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
Items 101–200 of 257
Age is reflected in the Fractal Dimensionality of MRI Diffusion Based Tractography
Reishofer, G., Studencnik, F., Koschutnig, K., Deutschmann, H., Ahammer, H., & Wood, G. M. D. O. (2018) · Scientific Reports · doi:10.1038/s41598-018-23769-6 · Cited by 31
Hozer, F., Sarrazin, S., Laidi, C., Favre, P., Pauling, M., Cannon, D. M., McDonald, C., Emsell, L., Mangin, J., Duchesnay, É., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291719004112 · Cited by 33
Decline Variability of Cortical and Subcortical Regions in Aging: A Longitudinal Study
Sele, S., Liem, F., Mérillat, S., & Jäncke, L. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00363 · Cited by 33
Canales-Rodríguez, E. J., Alonso-Lana, S., Verdolini, N., Sarró, S., Feria‐Raposo, I., Montoro, I., Garcia‐Ruiz, B., Jiménez, E., Varo, C., Albacete, À., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.06.002 · Cited by 34
Mareckova, K., Marecek, R., Andryskova, L., Brazdil, M., & Nikolova, Y. S. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa014 · Cited by 32
Effects of exergames on mood and cognition in healthy older adults: A randomized pilot study
Moret, B., Nucci, M., & Campana, G. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1018601 · Cited by 31
Cortical Thinning at Midlife: The PATH Through Life Study
Shaw, M. E., Abhayaratna, W. P., Sachdev, P. S., Anstey, K. J., & Cherbuin, N. (2016) · Brain Topography · doi:10.1007/s10548-016-0509-z · Cited by 34
Computed Tomography Assessment of Brain Atrophy in Centenarians
Chrzan, R. M., Gleń, A., Bryll, A., & Urbanik, A. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16193659 · Cited by 28
Wang, Z., Williams, V. J., Stephens, K. A., Kim, C., Bai, L., Zhang, M., & Salat, D. H. (2020) · Human Brain Mapping · doi:10.1002/hbm.24871 · Cited by 28
Shared and separate patterns in brain morphometry across transdiagnostic dimensions
McCutcheon, R. A., Pillinger, T., Guo, X., Rogdaki, M., Welby, G., Vano, L. J., Cummings, C., Heron, T. A., Brugger, S. P., Davies, D., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-022-00010-y · Cited by 26
Rast, P., Kennedy, K. M., Rodrigue, K. M., Robinson, P. R., Gross, A. L., McLaren, D. G., Grabowski, T., Schaie, K. W., & Willis, S. L. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx099 · Cited by 30
Reserve and Maintenance in the Aging Brain: A Longitudinal Study of Healthy Older Adults
Bagarinao, E., Watanabe, H., Maesawa, S., Kawabata, K., Hara, K., Ohdake, R., Ogura, A., Mori, D., Yoneyama, N., Imai, K., et al. (2022) · eNeuro · doi:10.1523/eneuro.0455-21.2022 · Cited by 26
Chained regularization for identifying brain patterns specific to HIV infection
Adeli, E., Kwon, D., Zhao, Q., Pfefferbaum, A., Zahr, N. M., Sullivan, E. V., & Pohl, K. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.022 · Cited by 25
Self-citationSelective neuronal vulnerability in Alzheimer’s disease
Wang, Z.-T., Zhang, C. M., Wang, Y., Dong, Q., Tan, L., & Yu, J. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101114 · Cited by 24
Pfefferbaum, A., Sullivan, E. V., Zahr, N. M., Pohl, K. M., & Saranathan, M. (2022) · Human Brain Mapping · doi:10.1002/hbm.26088 · Cited by 24
Self-citationZhang, Y., Kwon, D., Esmaeili‐Firidouni, P., Pfefferbaum, A., Sullivan, E. V., Javitz, H. S., Valcour, V., & Pohl, K. M. (2016) · Human Brain Mapping · doi:10.1002/hbm.23326 · Cited by 24
Self-citationVariation in longitudinal trajectories of cortical sulci in normal elderly
Shen, X., Liu, T., Tao, D., Fan, Y., Zhang, J., Li, S., Jiang, J., Zhu, W., Wang, Y., Wang, Y., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.010 · Cited by 24
Seemiller, L. R., & Gould, T. J. (2020) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2020.107234 · Cited by 23
Sex Differences in Alcohol Use: Is It All About Hormones?
