Abstract
Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks of brain dynamics. These approaches integrate over space instead of averaging over time and thereby greatly reduce or remove the temporal averaging effects, windowing artifacts, and noise at fine spatial scales that have bedeviled the analysis of dynamical functional connectivity (FC). The dependences of FC on dynamics at various timescales, and on windowing, are clarified and the results are demonstrated on simple test cases, demonstrating how modes provide directly interpretable insights that can be related to brain structure and function. It is shown that FC is dynamic even when the brain structure and effective connectivity are fixed, and that the observed patterns of FC are dominated by relatively few eigenmodes. Common artifacts introduced by statistical analyses that do not incorporate the physical nature of the brain are discussed and it is shown that these are avoided by spectral analysis using eigenmodes. Unlike most published artificially discretized “resting state networks” and other statistically-derived patterns, eigenmodes overlap, with every mode extending across the whole brain and every region participating in every mode—just like the vibrations that give rise to notes of a musical instrument. Despite this, modes are independent and do not interact in the linear limit. It is argued that for many purposes the intrinsic limitations of covariance-based FC instead favor the alternative of tracking eigenmode coefficients vs. time, which provide a compact representation that is directly related to biophysical brain dynamics.
Highlights
Brain activity spans many decades of spatial and temporal scale and constantly changes due to stimuli and internally generated signals (Raichle, 2011)
We showed that even strong perturbations that do not satisfy selection rules analogous to Equations (57, 58) do not significantly affect eigenmodes
We argue that these spatial modes can only be very slightly perturbed by short-term brain state changes because they are strongly pinned by overall brain curvature and any other permanent spatial parameter variations that are present, as seen in Figure 8, and because the selection rules (Equations 57, 58) prevent significant perturbations by local effects; so amplitude changes of activity in these modes are expected to dominate functional connectivity (FC) dynamics
Summary
Brain activity spans many decades of spatial and temporal scale and constantly changes due to stimuli and internally generated signals (Raichle, 2011) Some of these changes are due to changed activity (e.g., evoked by stimuli) in the pre-existing brain structure, while others are due to changes in this structure that alter the activity—i.e., changes in neural connections and their strengths (Bassett et al, 2011, 2017; Deco et al, 2011; Raichle, 2011; Hutchison et al, 2013a,b; Calhoun et al, 2014; Kopell et al, 2014; Preti et al, 2017; Babaie-Janvier and Robinson, 2019, 2020). In the intermediate range of roughly 5–100 s, processes such as plasticity certainly exist that can change functional brain connectivity, but their role is less understood
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