The DOI matches its Crossref record; 119 of the 122 references it cites were found in a registry or an index.
Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85years) measured with atlas-based parcellation of MRI
Open accessAbstract
Numerous cross-sectional MRI studies have characterized age-related differences in regional brain volumes that differ with structure and tissue type. The extent to which cross-sectional assumptions about change are accurate depictions of actual longitudinal measurement remains controversial. Even longitudinal studies can be limited by the age range of participants, sex distribution of the samples, and scan intervals. To address these issues, we calculated trajectories of regional brain volume changes from T1-weighted (SPGR) MRI data, quantified with our automated, unsupervised SRI24 atlas-based registration and parcellation method. Longitudinal MRIs were acquired at 3T in 17 boys and 12 girls, age 10 to 14 years, and 41 men and 41 women, age 20 to 85 years at first scan. Application of a regression-based correction function permitted merging of data acquired at 3T field strength with data acquired at 1.5T from additional subjects, thereby expanding the sample to a total of 55 men and 67 women, age 20 to 85 years at first scan. Adjustment for individual supratentorial volume removed regional volume differences between men and women due to sex-related differences in head size. Individual trajectories were computed from data collected on 2 to 6 MRIs at a single field strength over a ~1 to 8 year interval. Using linear mixed-effects models, the pattern of trajectories over age indicated: rises in ventricular and Sylvian fissure volumes, with older individuals showing faster increases than younger ones; declines in selective cortical volumes with faster tissue shrinkage in older than younger individuals; little effect of aging on volume of the corpus callosum; more rapid expansion of CSF-filled spaces in men than women after age 60 years; and evidence for continued growth in central white matter through early adulthood with accelerated decline in senescence greater in men than women.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Overcoming the drawbacks of cross-sectional studies that have largely dominated clinical studies on aging, newer research based on the longitudinal studies has reported significant tissue atrophy in the temporal and occipital regions in non-pathological aging (Pfefferbaum et al, 2013;Storsve et al, 2014).”
From Microglia Priming with Aging and Stress (2017) · cites this paper as Pfefferbaum et al, 2013
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,012
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,852
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,007
Fast robust automated brain extraction
Smith, S. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10062 · Cited by 10,496
KAUFMAN, J., BIRMAHER, B., BRENT, D., RAO, U., FLYNN, C., MORECI, P., WILLIAMSON, D., & RYAN, N. (1997) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/00004583-199707000-00021 · Cited by 10,148
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628
Flagged first, then by citations
3 of the 122 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A meta-analysis of sex differences in human brain structure
Ruigrok, A. N. V., Salimi-Khorshidi, G., Lai, M.-C., Baron-Cohen, S., Lombardo, M. V., Tait, R. J., & Suckling, J. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.12.004 · Cited by 1,192
Adolescence as a sensitive period of brain development
Fuhrmann, D., Knoll, L. J., & Blakemore, S.-J. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.07.008 · Cited by 1,158
Clinical epidemiology of Alzheimer’s disease: assessing sex and gender differences
Mielke, M., Vemuri, P., & Rocca, W. (2014) · Clinical Epidemiology · doi:10.2147/clep.s37929 · Cited by 1,021
Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M., & Walhovd, K. B. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2014.02.004 · Cited by 799
Storsve, A. B., Fjell, A. M., Tamnes, C. K., Westlye, L. T., Overbye, K., Aasland, H. W., & Walhovd, K. B. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0391-14.2014 · Cited by 542
The developmental mismatch in structural brain maturation during adolescence
Mills, K. L., Goddings, A.-L., Clasen, L. S., Giedd, J. N., & Blakemore, S.-J. (2014) · Developmental Neuroscience · doi:10.1159/000362328 · Cited by 440
Human cognitive aging: Corriger la fortune?
