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首页> 外文期刊>Medical image analysis >A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
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A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

机译：一种从静止状态fMRI数据中恢复有效连通性大脑网络的盲反卷积方法

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A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. In a recent study it has been proposed that relevant information in resting-state fMRI can be obtained by inspecting the discrete events resulting in relatively large amplitude BOLD signal peaks. Following this idea, we consider resting fMRI as 'spontaneous event-related', we individuate point processes corresponding to signal fluctuations with a given signature, extract a region-specific HRF and use it in deconvolution, after following an alignment procedure. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions.
机译：我们可以从功能磁共振成像数据中获得对大脑功能的见解的一项重大改进，可以来自有效的连通性分析，该分析通过预测性动力学模型的参数推断出即使是较远的大脑区域之间的信息流。与受生物学启发的模型相反，格兰杰因果关系（GC）等一些技术纯粹是数据驱动的，并且依赖于统计预测和时间优先级。尽管该方法功能强大且广泛适用，但在应用于BOLD fMRI数据时可能会遇到两个主要限制：血液动力学响应函数（HRF）的混杂效应以及在短时间序列存在下对大量变量的调节。对于与任务相关的功能磁共振成像，可以通过使用显式外源输入对信号动力学建模来捕获神经种群动力学。另一方面，对于静止状态的功能磁共振成像，除非明确依赖某些先前的生理假设，否则缺少明确的输入将使这项任务更加困难。为了克服这些问题并允许采用更通用的方法，在此我们提出一种简单新颖的BOLD-fMRI信号盲去卷积技术。在最近的研究中，已经提出可以通过检查导致相对较大幅度的BOLD信号峰的离散事件来获得静止状态fMRI中的相关信息。遵循这个想法，我们认为静息功能磁共振成像是“自发事件相关的”，我们按照给定程序将具有给定特征的信号波动对应的点过程进行个体化，提取区域特定的HRF并将其用于反卷积。出现第二个限制时，由于数据过拟合，使用短数据和嘈杂数据进行完全多变量处理会导致计算问题。此外，在存在冗余的情况下会出现概念上的问题。因此，正如最近所提出的，我们在信息论的框架中将部分条件应用于变量的有限子集。结合这两种改进，我们比较了BOLD和反卷积BOLD级有效网络之间的差异，并得出了一些结论。

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