Evidence map›Paper›PMID 35210268›Full record

ArticleAJNR. American journal of neuroradiology2022

Neuroanatomic Markers of Posttraumatic Epilepsy Based on MR Imaging and Machine Learning.

H Akrami, R M Leahy, A Irimia, P E Kim, C N Heck, A A Joshi

Open access · hybridAbstract read
In one paragraph

Article in AJNR. American journal of neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 26% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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  4. Review
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

6 authors at 5 institutions in 4 countries.

H AkramiFrom the Department of Biomedical Engineering (H.A., A.I.).ORCID 0000-0002-1678-8926
R M LeahyMing Hsieh Department of Electrical and Computer Engineering (R.M.L., A.A.J.).ORCID 0000-0002-7278-5471
A IrimiaFrom the Department of Biomedical Engineering (H.A., A.I.).ORCID 0000-0002-9254-9388
P E KimDepartments of Radiology (P.E.K.).ORCID 0000-0001-5726-5091
C N HeckNeurology (C.N.H.), University of Southern California, Los Angeles, California.
A A JoshiMing Hsieh Department of Electrical and Computer Engineering (R.M.L., A.A.J.) ajoshi@usc.edu.ORCID 0000-0002-9582-3848
Anna Needs Neuroblastoma Answers · USArt Institute of Portland · USMinghsin University of Science and Technology · TWResearch Institute of Radiology · BYUniversity of Southern California · US

Funding

BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG ImagingR01EB026299 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LEAHY, RICHARD M · 2018 to 2025
$5.4M
Interactive software for semiautomated analysis of structural brain imagesR01NS074980 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LEAHY, RICHARD M, SHATTUCK, DAVID W · 2011 to 2021
$5.0M
Effects of blood-brain barrier disruption upon white matter connectivity subsequent to traumatic brain injuryR01NS100973 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI IRIMIA, ANDREI · 2017 to 2021
$1.6M
NIBIB NIH HHS R01 EB026299NINDS NIH HHS R01 NS074980NINDS NIH HHS R01 NS100973
6 · The paper itself

Abstract

background and purposeAlthough posttraumatic epilepsy is a common complication of traumatic brain injury, the relationship between these conditions is unclear and early posttraumatic epilepsy detection and prevention remain major unmet clinical challenges. This study aimed to identify imaging biomarkers that predict posttraumatic epilepsy among survivors of traumatic brain injury on the basis of an MR imaging data set. MATERIALS AND

methodsWe performed tensor-based morphometry to analyze brain-shape changes associated with traumatic brain injury and to derive imaging features for statistical group comparison. Additionally, machine learning was used to identify structural anomalies associated with brain lesions. Automatically generated brain lesion maps were used to identify brain regions where lesion load may indicate an increased incidence of posttraumatic epilepsy. We used 138 non-posttraumatic epilepsy subjects for training the machine learning method. Validation of lesion delineation was performed on 15 subjects. Group analysis of the relationship between traumatic brain injury and posttraumatic epilepsy was performed on an independent set of 74 subjects (37 subjects with and 37 randomly selected subjects without epilepsy).

resultsWe observed significant

conclusionsStatistical analysis suggests that lesions in the temporal lobes, cerebellum, and the right occipital lobe are associated with an increased posttraumatic epilepsy incidence.

Indexed as

Brain Injuries, TraumaticEpilepsyEpilepsy, Post-TraumaticEpilepsy, Temporal LobeBiomarkersHumansMachine LearningMagnetic Resonance ImagingBiomarkers

Identifiers

PMID35210268
PMCPMC8910810
OpenAlexW4213432258

What OpenQuestion holds

Textmetadata
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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.