Maddern, X. J., Ursich, L. T., Bailey, G., Pearl, A. J., Anversa, R. G., Lawrence, A. J., & Walker, L. C. (2024) · Endocrinology · doi:10.1210/endocr/bqae088 · Cited by 23
Hasan, K. M., Moeller, F. G., & Narayana, P. A. (2014) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2014.01.014 · Cited by 23
Ridwan, A. R., Niaz, M. R., Wu, Y., Qi, X., Zhang, S., Kontzialis, M. S., Javierre‐Petit, C., Tazwar, M., Initiative, A. D. N., Bennett, D. A., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25327 · Cited by 23
Associations between subjective well-being and subcortical brain volumes
Ent, D. V. ‘., den Braber, A., Baselmans, B. M. L., Brouwer, R. M., Dolan, C. V., Pol, H. E. H., de Geus, E. J. C., & Bartels, M. (2017) · Scientific Reports · doi:10.1038/s41598-017-07120-z · Cited by 26
Dougherty, R. J., Lindheimer, J. B., Stegner, A. J., Riper, S. M. V., Okonkwo, O. C., & Cook, D. B. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170576 · Cited by 21
Size matters: Grey matter brain reserve predicts executive functioning in the elderly
Laubach, M., Lammers‐Lietz, F., Zacharias, N., Feinkohl, I., Pischon, T., Borchers, F., Slooter, A. J. C., Kühn, S., Spies, C., & Winterer, G. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.08.008 · Cited by 21
Age but no sex effects on subareas of the amygdala
Kurth, F., Cherbuin, N., & Eileen Luders, A. (2018) · Human Brain Mapping · doi:10.1002/hbm.24481 · Cited by 20
Age-Related Changes in Speech Understanding: Peripheral Versus Cognitive Influences
Gordon-Salant, S. M., Shader, M. J., & Wingfield, A. (2020) · Springer handbook of auditory research · doi:10.1007/978-3-030-49367-7_9 · Cited by 25
Dimech, C. J., Anderson, J. A. E., Lockrow, A. W., Spreng, R. N., & Turner, G. R. (2019) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01046.2018 · Cited by 21
Brain structural changes in women and men during midlife
Guo, J. Y., Isohanni, M. K., Miettunen, J., Jääskeläinen, E., Kiviniemi, V. J., Nikkinen, J., Remes, J. J., Huhtaniska, S., Veijola, J. M., Jones, P. B., et al. (2016) · Neuroscience Letters · doi:10.1016/j.neulet.2016.01.007 · Cited by 20
Structural neuroimaging in neuropsychology: History and contemporary applications.
Bigler, E. D. (2017) · Neuropsychology · doi:10.1037/neu0000418 · Cited by 20
Zhou, S., Lithfous, S., Després, O., Pébayle, T., Bi, X., & Dufour, A. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00131 · Cited by 19
Stickel, A. M., Tarraf, W., González, K. A., Ivanovic, V., Paredes, A. M., Zeng, D., Cai, J., Isasi, C. R., Kaplan, R. C., Lipton, R. B., et al. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.02.007 · Cited by 19
NOMIS: Quantifying morphometric deviations from normality over the lifetime of the adult human brainPreprint
Potvin, O., Dieumegarde, L., Duchesne, S., Initiative, T. A. D. N., CIMA-Q, T., & groups, T. C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.25.428063 · Cited by 21
Radhakrishnan, V., Galléa, C., Valabrègue, R., Krishnan, S., Kesavadas, C., Thomas, B., James, P., Menon, R. N., & Kishore, A. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1019239 · Cited by 18
Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics
Xu, X., Lin, L., Wu, S., & Sun, S. (2023) · Brain Sciences · doi:10.3390/brainsci13121651 · Cited by 20
Why monkeys do not get multiple sclerosis (spontaneously)
Bove, R. M. (2018) · Evolution Medicine and Public Health · doi:10.1093/emph/eoy002 · Cited by 19
Epigenetic clock as a correlate of anxiety
Marečková, K., Pačínková, A., Klasnja, A., Shin, J., Andrýsková, L., Kozubík, K. S., Pausová, Z., Brázdil, M., & Paus, T. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102458 · Cited by 18
Schättin, A., Gennaro, F., Egloff, M., Vogt, S., & de Bruin, E. D. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00242 · Cited by 18
Brusini, I., MacNicol, E., Kim, E., Smedby, Ö., Wang, C., Westman, E., Veronese, M., Turkheimer, F., & Cash, D. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.10.004 · Cited by 17
