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Review ARTICLE

This article is part of a Research Topic

Computational morphometry for detecting changes in brain structure due to development, aging, learning, disease and evolution

Structural Brain Mapping Group, Department of Psychiatry, University of Jena, D – 07743 Jena, Germany

The brain, like any living tissue, is constantly changing in response to genetic and environmental cues and their interaction, leading to changes in brain function and structure, many of which are now in reach of neuroimaging techniques. Computational morphometry on the basis of Magnetic Resonance (MR) images has become the method of choice for studying macroscopic changes of brain structure across time scales. Thanks to computational advances and sophisticated study designs, both the minimal extent of change necessary for detection and, consequently, the minimal periods over which such changes can be detected have been reduced considerably during the last few years. On the other hand, the growing availability of MR images of more and more diverse brain populations also allows more detailed inferences about brain changes that occur over larger time scales, way beyond the duration of an average research project. On this basis, a whole range of issues concerning the structures and functions of the brain are now becoming addressable, thereby providing ample challenges and opportunities for further contributions from neuroinformatics to our understanding of the brain and how it changes over a lifetime and in the course of evolution.

Keywords:

brain morphometry, MRI, development, aging, learning, brain disease, evolution, gyrification

Citation:

Mietchen D and 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

Received:

30 April 2009;

Paper pending published:

18 May 2009;

Accepted:

09 July 2009;

Published online:

11 August 2009.

Edited by:

Jussi Tohka, Tampere University of Technology, Finland

Reviewed by:

Jason Lerch, Toronto Center for Phenogenomics, Canada
Moo K. Chung, University of Wisconsin-Madison, USA

© 2009 Mietchen and Gaser. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

*Correspondence:

Christian Gaser, Department of Psychiatry, University of Jena, Jahnstr. 3, D – 07743 Jena, Germany. e-mail: christian.gaser@uni-jena.de