From connectome to cognition: The search for mechanism in human functional brain networks

doi:10.1016/j.neuroimage.2017.01.060

NeuroImage

Volume 160, 15 October 2017, Pages 124-139

https://doi.org/10.1016/j.neuroimage.2017.01.060 Get rights and content

Highlights

•
Mechanisms are needed to link descriptive network maps to cognitive function.
•
Network mechanisms involve explanatory interactions amongst brain regions.
•
Task manipulations and advanced analytic methods can reveal network mechanisms.
•
Each mechanism is characterized by its spatial, temporal and directional components.
•
Cognition emerges from mechanisms at macro network as well as micro cellular levels.

Abstract

Recent developments in functional connectivity research have expanded the scope of human neuroimaging, from identifying changes in regional activation amplitudes to detailed mapping of large-scale brain networks. However, linking network processes to a clear role in cognition demands advances in the theoretical frameworks, algorithms, and experimental approaches applied. This would help evolve the field from a descriptive to an explanatory state, by targeting network interactions that can mechanistically account for cognitive effects. In the present review, we provide an explicit framework to aid this search for “network mechanisms”, which anchors recent methodological advances in functional connectivity estimation to a renewed emphasis on careful experimental design. We emphasize how this framework can address specific questions in network neuroscience. These span ambiguity over the cognitive relevance of resting-state networks, how to characterize task-evoked and spontaneous network dynamics, how to identify directed or “effective” connections, and how to apply multivariate pattern analysis at the network level. In parallel, we apply the framework to highlight the mechanistic interaction of network components that remain “stable” across task domains and more “flexible” components associated with on-task reconfiguration. By emphasizing the need to structure the use of diverse analytic approaches with sound experimentation, our framework promotes an explanatory mapping between the workings of the cognitive mind and the large-scale network mechanisms of the human brain.

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)

D.S. Bassett et al.
Understanding complexity in the human brain
Trends Cogn. Sci.
(2011)
W. Bechtel et al.
Explanation: a mechanist alternative
Stud. Hist. Philos. Sci. Part C: Stud. Hist. Philos. Biol. Biomed. Sci.
(2005)
J.P. Borst et al.
Tracking cognitive processing stages with MEG: a spatio-temporal model of associative recognition in the brain
NeuroImage
(2016)
T.S. Braver et al.
Vive les differences! Individual variation in neural mechanisms of executive control
Curr. Opin. Neurobiol.
(2010)
J.M. Chein 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)
C.C. Chen et al.
Dynamic causal modelling of induced responses
NeuroImage
(2008)
M.W. Cole et al.
Intrinsic and task-evoked network architectures of the human brain
Neuron
(2014)
M.W. Cole et al.
Identifying the brain's most globally connected regions
NeuroImage
(2010)
M.W. Cole et al.
The cognitive control network: integrated cortical regions with dissociable functions
NeuroImage
(2007)
G. Deco et al.
Resting brains never rest: computational insights into potential cognitive architectures
Trends Neurosci.
(2013)

G. Deshpande et al.

Effect of hemodynamic variability on Granger causality analysis of fMRI

NeuroImage

(2010)

L.E. Dobrunz et al.

Heterogeneity of release probability, facilitation, and depletion at central synapses

Neuron

(1997)

I. Dumontheil et al.

Influence of the COMT Genotype on Working Memory and Brain Activity Changes During Development

Biol. Psychiatry

(2011)

J.H. Duyn

The future of ultra-high field MRI and fMRI for study of the human brain

NeuroImage

(2012)

J.A. Fodor et al.

Connectionism and cognitive architecture: a critical analysis

Cognition

(1988)

M.D. Fox et al.

Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS)

NeuroImage

(2012)

P. Fries

A mechanism for cognitive dynamics: neuronal communication through neuronal coherence

Trends Cogn. Sci.

(2005)

K.J. Friston et al.

Dynamic causal modelling

NeuroImage

(2003)

K.M. Gates et al.

Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples

NeuroImage

(2012)

L. Geerligs et al.

Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation

NeuroImage

(2016)

M.R. Gilmartin et al.

Prefrontal cortical regulation of fear learning

Trends Neurosci.

(2014)

D.A. Handwerker et al.

Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses

NeuroImage

(2004)

M. Havlicek et al.

Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering

NeuroImage

(2011)

J.-D. Haynes

A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives

Neuron

(2015)

J.F. Hipp et al.

BOLD fMRI correlation reflects frequency-specific neuronal correlation

Curr. Biol.

(2015)

D.K. Jones et al.

White matter integrity, fiber count, and other fallacies: the do's and don’ts of diffusion MRI

NeuroImage

(2013)

J.-R. King et al.

Characterizing the dynamics of mental representations: the temporal generalization method

Trends Cogn. Sci.

(2014)

P.J. Kohler et al.

