Assessment of the Role of Connection Length on Methodological Variance in Dynamic Functional Connections

Abstract

Dynamic functional connectivity (dFC) is the study of changes in the brain’s functional organization over time. dFC has been a growing field of research due to its importance in understanding cognitive processes and its potential applications as a biomarker for neurodegenerative diseases. However, the choice of dFC assessment methodology has been found to significantly impact dFC results, putting into question the reliability of the findings using these methods. Considering recent studies revealing the impact of structural connectivity on functional connectivity, we speculated that connection length, as a structural aspect, may indirectly influence dFC magnitudes and variability. We examined the impact that connection length had on dFC variability across methods. Furthermore, these connections were inspected according to whether they are intra- or inter- brain networks (i.e., the connection is between two regions that belong to the same or different brain network). We conducted our analysis in Python using resting-state functional MRI data of 395 subjects taken from the Human Connectome Project and evaluated them using seven well-known dFC assessment methodologies. The study revealed that longer connections lead to greater variation in dFC over methods for both intra- and inter-network connections. Interestingly, short inter-network connections show increased dFC variance across methods. Current limitations of this study include using Euclidean distance as a measure of connection length and assuming functional connections are independent in parametric statistical analyses. Our investigation is a step toward understanding the factors influencing the observed inconsistency in dFC pattern estimation obtained from different methodologies.