Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data
Open accessAbstract
We present an extension of the Linear Mixed Effects (LME) modeling approach to be applied to the mass-univariate analysis of longitudinal neuroimaging (LNI) data. The proposed method, called spatiotemporal LME or ST-LME, builds on the flexible LME framework and exploits the spatial structure in image data. We instantiated ST-LME for the analysis of cortical surface measurements (e.g. thickness) computed by FreeSurfer, a widely-used brain Magnetic Resonance Image (MRI) analysis software package. We validate the proposed ST-LME method and provide a quantitative and objective empirical comparison with two popular alternative methods, using two brain MRI datasets obtained from the Alzheimer's disease neuroimaging initiative (ADNI) and Open Access Series of Imaging Studies (OASIS). Our experiments revealed that ST-LME offers a dramatic gain in statistical power and repeatability of findings, while providing good control of the false positive rate.
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
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?
Multivariate LME models can increase statistical power and reliability compared to univariate methods.
“Multivariate LME models can increased statistical power and reliability compared to univariate methods30,31.”
From Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study (2018) · cites this paper as [30]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630 · see citing works
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913 · see citing works
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · see citing works
Self-citationFischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526 · see citing works
Self-citationCortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147 · see citing works
Self-citationMeasuring the thickness of the human cerebral cortex from magnetic resonance images
Fischl, B. R., & Dale, A. M. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200033797 · Cited by 5,789 · see citing works
Self-citationBrain development during childhood and adolescence: a longitudinal MRI study
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., Paus, T., Evans, A. C., & Rapoport, J. L. (1999) · Nature Neuroscience · doi:10.1038/13158 · Cited by 5,552 · see citing works
Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2011) · Wiley Series in Probability and Statistics · doi:10.1002/9781119513469 · Cited by 4,639 · see citing works
Small sample inference for fixed effects from restricted maximum likelihood
Kenward, M. G., & Roger, J. H. (1997) · Biometrics · doi:10.2307/2533558 · Cited by 4,552 · see citing works
Pratt, W. K. (2002) · Wiley eBooks · doi:10.1002/0471221325 · Cited by 4,348 · see citing works
Linear Mixed Models for Longitudinal Data
Verbeke, G. (1997) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2294-1_3 · Cited by 3,506 · see citing works
Statistical Parametric Mapping: The Analysis of Functional Brain Images
Penny, W. D., Friston, K. J., Ashburner, J., Kiebel, S. J., & Thomas, N. (2007) · UCL Discovery (University College London) · Cited by 3,339 · see citing works
Adaptive linear step-up procedures that control the false discovery rate
Benjamini, Y., Krieger, A. M., & Yekutieli, D. (2006) · Biometrika · doi:10.1093/biomet/93.3.491 · Cited by 3,228 · see citing works
Changes in grey matter induced by training
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004) · Nature · doi:10.1038/427311a · Cited by 2,373 · see citing works
Within-subject template estimation for unbiased longitudinal image analysis
Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.084 · Cited by 2,356 · see citing works
Self-citationMarcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.9.1498 · Cited by 2,163 · see citing works
Neurodevelopmental Trajectories of the Human Cerebral Cortex
Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., Greenstein, D., Clasen, L., Evans, A., Rapoport, J. L., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.5309-07.2008 · Cited by 1,644 · see citing works
Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, E., Pacheco, J., Albert, M., Killiany, R., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.051 · Cited by 1,508 · see citing works
Self-citationHighly accurate inverse consistent registration: A robust approach
Reuter, M., Rosas, H. D., & Fischl, B. R. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.020 · Cited by 1,378 · see citing works
Self-citationPantelis, C., Velakoulis, D., McGorry, P. D., Wood, S. J., John Suckling, A., Phillips, L., Yung, A. R., Bullmore, E. T., Brewer, W. J., Soulsby, B., et al. (2003) · The Lancet · doi:10.1016/s0140-6736(03)12323-9 · Cited by 1,309 · see citing works
Dickerson, B. C., Bakkour, A., Salat, D. H., Feczko, E., Pacheco, J., Greve, D. N., Grodstein, F., Wright, C. I., Blacker, D., Rosas, H. D., et al. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn113 · Cited by 1,153 · see citing works
Self-citationJack, C. R., Lowe, V. J., Weigand, S. D., Wiste, H. J., Senjem, M. L., Knopman, D. S., Shiung, M. M., Gunter, J. L., Boeve, B. F., Kemp, B. J., et al. (2009) · Brain · doi:10.1093/brain/awp062 · Cited by 1,099 · see citing works
Linear mixed models for longitudinal data
(2000) · Springer Series in Statistics · doi:10.1007/b98969 · Cited by 818 · see citing works
Classical and Bayesian Inference in Neuroimaging: Applications
Friston, K. J., Glaser, D. E., Henson, R. N., Kiebel, S. J., Phillips, C. L. M., & Ashburner, J. (2002) · NeuroImage · doi:10.1006/nimg.2002.1091 · Cited by 709 · see citing works
One-Year Brain Atrophy Evident in Healthy Aging