Lindenberger, U. (2014) · Science · doi:10.1126/science.1254403 · Cited by 414
Effect of alcohol use on the adolescent brain and behavior
Lees, B., Meredith, L. R., Kirkland, A. E., Bryant, B. E., & Squeglia, L. M. (2020) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2020.172906 · Cited by 411
Microglia Priming with Aging and Stress
Niraula, A., Sheridan, J. F., & Godbout, J. P. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2016.185 · Cited by 398
Accelerated Brain Aging in Schizophrenia: A Longitudinal Pattern Recognition Study
Schnack, H. G., van Haren, N. E. M., Nieuwenhuis, M., Pol, H. E. H., Cahn, W., & Kahn, R. S. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.15070922 · Cited by 376
Han, L. K. M., Dinga, R., Hahn, T., Ching, C. R. K., Eyler, L. T., Aftanas, L. I., Aghajani, M., Aleman, A., Baune, B. T., Berger, K., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0754-0 · Cited by 351
Thalamic structures and associated cognitive functions: Relations with age and aging
Fama, R., & Sullivan, E. V. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.03.008 · Cited by 323
Self-citationAlexander, R., Aragón, O. R., Bookwala, J., Cherbuin, N., Gatt, J. M., Kahrilas, I. J., Kästner, N., Lawrence, A. B., Lowe, L. J., Morrison, R., et al. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.12.002 · Cited by 364
Towards a unified analysis of brain maturation and aging across the entire lifespan: A MRI analysis
Coupé, P., Catheline, G., Lanuza, E., Manjón, J. V., & Initiative, A. D. N. (2017) · Human Brain Mapping · doi:10.1002/hbm.23743 · Cited by 283
Structural Imaging Measures of Brain Aging
Lockhart, S. N., & DeCarli, C. S. (2014) · Neuropsychology Review · doi:10.1007/s11065-014-9268-3 · Cited by 301
Snyder, H. M., Asthana, S., Bain, L., Brinton, R., Craft, S., Dubal, D. B., Espeland, M. A., Gatz, M., Mielke, M. M., Raber, J., et al. (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.08.004 · Cited by 262
Brain Development in Heavy-Drinking Adolescents
Squeglia, L. M., Tapert, S. F., Sullivan, E. V., Jacobus, J., Meloy, M. J., Rohlfing, T., & Pfefferbaum, A. (2015) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.14101249 · Cited by 245
Self-citationAlcohol and Drug Use and the Developing Brain
Squeglia, L. M., & Gray, K. M. (2016) · Current Psychiatry Reports · doi:10.1007/s11920-016-0689-y · Cited by 270
Alcohol's Effects on the Brain: Neuroimaging Results in Humans and Animal Models
Zahr, N. M. (2017) · Alcohol research · doi:10.35946/arcr.v38.2.04 · Cited by 231
Eileen Luders, A., Cherbuin, N., & Christian Gaser, B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.007 · Cited by 183
Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking
Pfefferbaum, A., Kwon, D., Brumback, T., Thompson, W. K., Cummins, K. M., Tapert, S. F., Brown, S. A., Colrain, I. M., Baker, F. C., Prouty, D., et al. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.17040469 · Cited by 182
Self-citationSchättin, A., Arner, R., Gennaro, F., & de Bruin, E. D. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00278 · Cited by 184
Trajectories of imaging markers in brain aging: the Rotterdam Study
Vinke, E. J., de Groot, M., Venkatraghavan, V., Klein, S., Niessen, W. J., Ikram, M. A., & Vernooij, M. W. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.07.001 · Cited by 170
Adolf Pfefferbaum, A., Torsten Rohlfing, B., Pohl, K. M., Lane, B., Chu, W., Kwon, D., Nichols, B. N., Brown, S. A., Tapert, S. F., Cummins, K. M., et al. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv205 · Cited by 172
Self-citationAssociation of Neighborhood-Level Disadvantage With Cerebral and Hippocampal Volume
Hunt, J. F. V., Buckingham, W. J., Kim, A. J., Oh, J. M., Vogt, N. M., Jonaitis, E. M., Hunt, T. K. A., Zuelsdorff, M. L., Powell, W. R., Norton, D., et al. (2020) · JAMA Neurology · doi:10.1001/jamaneurol.2019.4501 · Cited by 166
