Maggioni Eleonora, Tana Maria Gabriella, Arrigoni Filippo, Zucca Claudio, Bianchi Anna Maria
Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, P.za Leonardo da Vinci, 32, 20133 Milan, Italy; Scientific Institute IRCCS E.Medea, Via Don Luigi Monza 20, 23842 Bosisio Parini, Lecco, Italy.
Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, P.za Leonardo da Vinci, 32, 20133 Milan, Italy; BIND - Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio", Chieti, Italy; Department of Medicine and Aging Science, University "G. d'Annunzio", Chieti, Italy.
J Neurosci Methods. 2014 May 15;228:86-99. doi: 10.1016/j.jneumeth.2014.03.004. Epub 2014 Mar 25.
Functional Magnetic Resonance Imaging (fMRI) is used for exploring brain functionality, and recently it was applied for mapping the brain connection patterns. To give a meaningful neurobiological interpretation to the connectivity network, it is fundamental to properly define the network framework. In particular, the choice of the network nodes may affect the final connectivity results and the consequent interpretation.
We introduce a novel method for the intra subject topological characterization of the nodes of fMRI brain networks, based on a whole brain parcellation scheme. The proposed whole brain parcellation algorithm divides the brain into clusters that are homogeneous from the anatomical and functional point of view, each of which constitutes a node. The functional parcellation described is based on the Tononi's cluster index, which measures instantaneous correlation in terms of intrinsic and extrinsic statistical dependencies.
The method performance and reliability were first tested on simulated data, then on a real fMRI dataset acquired on healthy subjects during visual stimulation. Finally, the proposed algorithm was applied to epileptic patients' fMRI data recorded during seizures, to verify its usefulness as preparatory step for effective connectivity analysis. For each patient, the nodes of the network involved in ictal activity were defined according to the proposed parcellation scheme and Granger Causality Analysis (GCA) was applied to infer effective connectivity.
We showed that the algorithm 1) performed well on simulated data, 2) was able to produce reliable inter subjects results and 3) led to a detailed definition of the effective connectivity pattern.
功能磁共振成像（fMRI）用于探索大脑功能，最近它被应用于绘制大脑连接模式。为了对连接网络给出有意义的神经生物学解释，正确定义网络框架至关重要。特别是，网络节点的选择可能会影响最终的连接结果以及随之而来的解释。
我们基于全脑分割方案，介绍了一种用于fMRI脑网络节点的受试者内拓扑特征描述的新方法。所提出的全脑分割算法将大脑划分为从解剖学和功能角度来看是同质的簇，每个簇构成一个节点。所描述的功能分割基于托诺尼的簇指数，该指数根据内在和外在统计依赖性来测量瞬时相关性。
该方法的性能和可靠性首先在模拟数据上进行测试，然后在健康受试者在视觉刺激期间获取的真实fMRI数据集上进行测试。最后，将所提出的算法应用于癫痫患者发作期间记录的fMRI数据，以验证其作为有效连接分析准备步骤的有用性。对于每个患者，根据所提出的分割方案定义参与发作期活动的网络节点，并应用格兰杰因果分析（GCA）来推断有效连接。
我们表明该算法1）在模拟数据上表现良好，2）能够产生可靠的受试者间结果，3）导致有效连接模式的详细定义。
