Evidence map›Paper›PMID 26669858›Full record

ArticleScientific reports2015

The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions.

P Taylor, J N Hobbs, J Burroni, H T Siegelmann

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 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

35 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  14. Sensory Target Detection at Local and Global Timescales Reveals a Hierarchy of Supramodal Dynamics in the Human Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022
    Article
  15. Intrinsic dynamics and topography of sensory input systems.Cerebral cortex (New York, N.Y. : 1991) · 2022
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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

4 authors.

P TaylorCollege of Information and Computer Sciences. University of Massachusetts, Amherst, MA, USA.
J N HobbsCollege of Information and Computer Sciences. University of Massachusetts, Amherst, MA, USA.
J BurroniCollege of Information and Computer Sciences. University of Massachusetts, Amherst, MA, USA.
H T SiegelmannCollege of Information and Computer Sciences. University of Massachusetts, Amherst, MA, USA.

Funding

Meta-anaylsis in human brain mappingR01MH074457 · NIMH · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI FOX, PETER THORNTON · 2006 to 2024
$10.5M
Meta-analysis in human brain mappingR56MH074457 · NIMH · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI FOX, PETER THORNTON · 2020 to 2020
$543k
NIMH NIH HHS MH74457NIMH NIH HHS R01 MH074457NIMH NIH HHS R56 MH074457
6 · The paper itself

Abstract

Though widely hypothesized, limited evidence exists that human brain functions organize in global gradients of abstraction starting from sensory cortical inputs. Hierarchical representation is accepted in computational networks, and tentatively in visual neuroscience, yet no direct holistic demonstrations exist in vivo. Our methods developed network models enriched with tiered directionality, by including input locations, a critical feature for localizing representation in networks generally. Grouped primary sensory cortices defined network inputs, displaying global connectivity to fused inputs. Depth-oriented networks guided analyses of fMRI databases (~17,000 experiments;~1/4 of fMRI literature). Formally, we tested whether network depth predicted localization of abstract versus concrete behaviors over the whole set of studied brain regions. For our results, new cortical graph metrics, termed network-depth, ranked all databased cognitive function activations by network-depth. Thus, we objectively sorted stratified landscapes of cognition, starting from grouped sensory inputs in parallel, progressing deeper into cortex. This exposed escalating amalgamation of function or abstraction with increasing network-depth, globally. Nearly 500 new participants confirmed our results. In conclusion, data-driven analyses defined a hierarchically ordered connectome, revealing a related continuum of cognitive function. Progressive functional abstraction over network depth may be a fundamental feature of brains, and is observed in artificial networks.

Indexed as

Brain MappingAlgorithmsCerebral CortexCluster AnalysisCognitionConnectomeDatabases, FactualHumansMagnetic Resonance ImagingModels, BiologicalReproducibility of Results

Identifiers

PMID26669858
PMCPMC4681187

What OpenQuestion holds

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LicenceCC BY
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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.