Evidence map›Paper›PMID 34385902›Full record

ReviewFrontiers in neuroscience2021

Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy.

Daichi Sone

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2021. 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
1.7field-weighted citation impact, top 17% 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

6 citing papers in PubMed, 17 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  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

1 author at 1 institution in 2 countries.

Daichi SoneDepartment of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.
University College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It has been a clinically important, long-standing challenge to accurately localize epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to the detected focus, including resection neurosurgery, can provide significant seizure reduction. In addition to neurophysiological examinations, neuroimaging plays a crucial role in the detection of focus by providing morphological and neuroanatomical information. On the other hand, epileptogenic lesions in the brain may sometimes show only subtle or even invisible abnormalities on conventional MRI sequences, and thus, efforts have been made for better visualization and improved detection of the focus lesions. Recent advance in neuroimaging has been attracting attention because of the potentials to better visualize the epileptogenic lesions as well as provide novel information about the pathophysiology of epilepsy. While the progress of newer neuroimaging techniques, including the non-Gaussian diffusion model and arterial spin labeling, could non-invasively detect decreased neurite parameters or hypoperfusion within the focus lesions, advances in analytic technology may also provide usefulness for both focus detection and understanding of epilepsy. There has been an increasing number of clinical and experimental applications of machine learning and network analysis in the field of epilepsy. This review article will shed light on recent advances in neuroimaging for focal epilepsy, including both technical progress of images and newer analytical methodologies and discuss about the potential usefulness in clinical practice.

Indexed as

advanced neuroimagingdiffusion neuroimagingfocal epilepsyfunctional neuroimagingmagnetic resonance imagingstructural neuroimaging

Identifiers

PMID34385902
PMCPMC8353251
OpenAlexW3186048978

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.