皮质电图
计算机科学
人口
认知
人工智能
混合模型
机器学习
模式识别(心理学)
心理学
神经科学
脑电图
医学
环境卫生
作者
Cihan Mehmet Kadipasaoglu,Vatche G. Baboyan,Christopher R. Conner,G. Chen,Ziad S. Saad,Nitin Tandon
出处
期刊:NeuroImage
[Elsevier]
日期:2014-11-01
卷期号:101: 215-224
被引量:46
标识
DOI:10.1016/j.neuroimage.2014.07.006
摘要
Electrocorticography (ECoG) in humans yields data with unmatched spatio-temporal resolution that provides novel insights into cognitive operations. However, the broader application of ECoG has been confounded by difficulties in accurately depicting individual data and performing statistically valid population-level analyses. To overcome these limitations, we developed methods for accurately registering ECoG data to individual cortical topology. We integrated this technique with surface-based co-registration and a mixed-effects multilevel analysis (MEMA) to control for variable cortical surface anatomy and sparse coverage across patients, as well as intra- and inter-subject variability. We applied this surface-based MEMA (SB-MEMA) technique to a face-recognition task dataset (n = 22). Compared against existing techniques, SB-MEMA yielded results much more consistent with individual data and with meta-analyses of face-specific activation studies. We anticipate that SB-MEMA will greatly expand the role of ECoG in studies of human cognition, and will enable the generation of population-level brain activity maps and accurate multimodal comparisons.
科研通智能强力驱动
Strongly Powered by AbleSci AI