Title |
Computational anatomy for studying use-dependant brain plasticity
|
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Published in |
Frontiers in Human Neuroscience, June 2014
|
DOI | 10.3389/fnhum.2014.00380 |
Pubmed ID | |
Authors |
Bogdan Draganski, Ferath Kherif, Antoine Lutti |
Abstract |
In this article we provide a comprehensive literature review on the in vivo assessment of use-dependant brain structure changes in humans using magnetic resonance imaging (MRI) and computational anatomy. We highlight the recent findings in this field that allow the uncovering of the basic principles behind brain plasticity in light of the existing theoretical models at various scales of observation. Given the current lack of in-depth understanding of the neurobiological basis of brain structure changes we emphasize the necessity of a paradigm shift in the investigation and interpretation of use-dependent brain plasticity. Novel quantitative MRI acquisition techniques provide access to brain tissue microstructural properties (e.g., myelin, iron, and water content) in-vivo, thereby allowing unprecedented specific insights into the mechanisms underlying brain plasticity. These quantitative MRI techniques require novel methods for image processing and analysis of longitudinal data allowing for straightforward interpretation and causality inferences. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 20% |
United Kingdom | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 80% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 5 | 5% |
France | 1 | <1% |
Korea, Republic of | 1 | <1% |
Germany | 1 | <1% |
Hong Kong | 1 | <1% |
Italy | 1 | <1% |
Canada | 1 | <1% |
United Kingdom | 1 | <1% |
Unknown | 89 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 25 | 25% |
Researcher | 20 | 20% |
Student > Master | 12 | 12% |
Student > Doctoral Student | 8 | 8% |
Student > Bachelor | 7 | 7% |
Other | 16 | 16% |
Unknown | 13 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 25 | 25% |
Medicine and Dentistry | 17 | 17% |
Agricultural and Biological Sciences | 9 | 9% |
Psychology | 9 | 9% |
Engineering | 4 | 4% |
Other | 14 | 14% |
Unknown | 23 | 23% |