Fjell, A. M., Walhovd, K. B., Fennema-Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., Brewer, J. B., & Dale, A. M. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.3252-09.2009 · Cited by 692 · see citing works
Marcus, D. S., Fotenos, A. F., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2009) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21407 · Cited by 635 · see citing works
Classical and Bayesian Inference in Neuroimaging: Theory
Friston, K. J., Penny, W. D., Phillips, C. L. M., Kiebel, S. J., Hinton, G. E., & Ashburner, J. (2002) · NeuroImage · doi:10.1006/nimg.2002.1090 · Cited by 597 · see citing works
Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD
Fotenos, A. F., Snyder, A. Z., Girton, L. E., Morris, J. C., & Buckner, R. L. (2005) · Neurology · doi:10.1212/01.wnl.0000154530.72969.11 · Cited by 579 · see citing works
Analysis of a large fMRI cohort: statistical and methodological issues for group analyses
Thirion, B., Pinel, P., Mériaux, S., Roche, A., Dehaene, S., & Poline, J.-B. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.054 · Cited by 563 · see citing works
Schumann, C. M., Bloss, C. S., Barnes, C. C., Wideman, G. M., Carper, R. A., Akshoomoff, N., Pierce, K., Hagler, D., Schork, N., Lord, C., et al. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5714-09.2010 · Cited by 554 · see citing works
MISRA, C., FAN, Y., & DAVATZIKOS, C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.031 · Cited by 552 · see citing works
Progressive Structural Brain Abnormalities and Their Relationship to Clinical Outcome
Ho, B., Andreasen, N. C., Nopoulos, P., Arndt, S. V., Magnotta, V. A., & Flaum, M. A. (2003) · Archives of General Psychiatry · doi:10.1001/archpsyc.60.6.585 · Cited by 536 · see citing works
Chételat, G., Landeau, B., Eustache, F., Mézenge, F., Viader, F., de la Sayette, V., Desgranges, B., & Baron, J.-C. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.05.015 · Cited by 526 · see citing works
Hedman, A. M., van Haren, N. E. M., Schnack, H. G., Kahn, R. S., & Pol, H. E. H. (2011) · Human Brain Mapping · doi:10.1002/hbm.21334 · Cited by 485 · see citing works
Avoiding asymmetry-induced bias in longitudinal image processing
Reuter, M., & Fischl, B. R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.076 · Cited by 480 · see citing works
Self-citationFocal Decline of Cortical Thickness in Alzheimer's Disease Identified by Computational Neuroanatomy
Lerch, J. P., Pruessner, J. C., Zijdenbos, A. P., Hampel, H. J., Teipel, S. J., & Evans, A. C. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh200 · Cited by 431 · see citing works
Maximum Likelihood Computations with Repeated Measures: Application of the EM Algorithm
Laird, N. M., LANGE, N. D., & Stram, D. O. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478395 · Cited by 404 · see citing works
Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer’s disease
Singh, V., Chertkow, H., Lerch, J. P., Evans, A. C., Dorr, A. E., & Kabani, N. J. (2006) · Brain · doi:10.1093/brain/awl256 · Cited by 386 · see citing works
Growth of language-related brain areas after foreign language learning
Mårtensson, J., Eriksson, J., Bodammer, N., Lindgren, M. P., Johansson, M., Nyberg, L. H., & Lövdén, M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.043 · Cited by 374 · see citing works
The Dynamics of Cortical and Hippocampal Atrophy in Alzheimer Disease
Sabuncu, M. R. (2011) · Archives of Neurology · doi:10.1001/archneurol.2011.167 · Cited by 360 · see citing works
Self-citationStatistical analysis of longitudinal neuroimage data with Linear Mixed Effects models
Bernal-Rusiel, J. L., Greve, D. N., Reuter, M., Fischl, B., & Sabuncu, M. R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.065 · Cited by 360 · see citing works
Self-citationKasai, K., Shenton, M. E., Salisbury, D. F., Hirayasu, Y., Onitsuka, T., Spencer, M. H., Yurgelun-Todd, D. A., Kikinis, R., Jolesz, F. A., & McCarley, R. W. (2003) · Archives of General Psychiatry · doi:10.1001/archpsyc.60.8.766 · Cited by 352 · see citing works
Longitudinal changes in cortical thickness associated with normal aging
Thambisetty, M., Wan, J., Carass, A., An, Y., Prince, J. L., & Resnick, S. M. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.258 · Cited by 343 · see citing works
Resnick, S. M., Sojkova, J., Zhou, Y., An, Y., Ye, W., Holt, D. P., Dannals, R. F., Mathis, C. A., Klunk, W. E., Ferrucci, L., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181d3e3e9 · Cited by 288 · see citing works
Fully Bayesian Spatio-Temporal Modeling of FMRI Data
Woolrich, M. W., Jenkinson, M., Brady, J. M., & Smith, S. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.823065 · Cited by 248 · see citing works
Sex and age differences in atrophic rates: an ADNI study with n=1368 MRI scans
Hua, X., Hibar, D. P., Lee, S., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.033 · Cited by 234 · see citing works
β‐amyloid burden is not associated with rates of brain atrophy
Josephs, K. A., Whitwell, J. L., Ahmed, Z., Shiung, M. M., Weigand, S. D., Knopman, D. S., Boeve, B. F., Parisi, J. E., Petersen, R. C., Dickson, D. W., et al. (2007) · Annals of Neurology · doi:10.1002/ana.21223 · Cited by 224 · see citing works
Paviour, D. C., Price, S. L., Jahanshahi, M., Lees, A. J., & Fox, N. C. (2006) · Brain · doi:10.1093/brain/awl021 · Cited by 217 · see citing works
Subregional neuroanatomical change as a biomarker for Alzheimer's disease
Holland, D., Brewer, J. B., Hagler, D. J., Fennema-Notestine, C., Dale, A. M., Initiative, T. A. D. N., Weiner, M., Thal, L., Petersen, R., Jack, C. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906053106 · Cited by 210 · see citing works
