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Structural MRI: Morphometry

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Neuroeconomics

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.

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References

  • Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38(1):95–113

    Article  PubMed  Google Scholar 

  • Ashburner J, Friston KJ (2000) Voxel-based morphometry–the methods. Neuroimage 11(6 Pt 1):805–821

    Article  PubMed  Google Scholar 

  • Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851

    Article  PubMed  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • Gaser C (2005) Morphometrie. In: Walter H (ed) Funktionelle Bildgebung in Psychiatrie und Psychotherapie. Schattauer Verlag, Stuttgart, pp 89–104

    Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • 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

    Article  PubMed  PubMed Central  Google Scholar 

  • Nichols T, Hayasaka S (2003) Controlling the familywise error rate in functional neuroimaging: a comparative review. Stat Methods Med Res 12(5):419–446

    Article  PubMed  Google Scholar 

  • Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15(1):1–25

    Article  PubMed  Google Scholar 

  • Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315–337

    Article  PubMed  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • Takao H, Abe O, Ohtomo K (2010) Computational analysis of cerebral cortex. Neuroradiology 52(8):691–698

    Article  PubMed  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • Toga AW (ed) (1999) Brain warping. Academic Press, San Diego

    Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • Wilke M, Holland SK, Altaye M, Gaser C (2008) Template-O-matic: a toolbox for creating customized pediatric templates. Neuroimage 41(3):903–913

    Article  PubMed  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

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Correspondence to Christian Gaser .

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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

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