Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK.
Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK; Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK; ImmunoPsychiatry, GlaxoSmithKline Research and Development, Stevenage, UK.
Neuroimage. 2018 May 15;172:326-340. doi: 10.1016/j.neuroimage.2017.12.043. Epub 2017 Dec 20.
Functional connectomes are commonly analysed as sparse graphs, constructed by thresholding cross-correlations between regional neurophysiological signals. Thresholding generally retains the strongest edges (correlations), either by retaining edges surpassing a given absolute weight, or by constraining the edge density. The latter (more widely used) method risks inclusion of false positive edges at high edge densities and exclusion of true positive edges at low edge densities. Here we apply new wavelet-based methods, which enable construction of probabilistically-thresholded graphs controlled for type I error, to a dataset of resting-state fMRI scans of 56 patients with schizophrenia and 71 healthy controls. By thresholding connectomes to fixed edge-specific P value, we found that functional connectomes of patients with schizophrenia were more dysconnected than those of healthy controls, exhibiting a lower edge density and a higher number of (dis)connected components. Furthermore, many participants' connectomes could not be built up to the fixed edge densities commonly studied in the literature (∼5-30%), while controlling for type I error. Additionally, we showed that the topological randomisation previously reported in the schizophrenia literature is likely attributable to "non-significant" edges added when thresholding connectomes to fixed density based on correlation. Finally, by explicitly comparing connectomes thresholded by increasing P value and decreasing correlation, we showed that probabilistically thresholded connectomes show decreased randomness and increased consistency across participants. Our results have implications for future analysis of functional connectivity using graph theory, especially within datasets exhibiting heterogenous distributions of edge weights (correlations), between groups or across participants.
功能连接组通常被分析为稀疏图，通过对区域神经生理信号的互相关进行阈值处理来构建。阈值处理通常保留最强的边（相关性），要么保留超过给定绝对权重的边，要么限制边密度。后一种方法（更广泛使用）存在在高边密度时包含假阳性边和在低边密度时排除真阳性边的风险。在这里，我们应用了新的基于小波的方法，该方法可以构建控制第一类错误的概率阈值图，应用于 56 名精神分裂症患者和 71 名健康对照的静息态 fMRI 扫描数据集。通过将连接组阈值化为固定的边缘特定 P 值，我们发现精神分裂症患者的功能连接组比健康对照组更脱节，表现为更低的边缘密度和更高数量的（不）连接组件。此外，许多参与者的连接组无法达到文献中常见的固定边缘密度（约 5-30%），同时控制第一类错误。此外，我们表明，以前在精神分裂症文献中报道的拓扑随机化可能归因于在固定密度下基于相关性对连接组进行阈值处理时添加的“非显著”边缘。最后，通过显式比较按递增 P 值和递减相关性阈值化的连接组，我们表明概率阈值化的连接组在参与者之间表现出降低的随机性和增加的一致性。我们的结果对使用图论分析功能连接具有影响，特别是在边缘权重（相关性）分布不均的数据集、组间或参与者间。
