| Abstrakt: |
Background: Effective connectivity, the causal influence that functional activity in a source brain location exerts over functional activity in a target brain location, has the potential to provide richer information about brain networks than functional connectivity, which only quantifies activity synchrony between regions. However, head‐to‐head comparisons of effective and functional connectivity from functional MRI data are rare, especially among diverse cognitively healthy middle aged and older adults. Method: 100 Bogalusa Heart Study participants performed a Stroop task during functional MRI (Table 1). Effective connectivity and functional connectivity among 24 regions of interest (ROIs) previously identified as activated by this task [1] were calculated using deep stacking networks [2] and Pearson correlation, respectively. Graph metrics were then calculated from directed graphs resulting from effective connectivity and undirected graphs resulting from functional connectivity. Linear regression models related graph metrics to demographic, cardiometabolic, and cognitive measures. Result: Figure 1 shows the group‐consensus effective and functional connectivity graphs. Systolic blood pressure (SBP) was associated with effective connectivity based characteristic path length (Pearson's r = 0.22, p‐value = 0.038) and modularity (r = 0.25, p‐value = 0.02) (Figure 2). Diastolic blood pressure (DBP) was associated with effective connectivity based degree, density, clustering coefficient, modularity, transitivity, characteristic path length, and global efficiency (|r| = 0.26 ‐ 0.35, maximum p‐value = 0.013) (Figure 2). Word reading and vocabulary scores from the Wide Range Achievement Test (WRAT) were associated with effective connectivity based degree, density, clustering coefficient, transitivity, characteristic path length, modularity, and global efficiency (|r| = 0.21 ‐ 0.27, maximum p‐value = 0.049). Logical Memory II Recognition scores associated with effective connectivity based assortativity (r = 0.24, p‐value = 0.025). Associations between functional connectivity based graph metrics and demographic, cardiometabolic, and cognitive measures were not significant. Conclusion: In a diverse, cognitively healthy, middle‐aged community sample, graph metrics derived from effective connectivity based directed graphs tracked more closely with recognized indicators of brain health than traditional functional connectivity based undirected graph metrics. References: [1] Sheu, L. K. et al. (2012). Psychophysiology. [2] Chuang, K.‐C. et al. (2021). Machine Learning in Clinical Neuroimaging. [ABSTRACT FROM AUTHOR] |
