Surface visualization of electromagnetic brain activity

被引:6
作者
Badea, A
Kostopoulos, GK
Ioannides, AA
机构
[1] Univ Patras, Sch Med, Dept Physiol, Rion 26500, Greece
[2] RIKEN, Brain Sci Inst, Lab Human Brain Dynam, Wako, Saitama 3510198, Japan
关键词
MRI; functional imaging; segmentation; visualization; methodology; sensory evoked potentials; BIOMAGNETIC INVERSE PROBLEM; SPINAL-CORD INJURY; CORTICAL SURFACE; ELECTRICAL-ACTIVITY; MEG; LOCALIZATION; RECONSTRUCTION; SEGMENTATION; CORTEX; SPACE;
D O I
10.1016/S0165-0270(03)00100-6
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Advances in hardware and software have made possible the reconstruction of brain activity from non-invasive electrophysiological measurements over a large part of the brain. The appreciation of the information content in the data is enhanced when relevant anatomical detail is also available for visualization. Different neuroscientific questions give rise to different requirements for optimal superposition of structure and function. Most available software deal with scalar measures of activity, especially hemodynamic changes. In contrast, the electrophysiological observables are generated by electrical activity, which depends on the synchrony of neuronal assemblies and the geometry of the local cortical surface. We describe methods for segmentation and visualization of spatio-temporal brain activity, which allow the interplay of geometry and scalar as well as vector properties of the current density directly in the representations. The utility of these methods is demonstrated through displays of tomographic reconstructions of early sensory processing in the somatosensory and visual modality extracted from magnetoencephalography (MEG) data. The activation course characteristic to a specific area could be observed as current density or statistical maps independently and/or contrasted to the activity in other areas or the whole brain. MEG and functional magnetic resonance imaging (fMRI) activations were simultaneously visualized. Integrating and visualizing complementary functional data into a single environment helps evaluating analysis and understanding structure/function relationships in normal and diseased brain. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码:137 / 147
页数:11
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