Abstract
The general linear model (GLM) is the statistical method of choice used in brain morphometric analyses because of its ability to incorporate a multitude of effects. This chapter starts by presenting the theory, focusing on modeling, and then goes on discussing multiple comparisons issues specific to voxel-based approaches. The end of the chapter discusses practicalities: variable selection and covariates of no interest. Researchers have often a multitude of demographic and behavioral measures they wish to use, and methods to select such variables are presented. We end with a note of caution as the GLM can only reveal covariations between the brain and behavior, and prediction and causation mandate specific designs and analyses.
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Notes
- 1.
A polynomial function is a function, such as a quadratic, a cubic, a quartic, and so on, involving only nonnegative integer powers of x.
- 2.
In morphometric analyses, the total intracranial volume (or related measurement) is typically accounted for, either in the model or in the data. Here the hippocampal volume is normalized to the total brain volume—this transformation is mandatory as bigger heads give bigger volume and vice versa, and bias in a sample can lead to spurious results.
References
Christensen R (2002) Plane answers to complex questions. The theory of linear models, 3rd edn. Springer, New-York
Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. NeuroImage 11:805–821
Smith SM, Jenkinson M, Johansen-Berg H et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31:1487–1505
Frisoni GB, Ganzola R, Canu E et al (2008) Mapping local hippocampal changes in Alzheimer’s disease and normal ageing with MRI at 3 Tesla. Brain J Neurol 131:3266–3276
Worsley KJ, Marrett S, Neelin P et al (1996) A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapp 4:58–73
Hayasaka S, Phan KL, Liberzon I et al (2004) Nonstationary cluster-size inference with random field and permutation methods. NeuroImage 22:676–687
Smith S, Nichols T (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage 44:83–98
Salimi-Khorshidi G, Smith SM, Nichols TE (2009) Adjusting the neuroimaging statistical inferences for nonstationarity. Med Image Comput Comput Assist Interv 12. Springer:992–999
Salimi-Khorshidi G, Smith SM, Nichols TE (2011) Adjusting the effect of nonstationarity in cluster-based and TFCE inference. NeuroImage 54:2006–2019
Ridgway GR, Henley SMD, Rohrer JD et al (2008) Ten simple rules for reporting voxel-based morphometry studies. NeuroImage 40:1429–1435
Huang L, Rattner A, Liu H, Nathans J (2013) How to draw the line in biomedical research. elife 2:e00638
Salmond CH, Ashburner J, Vargha-Khadem F et al (2002) Distributional assumptions in voxel-based morphometry. NeuroImage 17:1027–1030
Scarpazza C, Sartori G, De Simone MS, Mechelli A (2013) When the single matters more than the group: very high false positive rates in single case voxel based morphometry. NeuroImage 70:175–188
Sink KM, Craft S, Smith SC et al (2015) Montreal cognitive assessment and modified mini mental state examination in African Americans. J Aging Res 2015:872018
Hutcheon JA, Chiolero A, Hanley JA (2010) Random measurement error and regression dilution bias. BMJ 340:c2289
Kraha A, Turner H, Nimon K et al (2012) Tools to support interpreting multiple regression in the face of multicollinearity. Front Psychol 3:44
Malone IB, Leung KK, Clegg S et al (2015) Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. NeuroImage 104:366–372
Henley SMD, Ridgway GR, Scahill RI et al (2010) Pitfalls in the use of voxel-based morphometry as a biomarker: examples from huntington disease. AJNR Am J Neuroradiol 31:711–719
Peelle JE, Cusack R, Henson RNA (2012) Adjusting for global effects in voxel-based morphometry: gray matter decline in normal aging. NeuroImage 60:1503–1516
Tu Y-K, Gunnell D, Gilthorpe MS (2008) Simpson’s paradox, Lord’s paradox, and suppression effects are the same phenomenon--the reversal paradox. Emerg Themes Epidemiol 5:2
Pernet CR, Poline J, Demonet J, Rousselet GA (2009) Brain classification reveals the right cerebellum as the best biomarker of dyslexia. BMC Neurosci 10:67
Lo A, Chernoff H, Zheng T, Lo S-H (2015) Why significant variables aren’t automatically good predictors. Proc Natl Acad Sci 112:13892–13897
Acknowledgments
Thank you to Ged Ridgway for providing useful references related to variables of no interest and reviewing the manuscript and to David Raffelt for pointing out difference between looking at voxel content (VBM/TBSSS) and morphometry per se (looking at shapes).
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Pernet, C.R. (2018). The General Linear Model: Theory and Practicalities in Brain Morphometric Analyses. In: Spalletta, G., Piras, F., Gili, T. (eds) Brain Morphometry. Neuromethods, vol 136. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7647-8_5
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DOI: https://doi.org/10.1007/978-1-4939-7647-8_5
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