Evolution of deep gray matter volume across the human lifespan
Narvacan, K., Treit, S., Camicioli, R. M., Martin, W. R. W., & Beaulieu, C. (2017) · Human Brain Mapping · doi:10.1002/hbm.23604 · Cited by 160
Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years
Dima, D., Modabbernia, A., Papachristou, E., Doucet, G. E., Agartz, I., Aghajani, M., Akudjedu, T. N., Albajes‐Eizagirre, A., Alnæs, D., Alpert, K. I., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25320 · Cited by 165
Normative data for subcortical regional volumes over the lifetime of the adult human brain
Potvin, O., Mouiha, A., Dieumegarde, L., & Duchesne, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.016 · Cited by 134
Pfefferbaum, A., Rogosa, D., Rosenbloom, M. J., Chu, W., Sassoon, S. A., Kemper, C. A., Deresinski, S. C., Torsten Rohlfing, B., Zahr, N. M., & Sullivan, E. V. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.01.008 · Cited by 122
Self-citationThe lifespan trajectory of neural oscillatory activity in the motor system
Heinrichs‐Graham, E., McDermott, T. J., Mills, M. S., Wiesman, A. I., Wang, Y., Stephen, J. M., Calhoun, V. D., & Wilson, T. W. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.02.013 · Cited by 116
Neurobiological and Cognitive Profile of Young Binge Drinkers: a Systematic Review and Meta-Analysis
Lees, B., Mewton, L., Stapinski, L. A., Squeglia, L. M., Rae, C. D., & Teesson, M. (2019) · Neuropsychology Review · doi:10.1007/s11065-019-09411-w · Cited by 109
Cerebellar volume in schizophrenia and bipolar I disorder with and without psychotic features
Laidi, C., d’Albis, M.-A., Wessa, M., Linke, J. O., Phillips, M. L., Delavest, M., Bellivier, F., Versace, A., Almeida, J., Sarrazin, S., et al. (2014) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12363 · Cited by 96
Sun, Y., Lee, R., Yu, C., Collinson, S. L., Thakor, N. V., Bezerianos, A., & Sim, K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0118857 · Cited by 96
A Life-Long Approach to Physical Activity for Brain Health
Macpherson, H., Teo, W.-P., Schneider, L. A., & Smith, A. E. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00147 · Cited by 104
Pohl, K. M., Sullivan, E. V., Rohlfing, T., Chu, W., Kwon, D., Nichols, B. N., Zhang, Y., Brown, S. A., Tapert, S. F., Cummins, K., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.061 · Cited by 85
Self-citationThe Role of Aging, Drug Dependence, and Hepatitis C Comorbidity in Alcoholism Cortical Compromise
Sullivan, E. V., Zahr, N. M., Sassoon, S. A., Thompson, W. K., Kwon, D., Pohl, K. M., & Pfefferbaum, A. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.0021 · Cited by 85
Self-citationNormative morphometric data for cerebral cortical areas over the lifetime of the adult human brain
Potvin, O., Dieumegarde, L., & Duchesne, S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.019 · Cited by 86
Adolf Pfefferbaum, A., Zahr, N. M., Sassoon, S. A., Kwon, D., Pohl, K. M., & Sullivan, E. V. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.06.006 · Cited by 86
Self-citationRegional vulnerability of hippocampal subfields to aging measured by structural and diffusion MRI
Pereira, J. B., Valls‐Pedret, C., Ros, E., Palacios, E. M., Falcón, C., Bargalló, N., Bartrés‐Faz, D., Wahlund, L., Westman, E., & Junqué, C. (2013) · Hippocampus · doi:10.1002/hipo.22234 · Cited by 81
Global and regional annual brain volume loss rates in physiological aging
Schippling, S., Ostwaldt, A., Suppa, P., Spies, L., Manogaran, P., Gocke, C., Huppertz, H., & Opfer, R. (2017) · Journal of Neurology · doi:10.1007/s00415-016-8374-y · Cited by 83
Bhattacharjee, S., Kashyap, R., Goodwill, A. M., O'Brien, B. A., Rapp, B., Oishi, K., Desmond, J. E., & Chen, S. H. A. (2022) · Brain Stimulation · doi:10.1016/j.brs.2021.11.018 · Cited by 80
“in parieto-occipital regions for elderly males compared to females [82,83].”
