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Computational Anatomy Going Beyond Brain Morphometry

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Computational Neuroscience

Part of the book series: Neuromethods ((NM,volume 199))

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Abstract

In this chapter, we provide a comprehensive historical overview of algorithms for feature extraction of structural magnetic resonance imaging data and the corresponding analytical frameworks. We then focus on the use of T1-weighted images, which still represent the working horse of surface- and voxel-based morphometry to elaborate on the complex relationships between magnetic resonance imaging contrast and underlying brain tissue properties. This critical point is the motivation for embarking on novel structural imaging protocols based on biophysical models, which are sensitive to tissue myelin, iron, and water content. We expand on the concept of voxel-based quantification to demonstrate the added value to existing methods using T1-weighted data—both in terms of robust brain tissue classification and of straightforward neurobiological interpretation of the obtained results. We do not stop short of considering the unresolved issues in voxel-based quantification currently implemented in the data processing and analysis framework of Statistical Parametric Mapping (SPM12). We conclude with an outlook to the future developments and perspectives in computational anatomy, particularly the need for integration of the available magnetic resonance imaging contrasts at the level of statistical analysis without hampering the interpretability of our findings.

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Acknowledgments

BD is supported by the Swiss National Science Foundation (grants 32003B_159780, CRSK-3_190185, 324730_192755, and the Sinergia VascX project), the EU ERA-NET iSee project, the SPHN SACR project, the CLIMACT project, the Leenaards Foundation, and the HeadFirst Innosuisse project. LREN is grateful to the ROGER DE SPOELBERCH and Partridge Foundations for their generous financial support.

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Authors and Affiliations

  1. LREN—Département des neurosciences cliniques, CHUV, Université de Lausanne, Lausanne, Switzerland

    Bogdan Draganski, Adeliya Latypova & Ferath Kherif

  2. Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

    Bogdan Draganski

  3. Department of Psychiatry and Medical Psychology, Research Institute Plovdiv, Medical University Plovdiv, Plovdiv, Bulgaria

    Rositsa Paunova

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  1. Bogdan Draganski
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  2. Rositsa Paunova
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  3. Adeliya Latypova
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  4. Ferath Kherif
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Correspondence to Bogdan Draganski .

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Editors and Affiliations

  1. Division of Translational Neuroscience, Medical University of Plovdiv, Plovdiv, Bulgaria

    Drozdstoy Stoyanov

  2. University of Lausanne, CHUV, Lausanne, Switzerland

    Bogdan Draganski

  3. Pathophysiology and Transplantation, University of Milan, Milan, Italy

    Paolo Brambilla

  4. Cognitive & Affective Neuroscience Unit, University of Vienna, Vienna, Austria

    Claus Lamm

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Draganski, B., Paunova, R., Latypova, A., Kherif, F. (2023). Computational Anatomy Going Beyond Brain Morphometry. In: Stoyanov, D., Draganski, B., Brambilla, P., Lamm, C. (eds) Computational Neuroscience. Neuromethods, vol 199. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3230-7_8

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  • DOI: https://doi.org/10.1007/978-1-0716-3230-7_8

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