Reconfigurations within resonating communities of brain regions following TMS reveal different scales of processing

Abstract

16An overarching goal of neuroscience research is to understand how heterogeneous neuronal 17 ensembles cohere into networks of coordinated activity to support cognition. To investigate how local 18 activity harmonizes with global signals, we measured electroencephalography (EEG) while single

Understanding diaschisis models of attention dysfunction with rTMS

“…7 and follows procedures recommended in a review of community distillation of neural networks 86 . This procedure is also consistent with a previous investigation of single-pulse TMS network dynamics within band-specific intrinsic oscillatory activity 87 .…”

Intermittent brain network reconfigurations and the resistance to social media influence

“…Here, we use dynamic community detection (for review, Garcia et al, 2018) to distill connectivity patterns derived from phase-based statistical dependencies into communities, or clusters of electrodes and then estimated shifts in communities across time. We have previously extended this method to EEG on a limited number of channels (Garcia et al, 2020), but there are lingering questions on robustness of the method to varying number of channels, the cognitive aspects the chosen temporal window might capture, and the parameter search. Future studies may explore other techniques to capture the processes underlying opinion formation, change, and generally complex decision making.…”

“…We swept the parameter space from .5-4 for each parameter, subject, and segment and compared the mean estimated modularity value Q to a shuffled null dataset. We chose a parameter set that on average produced more than 1 community and was the highest difference in modularity from the estimated modularity from the shuffled null dataset (see Garcia et al, 2020; Garcia et al, 2020 for a similar procedure). This resulted in ɣ = 1.1364 and ⍵ = 0.5.…”

“…First, when considering functional connectivity, EEG has been shown to be sensitive to a variety of task effects (e.g., Nidal & Malik, 2014) but it is also well established that its feature capture more rapid changes in the brain (Garcia et al, 2020) and even more stable effects as may be understood with disease models and genetics (e.g., Smit et al, 2008), spanning a large variety of temporal scales and a variety of behavior.…”

“…For example, the rate at which networks within the brain rapidly reconfigure to support cognition has been found to be highly predictive of a variety of cognitive processes. Dynamic community detection, a technique used to distill complex connectivity patterns into time-varying labels of communities (i.e., clusters of nodes) has been successful in capturing variability in a variety of behaviors in fMRI studies primarily, but has also been used for understanding band-specific EEG connectivity patterns (e.g., Garcia et al, 2020). Here, we have investigated the rapid fluctuations in network connectivity while individuals are exposed to an interactive social media platform containing factual and fake content attempting to simulate the real-world experience of social media and relate this to changes in opinions after exposure to this content, as we hypothesize that the flexible dynamics within the brain may be associated with complex decision making behind opinion change.…”