Evidence map›Paper›PMID 37725167›Full record

ReviewNeurosurgical review2023

A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI.

Daniel A Di Giovanni, D Louis Collins

Abstract readReview
PubMed Publisher
In one paragraph

Review in Neurosurgical review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Daniel A Di GiovanniIntegrated Program in Neuroscience at McGill University, Montreal, Canada. daniel.digiovanni@mail.mcgill.ca.
D Louis CollinsDepartment of Biomedical Engineering and Department of Neurology and Neurosurgery in McGill University, Montreal, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep learning algorithms have greatly improved our ability to estimate eloquent cortex regions from resting-state brain scans for patients about to undergo neurosurgery. The use of deep learning has the potential to fully automate functional mapping of cortex in this context. We present a highly focused state-of-the-art review on current technology for estimating eloquent cortex from resting-state functional magnetic resonance scans and identify potential paths to meet this goal in the future.

Indexed as

Deep LearningMagnetic Resonance ImagingAlgorithmsCerebral CortexHumansNeuroimagingEloquent cortexfMRIPresurgical

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.