Stickel, A. M., Tarraf, W., González, K. A., Paredes, A. M., Zeng, D., Cai, J., Isasi, C. R., Kaplan, R. C., Lipton, R. B., Daviglus, M. L., et al. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1390200 · Cited by 16
Meredith, L. R., Lim, A. C., & Ray, L. A. (2020) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2020.108269 · Cited by 15
HIV infection and age effects on striatal structure are additive
O’Connor, E. E., Zeffiro, T., López, O. L., Becker, J. T., & Zeffiro, T. A. (2019) · Journal of NeuroVirology · doi:10.1007/s13365-019-00747-w · Cited by 15
Xu, G., Zhou, M., Wang, J., Mao, D., & Sun, W. (2024) · BMC Geriatrics · doi:10.1186/s12877-024-05624-8 · Cited by 15
Cespedes, M. I., Fripp, J., McGree, J. M., Drovandi, C., Mengersen, K. L., & Doecke, J. D. (2017) · BMJ Open · doi:10.1136/bmjopen-2016-012174 · Cited by 16
Park, S. H., Zhang, Y., Kwon, D., Zhao, Q., Zahr, N. M., Pfefferbaum, A., Sullivan, E. V., & Pohl, K. M. (2018) · Scientific Reports · doi:10.1038/s41598-018-26627-7 · Cited by 14
Self-citationGiacobbo, B. L., Özalay, Ö., Mediavilla, T., Ericsson, M., Axelsson, J., Rieckmann, A., Sultan, F. R., & Marcellino, D. J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.795132 · Cited by 13
Piekarski, D. J., Zahr, N. M., Zhao, Q., Ferizi, U., Pohl, K. M., Sullivan, E. V., & Adolf Pfefferbaum, A. (2023) · Neuroimage Reports · doi:10.1016/j.ynirp.2023.100179 · Cited by 14
Self-citationSpeaking of aging: Changes in gray matter asymmetry in Broca’s area in later adulthood
Kurth, F., Cherbuin, N., & Eileen Luders, A. (2020) · Cortex · doi:10.1016/j.cortex.2020.03.028 · Cited by 12
Kozyrev, N., Albers, S. S., Yang, J., Prado, V. F., Prado, M. A. M., Fonseca, G. J., Rylett, R. J., & Dekaban, G. A. (2020) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlaa093 · Cited by 13
van Ruitenbeek, P., Serbruyns, L., Solesio‐Jofre, E., Meesen, R., Cuypers, K., & Swinnen, S. P. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1226-9 · Cited by 18
Brain Volume in Fetal Alcohol Spectrum Disorders Over a 20-Year Span
Adolf Pfefferbaum, A., Sullivan, E. V., Pohl, K. M., Bischoff‐Grethe, A., Stoner, S. A., Moore, E. M., & Riley, E. P. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.43618 · Cited by 12
Self-citationOccupation-related effects on motor cortex thickness among older, cognitive healthy individuals
Lenhart, L., Nagele, M., Steiger, R., Beliveau, V., Skalla, E., Zamarian, L., Gizewski, E. R., Benke, T., Delazer, M., & Scherfler, C. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02223-w · Cited by 11
Pfefferbaum, A., Zhao, Q., Pohl, K. M., Sassoon, S. A., Zahr, N. M., & Sullivan, E. V. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.07.023 · Cited by 11
Self-citationDima, D., Papachristou, E., Modabbernia, A., Doucet, G. E., Agartz, I., Aghajani, M., Akudjedu, T. N., Albajes‐Eizagirre, A., Alnæs, D., Alpert, K. I., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.05.079475 · Cited by 12
Men and women show partly distinct effects of physical activity on brain integrity
Gonneaud, J., Moreau, I., Felisatti, F., Arenaza‐Urquijo, E. M., Ourry, V., Touron, E., Sayette, V. D. L., Vivien, D., & Chételat, G. (2022) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12302 · Cited by 12
Advanced brain age correlates with greater rumination and less mindfulness in schizophrenia
Abram, S. V., Roach, B. J., Hua, J. P. Y., Han, L. K. M., Mathalon, D. H., Ford, J. M., & Fryer, S. L. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103301 · Cited by 9
Xie, Y., Sun, J., Man, W., Zhang, Z., & Zhang, N. (2023) · Molecular Autism · doi:10.1186/s13229-023-00558-1 · Cited by 14
Caballero, M. Á. A., Initiative, A. D. N., Klöppel, S., Dichgans, M., & Ewers, M. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160327 · Cited by 10
Gamma oscillatory activity is impaired in episodic memory encoding with age
Després, O., Lithfous, S., Tromp, D., Pébayle, T., & Dufour, A. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.12.019 · Cited by 10
