Douglas Greve – Publications – Laboratories for Computational Neuroimaging

Publications on Google scholar

Significant Works

Winkler, AM, Greve, DN, Bjuland, KJ, Nichols, TE, Sabuncu, MR, Håberg, AK, Skranes, J,
Rimol, LM, Joint analysis of area and thickness as a replacement for the analysis of cortical
volume. Cerebral Cortex 2018, vol 28 (2), 738-749
Greve, D.N., Fischl, B., False positive rates in surface-based anatomical analysis.
Neuroimage 171, 6-14, 2017.
Bernal-Rusiel, JL, Reuter, M, Greve, DN, Fischl, B, Sabuncu, MR, Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data, NeuroImage, 2015.
Greve, DN., Van der Haegen, L, Cai, Q., Stufflebeam, S., Sabuncu, MR, Fischl, B, and Bysbaert, M. “A surface-based analysis of language lateralization and cortical asymmetry.” (2013). Journal of Cognitive
Greve, D. N. and B. Fischl (2009). “Accurate and robust brain image alignment using boundary-based registration.” Neuroimage (48) 63-72. PMID: 19573611.
Salat, D. H.; Greve, D. N.; Pacheco, J. L.; Quinn, B. T.; Helmer, K. G.; Buckner, R. L.; Fischl, B. Regional white matter volume differences in nondemented aging and Alzheimer’s disease. Neuroimage 2009, 44, 1247-1258.
Westlye, L. T.; Walhovd, K. B.; Dale, A. M.; Espeseth, T.; Reinvang, I.; Raz, N.; Agartz, I.; Greve, D. N.; Fischl, B.; Fjell, A. M. Increased sensitivity to effects of normal aging and Alzheimer’s disease on cortical thickness by adjustment for local variability in gray/white contrast: a multi-sample MRI study. Neuroimage 2009, 47, 1545-1557.
Lindemer, E. R.; Salat, D. H.; Smith, E. E.; Nguyen, K.; Fischl, B.; Greve, D. N.; Alzheimer’s Disease Neuroimaging, I. White matter signal abnormality quality differentiates mild cognitive impairment that converts to Alzheimer’s disease from nonconverters. Neurobiology of aging 2015, 36, 2447-2457.
Lindemer, E.R., Greve, D.N., Fischl, B.R., Augustinack, J.C., Salat, D.H. 2017. Regional staging of whitematter signal abnormalities in aging and Alzheimer’s disease. Neuroimage Clin 14, 156-165.
Greve, D.N., C. Svarer, P.M. Fisher, L. Feng, A.E. Hansen, W. Baare, et al. Cortical surface
based analysis reduces bias and variance in kinetic modeling of brain PET data. Neuroimage 92: 225-236, 2014.
Beliveau, V.; Svarer, C.; Frokjaer, V. G.; Knudsen, G. M.; Greve, D. N. (joint senior authorship); Fisher, P. M. Functional connectivity of the dorsal and median raphe nuclei at rest. Neuroimage 2015.
Greve, D.N., Salat, D.H., Bowen, S.L., Izquierdo-Garcia, D., Schultz, A.P., Catana, C., Becker, J.A., Svarer, C., Knudsen, G.M., Sperling, R.A., Johnson, K.A., Different partial volume correction methods lead to different conclusions: An (18)F-FDG-PET study of aging. Neuroimage 132, 334-343, 2016.
Ganz, M., Feng, L., Hansen, H.D., Beliveau, V., Svarer, C., Knudsen, G.M., Greve, D.N., Cerebellar heterogeneity and its impact on PET data quantification of 5-HT receptor radioligands. J Cereb Blood Flow Metab, 2017.