A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice.
A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice.
复制标题
DOI：
10.1093/cercor/bhab282
复制
发表时间：
2021-09
期刊：
影响因子：
3.7
通讯作者：
L. Brier;Xiaohui Zhang;A. Bice;Seana H Gaines;E. Landsness;Jin-Moo Lee;M. Anastasio;J. Culver-
中科院分区：
文献类型：
--
作者：
L. Brier;Xiaohui Zhang;A. Bice;Seana H Gaines;E. Landsness;Jin-Moo Lee;M. Anastasio;J. Culver-
自发脑活动的时间相关性分析（例如，皮尔逊的“功能连接”（FC）提供了对人类大脑功能组织的见解。然而，诸如此类的双变量分析技术通常易受混淆生理过程（例如，睡眠、迈耶波、呼吸、运动），这使得难以准确地映射健康和疾病中的连接，因为这些生理过程影响FC。相比之下，一个多变量的方法来估算个人神经网络的自发神经成像数据可能会影响我们的概念理解FC，并提供性能优势。因此，我们分析了Thy 1-GCaMP 6 f小鼠在清醒、睡眠、麻醉、低强度和高强度运动期间或光血栓性中风前后的神经钙成像数据。使用线性支持向量回归方法来确定用于整合来自剩余像素的信号的最佳权重，以准确地预测感兴趣区域（ROI）中的神经活动。每个ROI的所得权重图被解释为多变量功能连接（MFC），类似于解剖连接，并显示出比传统FC更稀疏的强聚焦正连接集。虽然数据的全球变化对标准相关FC分析有很大影响，但MFC映射方法大多不受影响。最后，与传统FC相比，MFC分析提供了更强大的中风后连接缺陷检测。
Temporal correlation analysis of spontaneous brain activity (e.g., Pearson "functional connectivity," FC) has provided insights into the functional organization of the human brain. However, bivariate analysis techniques such as this are often susceptible to confounding physiological processes (e.g., sleep, Mayer-waves, breathing, motion), which makes it difficult to accurately map connectivity in health and disease as these physiological processes affect FC. In contrast, a multivariate approach to imputing individual neural networks from spontaneous neuroimaging data could be influential to our conceptual understanding of FC and provide performance advantages. Therefore, we analyzed neural calcium imaging data from Thy1-GCaMP6f mice while either awake, asleep, anesthetized, during low and high bouts of motion, or before and after photothrombotic stroke. A linear support vector regression approach was used to determine the optimal weights for integrating the signals from the remaining pixels to accurately predict neural activity in a region of interest (ROI). The resultant weight maps for each ROI were interpreted as multivariate functional connectivity (MFC), resembled anatomical connectivity, and demonstrated a sparser set of strong focused positive connections than traditional FC. While global variations in data have large effects on standard correlation FC analysis, the MFC mapping methods were mostly impervious. Lastly, MFC analysis provided a more powerful connectivity deficit detection following stroke compared to traditional FC.