From connectome to cognition: The search for mechanism in human functional brain networks
Section snippets
A framework for mechanistic discovery in network neuroscience
Since the emergence of human functional neuroimaging, researchers have sought the optimal analytic framework to link non-invasively acquired brain data to cognitive function. An initial focus on changes in regional activation amplitudes (Friston et al., 1994, Kanwisher, 2010) has given way to examination of functional connectivity (FC) between regions and large-scale networks of regions (Biswal et al., 1995, Medaglia et al., 2015, Petersen and Sporns, 2015, Raichle, 2010, Sporns, 2014). This
Implications of network mechanisms in advancing human FC research
To summarize the core tenets of our framework: i) network mechanisms are identified as interactions amongst brain regions that explain cognitive states; ii) experimental manipulations (both cognitive and neural) are key in reliably linking network interactions to a clear explanatory role in cognition; iii) task manipulations can be combined with recently developed FC estimation algorithms to characterize distinct functional components of each mechanism (spatial, temporal and directional); iv)
Clarifying the cognitive relevance of resting-state networks
Thus far, the dominant focus in human FC research has been on mapping spatial patterns of fMRI BOLD synchronization in the resting state. Computing the Pearson's correlation coefficient between pairwise regional (or voxel-wise) BOLD time series, in the absence of a controlled task, has yielded a highly reproducible set of large-scale networks spanning many domains of cognitive function. These canonical resting-state networks include low-level sensory and motor networks (Biswal et al., 1995,
Capturing functionally relevant network dynamics
In keeping with the dominant approach in FC research, the previous section characterized the cognitive relevance of resting-state networks in terms of a single functional component – spatial topology i.e. the spatial pattern of connections between brain regions. However, it is likely that temporal components of resting-state networks also provide critical insight into their function i.e. the spatial pattern of connections and when that pattern emerges during cognition (see Fig. 1b). The
Revealing asymmetries in activity propagation via directed functional connectivity
The majority of research into human brain networks has focused on one FC estimation algorithm – pairwise Pearson's correlation computed between regional time series – which conveys whether two regions A and B communicate in a general “undirected” sense (connectivity A-B). This is especially true for fMRI connectivity studies, whereas MEG/EEG connectivity has been commonly computed via both correlation and undirected coherence approaches. In contrast, a class of “directed” or “effective” FC
Increasing the sensitivity of network components via multivariate pattern analysis
Another feature of the standard FC estimation pipeline is the extraction of averaged time series from isolated brain voxels or as the average across neighboring voxels within putative brain regions. Extracting such “univariate” estimates of brain activation might occlude FC mechanisms encoded by “multivariate” representational patterns amongst multiple voxels or areas of cortex. Indeed, the application of multivariate pattern analysis (MVPA) methods to multi-unit recordings in animals has
Summary of key challenges and future directions
Whilst the preceding sections have detailed numerous advances in the study of human brain networks, a number of key challenges are posed to the search for network mechanisms. Firstly, there remains a broad need for more principled validations of FC estimation strategies. Confidence in available methodologies is a necessary precursor to generating meaningful mechanistic insight from them, and there remains much ambiguity over optimal preprocessing steps (e.g. minimization of artifacts), choice
Conclusion
The field of human functional connectivity research is tantalizingly poised. Recent technical and methodological advances have opened new avenues of inquiry, and the challenge now is to develop optimal strategies to navigate these avenues without getting lost in the labyrinth of “big data”. Our goal in this review was to demonstrate that the ongoing feedback loop between FC method development and insight into cognitive function would be fine-tuned by a specific focus on identifying network
Conflict of interest
None.
Acknowledgements
We acknowledge support by the US National Institutes of Health under awards K99-R00 MH096801 and R01 MH109520. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.
References (165)
- et al.
Understanding complexity in the human brain
Trends Cogn. Sci.
(2011) - et al.
