Abstract
Static and dynamic functional network connectivity (FNC) are typically studied separately, which makes us unable to see the full spectrum of connectivity in each analysis. Here, we propose an approach called filter-banked connectivity (FBC) to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach.First, we demonstrate that FBC can estimate connectivity across multiple frequencies missed by a sliding-window approach. Next, we use FBC to estimate FNC in a resting-state fMRI dataset including schizophrenia patients (SZ) and typical controls (TC). The FBC results are clustered into different network states. Some states showed weak low-frequency strength and as such were not captured in the window-based approach. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies compared with TCs who spent more time in lower frequency states. Finally, we show that FBC enables us to analyze static and dynamic connectivity in a unified way. In summary, FBC offers a novel way to unify static and dynamic connectivity analyses and can provide additional information about the frequency profile of connectivity patterns.
Highlights
Functional connectivity and its cross-network analog, functional network connectivity (FNC), have been the focus of many neuroimaging studies over the past few decades
The second and third rows demonstrate the mean and standard deviation of the centroids calculated from the toy example data, while the sixth and seventh rows demonstrate the same measures for sliding window is paired with Pearson correlation (SWPC)
Our proposed approach does not make any strong assumptions about connectivity frequency and performs the frequency tiling in the connectivity domain
Summary
A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banksAshkan Faghiri 1,2, Armin Iraji 1, Eswar Damaraju, Jessica Turner, and Vince D. A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks. FBC offers a novel way to unify static and dynamic connectivity analyses and can provide additional information about the frequency profile of connectivity patterns. A statistical relationship that is calculated using the whole length of the time series; that is, we have one single value for each connectivity pair. Functional connectivity: Statistical relationship between two or more time series, each belonging to a different brain region. Where 2Δ + 1 is window size and μx(t) and σx(t) are windowed sample mean and windowed standard deviation (for time series x), respectively Their definitions are as follows: μx(t) 1 2Δ + 1 x(i)
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