Executive Functions and Neurocognitive Aging
Reuter‐Lorenz, P. A., Festini, S. B., & Jantz, T. K. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-411469-2.00013-3 · Cited by 85
Rollins, C. P. E., Gallino, D., Kong, V., Ayranci, G., Devenyi, G. A., Germann, J., & Chakravarty, M. M. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.11.016 · Cited by 79
Knyazeva, M. G., Barzegaran, E., Vildavski, V. Y., & Démonet, J. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.05.018 · Cited by 97
Influence of age, disease onset and ApoE4 on visual medial temporal lobe atrophy cut‐offs
Pereira, J. B., Cavallin, L., Spulber, G., Aguilar, C., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H. S., Spenger, C., et al. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12148 · Cited by 80
Shaw, M. E., Sachdev, P. S., Anstey, K. J., & Cherbuin, N. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.12.009 · Cited by 77
George, R. J. S., Hinder, M. R., Puri, R., Walker, E., & Callisaya, M. L. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.10.027 · Cited by 79
Potential Brain Age Reversal after Pregnancy: Younger Brains at 4–6 Weeks Postpartum
Eileen Luders, A., Gingnell, M., Poromaa, I. S., Engman, J., Kurth, F., & Gaser, C. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2018.07.006 · Cited by 71
Storsve, A. B., Fjell, A. M., Yendiki, A., & Walhovd, K. B. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156770 · Cited by 71
Hughes, T. M., Ryan, C. M., Aizenstein, H. J., Nunley, K. A., Gianaros, P. J., Miller, R. G., Costacou, T., Strotmeyer, E. S., Orchard, T. J., & Rosano, C. (2013) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2013.07.001 · Cited by 70
Pfefferbaum, A., & Sullivan, E. V. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.05.005 · Cited by 69
Self-citationDougherty, R. J., Schultz, S. A., Boots, E. A., Ellingson, L. D., Meyer, J. D., Riper, S. M. V., Stegner, A. J., Edwards, D. F., Oh, J. M., Einerson, J. A., et al. (2017) · Brain and Behavior · doi:10.1002/brb3.625 · Cited by 72
Tullo, S., Patel, R., Devenyi, G. A., Salaciak, A., Bedford, S. A., Farzin, S., Wlodarski, N., Tardif, C. L., Breitner, J. C. S., & Chakravarty, M. M. (2019) · Human Brain Mapping · doi:10.1002/hbm.24771 · Cited by 63
Age-Related Differences in Brain Morphology and the Modifiers in Middle-Aged and Older Adults
Zhao, L., Matloff, W. J., Ning, K., Kim, H., Dinov, I. D., & Toga, A. W. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy300 · Cited by 71
Influence of BDNF Val66Met on the relationship between physical activity and brain volume
Brown, B. M., Bourgeat, P., Peiffer, J. J., Burnham, S. C., Laws, S. M., Rainey‐Smith, S. R., Bartrés‐Faz, D., Villemagne, V. L., Taddei, K., Rembach, A., et al. (2014) · Neurology · doi:10.1212/wnl.0000000000000867 · Cited by 67
Perspectives on fronto-fugal circuitry from human imaging of alcohol use disorders
Zahr, N. M., Adolf Pfefferbaum, A., & Sullivan, E. V. (2017) · Neuropharmacology · doi:10.1016/j.neuropharm.2017.01.018 · Cited by 67
Self-citationNeural predictors of alcohol use and psychopathology symptoms in adolescents
Brumback, T., Worley, M. J., Nguyen‐Louie, T. T., Squeglia, L. M., Jacobus, J., & Tapert, S. F. (2016) · Development and Psychopathology · doi:10.1017/s0954579416000766 · Cited by 301