Jiang, Y., Tian, Y., & Wang, Z. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00214 · Cited by 10
Zhuang, R., Zhu, S., Sui, Y., Zhou, M., Yang, T., Wang, C., Zhang, T., Wang, J., Kan, C., Shen, Y., et al. (2022) · Brain Sciences · doi:10.3390/brainsci12111479 · Cited by 9
Fowler, C., Goerzen, D., Madularu, D., Devenyi, G. A., Chakravarty, M. M., & Near, J. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.10.003 · Cited by 9
Rzechorzek, N. M., Saunders, O. M., Hiscox, L. V., Schwarz, T., Marioni‐Henry, K., Argyle, D. J., Schoenebeck, J. J., & Freeman, T. C. (2019) · Scientific Reports · doi:10.1038/s41598-019-48446-0 · Cited by 9
Morozova, M., Koschutnig, K., Klein, E., & Wood, G. M. D. O. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.003 · Cited by 9
Neuropsychology of aging, past, present and future: Contributions of Morris Moscovitch
Reuter‐Lorenz, P. A., & Cooke, K. A. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.06.018 · Cited by 10
Zahr, N. M., Sullivan, E. V., Pohl, K. M., & Pfefferbaum, A. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0744-6 · Cited by 9
Self-citationYang, J., Qu, J., & Ma, H. (2022) · Ageing and Neurodegenerative Diseases · doi:10.20517/and.2022.03 · Cited by 7
Diminished Age-Related Decline of the Amygdala in Long-Term Meditation Practitioners
Eileen Luders, A., Jain, F. A., & Kurth, F. (2021) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000913 · Cited by 8
Pretscher, A., Kauzner, S., Rohleder, N., & Becker, L. (2021) · European Journal of Ageing · doi:10.1007/s10433-021-00616-8 · Cited by 10
Cortical Thickness of the Orbitofrontal Cortex in Patients with Alcohol Use Disorder
Atmaca, M., Tabara, M. F., Koç, M., Gürok, M. G., Baykara, S., Korkmaz, S., & Mermi, O. (2023) · Brain Sciences · doi:10.3390/brainsci13040552 · Cited by 7
Investigating dynamic brain functional redundancy as a mechanism of cognitive reserve
Schwarz, J., Zistler, F., Usheva, A., Fix, A., Zinn, S., Zimmermann, J., Knolle, F., Schneider, G. H., & Nuttall, R. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1535657 · Cited by 8
Yang, M. H., Kim, E., Choi, E. S., & Ko, H. (2023) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2023.0006 · Cited by 7
Reduced Age-Related Gray Matter Loss in the Orbitofrontal Cortex in Long-Term Meditators
Kurth, F., Strohmaier, S., & Eileen Luders, A. (2023) · Brain Sciences · doi:10.3390/brainsci13121677 · Cited by 8
Reduced brain network segregation in alcohol use disorder: Associations with neurocognition
Wang, X., Manza, P., Li, X., Ramos-Rolón, A. P., Hager, N., Wang, G., Volkow, N. D., Hu, Y., Shi, Z., & Wiers, C. E. (2024) · Addiction Biology · doi:10.1111/adb.13446 · Cited by 7
Vilor‐Tejedor, N., Ikram, M. A., Roshchupkin, G. V., Vinke, E. J., Vernooij, M. W., & Adams, H. H. H. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00574 · Cited by 6
Hill, S. Y. (2018) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.13621 · Cited by 7
Dong, C., Thalamuthu, A., Jiang, J., Mather, K. A., Brodaty, H., Sachdev, P. S., & Wen, W. X. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab323 · Cited by 6
Predictive Modeling of Anatomy with Genetic and Clinical Data
Dalca, A. V., Sridharan, R., Sabuncu, M. R., & Golland, P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_62 · Cited by 6
Genius, P., Calle, M. L., Rodríguez‐Fernández, B., Minguillón, C., Cacciaglia, R., Garrido-Martín, D., Esteller, M., Navarro, A., Gispert, J. D., Vilor‐Tejedor, N., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14490 · Cited by 5
Biological Sex Disparities in Alzheimer’s Disease
Almutairi, J. A., & Kidd, E. J. (2024) · Current topics in behavioral neurosciences · doi:10.1007/7854_2024_545 · Cited by 4
Zhang, X., Huang, M., Yuan, X., Zhong, X., Dai, S., Wang, Y., Zhang, Q., Wongwitwichote, K., & Jiang, C. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101529 · Cited by 4
Circulating Cytokines Predict 1H-Proton MRS Cerebral Metabolites in Healthy Older Adults
