Effect of hemodynamic variability on Granger causality analysis of fMRI.
Effect of hemodynamic variability on Granger causality analysis of fMRI.
复制标题
DOI：
10.1016/j.neuroimage.2009.11.060
复制
发表时间：
2010-09
期刊：
影响因子：
5.7
通讯作者：
Hu, Xiaoping
中科院分区：
文献类型：
--
作者：
Deshpande, Gopikrishna;Sathian, K.;Hu, Xiaoping
在这项工作中，我们调查的区域变异性的血流动力学反应的灵敏度的格兰杰因果关系（GC）分析的功能磁共振成像（fMRI）数据神经元的因果关系的影响。我们通过将标准的经典血流动力学反应函数（HRF）与从猕猴皮层获得的局部场电位（LFP）进行卷积来模拟fMRI数据，并操纵LFP之间的因果影响和神经元延迟，HRF之间的血流动力学延迟，信噪比（SNR）和采样周期（TR）以评估这些因素中的每一个对来自功能MRI的GC分析的神经元延迟的可检测性的影响。在我们的第一个双变量实现中，我们假设血流动力学延迟的最差情况处于其正常生理范围的经验上限，并且与神经元延迟的方向相反。我们发现，在没有HRF混淆的情况下，甚至可以从fMRI中推断出数十毫秒的神经元延迟。然而，在存在HRF延迟的情况下，这与神经元延迟相反，最小可检测的神经元延迟为数百毫秒。在我们的第二个多变量模拟中，我们通过使用四个时间序列的多变量网络更接近地模拟了真实的情况，并假设血流动力学和神经元延迟是未知的，并且从均匀随机分布中提取。从fMRI中检测正确的多变量网络的准确性远远高于偶然性，并且在更快的采样下高达90%。一般来说，在所有条件下，更快的采样和低测量噪声提高了GC分析的灵敏度的fMRI数据神经元的因果关系。
In this work, we investigated the effect of the regional variability of the hemodynamic response on the sensitivity of Granger causality (GC) analysis of functional magnetic resonance imaging (fMRI) data to neuronal causal influences. We simulated fMRI data by convolving a standard canonical hemodynamic response function (HRF) with local field potentials (LFPs) acquired from the macaque cortex and manipulated the causal influence and neuronal delays between the LFPs, the hemodynamic delays between the HRFs, the signal to noise ratio (SNR) and the sampling period (TR) in order to assess the effect of each of these factors on the detectability of the neuronal delays from GC analysis of fMRI. In our first bivariate implementation, we assumed the worst case scenario of the hemodynamic delay being at the empirical upper limit of its normal physiological range and opposing the direction of neuronal delay. We found that, in the absence of HRF confounds, even tens of milliseconds of neuronal delays can be inferred from fMRI. However, in the presence of HRF delays which opposed neuronal delays, the minimum detectable neuronal delay was hundreds of milliseconds. In our second multivariate simulation, we mimicked the real situation more closely by using a multivariate network of four time series and assumed the hemodynamic and neuronal delays to be unknown and drawn from a uniform random distribution. The resulting accuracy of detecting the correct multivariate network from fMRI was well above chance and was up to 90% with faster sampling. Generically, under all conditions, faster sampling and low measurement noise improved the sensitivity of GC analysis of fMRI data to neuronal causality.
登录
查看更多内容
影响因子：
1.8
作者：
Stilla R;Hanna R;Hu X;Mariola E;Deshpande G;Sathian K
通讯作者：
Sathian K
影响因子：
4
作者：
THEILER, J;EUBANK, S;FARMER, JD
通讯作者：
FARMER, JD
影响因子：
5.8
作者：
Frischknecht, PM;Imboden, DM
通讯作者：
Imboden, DM
影响因子：
2.4
作者：
Blinowska, KJ;Kus, R;Kaminski, M
通讯作者：
Kaminski, M
影响因子：
4.8
作者：
Noseworthy, MD;Alfonsi, J;Bells, S
通讯作者：
Bells, S