Signal processing methods for interaction analysis of functional brain imaging data

Unrestricted Modeling functional brain interaction networks using non-invasive EEG and MEG data is more challenging than using intracranial recordings data. This is because most interaction measures are not robust to cross-talk (interference) between cortical regions, which may arise due to the limited spatial resolution of EEG/MEG inverse procedures. We describe a modified beamforming approach to accurately measure cortical interactions from EEG/MEG data, designed to suppress cross-talk between cortical regions. We estimate interaction measures from the output of the modified beamformer and test for statistical significance using permutation tests. Since the underlying neuronal sources and their interactions are unknown in real MEG data, we demonstrate the performance of the proposed beamforming method in a novel simulation scheme, where intracranial recordings from a macaque monkey are used as neural sources to simulate realistic MEG signals. The advantage of this approach is that local field potentials are more realistic representations of true neuronal sources than simulation models and therefore are more suitable to evaluate the performance of our nulling beamforming method.; Intracranial recordings have minimal cross-talk and therefore interactions can be measured more reliably. However, performing group level studies is a challenging task because of the sparsity and variable coverage of electrodes on each subjects' brain. We describe a set of group analysis procedures for intracranial EEG recordings, which include registration of MRI volumes and cortical surfaces, and parcellation of anatomical regions of interest. We use a parametric probability model to test for equality of phase synchrony, and use Fisher's combined p-value method to pool test results from electrodes on individual subjects into the parcellated regions of interest. We apply our group analysis procedure to intracranial EEG data recorded in a working memory experiment and find an interaction network that is modulated by memory load.采用无创脑电图（electroencephalogram, EEG）与脑磁图（magnetoencephalography, MEG）数据进行无约束功能性脑交互网络建模，相较于使用颅内记录（intracranial recordings）数据，其难度更大。这是因为多数交互测度对皮层区域间的串扰（干扰）不具备鲁棒性，而这类串扰源于EEG/MEG逆问题求解流程有限的空间分辨率。本研究提出一种改进的波束形成（beamforming）方法，可从EEG/MEG数据中精准估算皮层交互，旨在抑制皮层区域间的串扰。我们从改进后的波束形成器的输出中估算交互测度，并采用置换检验进行显著性统计验证。由于真实MEG数据中潜在的神经元源及其交互关系未知，本研究在一种全新的模拟方案中验证所提波束形成方法的性能：该方案以猕猴的颅内记录数据作为神经源，用以模拟符合真实生理特性的MEG信号。该方案的优势在于，局部场电位（local field potentials, LFP）相较于传统模拟模型，能更真实地反映真实神经元源的活动，因此更适合用于评估我们所提出的消串扰波束形成方法的性能；颅内记录的串扰极小，因此交互关系的测量结果更可靠。但开展组水平研究却颇具挑战：由于每名受试者脑内电极的分布稀疏且覆盖范围不均一。本研究提出一套针对颅内脑电图（intracranial EEG, iEEG）记录数据的组分析流程，涵盖磁共振成像（Magnetic Resonance Imaging, MRI）体积与皮层表面配准、以及感兴趣解剖区域的分区划分。我们采用参数化概率模型检验相位同步性的组间一致性，并运用费希尔组合p值法，将每名受试者各电极的检验结果合并至分区后的感兴趣区域。我们将该组分析流程应用于一项工作记忆实验中记录的颅内脑电图数据，发现了一组受记忆负荷调控的交互网络。