Evidence map›Paper›PMID 36451357›Full record

ArticleNeuroImage. Clinical2022

BMAT: An open-source BIDS managing and analysis tool.

Colin Vanden Bulcke, Maxence Wynen, Jules Detobel, Francesco La Rosa, Martina Absinta, Laurence Dricot, Benoît Macq, Meritxell Bach Cuadra, Pietro Maggi

Abstract read
In one paragraph

Article in NeuroImage. Clinical, 2022. 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. Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Observational
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

9 authors.

Colin Vanden BulckeICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium; Louvain Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium. Electronic address: colin.vandenbulcke@uclouvain.be.
Maxence WynenICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium; Louvain Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium; CIBM Center for Biomedical Imaging, Lausanne, Switzerland; Radiology Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Jules DetobelICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium; Louvain Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium.
Francesco La RosaSignal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; CIBM Center for Biomedical Imaging, Lausanne, Switzerland; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Martina AbsintaInstitute of Experimental Neurology, Division of Neuroscience, Vita-Salute San Raffaele University and Hospital, Milan, Italy.
Laurence DricotInstitute of NeuroScience, Université catholique de Louvain, Brussels, Belgium.
Benoît MacqICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium.
Meritxell Bach CuadraCIBM Center for Biomedical Imaging, Lausanne, Switzerland; Radiology Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Pietro MaggiLouvain Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium; Department of Neurology, Cliniques universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium; Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland. Electronic address: pietro.maggi@saintluc.uclouvain.be.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) has been recently proposed. The BIDS standard eases data sharing and processing within or between centers by providing guidelines for their description and organization. However, the transformation from the complex unstructured non-open file data formats coming directly from the MRI scanner to a correct BIDS structure can be cumbersome and time consuming. This hinders a wider adoption of the BIDS format across different study centers. To solve this problem and ease the day-to-day use of BIDS for the neuroimaging scientific community, we present the BIDS Managing and Analysis Tool (BMAT). The BMAT software is a complete and easy-to-use local open-source neuroimaging analysis tool with a graphical user interface (GUI) that uses the BIDS format to organize and process brain MRI data for MS imaging research studies. BMAT provides the possibility to translate data from MRI scanners to the BIDS structure, create and manage BIDS datasets as well as develop and run automated processing pipelines, and is faster than its competitor. BMAT software propose the possibility to download useful analysis apps, especially applied to MS research with lesion segmentation and processing of imaging contrasts for novel disease biomarkers such as the central vein sign and the paramagnetic rim lesions.

Indexed as

Multiple SclerosisNeuroimagingBrainHumansMagnetic Resonance ImagingSoftwareBIDSMRIMultiple SclerosisNeuroimagingSoftware

Identifiers

PMID36451357
PMCPMC9723304

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.