Mixed-effects and fMRI studies
Friston, K. J., Stephan, K. E., Lund, T. E., Morcom, A. M., & Kiebel, S. J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.08.055 · Cited by 204 · see citing works
Atrophy rates accelerate in amnestic mild cognitive impairment
Jack, C. R., Weigand, S. D., Shiung, M. M., Przybelski, S. A., O’Brien, P. C., Gunter, J. L., Knopman, D. S., Boeve, B. F., Smith, G. E., & Petersen, R. C. (2007) · Neurology · doi:10.1212/01.wnl.0000281688.77598.35 · Cited by 175 · see citing works
Whole-Brain Atrophy Rate and Cognitive Decline: Longitudinal MR Study of Memory Clinic Patients
Sluimer, J. D., van der Flier, W. M., Karas, G. B., Fox, N. C., Scheltens, P., Barkhof, F., & Vrenken, H. (2008) · Radiology · doi:10.1148/radiol.2482070938 · Cited by 173 · see citing works
Rates of cerebral atrophy differ in different degenerative pathologies
Whitwell, J. L., Jack, C. R., Parisi, J. E., Knopman, D. S., Boeve, B. F., Petersen, R. C., Ferman, T. J., Dickson, D. W., & Josephs, K. A. (2006) · Brain · doi:10.1093/brain/awm021 · Cited by 167 · see citing works
Ment, L. R., Kesler, S. R., Vohr, B. R., Katz, K. H., Baumgartner, H. A., Schneider, K. C., Delancy, S., Silbereis, J. C., Duncan, C. C., Constable, R. T., et al. (2009) · PEDIATRICS · doi:10.1542/peds.2008-0025 · Cited by 157 · see citing works
Longitudinal Brain Volume Measurement in Multiple Sclerosis
Kalkers, N. F., Ameziane, N., Bot, J., Minneboo, A., Polman, C. H., & Barkhof, F. (2002) · Archives of Neurology · doi:10.1001/archneur.59.10.1572 · Cited by 153 · see citing works
Lau, J. C., Lerch, J. P., Sled, J. G., Henkelman, R. M., Evans, A. C., & Bedell, B. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.252 · Cited by 151 · see citing works
Longitudinal brain metabolic changes from amnestic mild cognitive impairment to Alzheimer's disease
Fouquet, M., Desgranges, B., Landeau, B., Duchesnay, É., Mézenge, F., Sayette, V. D. L., Viader, F., Baron, J., Eustache, F., & Chételat, G. (2009) · Brain · doi:10.1093/brain/awp132 · Cited by 148 · see citing works
Thambisetty, M., An, Y., Kinsey, A. M., Koka, D., Saleem, M., Güntert, A., Kraut, M. A., Ferrucci, L., Davatzikos, C. A., Lovestone, S., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.056 · Cited by 148 · see citing works
Asami, T., Sylvain Bouix, A., Whitford, T. J., Shenton, M. E., Salisbury, D. F., & McCarley, R. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.066 · Cited by 143 · see citing works
Restricted Likelihood Ratio Testing for Zero Variance Components in Linear Mixed Models
Greven, S., Crainiceanu, C. M., Küchenhoff, H., & Peters, A. (2008) · Journal of Computational and Graphical Statistics · doi:10.1198/106186008x386599 · Cited by 131 · see citing works
A Bayesian hierarchical framework for spatial modeling of fMRI data
Bowman, F. D., Caffo, B. C., Bassett, S. S., & Kilts, C. D. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.08.012 · Cited by 129 · see citing works
Amyloid-β associated volume loss occurs only in the presence of phospho-tau
Desikan, R., McEvoy, L., Thompson, W., Holland, D., Roddey, J., Blennow, K., Aisen, P., Brewer, J., Hyman, B., & Dale, A. (2011) · Annals of Neurology · doi:10.1002/ana.22509 · Cited by 123 · see citing works
Bayesian Spatiotemporal Inference in Functional Magnetic Resonance Imaging
Gössl, C., Auer, D. P., & Fahrmeir, L. (2001) · Biometrics · doi:10.1111/j.0006-341x.2001.00554.x · Cited by 119 · see citing works
Delaloye, C., Moy, G., de Bilbao, F., Weber, K., Baudois, S., Haller, S., Xekardaki, A., Canuto, A., Giardini, U., Lövblad, K., et al. (2011) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.2683 · Cited by 103 · see citing works
Cortical thickness changes following spatial navigation training in adulthood and aging
Wenger, E., Schaefer, S., Noack, H., Kühn, S., Mårtensson, J., Heinze, H., Düzel, E., Bäckman, L., Lindenberger, U., & Lövdén, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.015 · Cited by 100 · see citing works
Accelerating regional atrophy rates in the progression from normal aging to Alzheimer’s disease
Sluimer, J. D., van der Flier, W. M., Karas, G. B., van Schijndel, R. A., Barnes, J., Boyes, R. G., Cover, K. S., Olabarriaga, S. D., Fox, N. C., Scheltens, P., et al. (2009) · European Radiology · doi:10.1007/s00330-009-1512-5 · Cited by 97 · see citing works
Wilde, E. A., Merkley, T. L., Bigler, E. D., Max, J. E., Schmidt, A., Ayoub, K. W., McCauley, S. R., Hunter, J. V., Hanten, G. R., Li, X., et al. (2012) · International Journal of Developmental Neuroscience · doi:10.1016/j.ijdevneu.2012.01.003 · Cited by 95 · see citing works
Unbiased comparison of sample size estimates from longitudinal structural measures in ADNI
Holland, D., McEvoy, L. K., Dale, A. M., & Initiative, T. A. D. N. (2011) · Human Brain Mapping · doi:10.1002/hbm.21386 · Cited by 83 · see citing works
Davatzikos, C. A. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.7.767 · Cited by 83 · see citing works
Midlife obesity and trajectories of brain volume changes in older adults
Driscoll, I. F., Beydoun, M. A., An, Y., Davatzikos, C. A., Ferrucci, L., Zonderman, A. B., & Resnick, S. M. (2011) · Human Brain Mapping · doi:10.1002/hbm.21353 · Cited by 80 · see citing works
Nonlinear time course of brain volume loss in cognitively normal and impaired elders
Schuff, N. W., Tosun, D., Insel, P. S., Chiang, G. C.-Y., Truran, D., Aisen, P. S. S., Jack, C. R., & Weiner, M. W. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.07.012 · Cited by 80 · see citing works
Detecting brain growth patterns in normal children using tensor‐based morphometry
