Evidence map›Paper›PMID 32572442›Full record

ArticleBrain : a journal of neurology2020

Post-stroke deficit prediction from lesion and indirect structural and functional disconnection.

Alessandro Salvalaggio, Michele De Filippo De Grazia, Marco Zorzi, Michel Thiebaut de Schotten, Maurizio Corbetta

Open access · bronzeAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 167 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
167citing papers in PubMed, 1 pooled it
19.0field-weighted citation impact, top 1% 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

167 citing papers in PubMed, 1 synthesis or guideline pooled it, 278 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. An MRI Atlas of Regional Brain Vulnerability to Metastatic Disease.medRxiv : the preprint server for health sciences · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Review
  19. Review
  20. Article

107 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 3 countries.

Alessandro SalvalaggioClinica Neurologica, Department of Neuroscience, and Padova Neuroscience Center (PNC), University of Padova, Italy.
Michele De Filippo De GraziaIRCCS San Camillo Hospital, Venice, Italy.
Marco ZorziIRCCS San Camillo Hospital, Venice, Italy.
Michel Thiebaut de SchottenBrain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.
Maurizio CorbettaClinica Neurologica, Department of Neuroscience, and Padova Neuroscience Center (PNC), University of Padova, Italy.
University of Padua · ITCentre National de la Recherche Scientifique · FRIRCCS San Camillo Hospital · IT

Funding

Stroke, Brain Networks, and BehaviorR01NS095741 · NINDS · WASHINGTON UNIVERSITY · PI CORBETTA, MAURIZIO · 2015 to 2019
$3.0M
NINDS NIH HHS R01 NS095741
6 · The paper itself

Abstract

Behavioural deficits in stroke reflect both structural damage at the site of injury, and widespread network dysfunction caused by structural, functional, and metabolic disconnection. Two recent methods allow for the estimation of structural and functional disconnection from clinical structural imaging. This is achieved by embedding a patient's lesion into an atlas of functional and structural connections in healthy subjects, and deriving the ensemble of structural and functional connections that pass through the lesion, thus indirectly estimating its impact on the whole brain connectome. This indirect assessment of network dysfunction is more readily available than direct measures of functional and structural connectivity obtained with functional and diffusion MRI, respectively, and it is in theory applicable to a wide variety of disorders. To validate the clinical relevance of these methods, we quantified the prediction of behavioural deficits in a prospective cohort of 132 first-time stroke patients studied at 2 weeks post-injury (mean age 52.8 years, range 22-77; 63 females; 64 right hemispheres). Specifically, we used multivariate ridge regression to relate deficits in multiple functional domains (left and right visual, left and right motor, language, spatial attention, spatial and verbal memory) with the pattern of lesion and indirect structural or functional disconnection. In a subgroup of patients, we also measured direct alterations of functional connectivity with resting-state functional MRI. Both lesion and indirect structural disconnection maps were predictive of behavioural impairment in all domains (0.16 < R2 < 0.58) except for verbal memory (0.05 < R2 < 0.06). Prediction from indirect functional disconnection was scarce or negligible (0.01 < R2 < 0.18) except for the right visual field deficits (R2 = 0.38), even though multivariate maps were anatomically plausible in all domains. Prediction from direct measures of functional MRI functional connectivity in a subset of patients was clearly superior to indirect functional disconnection. In conclusion, the indirect estimation of structural connectivity damage successfully predicted behavioural deficits post-stroke to a level comparable to lesion information. However, indirect estimation of functional disconnection did not predict behavioural deficits, nor was a substitute for direct functional connectivity measurements, especially for cognitive disorders.

Indexed as

AdultAgedBrainConnectomeFemaleHumansMaleMiddle AgedMultimodal ImagingNerve NetNeuroimagingRecovery of FunctionStrokeYoung Adultconnectivityfunctional disconnectionlesionstrokestructural disconnection

Identifiers

PMID32572442
PMCPMC7363494
OpenAlexW3037942253

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.