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The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer's disease
Open accessAbstract
In neurodegeneration research, normalization of regional volumes by intracranial volume (ICV) is important to estimate the extent of disease-driven atrophy. There is little agreement as to whether raw volumes, volume-to-ICV fractions or regional volumes from which the ICV factor has been regressed out should be used for volumetric brain imaging studies. Using multiple regional cortical and subcortical volumetric measures generated by Freesurfer (51 in total), the main aim of this study was to elucidate the implications of these adjustment approaches. Magnetic resonance imaging (MRI) data were analyzed from two large cohorts, the population-based PIVUS cohort (N = 406, all subjects age 75) and the Alzheimer disease Neuroimaging Initiative (ADNI) cohort (N = 724). Further, we studied whether the chosen ICV normalization approach influenced the relationship between hippocampus and cognition in the three diagnostic groups of the ADNI cohort (Alzheimer's disease, mild cognitive impairment, and healthy individuals). The ability of raw vs. adjusted hippocampal volumes to predict diagnostic status was also assessed. In both cohorts raw volumes correlate positively with ICV, but do not scale directly proportionally with it. The correlation direction is reversed for all volume-to-ICV fractions, except the lateral and third ventricles. Most gray matter fractions are larger in females, while lateral ventricle fractions are greater in males. Residual correction effectively eliminated the correlation between the regional volumes and ICV and removed gender differences. The association between hippocampal volumes and cognition was not altered by ICV normalization. Comparing prediction of diagnostic status using the different approaches, small but significant differences were found. The choice of normalization approach should be carefully considered when designing a volumetric brain imaging study.
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Cites
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
A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0786 · Cited by 4,561
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
A new rating scale for Alzheimer's disease
(1984) · American Journal of Psychiatry · doi:10.1176/ajp.141.11.1356 · Cited by 4,175
Rate of medial temporal lobe atrophy in typical aging and Alzheimer's disease
Jack, C. R., Petersen, R. C., Xu, Y., O'Brien, P. C., Smith, G. E., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1998) · Neurology · doi:10.1212/wnl.51.4.993 · Cited by 666
Consistent neuroanatomical age-related volume differences across multiple samples
Walhovd, K. B., Westlye, L. T., Amlien, I., Espeseth, T., Reinvang, I., Raz, N., Agartz, I., Salat, D. H., Greve, D. N., Fischl, B., et al. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2009.05.013 · Cited by 506
Research works printed but not linked: 29
- Arndt, 1991, Problems with ratio and proportion measures of imaged cerebral structures, Psychiatry Res, 40, 79, 10.1016/0925-4927(91)90031-K · DOI · Paper page
- Barnes, 2010, Head size, age and gender adjustment in MRI studies: a necessary nuisance?, Neuroimage, 53, 1244, 10.1016/j.neuroimage.2010.06.025 · DOI · Paper page
- Blatter, 1995, Quantitative volumetric analysis of brain MR: normative database spanning 5 decades of life, Am. J. Neuroradiol, 16, 241
- Buckner, 2004, A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume, Neuroimage, 23, 724, 10.1016/j.neuroimage.2004.06.018 · DOI · Paper page
- Coffey, 1998, Sex differences in brain aging: a quantitative magnetic resonance imaging study, Arch. Neurol, 55, 169, 10.1001/archneur.55.2.169 · DOI · Paper page
- Dale, 1999, Cortical surface-based analysis. I. Segmentation and surface reconstruction, Neuroimage, 9, 179, 10.1006/nimg.1998.0395 · DOI · Paper page
- Dekaban, 1978, Changes in brain weights during the span of human life: relation of brain weights to body heights and body weights, Ann. Neurol, 4, 345, 10.1002/ana.410040410 · DOI · Paper page
- Fischl, 1999, Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system, Neuroimage, 9, 195, 10.1006/nimg.1998.0396 · DOI · Paper page
- Goodro, 2012, Age effect on subcortical structures in healthy adults, Psychiatry Res, 203, 38, 10.1016/j.pscychresns.2011.09.014 · DOI
- Greenberg, 2008, Aging, gender, and the elderly adult brain: an examination of analytical strategies, Neurobiol. Aging, 29, 290, 10.1016/j.neurobiolaging.2006.09.016 · DOI
- Guo, 2013, Brain size and the compensation of Alzheimer's disease symptoms: a longitudinal cohort study, Alzheimers Dement, 9, 580, 10.1016/j.jalz.2012.10.002 · DOI