Hua, X., Leow, A., Levitt, J. G., Caplan, R., Thompson, P. M., & Toga, A. W. (2007) · Human Brain Mapping · doi:10.1002/hbm.20498 · Cited by 77 · see citing works
Multiscale Adaptive Regression Models for Neuroimaging Data
Li, Y., Hongtu Zhu, B., Shen, D. G., Lin, W., Gilmore, J. H., & Ibrahim, J. G. (2011) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2010.00767.x · Cited by 72 · see citing works
Rosas, H. D., Reuter, M., Doros, G. D., Lee, S. Y., Triggs, T. D., Malarick, K., Fischl, B. R., Salat, D. H., & Hersch, S. M. (2011) · Movement Disorders · doi:10.1002/mds.23762 · Cited by 61 · see citing works
Self-citationSullivan, E. V., Pfefferbaum, A., Torsten Rohlfing, B., Baker, F. C., Padilla, M. L., & Colrain, I. M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.04.003 · Cited by 60 · see citing works
Spatiotemporal Models for Region of Interest Analyses of Functional Neuroimaging Data
Bowman, F. D. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001347 · Cited by 58 · see citing works
Kaladjian, A., Jeanningros, R., Azorin, J., Nazarian, B., Roth, M., Anton, J., & Mazzola‐Pomietto, P. (2009) · Bipolar Disorders · doi:10.1111/j.1399-5618.2009.00722.x · Cited by 58 · see citing works
Bernal-Rusiel, J. L., Atienza, M., & Cantero, J. L. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.074 · Cited by 57 · see citing works
Self-citationTrajectories of brain and hippocampal atrophy in FTD with mutations in MAPT or GRN
Whitwell, J. L., Weigand, S. D., Gunter, J. L., Boeve, B. F., Rademakers, R., Baker, M., Knopman, D. S., Wszołek, Z. K., Petersen, R. C., Jack, C. R., et al. (2011) · Neurology · doi:10.1212/wnl.0b013e318227047f · Cited by 55 · see citing works
Zipunnikov, V. M., Greven, S., Shou, H., Caffo, B. S., Reich, D. S., & Crainiceanu, C. M. (2014) · The Annals of Applied Statistics · doi:10.1214/14-aoas748 · Cited by 45 · see citing works
Multiscale adaptive generalized estimating equations for longitudinal neuroimaging data
Li, Y., Gilmore, J. H., Shen, D. G., Styner, M. A., Lin, W., & Hongtu Zhu, B. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.01.034 · Cited by 39 · see citing works
Frings, L., Mader, I., Landwehrmeyer, B. G., Weiller, C., Hüll, M., & Huppertz, H. (2012) · Human Brain Mapping · doi:10.1002/hbm.21304 · Cited by 38 · see citing works
Guo, Y., Bowman, F. D., & Kilts, C. D. (2007) · Human Brain Mapping · doi:10.1002/hbm.20450 · Cited by 34 · see citing works
Genotype specific age related changes in a transgenic rat model of Huntington's disease
Blockx, I., Camp, N. V., Verhoye, M., Boisgard, R., Dubois, A., Jégo, B., Jonckers, E., Raber, K. A., Siquier-Pernet, K., Kühnast, B., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.007 · Cited by 26 · see citing works
Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time‐Varying Covariates
Skup, M., Zhu, H., & Zhang, H. (2012) · Biometrics · doi:10.1111/j.1541-0420.2012.01767.x · Cited by 25 · see citing works
LONGITUDINAL HIGH-DIMENSIONAL DATA ANALYSIS
Zipunnikov, V. M., Greven, S., Caffo, B. C., Reich, D. S., & Crainiceanu, C. M. (2011) · Collection of Biostatistics Research Archive · Cited by 23 · see citing works
Longitudinal cerebral blood flow changes during speech in hereditary ataxia
Sidtis, J. J., Strother, S. C., Naoum, A., Rottenberg, D. A., & Gomez, C. M. (2010) · Brain and Language · doi:10.1016/j.bandl.2010.03.007 · Cited by 22 · see citing works
Predicting brain activity using a Bayesian spatial model
Derado, G., Bowman, F. D., Zhang, L., & Initiative, A. T. A. D. N. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280212448972 · Cited by 21 · see citing works
Bowman, F. D., & Kilts, C. D. (2003) · Human Brain Mapping · doi:10.1002/hbm.10127 · Cited by 15 · see citing works
Shinohara, R. T., Crainiceanu, C. M., Caffo, B. S., & Reich, D. S. (2011) · Collection of Biostatistics Research Archive · Cited by 9 · see citing works
Longitudinal Image Analysis of Tumour–Healthy Brain Change in Contrast Uptake Induced by Radiation
Zhang, X., Johnson, T. D., Little, R. J. A., & Cao, Y. (2010) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2010.00718.x · Cited by 6 · see citing works
Longitudinal Image Analysis of Tumor/Brain Change in Contrast Uptake Induced by Radiation
Zhang, X., Johnson, T., Little, R., & Cao, Y. (2009) · Collection of Biostatistics Research Archive · Cited by 1 · see citing works
High-resolution intersubject averaging and a coordinate system for the cortical surface
Fischl, B., Sereno, M. I., Tootell, R. B. H., & Dale, A. M. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Zuo, X., & Xing, X.-X. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.05.009 · Cited by 657 · see citing works
Gordon, B. A., Blazey, T. M., Su, Y., Hari-Raj, A., Dincer, A., Flores, S., Christensen, J., McDade, E., Wang, G., Xiong, C., et al. (2018) · The Lancet Neurology · doi:10.1016/s1474-4422(18)30028-0 · Cited by 602 · see citing works
Head motion during MRI acquisition reduces gray matter volume and thickness estimates
Reuter, M., Tisdall, M. D., Qureshi, A., Buckner, R. L., van der Kouwe, A. J. W., & Fischl, B. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.006 · Cited by 457 · see citing works
Self-citationWeiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.11.001 · Cited by 348 · see citing works
On the right side? A longitudinal study of left- versus right-lateralized semantic dementia
Kumfor, F., Landin-Romero, R., Devenney, E., Hutchings, R., Grasso, R., Hodges, J. R., & Piguet, O. (2016) · Brain · doi:10.1093/brain/awv387 · Cited by 224 · see citing works
Regional growth and atlasing of the developing human brain
