Evidence map›Paper›PMID 28131891›Full record

ReviewNeuroImage2017

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

Ravi D Mill, Takuya Ito, Michael W Cole

Abstract readReview
In one paragraph

Review in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  14. Rescuing missing data in connectome-based predictive modeling.Imaging neuroscience (Cambridge, Mass.) · 2024
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ravi D MillCenter for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA. Electronic address: rdm146@rutgers.edu.
Takuya ItoCenter for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA.
Michael W ColeCenter for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07120, USA.

Funding

Brain Network Mechanisms of Instructed LearningR01MH109520 · NIMH · RUTGERS THE STATE UNIV OF NJ NEWARK · PI COLE, MICHAEL WILLIAM · 2016 to 2020
$2.0M
Network Mechanisms of Flexible Cognitive ControlR00MH096801 · NIMH · RUTGERS THE STATE UNIV OF NJ NEWARK · PI COLE, MICHAEL WILLIAM · 2014 to 2016
$747k
Network Mechanisms of Flexible Cognitive ControlK99MH096801 · NIMH · WASHINGTON UNIVERSITY · PI COLE, MICHAEL WILLIAM · 2012 to 2013
$165k
NIMH NIH HHS K99 MH096801NIMH NIH HHS R00 MH096801NIMH NIH HHS R01 MH109520
6 · The paper itself

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.

Indexed as

BrainCognitionConnectomeHumansNerve NetNeural PathwaysComputational modelingDirected connectivityDynamic connectivityFunctional connectivityMulti-modal neuroimagingMVPA

Identifiers

PMID28131891
PMCPMC5529276

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.