The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery

By Vince D. Calhoun, Robyn Miller, Godfrey D. Pearlson, T. Adalı. 2014

1,364 citations recorded by the source corpus.

Recent years have witnessed a rapid growth of interest in moving functional magnetic resonance imaging (fMRI) beyond simple scan-length averages and into approaches that capture time-varying properties of connectivity. In this Perspective we use the term “chronnectome” to describe metrics that allow a dynamic view of coupling. In the chronnectome, coupling refers to possibly time-varying levels of correlated or mutually informed activity between brain regions whose spatial properties may also be temporally evolving. We primarily focus on multivariate approaches developed in our group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component analysis. We also discuss the potential these approaches offer to improve characterization and understanding of brain function. There are a number of methodological directions that need to be developed further, but chronnectome approaches already show great promise for the study of both the healthy and the diseased brain.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “sion residual is independent of V 1. Thus, we cannot correctly determine the causal direction. 6 To tackle the issue of changing causal models, one may try to nd causal models in each sliding window (Calhoun et al., 2014) (for nonstationary data) or in dierent domains (for data from multiple domains”
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, neutral): “To tackle the issue of changing causal models, one may try to ﬁnd causal models on sliding windows [Calhoun et al. , 2014] (for nonstationary data) or in different domains (for data from multiple domains) separately, and then compare them.”
Abnormal intrinsic brain functional network dynamics in Parkinson’s disease (2017, background): “Recent research has identified and explored dynamic functional connectivity in healthy subjects, which was shown to represent mental activity (Chang and Glover, 2010; Kang et al., 2011; Calhoun et al., 2014) and cogni- tive functions (Thompson et al., 2013; Allen et al., 2014).”
Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, background): “…during tasks (Cole et al., 2014; Gratton et al., 2016), other reports emphasize variability of functional networks (Allen et al., 2014; Calhoun et al., 2014; Hutchison et al., 2013) and the sensitivity of these networks to ongoing cognition (Gonzalez-Castillo et al., 2015; Shine et…”
Communication dynamics in complex brain networks (2017, background): “Notably, functional connections, whether static or dynamic 60,61 , do not resolve the trajectories or temporal patterns of signal propagation through anatomical connections; instead, the co-variation of the activity of two neural elements expresses the aggregation of local, global, feedforward and…”
Network science of biological systems at different scales: A review. (2017, background): “The first application of multilayer analysis addressed the temporal evolution of brain network dynamics [176].”
Network neuroscience (2017, background): “A current fundamental challenge lies in constructing appropriate generative models that can be used to infer the dynamic mechanisms potentially driving these network reconfigurations and how they might be altered in disease states 92 .”
Discovery and Visualization of Nonstationary Causal Models (2015, background): “Likewise, the causal inﬂuences may also vary as a function of the experimental condition (e.g., health, disease, and behavior) [11].”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, neutral): “An integral component of modern neuroimaging research, FC is traditionally calculated over an entire scan or experimental condition (“static” functional connectivity), but recent years have seen rapidly growing interest in studying time-resolved ﬂuctuations in FC (often referred to as “dynamic” or t”
CLASSIFICATION OF SCHIZOPHRENIA AND BIPOLAR PATIENTS USING STATIC AND DYNAMIC RESTING-STATE FMRI BRAIN CONNECTIVITY (2016, methodology): “Estimate dynamic FNC matrices for all the subjects using a windowed FNC approach (Allen et al., 2012; Calhoun et al., 2014; Rashid et al., 2014).”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, background): “…Functional Connectome is not stationary, but instead evolves over time (Chang and Glover, 2010, Handwerker et al., 2012, Jones et al., 2012, Hutchison et al., 2013a, Hutchison et al., 2013b, Allen et al., 2014, Calhoun et al., 2014, Leonardi et al., 2014, Zalesky et al., 2014, Hansen et al., 2015).”