Makropoulos, A., Aljabar, P., Wright, R., Hüning, B. M., Merchant, N. M., Arichi, T., Tusor, N., Hajnal, J. V., Edwards, A. D., Counsell, S. J., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.047 · Cited by 200 · see citing works
Fast and accurate modelling of longitudinal and repeated measures neuroimaging data
Guillaume, B., Hua, X., Thompson, P. M., Waldorp, L., & Nichols, T. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.029 · Cited by 187 · see citing works
Hippocampal plasticity underpins long-term cognitive gains from resistance exercise in MCI
Broadhouse, K. M., Singh, M. F., Suo, C., Gates, N., Wen, W., Brodaty, H., Jain, N., Wilson, G. C., Meiklejohn, J., Singh, N., et al. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102182 · Cited by 172 · see citing works
Mohammed, B., Senan, E. M., Rassem, T. H., Makbol, N. M., Alanazi, A., Al-Mekhlafi, Z., Almurayziq, T. S., & Ghaleb, F. A. (2021) · Electronics · doi:10.3390/electronics10222860 · Cited by 150 · see citing works
Sleep Duration and Subsequent Cortical Thinning in Cognitively Normal Older Adults
Spira, A. P., Gonzalez, C. E., Venkatraman, V. K., Wu, M. N., Pacheco, J. L., Simonsick, E. M., Ferrucci, L., & Resnick, S. M. (2016) · SLEEP · doi:10.5665/sleep.5768 · Cited by 157 · see citing works
Bernardoni, F., King, J. A., Geisler, D., Stein, E., Jaite, C., Nätsch, D., Tam, F. I., Boehm, I., Seidel, M., Roessner, V., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.003 · Cited by 148 · see citing works
Wachinger, C., Salat, D. H., Weiner, M. W., Reuter, M., & Initiative, A. D. N. (2016) · Brain · doi:10.1093/brain/aww243 · Cited by 150 · see citing works
Self-citationLandin-Romero, R., Kumfor, F., Leyton, C. E., Irish, M., Hodges, J. R., & Piguet, O. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.032 · Cited by 116 · see citing works
Deprez, S., Kesler, S. R., Saykin, A. J., Silverman, D. H. S., de Ruiter, M. B., & McDonald, B. C. (2017) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djx285 · Cited by 108 · see citing works
Liem, F., Mérillat, S., Bezzola, L., Hirsiger, S., Philipp, M., Madhyastha, T. M., & Jäncke, L. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.035 · Cited by 100 · see citing works
Brain volumes and cognitive function in very-low-birth-weight (VLBW) young adults
Bjuland, K. J., Rimol, L. M., Løhaugen, G. C. C., & Skranes, J. S. (2014) · European Journal of Paediatric Neurology · doi:10.1016/j.ejpn.2014.04.004 · Cited by 104 · see citing works
Wenger, E., Kühn, S., Verrel, J., Mårtensson, J., Bodammer, N., Lindenberger, U., & Lövdén, M. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw141 · Cited by 94 · see citing works
James, C. E., ALTENMÜLLER, E. O., Kliegel, M., Krüger, T. H. C., Ville, D. V. D., Worschech, F., Abdili, L., Scholz, D. S., Jünemann, K., Hering, A., et al. (2020) · BMC Geriatrics · doi:10.1186/s12877-020-01761-y · Cited by 95 · see citing works
Tract-based analysis of white matter degeneration in Alzheimer’s disease
Lee, S., Coutu, J., Wilkens, P. J., Yendiki, A., Rosas, H. D., & Salat, D. H. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.05.049 · Cited by 88 · see citing works
Gordon, B. A., McCullough, A. A., Mishra, S., Blazey, T., Su, Y., Christensen, J., Dincer, A., Jackson, K., Hornbeck, R. C., Morris, J. C., et al. (2018) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.02.003 · Cited by 86 · see citing works
Legault, J., Fang, S.-Y., Lan, Y.-J., & Li, P. (2019) · Brain and Cognition · doi:10.1016/j.bandc.2018.09.004 · Cited by 87 · see citing works
Bilgel, M., Prince, J. L., Wong, D. F., Resnick, S. M., & Jedynak, B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.001 · Cited by 82 · see citing works
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 · see citing works
Collins, M. A., Ji, J. L., Chung, Y., Lympus, C. A., Afriyie-Agyemang, Y., Addington, J., Goodyear, B. G., Bearden, C. E., Cadenhead, K. S., Mirzakhanian, H., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01870-7 · Cited by 70 · see citing works
Body mass index is associated with cortical thinning with different patterns in mid- and late-life
Shaw, M. E., Sachdev, P. S., Abhayaratna, W. P., Anstey, K. J., & Cherbuin, N. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.254 · Cited by 69 · see citing works
Nutritional Status Affects Cortical Folding: Lessons Learned From Anorexia Nervosa
Bernardoni, F., King, J. A., Geisler, D., Birkenstock, J., Tam, F. I., Weidner, K., Roessner, V., White, T. J. H., & Ehrlich, S. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.05.008 · Cited by 69 · see citing works
Irish, M., Landin-Romero, R., Mothakunnel, A., Ramanan, S., Hsieh, S., Hodges, J. R., & Piguet, O. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.03.014 · Cited by 65 · see citing works
Abé, C., Liberg, B., Song, J., Bergen, S. E., Petrović, P., Ekman, C. J., Sellgren, C. M., Ingvar, M., & Landén, M. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.08.015 · Cited by 66 · see citing works
Pagliaccio, D., Pine, D. S., Barch, D. M., Luby, J. L., & Leibenluft, E. (2018) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2018.02.010 · Cited by 65 · see citing works
Lammer, L., Beyer, F., Luppa, M., Sander, C., Baber, R., Engel, C., Wirkner, K., Löffler, M., Riedel-Heller, S. G., Villringer, A., et al. (2023) · eLife · doi:10.7554/elife.83660 · Cited by 65 · see citing works
Morphometricity as a measure of the neuroanatomical signature of a trait
Sabuncu, M. R., Ge, T., Holmes, A. J., Smoller, J. W., Buckner, R. L., Fischl, B. R., Initiative, T. A. D. N., Weiner, M. W., Aisen, P., Weiner, M. W., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604378113 · Cited by 58 · see citing works