- Gur, 1991, Gender differences in age effect on brain atrophy measured by magnetic resonance imaging, Proc. Natl. Acad. Sci. U.S.A, 88, 2845, 10.1073/pnas.88.7.2845 · DOI · Paper page
- Kruggel, 2006, MRI-based volumetry of head compartments: normative values of healthy adults, Neuroimage, 30, 1, 10.1016/j.neuroimage.2005.09.063 · DOI
- Lind, 2006, A comparison of three different methods to determine artierial compliance in the elderly: the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study, J. Hypertens, 24, 1075, 10.1097/01.hjh.0000226197.67052.89 · DOI
- Nordenskjöld, 2013, Intracranial volume estimated with commonly used methods could introduce bias in studies including brain volume measurements, Neuroimage, 83, 355, 10.1016/j.neuroimage.2013.06.068 · DOI
- O'Brien, 2011, Statistical adjustments for brain size in volumetric neuroimaging studies: some practical implications in methods, Psychiatry Res, 193, 113, 10.1016/j.pscychresns.2011.01.007 · DOI · Paper page
- Perneczky, 2010, Head circumference, atrophy, and cognition: implications for brain reserve in Alzheimer disease, Neurology, 75, 137, 10.1212/WNL.0b013e3181e7ca97 · DOI
- Raz, 2004, Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume, Neurobiol. Aging, 25, 377, 10.1016/S0197-4580(03)00118-0 · DOI · Paper page
- Raz, 2006, Differential aging of the brain: patterns, cognitive correlates and modifiers, Neurosci. Biobehav. Rev, 30, 730, 10.1016/j.neubiorev.2006.07.001 · DOI · Paper page
- Sanfilipo, 2004, Correction for intracranial volume in analysis of whole brain atrophy in multiple sclerosis: the proportion vs. residual method, Neuroimage, 22, 1732, 10.1016/j.neuroimage.2004.03.037 · DOI · Paper page
- Scahill, 2003, A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging, Arch. Neurol, 60, 989, 10.1001/archneur.60.7.989 · DOI · Paper page
- Simmons, 2009, MRI measures of Alzheimer's disease and the addneuromed study, Ann. N.Y. Acad. Sci, 1180, 47, 10.1111/j.1749-6632.2009.05063.x · DOI
- Simmons, 2011, The AddNeuroMed framework for multi-centre MRI assessment of longitudinal changes in Alzheimer's disease: experience from the first 24 months, Int. J. Geriatr. Psychiatry, 26, 75, 10.1002/gps.2491 · DOI
- Smith, 2007, Age and gender effects on human brain anatomy: a voxel-based morphometric study in healthy elderly, Neurobiol. Aging, 28, 1075, 10.1016/j.neurobiolaging.2006.05.018 · DOI · Paper page
- Sowell, 2007, Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age, Cereb. Cortex, 17, 1550, 10.1093/cercor/bhl066 · DOI · Paper page
- Westman, 2013, Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment, Brain Topogr, 26, 9, 10.1007/s10548-012-0246-x · DOI · Paper page
- Westman, 2012, Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion, Neuroimage, 62, 229, 10.1016/j.neuroimage.2012.04.056 · DOI · Paper page
- Westman, 2011, AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America, Neuroimage, 58, 818, 10.1016/j.neuroimage.2011.06.065 · DOI
- Whitwell, 2010, The protective role of brain size in Alzheimer's disease, Expert Rev. Neurother, 10, 1799, 10.1586/ern.10.168 · DOI
Cited by
Schwarz, C. G., Gunter, J. L., Wiste, H. J., Przybelski, S. A., Weigand, S. D., Ward, C. P., Senjem, M. L., Vemuri, P., Murray, M. E., Dickson, D. W., et al. (2016) · NeuroImage: Clinical · doi:10.1016/j.nicl.2016.05.017 · Cited by 429
Zhang, Y., Dong, Z., Phillips, P., Wang, S., Ji, G., Yang, J., & Yuan, T.-F. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00066 · Cited by 279
Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging
Anatürk, M., Kaufmann, T., Cole, J. H., Suri, S., Griffanti, L., Zsoldos, E., Filippini, N., Singh‐Manoux, A., Kivimäki, M., Westlye, L. T., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25316 · Cited by 132
Structural Brain Abnormalities in Youth With Psychosis Spectrum Symptoms
Satterthwaite, T. D., Wolf, D. H., Calkins, M. E., Vandekar, S. N., Erus, G., Ruparel, K., Roalf, D. R., Linn, K. A., Elliott, M. A., Moore, T. M., et al. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.3463 · Cited by 132
Landin-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
Metadata
- Authors
- Voevodskaya, O., Simmons, A., Nordenskjöld, R., Kullberg, J., Ahlström, H., Lind, L., Wahlund, L.-O., Larsson, E.-M., Westman, E., & Initiative, A. D. N.
- Venue
- Frontiers in Aging Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2014-10-07
- Volume
- 6
- Pages
- 264-264
- ISSN
- 1663-4365 (ISSN-L)
- OpenAlex
- W2035771001
- Cited by
- 468
- References
- 6
- FWCI
- 9.43
- Type
- journal article
- Language
- en
- Open access
- gold
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