Graphical neuroimaging informatics: Application to Alzheimer’s disease

被引:0
作者
John Darrell Van Horn
Ian Bowman
Shantanu H. Joshi
Vaughan Greer
机构
[1] University of Southern California,The Institute for Neuroimaging and Informatics, Keck School of Medicine
[2] UCLA School of Medicine,Department of Neurology
来源
Brain Imaging and Behavior | 2014年 / 8卷
关键词
Neuroimaging; Graphical informatics; Aging; Alzheimer’s Disease; GPU processing; OpenGL;
D O I
暂无
中图分类号
学科分类号
摘要
The Informatics Visualization for Neuroimaging (INVIZIAN) framework allows one to graphically display image and meta-data information from sizeable collections of neuroimaging data as a whole using a dynamic and compelling user interface. Users can fluidly interact with an entire collection of cortical surfaces using only their mouse. In addition, users can cluster and group brains according in multiple ways for subsequent comparison using graphical data mining tools. In this article, we illustrate the utility of INVIZIAN for simultaneous exploration and mining a large collection of extracted cortical surface data arising in clinical neuroimaging studies of patients with Alzheimer’s Disease, mild cognitive impairment, as well as healthy control subjects. Alzheimer’s Disease is particularly interesting due to the wide-spread effects on cortical architecture and alterations of volume in specific brain areas associated with memory. We demonstrate INVIZIAN’s ability to render multiple brain surfaces from multiple diagnostic groups of subjects, showcase the interactivity of the system, and showcase how INVIZIAN can be employed to generate hypotheses about the collection of data which would be suitable for direct access to the underlying raw data and subsequent formal statistical analysis. Specifically, we use INVIZIAN show how cortical thickness and hippocampal volume differences between group are evident even in the absence of more formal hypothesis testing. In the context of neurological diseases linked to brain aging such as AD, INVIZIAN provides a unique means for considering the entirety of whole brain datasets, look for interesting relationships among them, and thereby derive new ideas for further research and study.
引用
收藏
页码:300 / 310
页数:10
相关论文
共 99 条
  • [1] Berretta R(2010)Cancer biomarker discovery: the entropic hallmark PLoS ONE 5 e12262-4739
  • [2] Moscato P(2010)Toward discovery science of human brain function Proceedings of the National Academy of Sciences of the United States of America 107 4734-377
  • [3] Biswal BB(2003)A guide to building image-centric databases Neuroinformatics 1 359-990
  • [4] Mennes M(2005)Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment NeuroImage 27 979-1378
  • [5] Zuo XN(2010)Longitudinal changes in white matter disease and cognition in the first year of the Alzheimer disease neuroimaging initiative Archives of Neurology 67 1370-1721
  • [6] Bug W(2013)Computational identification of specific splicing regulatory elements from RNA-seq in lung cancer European Review for Medical and Pharmacological Sciences 17 1716-205
  • [7] Nissanov J(1994)Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space Journal of Computer Assisted Tomography 18 192-194
  • [8] Carmichael OT(1999)Cortical surface-based analysis. I. Segmentation and surface reconstruction NeuroImage 9 179-10
  • [9] Aizenstein HA(2010)Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline Frontiers in Neuroinformatics 3 1-207
  • [10] Davis SW(1999)Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system NeuroImage 9 195-401