Self-citationChuang, Y., Elango, P., Gonzalez, C. E., & Thambisetty, M. (2017) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1016/j.trci.2017.06.004 · Cited by 61 · see citing works
Stage-dependent loss of cortical gyrification as Parkinson disease “unfolds”
Sterling, N. W., Wang, M., Zhang, L., Lee, E.-Y., Du, G., Lewis, M. M., Styner, M. A., & Huang, X. (2016) · Neurology · doi:10.1212/wnl.0000000000002492 · Cited by 53 · see citing works
Instantiated mixed effects modeling of Alzheimer's disease markers
Guerrero, R., Schmidt-Richberg, A., Ledig, C., Tong, T., Wolz, R., & Rueckert, D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.049 · Cited by 50 · see citing works
Racine, A. M., Merluzzi, A. P., Adluru, N., Norton, D., Koscik, R. L., Clark, L. R., Berman, S. E., Nicholas, C. R. N., Asthana, S., Alexander, A. L., et al. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9732-9 · Cited by 47 · see citing works
Patterns of progressive atrophy vary with age in Alzheimer's disease patients
Fiford, C. M., Ridgway, G. R., Cash, D. M., Marc Modat, A., Nicholas, J. M., Manning, E. N., Malone, I. B., Biessels, G. J., Sebastien Ourselin, A., Carmichael, O. T., et al. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.11.002 · Cited by 46 · see citing works
von Schwanenflug, N., Müller, D. K., King, J. A., Ritschel, F., Bernardoni, F., Mohammadi, S., Geisler, D., Roessner, V., Biemann, R., Marxen, M., et al. (2018) · Psychological Medicine · doi:10.1017/s003329171800212x · Cited by 46 · see citing works
Abunadi, I. (2022) · Connection Science · doi:10.1080/09540091.2022.2123450 · Cited by 43 · see citing works
Longitudinal changes in cortical thinning associated with hypertension
Gonzalez, C. E., Pacheco, J. L., Beason‐Held, L. L., & Resnick, S. M. (2015) · Journal of Hypertension · doi:10.1097/hjh.0000000000000531 · Cited by 41 · see citing works
Platero, C. A., Tobar, M. C., & Initiative, A. D. N. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00366-8 · Cited by 37 · see citing works
Estimating anatomical trajectories with Bayesian mixed-effects modeling
Ziegler, G., Penny, W. D., Ridgway, G. R., Sebastien Ourselin, A., & Friston, K. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.094 · Cited by 36 · see citing works
Cortical trajectories during adolescence in preterm born teenagers with very low birthweight
Rimol, L. M., Bjuland, K. J., Løhaugen, G. C. C., Martinussen, M., Evensen, K. A. I., Indredavik, M. S., Brubakk, A., Eikenes, L., Haberg, A. K., & Skranes, J. S. (2015) · Cortex · doi:10.1016/j.cortex.2015.12.001 · Cited by 35 · see citing works
Fjell, A. M., Sørensen, Ø., Amlien, I. K., David Bartrés-faz, A., Brandmaier, A. M., Buchmann, N., Demuth, I., Drevon, C. A., Düzel, S., Ebmeier, K. P., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa332 · Cited by 35 · see citing works
Age influences structural brain restoration during weight gain therapy in anorexia nervosa
Kaufmann, L., Hänggi, J., Jäncke, L., Baur, V., Piccirelli, M., Kollias, S., Schnyder, U., Martin-Söelch, C., & Milos, G. F. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0809-7 · Cited by 36 · see citing works
Zarkali, A., McColgan, P., Leyland, L. A., Lees, A. J., & Weil, R. S. (2022) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2021-326630 · Cited by 32 · see citing works
Roe, J. M., Vidal-Piñeiro, D., Amlien, I., Pan, M., Sneve, M., de Schotten, M. T., Friedrich, P., Sha, Z., Francks, C., Eilertsen, E., et al. (2023) · eLife · doi:10.7554/elife.84685 · Cited by 6 · see citing works
Brett, B. L., Bobholz, S. A., España, L. Y., Huber, D. L., Mayer, A. R., Harezlak, J., Broglio, S. P., McAllister, T. W., McCrea, M., & Meier, T. B. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00673 · Cited by 32 · see citing works
Event time analysis of longitudinal neuroimage data
Sabuncu, M. R., Bernal-Rusiel, J. L., Reuter, M., Greve, D. N., & Fischl, B. R. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.015 · Cited by 31 · see citing works
Self-citationWeygandt, M., Spranger, J., Leupelt, V., Maurer, L., Bobbert, T., Mai, K., & Haynes, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.058 · Cited by 30 · see citing works
No phenotypic or genotypic evidence for a link between sleep duration and brain atrophy
Fjell, A. M., Sørensen, Ø., Wang, Y., Amlien, I. K., Baaré, W. F. C., Bartrés-Faz, D., Bertram, L., Boraxbekk, C.-J., Brandmaier, A. M., Demuth, I., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01707-5 · Cited by 29 · see citing works
Ge, T., Nichols, T. E., Ghosh, D., Mormino, E. C., Smoller, J. W., & Sabuncu, M. R. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.029 · Cited by 29 · see citing works
Self-citationShaw, M. E., Nettersheim, J., Sachdev, P. S., Anstey, K. J., & Cherbuin, N. (2017) · Brain Topography · doi:10.1007/s10548-017-0544-4 · Cited by 29 · see citing works
Structural MRI outcomes and predictors of disease progression in amyotrophic lateral sclerosis
Spinelli, E. G., Riva, N., Rancoita, P. M. V., Schito, P., Doretti, A., Poletti, B., Serio, C. D., Silani, V., Filippi, M., & Agosta, F. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102315 · Cited by 27 · see citing works
Correlates of anomia in non-semantic variants of primary progressive aphasia converge over time
Leyton, C. E., Landín-Romero, R., Liang, C. T., Burrell, J. R., Kumfor, F., Hodges, J. R., & Piguet, O. (2019) · Cortex · doi:10.1016/j.cortex.2019.06.008 · Cited by 28 · see citing works
Self-supervised learning of neighborhood embedding for longitudinal MRI
Ouyang, J., Zhao, Q., Adeli, E., Zaharchuk, G., & Pohl, K. M. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102571 · Cited by 29 · see citing works
Brain aging differs with cognitive ability regardless of education
