Evidence map›Paper›PMID 33353461›Full record

ArticleNeurorehabilitation and neural repair2021

Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery.

E Susan Duncan, A Duke Shereen, Thanos Gentimis, Steven L Small

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurorehabilitation and neural repair, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 84% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 3 institutions in 1 country.

E Susan DuncanLouisiana State University, Baton Rouge, LA, USA.ORCID 0000-0002-1943-0903
A Duke ShereenCity University of New York, New York, NY, USA.
Thanos GentimisLouisiana State University, Baton Rouge, LA, USA.
Steven L SmallUniversity of Texas at Dallas, Richardson, TX, USA.
Louisiana State University · USCity University of New York · USThe University of Texas at Dallas · US

Funding

Neurophysiological Measurement in Aphasia TreatmentR01DC007488 · NIDCD · UNIVERSITY OF CHICAGO · PI SMALL, STEVEN L · 2005 to 2009
$2.0M
Bioinformatics Infrastructure for Large Scale Studies of Aphasia RecoveryR33DC008638 · NIDCD · UNIVERSITY OF CHICAGO · PI SMALL, STEVEN L · 2008 to 2010
$1.9M
NIDCD NIH HHS R01 DC007488NIDCD NIH HHS R33 DC008638
6 · The paper itself

Abstract

backgroundConsiderable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint.

objectiveWe investigated the stability of functional connectivity across tasks and sessions in a cohort of individuals with chronic stroke using a supervised machine learning approach.

methodsTwelve individuals with chronic stroke underwent functional magnetic resonance imaging (fMRI) seven times over 18 weeks. The middle 6 weeks consisted of intensive aphasia therapy. We collected fMRI data during rest and performance of 2 tasks. We calculated functional connectivity metrics for each imaging run, then applied a support vector machine to classify data on the basis of participant, task, and time point (pre- or posttherapy). Permutation testing established statistical significance.

resultsWhole brain functional connectivity matrices could be classified at levels significantly greater than chance on the basis of participant (87.1% accuracy;

conclusionsFindings suggest the profound importance of considering interindividual variability when interpreting mechanisms of recovery in studies of functional connectivity in stroke.

Indexed as

ConnectomeLanguage TherapyOutcome Assessment, Health CareStroke RehabilitationSupport Vector MachineAdultAgedAphasiaChronic DiseaseFemaleHumansIndividualityIschemic StrokeMagnetic Resonance ImagingMaleMiddle Agedaphasiafunctional neuroimagingmagnetic resonance imagingrehabilitationstrokesupervised machine learning

Identifiers

PMID33353461
PMCPMC8669773
OpenAlexW3114691759

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.