FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom; Department of Paediatrics, University of Oxford, Oxford, OX3 7JX, United Kingdom.
FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom; Institute of Cognitive Neuroscience, University College London, WC1N 3AZ, United Kingdom.
Neuroimage. 2018 Jun;173:540-550. doi: 10.1016/j.neuroimage.2018.01.053. Epub 2018 Feb 21.
Functional connectivity (FC) analyses of correlations of neural activity are used extensively in neuroimaging and electrophysiology to gain insights into neural interactions. However, analyses assessing changes in correlation fail to distinguish effects produced by sources as different as changes in neural signal amplitudes or noise levels. This ambiguity substantially diminishes the value of FC for inferring system properties and clinical states. Network modelling approaches may avoid ambiguities, but require specific assumptions. We present an enhancement to FC analysis with improved specificity of inferences, minimal assumptions and no reduction in flexibility. The Additive Signal Change (ASC) approach characterizes FC changes into certain prevalent classes of signal change that involve the input of additional signal to existing activity. With FMRI data, the approach reveals a rich diversity of signal changes underlying measured changes in FC, suggesting that it could clarify our current understanding of FC changes in many contexts. The ASC method can also be used to disambiguate other measures of dependency, such as regression and coherence, providing a flexible tool for the analysis of neural data.
功能连接（FC）分析是神经影像学和电生理学中广泛用于深入了解神经相互作用的方法，用于分析神经活动相关性。然而，评估相关性变化的分析方法无法区分源产生的影响，例如神经信号幅度或噪声水平的变化。这种不明确性大大降低了 FC 用于推断系统特性和临床状态的价值。网络建模方法可以避免这种不明确性，但需要特定的假设。我们提出了一种 FC 分析的增强方法，具有更高的推断特异性、最小的假设和不降低灵活性。加性信号变化（ASC）方法将 FC 变化特征化为涉及现有活动中输入额外信号的某些常见信号变化类别。使用 fMRI 数据，该方法揭示了测量的 FC 变化背后丰富多样的信号变化，表明它可以澄清我们目前在许多情况下对 FC 变化的理解。ASC 方法还可用于消除其他依赖性度量（如回归和相干性）的歧义，为神经数据的分析提供了一种灵活的工具。