Walhovd, K. B., Nyberg, L. H., Lindenberger, U., Amlien, I. K., Sørensen, Ø., Wang, Y., Mowinckel, A. M., Kievit, R., Ebmeier, K. P., David Bartrés-faz, A., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-17727-6 · Cited by 25 · see citing works
Ríos, R., de Crevoisier, R., Ospina, J. D., Commandeur, F., Lafond, C., Simon, A., Haigron, P., Espinosa, J. J., & Acosta, O. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.03.001 · Cited by 25 · see citing works
Krogsrud, S. K., Mowinckel, A. M., Sederevičius, D., Dídac Vidal-piñeiro, A., Amlien, I. K., Wang, Y., Sørensen, Ø., Walhovd, K. B., & Fjell, A. M. (2021) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2021.100997 · Cited by 24 · see citing works
Huang, C., Xu, Z., Shen, Z., Luo, T., Li, T., Nissman, D., Nelson, A. E., Golightly, Y. M., Niethammer, M., & Hongtu Zhu, B. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102343 · Cited by 23 · see citing works
Bobholz, S. A., Brett, B. L., España, L. Y., Huber, D. L., Mayer, A. R., Harezlak, J., Broglio, S. P., McAllister, T. W., McCrea, M., & Meier, T. B. (2020) · British Journal of Sports Medicine · doi:10.1136/bjsports-2020-102002 · Cited by 21 · see citing works
Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs
Ouyang, J., Zhao, Q., Sullivan, E. V., Pfefferbaum, A., Tapert, S. F., Adeli, E., & Pohl, K. M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3042447 · Cited by 22 · see citing works
Brain changes underlying progression of speech motor programming impairment
Landín-Romero, R., Liang, C. T., Monroe, P., Higashiyama, Y., Leyton, C. E., Hodges, J. R., Piguet, O., & Ballard, K. J. (2021) · Brain Communications · doi:10.1093/braincomms/fcab205 · Cited by 21 · see citing works
Effortful control and resting state networks: A longitudinal EEG study
Knyazev, G. G., Savostyanov, A. N., Bocharov, A. V., Slobodskaya, H. R., Bairova, N. B., Tamozhnikov, S. S., & Stepanova, V. V. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.01.031 · Cited by 21 · see citing works
Platero, C. A., & Tobar, M. C. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108698 · Cited by 21 · see citing works
Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes
Pur, D. R., Eagleson, R. A., Ribaupierre, A. D., Mella, N., & de Ribaupierre, S. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00046 · Cited by 20 · see citing works
Guo, X., Chen, K., Chen, Y., Xiong, C., Su, Y., Yao, L., & Reiman, E. M. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3106939 · Cited by 20 · see citing works
Sauty, B., & Durrleman, S. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1161527 · Cited by 20 · see citing works
CSF sTREM2 and Tau Work Together in Predicting Increased Temporal Lobe Atrophy in Older Adults
Halaas, N. B., Henjum, K., Blennow, K., Dakhil, S., Idland, A., Nilsson, L. N. G., Sederevičius, D., Dídac Vidal-piñeiro, A., Walhovd, K. B., Wyller, T. B., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz240 · Cited by 20 · see citing works
Shaw, M. E., Abhayaratna, W. P., Anstey, K. J., & Cherbuin, N. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170055 · Cited by 19 · see citing works
Liu, G., Liu, C., Qiu, A., & Initiative, T. A. D. N. (2021) · Human Brain Mapping · doi:10.1002/hbm.25414 · Cited by 18 · see citing works
Modeling longitudinal imaging biomarkers with parametric Bayesian multi‐task learning
Aksman, L., Scelsi, M. A., Marquand, A. F., Alexander, D. C., Sebastien Ourselin, A., Altmann, A., & for, A. D. N. I. (2019) · Human Brain Mapping · doi:10.1002/hbm.24682 · Cited by 18 · see citing works
Sex differences in trajectories of cortical development in autistic children from 2–13 years of age
Andrews, D. S., Diers, K., Lee, J. K., Harvey, D. J., Heath, B. E., Cordero, D., Rogers, S. J., Reuter, M., Solomon, M., Amaral, D. G., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02592-8 · Cited by 16 · see citing works
Self-citationModeling sparse longitudinal data in early neurodevelopment
Chen, Y., Dubey, P., Müller, H., Bruchhage, M. M. K., Wang, J.-L., & Deoni, S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118079 · Cited by 15 · see citing works
Serum neurofilament light concentrations are associated with cortical thinning in anorexia nervosa
Hellerhoff, I., Bernardoni, F., Bahnsen, K., King, J. A., Doose, A., Pauligk, S., Tam, F. I., Mannigel, M., Gramatke, K., Roessner, V., et al. (2023) · Psychological Medicine · doi:10.1017/s0033291723000387 · Cited by 17 · see citing works
Wei, D., Zhang, H., Broekman, B. F. P., Chong, Y. S., Shek, L. P., Yap, F. K. P., Tan, K. H., Gluckman, P. D., Meaney, M. J., Fortier, M. V., et al. (2021) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2021.05.021 · Cited by 15 · see citing works
MIDAS: Regionally linear multivariate discriminative statistical mapping
Varol, E., Sotiras, A., & Davatzikos, C. A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.060 · Cited by 15 · see citing works
Computing group cardinality constraint solutions for logistic regression problems
Zhang, Y., Kwon, D., & Pohl, K. M. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.05.011 · Cited by 14 · see citing works
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 14 · see citing works
Prospective Study Examining the Effects of Extreme Drinking on Brain Structure in Emerging Adults
Hua, J. P. Y., Sher, K. J., Boness, C. L., Trela, C. J., McDowell, Y. E., Merrill, A. M., Piasecki, T. M., & Kerns, J. G. (2020) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14446 · Cited by 14 · see citing works
Solé‐Padullés, C., Macià, D., Andersson, M., Stiernstedt, M., Pudas, S., Düzel, S., Zsoldos, E., Ebmeier, K. P., Binnewies, J., Drevon, C. A., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.795764 · Cited by 13 · see citing works