Pattern classification precedes region-average hemodynamic response in early visual cortex

NeuroImage

(2013)

D. Kumaran et al.

What learning systems do intelligent agents need? Complementary learning systems theory updated

Trends Cogn. Sci.

(2016)

A.R. Laird et al.

Networks of task co-activations

NeuroImage

(2013)

F. Lopes da Silva

Neural mechanisms underlying brain waves: from neural membranes to networks

Electroencephalogr. Clin. Neurophysiol.

(1991)

D.G. McLaren et al.

A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches

NeuroImage

(2012)

E.A. Allen et al.

Tracking whole-brain connectivity dynamics in the resting state

Cereb. Cortex

(2014)

G. Aston-Jones et al.

An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance

Annu. Rev. Neurosci.

(2005)

A.P. Baker et al.

Fast transient networks in spontaneous human brain activity

eLife

(2014)

J. Barredo et al.

Ventral fronto-temporal pathway supporting cognitive control of episodic memory retrieval

Cereb. Cortex

(2015)

D.S. Bassett et al.

Learning-induced autonomy of sensorimotor systems

Nat. Neurosci.

(2015)

M. Bedau

Weak emergence

B. Biswal et al.

Functional connectivity in the motor cortex of resting human brain using echo-planar MRI

Magn. Reson. Med.

(1995)

M.J. Brookes et al.

Investigating the electrophysiological basis of resting state networks using magnetoencephalography

Proc. Natl. Acad. Sci. USA

(2011)

E. Bullmore et al.

Complex brain networks: graph theoretical analysis of structural and functional systems

Nat. Rev. Neurosci.

(2009)

W.A. Catterall et al.

The Hodgkin-Huxley Heritage: from Channels to Circuits

J. Neurosci.

(2012)

Chen, R.H., Ito, T., Kulkarni, K.R., Cole, M.W., 2016. Large-scale multivariate activation states of the human brain...

M.W. Cole et al.

Prefrontal dynamics underlying rapid instructed task learning reverse with practice

J. Neurosci.

(2010)

M.W. Cole et al.

Rapid transfer of abstract rules to novel contexts in human lateral prefrontal cortex

Front. Hum. Neurosci.

(2011)

Cole, M.W., Ito, T., Bassett, D.S., Schultz, D.H., 2016. Activity flow over resting-state networks shapes cognitive...

M.W. Cole et al.

Rapid instructed task learning: a new window into the human brain's unique capacity for flexible cognitive control

Cogn. Affect. Behav. Neurosci.

(2013)

M.W. Cole et al.

The frontoparietal control system: a central role in mental health

Neuroscientist

(2014)

M.W. Cole et al.

Multi-task connectivity reveals flexible hubs for adaptive task control

Nat. Neurosci.

(2013)

M. Corbetta et al.

Control of goal-directed and stimulus-driven attention in the brain

Nat. Rev. Neurosci.

(2002)

Cited by (76)

