Evidence map›Paper›PMID 38582867›Full record

ArticleCommunications biology2024

Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations.

Joan Falcó-Roget, Alberto Cacciola, Fabio Sambataro, Alessandro Crimi

Abstract read
In one paragraph

Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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.

Joan Falcó-RogetBrain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland. j.roget@sanoscience.org.ORCID 0000-0002-9410-6361
Alberto CacciolaBrain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Imaging, University of Messina, Messina, Italy.ORCID 0000-0001-9412-4116
Fabio SambataroDepartment of Neuroscience, University of Padova, Padua, Italy.ORCID 0000-0003-2102-416X
Alessandro CrimiBrain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland. a.crimi@sanoscience.org.ORCID 0000-0001-5397-6363

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.

Indexed as

Brain MappingBrain NeoplasmsBrainDiffusion Tensor ImagingHumansMachine Learning

Identifiers

PMID38582867
PMCPMC10998892

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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.