Quantifying the Variability in Resting-State Networks

Abstract

Recent precision functional mapping of individual human brains has shown that individual brain organization is qualitatively different from group average estimates and that individuals exhibit distinct brain network topologies. How this variability affects the connectivity within individual resting-state networks remains an open question. This is particularly important since certain resting-state networks such as the default mode network (DMN) and the fronto-parietal network (FPN) play an important role in the early detection of neurophysiological diseases like Alzheimer’s, Parkinson’s, and attention deficit hyperactivity disorder. Using different types of similarity measures including conditional mutual information, we show here that the backbone of the functional connectivity and the direct connectivity within both the DMN and the FPN does not vary significantly between healthy individuals for the AAL brain atlas. Weaker connections do vary however, having a particularly pronounced effect on the cross-connections between DMN and FPN. Our findings suggest that the link topology of single resting-state networks is quite robust if a fixed brain atlas is used and the recordings are sufficiently long—even if the whole brain network topology between different individuals is variable.

Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress

“…Crucially, this approach has also been extended to take into account one or more control variables through the so-called partial correlation ( Marrelec et al, 2006 ; Wang et al, 2016 ). The latter has been widely employed for the study of brain connectivity, where the coupling between two time series is often assessed removing indirect effects from other multiple series through the use of partial correlation matrices ( Marrelec et al, 2006 ; Oliver et al, 2019 ). More sophisticated analysis techniques have been proposed for the study of dynamic brain–heart and brain–body interactions, e.g., information-theoretic-based measures able to assess the information produced by each physiological system and transferred to the other connected systems starting from their output time series, which exploit, for example, Granger Causality or penalized regression (we refer the reader to Faes et al, 2014 ; Duggento et al, 2016 ; Greco et al, 2019 ; Zanetti et al, 2019a ; Antonacci et al, 2020 for further details) or different approaches like the one calculating the maximal information coefficient ( Valenza et al, 2016 ).…”

Prenatal Stress Dysregulates Resting-State Functional Connectivity and Sensory Motifs

“…4 for partial correlation matrices). To limit the number of spurious connections from the estimating process of partial correlation (Martin et al, 2017; Oliver et al, 2019), all partial correlations were tested for statistical significance (Epskamp and Fried, 2018). Then the strongest 80% of connections in each network were preserved to equalizes the network sizes.…”

Promises and pitfalls of Topological Data Analysis for brain connectivity analysis

“…Furthermore, to remove possible signal drift, time-series were linearly detrended and filtered by band-pass filter [0.009-0.08Hz]. See Kopal et al (2020) and Oliver et al (2019) for detailed prepocessing description. To extract the time series for further analysis, the brain’s spatial domain was divided into 90 non-overlapping regions of interest (ROIs) according to the AAL atlas; from each ROI we extract one BOLD time series by averaging the time series of all voxels in the ROI.…”