Langer, K. G., Cohen, R. A., Porges, E. C., Williamson, J. B., & Woods, A. J. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.690923 · Cited by 4
Kawagoe, T. (2022) · Life · doi:10.3390/life12030416 · Cited by 6
The effect of age on interhemispheric transfer time: an event related potential study
Boyson, A. (2024) · PEARL (University of Plymouth) · doi:10.24382/0t1m-dq74 · Cited by 4
Age, Alcohol Use, and Brain Function
Koob, G. F. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.0009 · Cited by 4
Characterizing the middle-age neurophysiology using EEG/MEGPreprint
Gula, J., Moiseeva, V., Ruiz, M. H., & Cappelletti, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.04.447084 · Cited by 4
Longitudinal Characterisation of Healthy Ageing in Rats using Multimodal Magnetic Resonance ImagingPreprint
MacNicol, E. (2021) · Thesis Commons (OSF Preprints) · doi:10.31237/osf.io/3vpyg · Cited by 3
Rohan, A., Fugiel, J., Izabela, W., Lindner, K., Kołodziej, M., & Sobieszczańska, M. I. (2021) · Experimental Aging Research · doi:10.1080/0361073x.2021.1982350 · Cited by 3
Ventromedial Prefrontal Cortex and Ventral Striatum Volumes in Patients with Alcohol Use Disorder
Tabara, M. F., Koç, M., Gürok, M. G., Baykara, S., Korkmaz, S., Mermi, O., & Atmaca, M. (2023) · Alcoholism Treatment Quarterly · doi:10.1080/07347324.2023.2299258 · Cited by 4
Longitudinal non-linear changes in the microstructure of the hippocampal subfields in older adults
Zia, G., Shahid, S. S., Yang, H., Gao, S., Risacher, S. L., Saykin, A. J., & Wu, Y. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.07.008 · Cited by 3
Joint Data Harmonization and Group Cardinality Constrained Classification
Zhang, Y., Park, S. H., & Pohl, K. M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46720-7_33 · Cited by 3
Subcortical volume in middle-aged adults with fetal alcohol spectrum disorders
Bischoff‐Grethe, A., Stoner, S. A., Riley, E. P., & Moore, E. M. (2024) · Brain Communications · doi:10.1093/braincomms/fcae273 · Cited by 3
Munivenkatappa, A., Bagepally, B. S., Saini, J. S., & Pal, P. K. (2013) · Europe PMC (PubMed Central) · Cited by 3
Neuroscience and death as a penalty for late adolescents.
McCaffrey, R. J., & Reynolds, C. R. (2021) · Journal of Pediatric Neuropsychology · doi:10.1007/s40817-021-00104-y · Cited by 3
Luo, W., Wen, H., Ge, S.-Q., Tang, C.-L., Liu, X.-F., & Lu, L.-M. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.774804 · Cited by 3
A Bayesian hierarchical approach to jointly model structural biomarkers and covariance networks
Cespedes, M. I., McGree, J. M., Drovandi, C., Mengersen, K. L., Reid, L. B., Doecke, J. D., & Fripp, J. (2017) · QUT ePrints (Queensland University of Technology) · Cited by 3
Zhao, L., Matloff, W., Shi, Y.-G., Cabeen, R. P., & Toga, A. (2021) · bioRxiv · doi:10.1101/2021.03.09.434625 · Cited by 5
Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age rangePreprint
Hata, J., Nakae, K., Tsukada, H., Woodward, A., Haga, Y., Iida, M., Uematsu, A., Seki, F., Ichinohe, N., Gong, R., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.21.508952 · Cited by 3
Prior test experience confounds longitudinal tracking of adolescent cognitive and motor development
Sullivan, E., Thompson, W., Brumback, T., Prouty, D., Tapert, S., Brown, S. A., Bellis, M. D., Nooner, K., Baker, F., Colrain, I., et al. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01606-9 · Cited by 4
Self-citationStable individual differences dominate adult brain volume variation until later life
Grødem, E., Dídac Vidal-piñeiro, A., Sørensen, Ø., Bartrés‐Faz, D., Brandmaier, A. M., Cattaneo, G., Garrido, P. F., Henson, R. N., Kühn, S., Lindenberger, U., et al. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1242 · Cited by 3
Brain parenchymal changes during normal aging in domestic cats
Babicsak, V. R., Klein, A. V., Tsunemi, M. H., & Vulcano, L. C. (2018) · Pesquisa Veterinária Brasileira · doi:10.1590/1678-5150-pvb-5397 · Cited by 2
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