Frodl, T., Strehl, K., Carballedo, A., Tozzi, L., Doyle, M., Amico, F., Gormley, J., Lavelle, G., & O’Keane, V. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00088-6 · Cited by 70
Sele, S., Liem, F., Mérillat, S., & Jäncke, L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118370 · Cited by 60
Morphometric network differences in ageing versus Alzheimer’s disease dementia
Binette, A. P., Gonneaud, J., Vogel, J. W., Joie, R. L., Rosa‐Neto, P., Collins, D. L., Poirier, J., Breitner, J. C. S., Villeneuve, S., Vachon‐Presseau, É., et al. (2019) · Brain · doi:10.1093/brain/awz414 · Cited by 54
Zheng, F., Liu, Y., Yuan, Z., Gao, X., He, Y., Liu, X., Cui, D., Rui, Q., Chen, T., & Qiu, J. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26037 · Cited by 55
Walhovd, K. B., Fjell, A. M., & Espeseth, T. (2014) · Scandinavian Journal of Psychology · doi:10.1111/sjop.12120 · Cited by 60
Impact of age on cerebrovascular dilation versus reactivity to hypercapnia
Coverdale, N. S., Badrov, M. B., & Shoemaker, J. K. (2016) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x15626156 · Cited by 55
Structural and microstructral imaging of the brain in alcohol use disorders
Zahr, N. M. (2014) · Handbook of clinical neurology · doi:10.1016/b978-0-444-62619-6.00017-3 · Cited by 61
Monkeys that Voluntarily and Chronically Drink Alcohol Damage their Brains: a Longitudinal MRI Study
Kroenke, C. D., Torsten Rohlfing, B., Park, B. S., Sullivan, E. V., Pfefferbaum, A., & Grant, K. A. (2013) · Neuropsychopharmacology · doi:10.1038/npp.2013.259 · Cited by 54
Self-citationAge and sex effects on corpus callosum morphology across the lifespan
Prendergast, D. M., Ardekani, B. A., Ikuta, T., John, M., Peters, B. D., DeRosse, P., Wellington, R. L., Malhotra, A. K., & Szeszko, P. R. (2015) · Human Brain Mapping · doi:10.1002/hbm.22800 · Cited by 54
Age Differences in Prefrontal Surface Area and Thickness in Middle Aged to Older Adults
Dotson, V. M., Szymkowicz, S. M., Sozda, C. N., Kirton, J. W., Green, M. L., O’Shea, A. M., McLaren, M. E., Anton, S. D., Manini, T. M., & Woods, A. J. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00250 · Cited by 63
Garza‐Villarreal, E. A., Chakravarty, M. M., Hansen, B., Eskildsen, S. F., Devenyi, G. A., Castillo-Padilla, D. V., Balducci, T., Reyes-Zamorano, E., Jespersen, S. N., Perez-Palacios, P., et al. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.92 · Cited by 54
Vojinovic, D., Adams, H. H., Jian, X., Yang, Q., Smith, A. V., Bis, J. C., Teumer, A., Scholz, M., Armstrong, N. J., Hofer, E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06234-w · Cited by 52
Thinning faster? Age-related cortical thickness differences in adults with autism spectrum disorder
Braden, B. B., & Riecken, C. (2019) · Research in Autism · doi:10.1016/j.rasd.2019.03.005 · Cited by 52
Sun, Y., Yu, C., Lee, R., Bezerianos, A., Collinson, S. L., & Sim, K. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.01.025 · Cited by 52
Suttie, M., Wozniak, J. R., Parnell, S. E., Wetherill, L. F., Mattson, S. N., Sowell, E. R., Kan, E., Riley, E. P., Jones, K. L., Coles, C. D., et al. (2018) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.13820 · Cited by 50
