Evidence map›Paper›PMID 37409023›Full record

ArticleFrontiers in neurology2023

Patterns of gray and white matter functional networks involvement in glioblastoma patients: indirect mapping from clinical MRI scans.

Giulio Sansone, Lorenzo Pini, Alessandro Salvalaggio, Matteo Gaiola, Francesco Volpin, Valentina Baro, Marta Padovan, Mariagiulia Anglani, Silvia Facchini, Franco Chioffi and 5 more

Abstract read
In one paragraph

Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Pigs: Large Animal Preclinical Cancer Models.World journal of oncology · 2024
    Review
  6. 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

15 authors.

Giulio SansoneDepartment of Neuroscience, University of Padova, Padova, Italy.
Lorenzo PiniPadova Neuroscience Center (PNC), University of Padova, Padova, Italy.
Alessandro SalvalaggioDepartment of Neuroscience, University of Padova, Padova, Italy.
Matteo GaiolaDepartment of Neuroscience, University of Padova, Padova, Italy.
Francesco VolpinDivision of Neurosurgery, Azienda Ospedaliera Università di Padova, Padova, Italy.
Valentina BaroAcademic Neurosurgery, Department of Neurosciences, University of Padova, Padova, Italy.
Marta PadovanDepartment of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy.
Mariagiulia AnglaniNeuroradiology Unit, University Hospital of Padova, Padova, Italy.
Silvia FacchiniDepartment of Neuroscience, University of Padova, Padova, Italy.
Franco ChioffiDivision of Neurosurgery, Azienda Ospedaliera Università di Padova, Padova, Italy.
Vittorina ZagonelDepartment of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy.
Domenico D'AvellaAcademic Neurosurgery, Department of Neurosciences, University of Padova, Padova, Italy.
Luca DenaroAcademic Neurosurgery, Department of Neurosciences, University of Padova, Padova, Italy.
Giuseppe LombardiDepartment of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padova, Italy.
Maurizio CorbettaDepartment of Neuroscience, University of Padova, Padova, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Resting-state functional-MRI studies identified several cortical gray matter functional networks (GMNs) and white matter functional networks (WMNs) with precise anatomical localization. Here, we aimed at describing the relationships between brain's functional topological organization and glioblastoma (GBM) location. Furthermore, we assessed whether GBM distribution across these networks was associated with overall survival (OS). Materials and methods: We included patients with histopathological diagnosis of IDH-wildtype GBM, presurgical MRI and survival data. For each patient, we recorded clinical-prognostic variables. GBM core and edema were segmented and normalized to a standard space. Pre-existing functional connectivity-based atlases were used to define network parcellations: 17 GMNs and 12 WMNs were considered in particular. We computed the percentage of lesion overlap with GMNs and WMNs, both for core and edema. Differences between overlap percentages were assessed through descriptive statistics, ANOVA, post-hoc tests, Pearson's correlation tests and canonical correlations. Multiple linear and non-linear regression tests were employed to explore relationships with OS. Results: 99 patients were included (70 males, mean age 62  years). The most involved GMNs included ventral somatomotor, salient ventral attention and default-mode networks; the most involved WMNs were ventral frontoparietal tracts, deep frontal white matter, and superior longitudinal fasciculus system. Superior longitudinal fasciculus system and dorsal frontoparietal tracts were significantly more included in the edema ( Discussion and conclusion: Both GBM core and edema preferentially overlap with specific GMNs and WMNs, especially associative networks, and GBM core follows five main distribution patterns. Some inter-related GMNs and WMNs were co-lesioned by GBM, suggesting that GBM distribution is not independent of the brain's structural and functional organization. Although the involvement of ventral frontoparietal tracts (N12) seems to have some role in predicting survival, network-topology information is overall scarcely informative about OS. fMRI-based approaches may more effectively demonstrate the effects of GBM on brain networks and survival.

Indexed as

functional gray matter networksfunctional white matter networksglioblastomaMRIoverall survivalpatterns

Identifiers

PMID37409023
PMCPMC10318144

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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.