Explanation: a mechanist alternative
Stud. Hist. Philos. Sci. Part C: Stud. Hist. Philos. Biol. Biomed. Sci.
(2005) - et al.
Tracking cognitive processing stages with MEG: a spatio-temporal model of associative recognition in the brain
NeuroImage
(2016) - et al.
Vive les differences! Individual variation in neural mechanisms of executive control
Curr. Opin. Neurobiol.
(2010) - et al.
Neuroimaging studies of practice-related change: fmri and meta-analytic evidence of a domain-general control network for learning
Cogn. Brain Res.
(2005) - et al.
Dynamic causal modelling of induced responses
NeuroImage
(2008) - et al.
Intrinsic and task-evoked network architectures of the human brain
Neuron
(2014) - et al.
Identifying the brain's most globally connected regions
NeuroImage
(2010) - et al.
The cognitive control network: integrated cortical regions with dissociable functions
NeuroImage
(2007) - et al.
Resting brains never rest: computational insights into potential cognitive architectures
Trends Neurosci.
(2013)
Effect of hemodynamic variability on Granger causality analysis of fMRI
NeuroImage
Heterogeneity of release probability, facilitation, and depletion at central synapses
Neuron
Influence of the COMT Genotype on Working Memory and Brain Activity Changes During Development
Biol. Psychiatry
The future of ultra-high field MRI and fMRI for study of the human brain
NeuroImage
Connectionism and cognitive architecture: a critical analysis
Cognition
Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS)
NeuroImage
A mechanism for cognitive dynamics: neuronal communication through neuronal coherence
Trends Cogn. Sci.
Dynamic causal modelling
NeuroImage
Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples
NeuroImage
Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation
NeuroImage
Prefrontal cortical regulation of fear learning
Trends Neurosci.
Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses
NeuroImage
Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering
NeuroImage
A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives
Neuron
BOLD fMRI correlation reflects frequency-specific neuronal correlation
Curr. Biol.
White matter integrity, fiber count, and other fallacies: the do's and don’ts of diffusion MRI
NeuroImage
Characterizing the dynamics of mental representations: the temporal generalization method
Trends Cogn. Sci.
Pattern classification precedes region-average hemodynamic response in early visual cortex
NeuroImage
What learning systems do intelligent agents need? Complementary learning systems theory updated
Trends Cogn. Sci.
Networks of task co-activations
NeuroImage
Neural mechanisms underlying brain waves: from neural membranes to networks
Electroencephalogr. Clin. Neurophysiol.
A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches
NeuroImage
Tracking whole-brain connectivity dynamics in the resting state
Cereb. Cortex
An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance
Annu. Rev. Neurosci.
Fast transient networks in spontaneous human brain activity
eLife
Ventral fronto-temporal pathway supporting cognitive control of episodic memory retrieval
Cereb. Cortex
Learning-induced autonomy of sensorimotor systems
Nat. Neurosci.
Weak emergence
Functional connectivity in the motor cortex of resting human brain using echo-planar MRI
Magn. Reson. Med.
Investigating the electrophysiological basis of resting state networks using magnetoencephalography
Proc. Natl. Acad. Sci. USA
Complex brain networks: graph theoretical analysis of structural and functional systems
Nat. Rev. Neurosci.
The Hodgkin-Huxley Heritage: from Channels to Circuits
J. Neurosci.
Prefrontal dynamics underlying rapid instructed task learning reverse with practice
J. Neurosci.
Rapid transfer of abstract rules to novel contexts in human lateral prefrontal cortex
Front. Hum. Neurosci.
Rapid instructed task learning: a new window into the human brain's unique capacity for flexible cognitive control
Cogn. Affect. Behav. Neurosci.
The frontoparietal control system: a central role in mental health
Neuroscientist
Multi-task connectivity reveals flexible hubs for adaptive task control
Nat. Neurosci.
Control of goal-directed and stimulus-driven attention in the brain
Nat. Rev. Neurosci.
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