Scale-resolved analysis of brain functional connectivity networks with spectral entropy

Abstract

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between an empirical functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure.We apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks.

Increased network centrality of the anterior insula in early abstinence from alcohol

“…The heterogeneous nature of the connectivity changes in AUD patients would have likely gone unnoticed by common functional connectivity analysis. Here, we took advantage of important methodological advances in graph‐based analyses of functional connectivity, finer network representation (>600 nodes), improved resolution 20 and lower risk of motion‐related biases between different experimental groups 24,31 …”

“…Comparisons of motion parameters DVARS and FD for the experimental groups are reported in the Supporting Information (Tables S4 and S5, Figures S1 and S2). Sparsification of the resulting networks was also applied to further control potential residual in‐scanner motion (see below) 31 . Structural MR images were also acquired and analyzed within a voxel‐based morphometry (VBM) framework to assess potential differences in grey matter density and structure.…”

The thresholding problem and variability in the EEG graph network parameters

“…However, it is still important to provide a basis under the used threshold level. Recently, an approach to choose the thresholding value according to the entropy of the functional networks has been suggested (Nicolini et al, 2020), however, its validity and reliability are yet to be established.…”

The Structural Information Potential and its Application to Document Triage

“…Understanding the properties of these analytical expressions so as to be able to thoroughly explain their effects on empirical data remains an active research field [55]. This is especially critical for the analysis of biomedical data, a major application domain of spectral entropy [56]- [58], where it is used in particular for the clinical interpretation of EEG signals (e.g., for monitoring depth of patient sedation during anesthesia [59]). Audio signals analysis (e.g., urban soundscape classification [60], dolphin whistle segmentation [61], abnormal milling sounds detection [62]) and speech analysis (e.g., speaker identification [63], noise quality assessment [64]) are other common application domains.…”