among the 1034 participants with neurocognitive testing and brain MRI, there were non-linear relationships between left anterior (ACA) and middle cerebral artery (MCA) diameter and semantic memory Z-scores (χ2=10.00; DF=3; p=.019), and left posterior cerebral artery (PCA) and posterior communicating artery (Pcomm) mean diameter and episodic memory Z-scores (χ2=9.88; DF=3; p=.020). Among the 745 participants who returned for 2nd neuropsychological testing, on average 5.0±0.4 years after their MRI, semantic memory change was associated non-linearly with the left PCA/Pcomm mean diameter (χ2=13.09; DF=3; p=.004) and with the right MCA/ACA mean diameter (χ2=8.43; DF=3; p=.03). In both cross-sectional and longitudinal analyses, participants with the larger brain arterial diameters had more consistently lower Z-scores and greater decline than the rest of the participants. Conclusions: Brain arterial diameters may have downstream effects in brain function presenting as poorer cognition. Identifying the mechanisms and the directionality of such interactions may increase the understanding of the vascular contribution to cognitive impairment and dementia. (JINS, 2018, 24, 335–346)
Contributors
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- By Lenard A. Adler, Pinky Agarwal, Rehan Ahmed, Jagga Rao Alluri, Fawaz Al-Mufti, Samuel Alperin, Michael Amoashiy, Michael Andary, David J. Anschel, Padmaja Aradhya, Vandana Aspen, Esther Baldinger, Jee Bang, George D. Baquis, John J. Barry, Jason J. S. Barton, Julius Bazan, Amanda R. Bedford, Marlene Behrmann, Lourdes Bello-Espinosa, Ajay Berdia, Alan R. Berger, Mark Beyer, Don C. Bienfang, Kevin M. Biglan, Thomas M. Boes, Paul W. Brazis, Jonathan L. Brisman, Jeffrey A. Brown, Scott E. Brown, Ryan R. Byrne, Rina Caprarella, Casey A. Chamberlain, Wan-Tsu W. Chang, Grace M. Charles, Jasvinder Chawla, David Clark, Todd J. Cohen, Joe Colombo, Howard Crystal, Vladimir Dadashev, Sarita B. Dave, Jean Robert Desrouleaux, Richard L. Doty, Robert Duarte, Jeffrey S. Durmer, Christyn M. Edmundson, Eric R. Eggenberger, Steven Ender, Noam Epstein, Alberto J. Espay, Alan B. Ettinger, Niloofar (Nelly) Faghani, Amtul Farheen, Edward Firouztale, Rod Foroozan, Anne L. Foundas, David Elliot Friedman, Deborah I. Friedman, Steven J. Frucht, Oded Gerber, Tal Gilboa, Martin Gizzi, Teneille G. Gofton, Louis J. Goodrich, Malcolm H. Gottesman, Varda Gross-Tsur, Deepak Grover, David A. Gudis, John J. Halperin, Maxim D. Hammer, Andrew R. Harrison, L. Anne Hayman, Galen V. Henderson, Steven Herskovitz, Caitlin Hoffman, Laryssa A. Huryn, Andres M. Kanner, Gary P. Kaplan, Bashar Katirji, Kenneth R. Kaufman, Annie Killoran, Nina Kirz, Gad E. Klein, Danielle G. Koby, Christopher P. Kogut, W. Curt LaFrance, Patrick J.M. Lavin, Susan W. Law, James L. Levenson, Richard B. Lipton, Glenn Lopate, Daniel J. Luciano, Reema Maindiratta, Robert M. Mallery, Georgios Manousakis, Alan Mazurek, Luis J. Mejico, Dragana Micic, Ali Mokhtarzadeh, Walter J. Molofsky, Heather E. Moss, Mark L. Moster, Manpreet Multani, Siddhartha Nadkarni, George C. Newman, Rolla Nuoman, Paul A. Nyquist, Gaia Donata Oggioni, Odi Oguh, Denis Ostrovskiy, Kristina Y. Pao, Juwen Park, Anastas F. Pass, Victoria S. Pelak, Jeffrey Peterson, John Pile-Spellman, Misha L. Pless, Gregory M. Pontone, Aparna M. Prabhu, Michael T. Pulley, Philip Ragone, Prajwal Rajappa, Venkat Ramani, Sindhu Ramchandren, Ritesh A. Ramdhani, Ramses Ribot, Heidi D. Riney, Diana Rojas-Soto, Michael Ronthal, Daniel M. Rosenbaum, David B. Rosenfield, Durga Roy, Michael J. Ruckenstein, Max C. Rudansky, Eva Sahay, Friedhelm Sandbrink, Jade S. Schiffman, Angela Scicutella, Maroun T. Semaan, Robert C. Sergott, Aashit K. Shah, David M. Shaw, Amit M. Shelat, Claire A. Sheldon, Anant M. Shenoy, Yelizaveta Sher, Jessica A. Shields, Tanya Simuni, Rajpaul Singh, Eric E. Smouha, David Solomon, Mehri Songhorian, Steven A. Sparr, Egilius L. H. Spierings, Eve G. Spratt, Beth Stein, S.H. Subramony, Rosa Ana Tang, Cara Tannenbaum, Hakan Tekeli, Amanda J. Thompson, Michael J. Thorpy, Matthew J. Thurtell, Pedro J. Torrico, Ira M. Turner, Scott Uretsky, Ruth H. Walker, Deborah M. Weisbrot, Michael A. Williams, Jacques Winter, Randall J. Wright, Jay Elliot Yasen, Shicong Ye, G. Bryan Young, Huiying Yu, Ryan J. Zehnder
- Edited by Alan B. Ettinger, Albert Einstein College of Medicine, New York, Deborah M. Weisbrot, State University of New York, Stony Brook
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- Book:
- Neurologic Differential Diagnosis
- Published online:
- 05 June 2014
- Print publication:
- 17 April 2014, pp xi-xx
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