Abstract
Human brains are characterised by considerable intersubject anatomical variability, which is of interest in both clinical practice and research. Computational morphometry of magnetic resonance images has emerged as the method of choice for studying macroscopic changes in brain structure. Magnetic resonance imaging not only allows the acquisition of images of the entire brain in vivo but also the tracking of changes over time using repeated measurements, while computational morphometry enables the automated analysis of subtle changes in brain structure. In this section, several voxel-based morphometric methods for the automated analysis of brain images are presented. In the first part, some basic principles and techniques are introduced, while deformation- and voxel-based morphometry are discussed in the second part.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38(1):95–113
Ashburner J, Friston KJ (2000) Voxel-based morphometry–the methods. Neuroimage 11(6 Pt 1):805–821
Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate—a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 57(1):289–300
Friston KJ, Holmes A, Poline JB, Price CJ, Frith CD (1996) Detecting activations in PET and fMRI: levels of inference and power. Neuroimage 4(3 Pt 1):223–235
Gaser C (2005) Morphometrie. In: Walter H (ed) Funktionelle Bildgebung in Psychiatrie und Psychotherapie. Schattauer Verlag, Stuttgart, pp 89–104
Gaser C, Volz HP, Kiebel S, Riehemann S, Sauer H (1999) Detecting structural changes in whole brain based on nonlinear deformations-application to schizophrenia research. Neuroimage 10(2):107–113
Gaser C, Nenadic I, Buchsbaum BR, Hazlett EA, Buchsbaum MS (2001) Deformation-based morphometry and its relation to conventional volumetry of brain lateral ventricles in MRI. Neuroimage 13(6 Pt 1):1140–1145
Mietchen D, Gaser C (2009) Computational morphometry for detecting changes in brain structure due to development, aging, learning, disease and evolution. Front Neuroinform 3:25. doi:10.3389/neuro.11.025.2009
Nichols T, Hayasaka S (2003) Controlling the familywise error rate in functional neuroimaging: a comparative review. Stat Methods Med Res 12(5):419–446
Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15(1):1–25
Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315–337
Smith SM, Nichols TE (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44(1):83–98
Takao H, Abe O, Ohtomo K (2010) Computational analysis of cerebral cortex. Neuroradiology 52(8):691–698
Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system—an approach to cerebral imaging. Thieme, New York
Thompson PM, MacDonald D, Mega MS, Holmes CJ, Evans AC, Toga AW (1997) Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces. J Comput Assist Tomogr 21(4):567–581
Toga AW (ed) (1999) Brain warping. Academic Press, San Diego
Tohka J, Zijdenbos A, Evans A (2004) Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 23(1):84–97
Wilke M, Holland SK, Altaye M, Gaser C (2008) Template-O-matic: a toolbox for creating customized pediatric templates. Neuroimage 41(3):903–913
Worsley KJ, Marrett S, Neelin P, Vandal AC, Friston KJ, Evans AC (1996) A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapp 4(1):58–73
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Gaser, C. (2016). Structural MRI: Morphometry. In: Reuter, M., Montag, C. (eds) Neuroeconomics. Studies in Neuroscience, Psychology and Behavioral Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35923-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-35923-1_21
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35922-4
Online ISBN: 978-3-642-35923-1
eBook Packages: Behavioral Science and PsychologyBehavioral Science and Psychology (R0)