Trajectories of brain volumes in young children are associated with maternal education
Zhu, C., Chen, Y., Müller, H., Wang, J., O’Muircheartaigh, J., Bruchhage, M. M. K., & Deoni, S. (2023) · Human Brain Mapping · doi:10.1002/hbm.26271 · Cited by 10 · see citing works
Collins, M. A., Chung, Y., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Seidman, L. J., et al. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102757 · Cited by 12 · see citing works
Landín-Romero, R., Kumfor, F., Lee, A. Y., Leyton, C. E., & Piguet, O. (2024) · Brain Research · doi:10.1016/j.brainres.2024.148777 · Cited by 11 · see citing works
CLEAN: Leveraging spatial autocorrelation in neuroimaging data in clusterwise inference
Park, J. Y., & Fiecas, M. J. A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119192 · Cited by 11 · see citing works
Wang, Q., Guo, L., Thompson, P. M., Jack, C. R., Dodge, H. H., Zhan, L., & Zhou, J. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-171048 · Cited by 10 · see citing works
Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments
Risk, B. B., Matteson, D. S., Spreng, R. N., & Ruppert, D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.038 · Cited by 9 · see citing works
Kernel machine regression in neuroimaging genetics
Ge, T., Smoller, J. W., & Sabuncu, M. R. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804076-8.00002-5 · Cited by 8 · see citing works
Self-citationPermutation-based inference for spatially localized signals in longitudinal MRI data
Park, J. Y., & Fiecas, M. J. A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118312 · Cited by 8 · see citing works
BLMM: Parallelised computing for big linear mixed models
Maullin-Sapey, T., & Nichols, T. E. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119729 · Cited by 8 · see citing works
Chang, H., Huang, C., Huang, S., Hsu, S., Lin, K., Ho, T., Wu, H., & Chang, C. (2024) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13680 · Cited by 7 · see citing works
Hsiao, H., Ma, M.-C., Chang, H.-I., Lin, C.-H., Hsu, S., Huang, S., Lee, C., Huang, C., & Chang, C.-C. (2022) · Nutrients · doi:10.3390/nu14245300 · Cited by 7 · see citing works
Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model
Kim, S., Kim, S.-W., Noh, Y., Lee, P. H., Na, D. L., Seo, S. W., & Seong, J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.869387 · Cited by 5 · see citing works
Computing Brain Change over Time
Ridgway, G. R., Leung, K. K., & Ashburner, J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00313-4 · Cited by 5 · see citing works
Gao, F., Zhang, M., Wang, Q., Ni, M., Liu, C., Deng, K., Xie, Q., Wang, S., Shi, J., & Shen, Y. (2024) · Acta Neuropathologica · doi:10.1007/s00401-024-02750-w · Cited by 4 · see citing works
Solving Logistic Regression with Group Cardinality Constraints for Time Series Analysis
Zhang, Y., & Pohl, K. M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_55 · Cited by 4 · see citing works
Dai, N., Kang, H., Jones, G. L., Fiecas, M. J. A., & Initiative, A. D. N. (2021) · Canadian Journal of Statistics · doi:10.1002/cjs.11588 · Cited by 4 · see citing works
EBLUPs : Empirical Best Linear Unbiased Predictors
Haslett, S. J., & Welsh, A. H. (2019) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat08180 · Cited by 4 · see citing works
Spatial-extent inference for testing variance components in reliability and heritability studies
Pan, R., Dickie, E. W., Hawco, C. S., Reid, N. M., Voineskos, A. N., & Park, J. Y. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00058 · Cited by 3 · see citing works
Spatial Linear Mixed Effects Modelling for OCT Images: SLME Model
Zhu, W., Ku, J. Y., Zheng, Y., Knox, P. C., Kolamunnage‐Dona, R., & Czanner, G. (2020) · Journal of Imaging · doi:10.3390/jimaging6060044 · Cited by 4 · see citing works
Accurate non-iterative modelling and inference of longitudinal neuroimaging data
Guillaume, B. (2015) · doi:10.26481/dis.20150930bg · Cited by 3 · see citing works
- Authors
- Bernal-Rusiel, J. L., Reuter, M., Greve, D. N., Fischl, B., & Sabuncu, M. R.
- Affiliations
- Harvard University (US)
- Corresponding author
- Sabuncu, Mert R.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-11
- Volume
- 81
- Pages
- 358-370
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3816382
- OpenAlex
- W2132114367
- Cited by
- 125
- References
- 93
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 7 · 2023: 9 · 2022: 13 · 2021: 11 · 2020: 16 · 2019: 6 · 2018: 10 · 2017: 12 · 2016: 13 · 2015: 11 · 2014: 6
- FWCI
- 5.46
- Type
- journal article
- Language
- en
- Update notices
- erratum (2015-03-01)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute on Aging (AG022381)
- National Institute of Neurological Disorders and Stroke (1R01NS070963, 1R21NS072652-01, 2R01NS042861-06A1, 5P01NS058793-03, R01 NS052585-01)
- BrightFocus Foundation (AHAF-A2012333)
- National Center for Research Resources (P41-RR14075)
- NIH Blueprint for Neuroscience Research (5U01-MH093765)
- Ellison Medical Foundation
- National Institute of Biomedical Imaging and Bioengineering (1K25EB013649-01, R01EB006758)
- National Institute of Child Health and Human Development (1S10RR019307, 1S10RR023043, 1S10RR023401, R01-HD071664)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- longitudinal neuroimaging, mass univariate analysis, cortical thickness, FreeSurfer, Alzheimer's disease, statistical power, false positive rate control, brain MRI
- MeSH terms
- Brain, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Software, Linear Models
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
