Evidence map›Paper›PMID 37104929›Full record

ArticleNeuroImage. Clinical2023

Neural correlates of verbal fluency revealed by longitudinal T1, T2 and FLAIR imaging in stroke.

Yanyu Xiong, Mohamed Salah Khlif, Natalia Egorova-Brumley, Amy Brodtmann, Brielle C Stark

Open access · goldAbstract read
In one paragraph

Article in NeuroImage. Clinical, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
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

5 authors at 2 institutions in 2 countries.

Yanyu XiongDepartment of Speech, Language and Hearing Sciences, Indiana University, Bloomington IN 47408, USA. Electronic address: yxiong5@ua.edu.
Mohamed Salah KhlifThe Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia.
Natalia Egorova-BrumleyThe Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia.
Amy BrodtmannThe Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia.
Brielle C StarkDepartment of Speech, Language and Hearing Sciences, Indiana University, Bloomington IN 47408, USA.
The University of Melbourne · AUIndiana University Bloomington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diffusion-weighted imaging has been widely used in the research on post-stroke verbal fluency but acquiring diffusion data is not always clinically feasible. Achieving comparable reliability for detecting brain variables associated with verbal fluency impairments, based on more readily available anatomical, non-diffusion images (T1, T2 and FLAIR), enables clinical practitioners to have complementary neurophysiological information at hand to facilitate diagnosis and treatment of language impairment. Meanwhile, although the predominant focus in the stroke recovery literature has been on cortical contributions to verbal fluency, it remains unclear how subcortical regions and white matter disconnection are related to verbal fluency. Our study thus utilized anatomical scans of ischaemic stroke survivors (n = 121) to identify longitudinal relationships between subcortical volume, white matter tract disconnection, and verbal fluency performance at 3- and 12-months post-stroke. Subcortical grey matter volume was derived from FreeSurfer. We used an indirect probabilistic approach to quantify white matter disconnection in terms of disconnection severity, the proportion of lesioned voxel volume to the total volume of a tract, and disconnection probability, the probability of the overlap between the stroke lesion and a tract. These disconnection variables of each subject were identified based on the disconnectome map of the BCBToolkit. Using a linear mixed multiple regression method with 5-fold cross-validations, we correlated the semantic and phonemic fluency scores with longitudinal measurements of subcortical grey matter volume and 22 bilateral white matter tracts, while controlling for demographic variables (age, sex, handedness and education), total brain volume, lesion volume, and cortical thickness. The results showed that the right subcortical grey matter volume was positively correlated with phonemic fluency averaged over 3 months and 12 months. The finding generalized well on the test data. The disconnection probability of left superior longitudinal fasciculus II and left posterior arcuate fasciculus was negatively associated with semantic fluency only on the training data, but the result aligned with our previous study using diffusion scans in the same clinical population. In sum, our results presented evidence that routinely acquired anatomical scans can serve as a reliable source for deriving neural variables of post-stroke verbal fluency performance. The use of this method might provide an ecologically valid and more readily implementable analysis tool.

Indexed as

Brain IschemiaStrokeWhite MatterBrainDiffusion Magnetic Resonance ImagingHumansReproducibility of ResultsAnatomical scansPhonemic fluencySemantic fluencyStrokeSubcortical grey matter volumeWhite matter tract disconnection

Identifiers

PMID37104929
PMCPMC10165164
OpenAlexW4366503420

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.