Reconstructing fMRI BOLD signals arising from cerebellar granule neurons - comparing GLM and balloon models

Chaitanya Medini, G. Naldi, B. Nair, E. D’Angelo, S. Sunitha Diwakar
{"title":"Reconstructing fMRI BOLD signals arising from cerebellar granule neurons - comparing GLM and balloon models","authors":"Chaitanya Medini, G. Naldi, B. Nair, E. D’Angelo, S. Sunitha Diwakar","doi":"10.1109/IJCNN.2015.7280638","DOIUrl":null,"url":null,"abstract":"Understanding the relationship between fMRI BOLD and underlying neuronal activity has been crucial to connect circuit behavior to cognitive functions. In this paper, we modeled fMRI BOLD reconstructions with general linear model and balloon modeling using biophysical models of rat cerebellum granular layer and stimuli spike trains of various response times. Linear convolution of the hemodynamic response function with the known spiking information reconstructed activity similar to experimental BOLD-like signals with the limitation of short stimuli trains. Balloon model through Volterra kernels gave seemingly similar results to that of general linear model. Our main goal in this study was to understand the activity role of densely populated clusters through BOLD-like reconstructions given neuronal responses and by varying response times for the whole stimulus duration.","PeriodicalId":6539,"journal":{"name":"2015 International Joint Conference on Neural Networks (IJCNN)","volume":"24 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Joint Conference on Neural Networks (IJCNN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2015.7280638","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Understanding the relationship between fMRI BOLD and underlying neuronal activity has been crucial to connect circuit behavior to cognitive functions. In this paper, we modeled fMRI BOLD reconstructions with general linear model and balloon modeling using biophysical models of rat cerebellum granular layer and stimuli spike trains of various response times. Linear convolution of the hemodynamic response function with the known spiking information reconstructed activity similar to experimental BOLD-like signals with the limitation of short stimuli trains. Balloon model through Volterra kernels gave seemingly similar results to that of general linear model. Our main goal in this study was to understand the activity role of densely populated clusters through BOLD-like reconstructions given neuronal responses and by varying response times for the whole stimulus duration.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
重建小脑颗粒神经元产生的fMRI BOLD信号——比较GLM和球囊模型
了解fMRI BOLD与潜在神经元活动之间的关系对于将回路行为与认知功能联系起来至关重要。在本文中,我们利用大鼠小脑颗粒层的生物物理模型和不同反应时间的刺激峰列,用一般线性模型和球囊模型对fMRI BOLD重建进行了建模。血液动力学响应函数与已知尖峰信息的线性卷积重建了类似于实验类bold信号的活动,但受短刺激序列的限制。通过Volterra核的气球模型得到了与一般线性模型相似的结果。在这项研究中,我们的主要目标是通过在整个刺激持续时间内对神经元的反应和不同的反应时间进行类似bold的重建,来了解密集群集的活动作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Efficient conformal regressors using bagged neural nets Repeated play of the SVM game as a means of adaptive classification Unit commitment considering multiple charging and discharging scenarios of plug-in electric vehicles High-dimensional function approximation using local linear embedding A label compression coding approach through maximizing dependence between features and labels for multi-label classification
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1