Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher’s Choice Paths

Abstract

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple preprocessing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.

Intrinsic functional brain connectivity changes following aerobic exercise, computerized cognitive training, and their combination in physically inactive healthy late-middle-aged adults: the Projecte Moviment

“…In our study, the construction of a static functional connectivity network (sFCN) incorporates the wavelet decomposition of voxel-based time series and a distance correlation metric to quantify the multiplexity between two brain areas. We performed a wavelet decomposition on every voxel-based time series within every ROI by adopting the maximal overlap discrete wavelet transform (MODWT) selecting the Daubechies family implemented with a wavelet length equal to 6 [ 52 ]. Wavelet coefficients were extracted for the first four wavelet scales for every voxel-based time series which were further averaged to produce 4 frequency-dependent regional time series.…”

“…1 Wavelet decomposition of voxel-based time series and a multiplex coupling strength index. (A, B) A pair set of voxel-based time series (blue) was decomposed with the maximal overlap discrete wavelet transform (MODWT) in four frequency bands by adopting the Daubechies family implemented with a wavelet length equal to 6 [ 52 ]. The four wavelet scales, which correspond to the frequency ranges 0.125∼0.25 Hz (scale 1), 0.06∼0.125 Hz (scale 2), 0.03∼0.06 Hz (scale 3), and 0.015∼0.03 Hz (scale 4) [ 179 ].We then averaged the voxel-based time series producing four representative time series per frequency scale and per ROI (red).…”

Intrinsic functional brain connectivity changes following aerobic exercise, computerized cognitive training and their combination in healthy late-middle-aged adults: the Projecte Moviment

“…In our study, the construction of a static functional connectivity network (sFCN) incorporates the wavelet decomposition of voxel-based time series and a distance correlation metric to quantify the multiplexity between two brain areas. We performed a wavelet decomposition on every voxel-based time series within every ROI by adopting the maximal overlap discrete wavelet transform (MODWT) (Dimitriadis, 2022). Wavelet coefficients were extracted for the first four wavelet scales for every voxel-based time series which were further averaged to produce 4 frequency-dependent regional time series.…”