Advancing functional connectivity research from association to causation

By A. Reid, Drew B. Headley, R. Mill, Ruben Sanchez-Romero, Lucina Q. Uddin, Daniele Marinazzo, Daniel J. Lurie, Pedro A. Valdes-Sosa, Stephen Jose Hanson, Bharat B. Biswal and 3 more. Nature Neuroscience, 2019

314 citations recorded by the source corpus.

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series—functional connectivity (FC) methods—are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods (‘effective connectivity’) is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures. Many studies focus on neural associations yet understanding the brain will ultimately depend on discovering the causal interactions underlying its functionality. Moving from association to causation will thus be essential for advancing neuroscience.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/363c445fb24bc707962ccac4341b9fa10e73aec0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, unknown): “Beyond the speciﬁc case of TVFC, there is also ongoing debate about the use of “functional connectivity” to refer to methods that attempt to infer neural interactions from time series data (Mehler & Kording, 2018; Reid et al., 2019).”
Granger Causality: A Review and Recent Advances (2021, background): “Researchers in various applied domains, from neuroscience (Bergmann and Hartwigsen, 2021; Reid et al., 2019) to environmental sciences (Cox Jr and Popken, 2015), have used Granger’s framework to (informally) draw causal conclusions.”
Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks (2019, unknown): “Other groups have worked towards standardized deﬁnitions of functional and e ﬀ ective connectivity (Reid et al. 2019), and best practices in MRI data analysis and sharing (Nichols et al. 2017).”
Neural interactions in the human frontal cortex dissociate reward and punishment learning (2024, background): “…emerge from the dynamic coordination over large-scale and hierarchically organized networks (Varela et al., 2001; Bressler and Menon, 2010; Reid et al., 2019; Panzeri et al., 2022; Thiebaut de Schotten and Forkel, 2022; Miller et al., 2024; Noble et al., 2024) and accumulating evidence…”
Inferring Causality from Noninvasive Brain Stimulation in Cognitive Neuroscience (2020, background): “This approach has gained increasing interest in the functional neuroimaging community as well, where its applicability is fiercely debated (Reid et al., 2019; Mehler & Kording, 2018; GrosseWentrup, Janzing, Siegel, & Schölkopf, 2016; Weichwald et al., 2015; Ramsey et al., 2010).”