Tracking whole-brain connectivity dynamics in the resting state.

By Elena A. Allen, Eswar Damaraju, Sergey M. Plis, Erik B. Erhardt, Tom Eichele, Vince D. Calhoun. Cerebral Cortex, 2014

447 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/8ca5820081f8c3a85b8125148124238d6d7d8c77

Citations in the corpus

• Evaluating Dynamic Bivariate Correlations in Resting-state fMRI: A comparison study and a new approach (2014, unknown): “Recently, there has been an increased interest in attempting to quantify the dynamic changes in FC during the course of an fMRI experiment (e.g., Allen et al., 2012; Chang and Glover, 2010; Cribben et al., 2012; Handwerker et al., 2012; Hutchison et al., 2013; Jones et al., 2012; Kiviniemi et al.,…”
• Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, unknown): “After the SWC is performed pairwise for the brain areas of interest, clustering is often used to find the number of ‘states’ that occur over the length of the scan, and the times at which transitions occur (Hutchison et al., 2013a, 2013b; Allen et al., 2014; Damaraju et al., 2014; Shakil et al., 201”
• Discovering dynamic brain networks from big data in rest and task (2017, unknown): “The most common analysis technique used for describing brain network dynamics in both task and rest is the use of sliding windows [3,4].”
• Dynamic functional connectivity: Promise, issues, and interpretations (2013, unknown): “Both of these studies, as well as Allen et al. (2013), also observed that increases in the power of slower EEG oscillations showed the opposite behavior, correlating with increases in FC. Tagliazzucchi et al. additionally reported a correlation between increased gamma-band power and increased FC,…”
• Multi-scale brain networks (2016, unknown): “These fluctuations in network organization, especially over short timescales (< that of a single scan session), have become frequent topics of investigation (Bassett et al., 2011b; Allen et al., 2012; Bassett et al., 2015b; Zalesky et al., 2014; Betzel et al., 2016b).”
• Spectrally resolved fast transient brain states in electrophysiological data (2016, unknown): “Published by Elsevier Inc a b s t r a c t a r t i c l e i n f o”
• The Virtual Epileptic Patient: Individualized whole‐brain models of epilepsy spread (2017, unknown): “In fact, most meaningful situations and tasks in neuroscience pose themselves as non-stationary processes including the resting state, as well as cognitive and motor behaviors (Allen et al., 2012; Hansen et al., 2014).”
• Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, unknown): “One straightforward approach for evaluating dynamics in fMRI data is by evaluating the FNC correlations in either task or rest fMRI over time via a sliding windowed approach [86], [89] (called dynamic FNC).”
• The graphical brain: Belief propagation and active inference (2017, unknown): “We focus on the modulation of intrinsic excitability; specifically, the afferents to superficial pyramidal cells—and, as a more abstract level, the implications for self-organized criticality of the sort entailed by dynamic fluctuations in connectivity (Aertsen, Gerstein, Habib, & Palm, 1989; Allen ”