Evidence map›Paper›PMID 32398669›Full record

ArticleScientific reports2020

Dynamic activity of human brain task-specific networks.

Jie Huang

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

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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

1 author.

Jie HuangDepartment of Radiology, Michigan State University, East Lansing, MI, USA. huangj@msu.edu.ORCID http://orcid.org/0000-0002-4096-5128

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A simple motor behaviour or a more complex behaviour is the result of the neural activity of those neural networks responsible for the behaviour. To understand how the network activity is transformed into human behaviours, it is necessary to identify task-specific networks and analyse the dynamic network activity that changes with time. Here we report a novel task-fMRI technique to identify task-specific networks and analyse their dynamic activity. Nine subjects participated in a task-fMRI study in which the subjects were cued to perform three different tasks of word-reading, pattern-viewing and finger-tapping. A functional area of unitary pooled activity (FAUPA) is defined as an area in which the temporal variation of the activity is the same across the entire area, and a task-associated FAUPA plays the role of a functional unit for the task. A novel method is presented to (1) identify FAUPAs that are associated with each task as well as their functional groupings; (2) identify the three task-specific networks; and (3) analyse the network activity from trial to trial. Task-associated FAUPAs were identified for each task and each subject. A task-specific large-scale neural network across the whole brain consisted of all FAUPAs that were activated each time the task was performed, and all three task-specific networks were identified for each individual subject. The temporal changes of activation and functional connectivity of the FAUPAs within each network from trial to trial characterized the dynamic activity of the network. The results demonstrated a one-to-one relation between the network activity and the task performance from trial to trial, offering a means of testing the causal relationship between network activity and human task performance by systematically manipulating task performance and measuring corresponding network activity change.

Indexed as

AdultBrainBrain MappingFemaleFingersHumansMagnetic Resonance ImagingMaleMiddle AgedMotor CortexMovementSomatosensory CortexYoung Adult

Identifiers

PMID32398669
PMCPMC7217945

What OpenQuestion holds

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Registered trials

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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.