Wolf, D. G. F., Fischer, F. U., de Florès, R., Chételat, G., & Fellgiebel, A. (2015) · Human Brain Mapping · doi:10.1002/hbm.22880 · Cited by 45
Dadar, M., Potvin, O., Camicioli, R. M., Duchesne, S., & Initiative, A. D. N. (2021) · Human Brain Mapping · doi:10.1002/hbm.25398 · Cited by 48
Using Developmental Science to Distinguish Adolescents and Adults Under the Law
Steinberg, L. D., & Icenogle, G. (2019) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-121318-085105 · Cited by 50
Takeuchi, H., Taki, Y., Nouchi, R., Yokoyama, R., Kotozaki, Y., Nakagawa, S., Sekiguchi, A., Iizuka, K., Yamamoto, Y., Hanawa, S., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-10104-8 · Cited by 46
Metzler‐Baddeley, C., Mole, J. P., Sims, R., Fasano, F., Evans, J., Jones, D. K., Aggleton, J. P., & Baddeley, R. (2019) · Scientific Reports · doi:10.1038/s41598-018-37658-5 · Cited by 50
Age-related changes in hand dominance and functional asymmetry in older adults
Sebastjan, A., Skrzek, A., Ignasiak, Z., & Sławińska, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177845 · Cited by 46
Caspi, Y., Brouwer, R. M., Schnack, H. G., van de Nieuwenhuijzen, M. E., Cahn, W., Kahn, R. S., Niessen, W. J., van der Lugt, A., & Pol, H. E. H. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116842 · Cited by 50
Dopaminergic system and dream recall: an MRI study in Parkinson's disease patients
De Gennaro, L., Lanteri, O., Piras, F., Scarpelli, S., Assogna, F., Ferrara, M., Caltagirone, C., & Spalletta, G. (2016) · Human Brain Mapping · doi:10.1002/hbm.23095 · Cited by 45
Long, Y., Pan, N., Ji, S., Qin, K., Chen, Y., Zhang, X., He, M., Suo, X., Yu, Y., Wang, S., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02130-6 · Cited by 44
Kennedy, K. M., & Raz, N. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00068-3 · Cited by 48
Eight-Year Follow-Up of Neuropsychiatric Symptoms and Brain Structural Changes in Fabry Disease
Lelieveld, I. M., Böttcher, A., Hennermann, J. B., Beck, M., & Fellgiebel, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137603 · Cited by 44
Structural Brain Network Changes across the Adult Lifespan
Liu, K., Yao, S., Chen, K., Zhang, J., Yao, L., Li, K., Jin, Z., & Guo, X. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00275 · Cited by 45
Yuan, P., Voelkle, M. C., & Raz, N. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.032 · Cited by 46
Westacott, L. J., & Wilkinson, L. S. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.840266 · Cited by 48
Gray matter volume drives the brain age gap in schizophrenia: a SHAP study
Ballester, P. L., Suh, J. S., Ho, N. C. W., Liang, L., Hassel, S., Strother, S. C., Arnott, S. R., Minuzzi, L., Sassi, R. B., Lam, R. W. M., et al. (2023) · Schizophrenia · doi:10.1038/s41537-022-00330-z · Cited by 42
Marié, D., Müller, C. A. H., ALTENMÜLLER, E. O., Ville, D. V. D., Jünemann, K., Scholz, D. S., Krüger, T. H. C., Worschech, F., Kliegel, M., Sinke, C., et al. (2023) · Neuroimage Reports · doi:10.1016/j.ynirp.2023.100166 · Cited by 48
A Lifespan fMRI Study of Neurodevelopment Associated with Reading Chinese
Siok, W. T., Jia, F., Liu, C. Y., Perfetti, C. A., & Tan, L. H. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa038 · Cited by 37
Sullivan, E. V., & Adolf Pfefferbaum, A. (2022) · Alcohol · doi:10.1016/j.alcohol.2022.06.002 · Cited by 44
