Evidence map›Paper›PMID 27368185›Full record

ArticleInternational journal of computer assisted radiology and surgery2016

Ensemble average propagator-based detection of microstructural alterations after stroke.

Lorenza Brusini, Silvia Obertino, Ilaria Boscolo Galazzo, Mauro Zucchelli, Gunnar Krueger, Cristina Granziera, Gloria Menegaz

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

7 authors.

Lorenza BrusiniDepartment of Computer Science, University of Verona, Verona, Italy. lorenza.brusini@univr.it.ORCID http://orcid.org/0000-0001-5357-5180
Silvia ObertinoDepartment of Computer Science, University of Verona, Verona, Italy.
Ilaria Boscolo GalazzoInstitute of Nuclear Medicine, University College of London, London, UK.
Mauro ZucchelliDepartment of Computer Science, University of Verona, Verona, Italy.
Gunnar KruegerSiemens Healthcare USA, Boston, MA, USA.
Cristina GranzieraDepartment of Clinical Neuroscience, CHUV and University of Lausanne, Lausanne, Switzerland.
Gloria MenegazDepartment of Computer Science, University of Verona, Verona, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeNew analytical reconstruction techniques of diffusion weighted signal have been proposed. A previous work evidenced the exploitability of some indices derived from the simple harmonic oscillator-based reconstruction and estimation (3D-SHORE) model as numerical biomarkers of neural plasticity after stroke. Here, the analysis is extended to two additional indices: return to the plane/origin (RTPP/RTOP) probabilities. Moreover, several motor networks were introduced and the results were analyzed at different time scales.

methodsTen patients underwent three diffusion spectrum imaging (DSI) scans [1 week (tp1), 1 month (tp2) and 6 months (tp3) after stroke]. Ten matched controls underwent two DSI scans 1 month apart. 3D-SHORE was used for reconstructing the signal and the microstructural indices were derived. Tract-based analysis was performed along motor cortical, subcortical and transcallosal networks in the contralesional area.

resultsThe optimal intra-class correlation coefficient (ICC) was obtained in the subcortical loop for propagator anisotropy (ICC [Formula: see text] 0.96), followed by generalized fractional anisotropy (ICC [Formula: see text] 0.94). The new indices reached the highest stability in the transcallosal network and performed well in the cortical and subcortical networks with the exception of RTOP in the cortical loop (ICC [Formula: see text] 0.59). They allowed discriminating patients from controls at the majority of the timescales. Finally, the regression model using indices calculated along the subcortical loop at tp1 resulted in the best prediction of clinical outcome.

conclusionsThe whole set of microstructural indices provide measurements featuring high precision. The new indices allow discriminating patients from controls in all networks, except for RTPP in the cortical loop. Moreover, the 3D-SHORE indices in subcortical connections constitute a good regression model for predicting the clinical outcome at 6 months, supporting their suitability as numerical biomarkers for neuronal plasticity after stroke.

Indexed as

AnisotropyBrainDiffusion Magnetic Resonance ImagingFemaleHumansMaleMiddle AgedStroke3D-SHOREBiomarkerGFAStroke

Identifiers

What OpenQuestion holds

Textmetadata
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.