Functional and Effective Connectivity: A Review

By Karl J. Friston. Brain Connectivity, 2011

3,402 citations recorded by the source corpus.

Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/56052b0740f2b6c39c16c22087d7397f1b31a281

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In Cognitive Neuroscience

Citations in the corpus

Information Processing in the Mental Workspace Is Fundamentally Distributed (2016, methodology): “Established methods for assessing directed connectivity produce a single value that characterizes the degree to which processing in one region is predictive of later processing in another region (Barnett & Seth, 2014; Friston, 2011; Lizier et al., 2011).”
Change-point detection in high-dimensional covariance structure (2016, background): “The covariance structure of data in functional Magnetic Resonance Imaging has recently drawn a lot of interest, as it encodes so-called functional connectivity networks [40] which refer to the explicit influence among neural systems [22].”
Tools of the trade: psychophysiological interactions and functional connectivity. (2012, methodology): “…can investigate not only which individual brain areas are involved in a task, but also how information flows between brain areas (Stephan, 2004; Friston, 2011; Smith et al., 2012) and how functional areas can change their connectivity to participate in different networks at different times…”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, methodology): “…remarkably as neuroscientists seek to understand the comprehensive information underlying cognition, behavior, and perception (Bassett and Bullmore, 2006; Reijneveld et al., 2007; Bullmore and Sporns, 2009, 2012; He and Evans, 2010; Friston, 2011; Craddock et al., 2013; Park and Friston, 2013).”
Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations (2013, methodology): “…propagation through the eigenvectors of the Jacobian matrix can be seen as the effective connectivity (i.e., how different cortical areas influence each other, even in the absence of direct connection between them) (Aertsen et al., 1989; Bullmore and Sporns, 2009; Friston, 2011; Robinson, 2012).”
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, methodology): “Although several studies have reported the presence of dynamic functional connectivity (dFC), statistical assessment of the results is not always carried out in a sound way and, in some studies, is even omitted.”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “…choices facing the researcher are the spatial scale most relevant for the analysis (single voxels versus voxel aggregates), and in the case of voxel aggregates the choice of howto combine voxels (parcellation based on prior anatomical knowledge, a data-driven approach, or a hybrid technique [20]).”
Task-Based Core-Periphery Organization of Human Brain Dynamics (2012, methodology): “(which we estimated using the magnitude squared coherence) between pairs of regional BOLD time series as functional connectivity [63].”
Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, background): “…2009; Deco et al. 2011; Ghosh et al., 2008; Honey et al., 2007) have demonstrated the importance of the interplay between anatomical structure, local neural dynamics and noise in the emergence of resting-state inter-regional correlations described by functional connectivity (FC) (Friston, 2011).”
Activity flow over resting-state networks shapes cognitive task activations (2016, background): “…insights and the activity flow mapping procedure introduced here will facilitate future investigation into the functional relevance of resting-state FC, the factors that influence cognitive task activations and the balance of large-scale distributed versus localized processing in the human brain.”
Tractography - where do we go from here? (2011, neutral): “In a nutshell, although a complete understanding of brain structural architecture is certainly not sufﬁcient to understand brain function (Friston, 2011), it is still a valuable tool to understand its degrees of freedom.”
The future of FMRI connectivity (2012, background): “This brief paper does not give a detailed retrospective review, as it is supposed to be a future-looking paper, and there are several relevant reviews by other authors in this special issue, in addition to excellent recent reviews elsewhere (e.g., (Friston, 2011)).”
The human connectome: Origins and challenges (2013, result): “This stands in sharp contrast to “functional connections” that describe statistical dependencies derived from observations of neural time series (Friston, 2011).”
Motor primitives in space and time via targeted gain modulation in cortical networks (2018, neutral): “Alternatively, one can change the underlying dynamical system through modification of the effective connectivity [42] even when a preparatory state is the same across movements.”