Self-citationPerson-Based Brain Morphometric Similarity is Heritable and Correlates With Biological Features
Doucet, G. E., Moser, D. A., Rodrigue, A., Bassett, D. S., Glahn, D. C., & Frangou, S. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy287 · Cited by 41
Monitoring the Cortical Activity of Children and Adults during Cognitive Task Completion
Khramova, M. V., Kuc, A. K., Maksimenko, V., Frolov, N. S., Grubov, V., Kurkin, S., Pisarchik, A. N., Shusharina, N. N., Fedorov, A., & Hramov, A. E. (2021) · Sensors · doi:10.3390/s21186021 · Cited by 37
van Ruitenbeek, P., Monteiro, T. S., Chalavi, S., King, B. R., Cuypers, K., Sunaert, S. G., Peeters, R. R., & Swinnen, S. P. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac514 · Cited by 36
Kang, D. W., Wang, S., Na, H.-R., Park, S. Y., Kim, N. Y., Lee, C. U., Kim, D., Son, S.-J., & Lim, H. K. (2020) · Scientific Reports · doi:10.1038/s41598-020-77848-8 · Cited by 39
Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range
Hata, J., Nakae, K., Tsukada, H., Woodward, A., Haga, Y., Iida, M., Uematsu, A., Seki, F., Ichinohe, N., Gong, R., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02121-2 · Cited by 37
Alcohol Use Disorder and Dementia: A Review
Zahr, N. M. (2024) · Alcohol research · doi:10.35946/arcr.v44.1.03 · Cited by 42
Executive functions and neurocognitive aging
Reuter‐Lorenz, P. A., Festini, S. B., & Jantz, T. K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-816094-7.00019-2 · Cited by 38
Neurobiological Correlates of Change in Adaptive Behavior in Autism
Pretzsch, C. M., Schäfer, T., Lombardo, M. V., Warrier, V., Mann, C., Bletsch, A., Chatham, C., Floris, D. L., Tillmann, J., Yousaf, A., et al. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21070711 · Cited by 35
Neurochemical and cognitive changes precede structural abnormalities in the TgF344-AD rat model
Fowler, C., Goerzen, D., Devenyi, G. A., Madularu, D., Chakravarty, M. M., & Near, J. (2022) · Brain Communications · doi:10.1093/braincomms/fcac072 · Cited by 33
The aging mind: A complex challenge for research and practice
Zihl, J., & Reppermund, S. (2022) · Aging Brain · doi:10.1016/j.nbas.2022.100060 · Cited by 40
Metadata
- Authors
- Pfefferbaum, A., Rohlfing, T., Rosenbloom, M. J., Chu, W., Colrain, I. M., & Sullivan, E. V.
- Affiliations
- SRI International (US)
- Stanford Medicine (US)
- Corresponding author
- Sullivan, Edith V.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 65
- Pages
- 176-193
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3516371
- OpenAlex
- W2126769529
- Cited by
- 256
- References
- 122
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 12 · 2023: 19 · 2022: 22 · 2021: 26 · 2020: 31 · 2019: 18 · 2018: 25 · 2017: 23 · 2016: 22 · 2015: 17 · 2014: 14 · 2013: 5
- FWCI
- 5.89
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (AA017347, AA005965, AA012388, AA010723, AA017168, AG017919, EB008381)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- regional brain volume, brain aging, ventriculomegaly, Sylvian fissure, corpus callosum, linear mixed models
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Anatomy, Artistic, Atlases as Topic, Brain, Child, Female, Humans, Image Interpretation, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review