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/lp/ou_press/book-review-stereotactic-brain-microanatomy-mathematical-principles-nNROB1VGH7
- Publisher
- Oxford University Press
- Copyright
- Copyright © 2018 by the Congress of Neurological Surgeons
- ISSN
- 0148-396X
- eISSN
- 1524-4040
- DOI
- 10.1093/neuros/nyy078
- Publisher site
- See Article on Publisher Site
Abstract
With advances in neuroimaging and intraoperative registration, stereotactic localization has developed into a routine tool for electrode placement, lesioning, tissue biopsy, endoscopy, and other minimally invasive techniques for access. Every neurosurgeon is well familiar with the standard axial, coronal, and sagittal projections, but what may not be as familiar is the early history of describing neuroanatomy and navigating stereotactically. This book is intended for neuroanatomists and functional neurosurgeons interested in a review of basic stereotactic coordinate definitions and the history of localization methods. Author Ioannis Mavridis has published extensively on what he calls “Mavridis’ area,” a part of the nucleus accumbens which he claims is at a reliable absolute stereotactic location across the population. The author starts with a basic introduction to Cartesian coordinates and the history of where to place the origin. It touches on the development of historic atlases by Schaltenbrand, Wahren, Talairach, Tournoux, and Jürgen over the last century. Because of their relatively invariant positions, the anterior and posterior commissures were chosen to help define the origin of most standard coordinate systems. With these refinements in atlas descriptions, the author briefly reviews the parallel efforts by Dandy, Leksell, and others to develop accurate tools for localization and access. After this brief history, the book goes on to review some basic geometric concepts such as the definition of lines and planes in Cartesian 3-dimensional space, calculating distance, and formulas for converting to other coordinate systems (eg polar). A significant portion of the text is spent on these basic geometric concepts. The author repeatedly returns to the utility of these definitions in localizing the nucleus accumbens and subthalamic nucleus, and concludes with a chapter dedicated to defining “Mavridis’ area” and its utility for reliable electrode targeting. This is a relatively compact and short text coming in at a little over 100 pages with roughly 30 figures illustrating coordinate system concepts. The most interesting figures are color photographs of human brain dissections with overlaid subcortical landmarks. One important factor the book did not explore is the variability in neuroanatomy. It is well established that neurodegenerative disorders and healthy aging are associated with structural changes in the brain affecting the volume, shape, and relative position of structures throughout the brain.1 Advances in genetics have suggested specific genes to explain this variance.2 The inherent variability and time consuming nature of manually tracing out the intricate anatomy has driven the development of automated methods to parcellate the whole brain.3,4 Many algorithms have been developed for registering a patient's scan to a reference atlas for automatic parcellation.5 Studies have demonstrated that these automatic methods can outperform manual targeting by the surgeon, although it is unclear if this is enough to make a clinical difference.6 Diffusion and functional imaging has given us further structural and functional connectivity to increase our understanding and guide treatment, and the use of these additional modalities has improved outcomes in deep brain stimulation.7 With each passing year, these tools become more robust, reliable, and see increased utilization in practice. View largeDownload slide View largeDownload slide Overall, I do not recommend this book for the practicing neurosurgeon. Its main utility would be its review of history. Nowadays preoperative targeting and intraoperative registration have become quite simple, and we expect that our tools will only improve in reliability, accuracy, and ease of use. To those interested in the topic, I would recommend the methods and studies referenced below as a starting point. Disclosure The author has no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article. REFERENCES 1. Jernigan TL , Archibald SL , Berhow MT , Sowell ER , Foster DS , Hesselink JR . Cerebral structure on MRI, part I: localization of age-related changes . Biol Psychiatry . 1991 ; 29 ( 1 ): 55 - 67 . Google Scholar CrossRef Search ADS PubMed 2. Hibar DP , Stein JL , Renteria ME et al. Common genetic variants influence human subcortical brain structures . Nature . 2015 ; 520 ( 7546 ): 224 - 229 . Google Scholar CrossRef Search ADS PubMed 3. Fischl B , Salat DH , Busa E et al. Whole brain segmentation . Neuron . 2002 ; 33 ( 3 ): 341 - 355 . Google Scholar CrossRef Search ADS PubMed 4. Patenaude B , Smith SM , Kennedy DN , Jenkinson M . A Bayesian model of shape and appearance for subcortical brain segmentation . Neuroimage . 2011 ; 56 ( 3 ): 907 - 922 . Google Scholar CrossRef Search ADS PubMed 5. Klein A , Andersson J , Ardekani BA et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration . Neuroimage . 2009 ; 46 ( 3 ): 786 - 802 . Google Scholar CrossRef Search ADS PubMed 6. Pallavaram S , D'Haese P-F , Lake W , Konrad PE , Dawant BM , Neimat JS . Fully automated targeting using nonrigid image registration matches accuracy and exceeds precision of best manual approaches to subthalamic deep brain stimulation targeting in Parkinson disease . Neurosurgery . 2015 ; 76 ( 6 ): 756 - 765 . Google Scholar CrossRef Search ADS PubMed 7. See AAQ , King NKK . Improving surgical outcome using diffusion tensor imaging techniques in deep brain stimulation . Front Surg . 2017 ; 4 : 54 . Google Scholar CrossRef Search ADS PubMed Copyright © 2018 by the Congress of Neurological Surgeons This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
Journal
Neurosurgery – Oxford University Press
Published: May 8, 2018
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