Mechanisms of Change in Cognitive Function Domains Among Older Adults in Cognitive Deterioration and Improvement Groups: Evidence From Phenotypic Network Structure
2023, Journal of the American Medical Directors Association
To investigate how cognitive function domains change in phenotypic networks in cognitive deterioration and improvement groups.
Secondary data analysis.
Respondents in the Aging, Demographics, and Memory Study (ADAMS) who were 70 years or older at the time of the data collection in 2000 or 2002.
This study used data from the ADAMS in Wave A and Wave B. We assessed 12 cognitive function domains. Latent profile transition analysis (LPTA) and the cross-lagged panel network model were used to the dynamic interactions of the 12 cognitive function domains over time in both the deterioration and improvement groups.
A total of 252 participants were included in the final analysis. LPTA identified 5 subgroups and categorized all samples into 3 main categories: improvement group (n = 61), deterioration group (n = 54), and no change group (n = 137). “D9: psychomotor processing” showed the largest value of out-strength in the deterioration group (r = 0.941) and improvement group (r = 0.969). The strongest direct positive effect in the deterioration group was “C9: psychomotor processing” -> “C8: attention” [β = 0.39 (0.00, 1.13)]. In the improvement group, the strongest direct positive effect was “C9 = psychomotor processing” -> “C7 = visual memory” [β = 0.69 (0.07, 1.30)].
Psychomotor processing affected other cognitive domains, and it played a crucial role in changes of cognitive function. The paths of psychomotor processing to attention and visual memory were found to be major factors in cognitive deterioration and improvement. Targeting psychomotor processing may lead to the development of more effective and precise interventions.
Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia
2023, Asian Journal of Psychiatry
Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis.
Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes.
Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x = −04,y = +30,z = +26;k = 12;cluster-p_FWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively.
ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.
The mathematical brain at rest
2023, Current Opinion in Behavioral Sciences
In this review, we consider the scope of work that uses resting-state functional connectivity (RSFC) analyses of functional magnetic resonance imaging data relevant for understanding the mathematical brain. After briefly introducing this technique and its utility, we identify three main themes this research has focused on: (1) using RSFC to identify patterns in brain connectivity that relate to individual differences in math ability or may be associated with math learning difficulties, (2) the use of RSFC to understand the effects of math training or intervention on the brain, and (3) insights that RSFC provides regarding the development and plasticity of mechanisms of the mathematical brain. The major contributions within each research theme are discussed alongside unresolved issues in RSFC study design related to understanding the development of math skills.
Immunity and cognition
2023, Translational Neuroimmunology: Neuroinflammation: Volume 7
The central nervous system (CNS) has been long considered an immune-privileged site, underscoring the potential influence exerted by the immune system on human cognition and behavior. During last decades, the complex bidirectional communication occurring between immune cells and neurons has begun to be investigated, unveiling unexpected neuroimmune interactions shaping synaptic connections and neural network activity during both physiological and pathological conditions. Such immune modulation of cognitive abilities and behavioral responses could represent a beneficial evolutionistic protective mechanism, being required to prevent and cope with inflammatory/infectious diseases as well as to limit the potential spreading of pathogens within a close-contact community. However, when the inflammatory response becomes persistent or compartmentalized, such as during autoimmune or neuroinflammatory disorders, the overactivation of the immune system in the brain could alter the physiological function of neural networks, leading to disabling disease features like cognitive impairment, mood disorders, and altered social interactions. In this scenario, the investigation of pathological neuroimmune interactions occurring during multiple sclerosis (MS), the most frequent inflammatory neurological disorder, has significantly contributed to decipher the synaptic mechanisms potentially underlying the detrimental effects of chronic inflammation on human cognition and behavior. In this chapter, we will review the preclinical and clinical evidence pointing toward an immune-mediated disruption of synaptic plastic abilities during chronic CNS inflammation, discussing the potential molecular pathways involved in the alteration of human cognition and social skills during autoimmune inflammatory disorders.
Alternative model systems for cognitive variation: eusocial-insect colonies
2022, Trends in Cognitive Sciences
Understanding the origins and maintenance of cognitive variation in animal populations is central to the study of the evolution of cognition. However, the brain is itself a complex, hierarchical network of heterogeneous components, from diverse cell types to diverse neuropils, each of which may be of limited use to study in isolation or prohibitively challenging to manipulate in situ. Consequently, highly tractable alternative model systems may be valuable tools. Eusocial-insect colonies display emergent cognitive-like properties from relatively simple social interactions between diverse subunits that can be observed and manipulated while operating collectively. Here, we review the individual-scale mechanisms that cause group-level variation in how colonies solve problems analogous to cognitive challenges faced by brains, like decision-making, attention, and search.
Protocol for activity flow mapping of neurocognitive computations using the Brain Activity Flow Toolbox
2022, STAR Protocols
Traditional cognitive neuroscience uses task-evoked activations to map neurocognitive processes (and information) to brain regions; however, how those processes are generated is unknown. We developed activity flow mapping to identify and empirically validate network mechanisms underlying the generation of neurocognitive processes. This approach models the movement of task-evoked activity over brain connections to predict task-evoked activations. We present a protocol for using the Brain Activity Flow Toolbox (https://colelab.github.io/ActflowToolbox/) to identify network mechanisms underlying neurocognitive processes of interest.
For complete details on the use and execution of this protocol, please refer to Cole et al., 2021.

View all citing articles on Scopus

View full text

From connectome to cognition: The search for mechanism in human functional brain networks

Highlights

Abstract

Section snippets

A framework for mechanistic discovery in network neuroscience

Implications of network mechanisms in advancing human FC research

Clarifying the cognitive relevance of resting-state networks

Capturing functionally relevant network dynamics

Revealing asymmetries in activity propagation via directed functional connectivity

Increasing the sensitivity of network components via multivariate pattern analysis

Summary of key challenges and future directions

Conclusion

Conflict of interest

Acknowledgements

Trends Cogn. Sci.

Stud. Hist. Philos. Sci. Part C: Stud. Hist. Philos. Biol. Biomed. Sci.

NeuroImage

Curr. Opin. Neurobiol.

Cogn. Brain Res.

NeuroImage

Neuron

NeuroImage

NeuroImage

Trends Neurosci.

NeuroImage

Neuron

Biol. Psychiatry

NeuroImage

Cognition

NeuroImage

Trends Cogn. Sci.

NeuroImage

NeuroImage

NeuroImage

Trends Neurosci.

NeuroImage

NeuroImage

Neuron

Curr. Biol.

NeuroImage

Trends Cogn. Sci.

NeuroImage

Trends Cogn. Sci.

NeuroImage

Electroencephalogr. Clin. Neurophysiol.

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.