Evidence map›Paper›PMID 38234795›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Portable Multi-focal Visual Evoked Potential Diagnostics for Multiple Sclerosis/Optic Neuritis patients.

S Mohammad Ali Banijamali, Craig Versek, Kristen Babinski, Sagar Kamarthi, Deborah Green-LaRoche, Srinivas Sridhar

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

S Mohammad Ali BanijamaliDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA.ORCID 0009-0004-1467-1726
Craig VersekNeuroFieldz Inc, Newton, MA, USA.ORCID 0000-0003-3553-4355
Kristen BabinskiDepartment of Neurology, Tufts University School of Medicine, Tufts Medical Center, Boston, MA, USA.
Sagar KamarthiDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA.ORCID 0000-0001-5845-7714
Deborah Green-LaRocheDepartment of Neurology, Tufts University School of Medicine, Tufts Medical Center, Boston, MA, USA.ORCID 0000-0001-8158-0788
Srinivas SridharDepartment of Physics, Department of Bioengineering and Department of Chemical Engineering, Northeastern University, Boston, MA 02115, NeuroFieldz Inc, Newton, MA, USA.
Northeastern University · USTufts University · USNeuroMetrix (United States) · US

Funding

Tufts Clinical and Translational Science Institute (Pilot Supplement)UL1TR002544 · NCATS · TUFTS UNIVERSITY BOSTON · PI SELKER, HARRY P. · 2018 to 2022
$50.2M
NCATS NIH HHS UL1 TR002544
6 · The paper itself

Abstract

Purpose: Multiple Sclerosis (MS) is a neuro-inflammatory disease of the Central Nervous System (CNS) in which the body's immune system attacks and destroys myelin sheath that protects nerve fibers and causes disruption in axonal signal transmission. Demyelinating Optic Neuritis (ON) is often a manifestation of MS and involves inflammation of the optic nerve. ON can cause vision loss, pain and discomfort in the eyes, and difficulties in color perception.In this study, we developed NeuroVEP, a portable, wireless diagnostic system that delivers visual stimuli through a smartphone in a headset and measures evoked potentials at the visual cortex from near the O1, Oz, O2, O9 and O10 locations on the scalp (extended 10-20 system) using custom electroencephalography (EEG) electrodes. Methods: Each test session is constituted by a short 2.5-minute full-field visual evoked potentials (ffVEP) test, followed by a 12.5-minute multifocal VEP (mfVEP) test. The ffVEP test evaluates the integrity of the visual pathway by analyzing the P1 (also known as P100) component of responses from each eye, while the mfVEP test evaluates 36 individual regions of the visual field for abnormalities. Extensive signal processing, feature extraction methods, and machine learning algorithms were explored for analyzing the mfVEP responses. The results of the ffVEP test for patients were evaluated against normative data collected from a group of subjects with normal vision. Custom visual stimuli with simulated defects were used to validate the mfVEP results which yielded 91% accuracy of classification. Results: 20 subjects, 10 controls and 10 with MS and/or ON were tested with the NeuroVEP device and a standard-of-care (SOC) VEP testing device which delivers only ffVEP stimuli. In 91% of the cases, the ffVEP results agreed between NeuroVEP and SOC device. Where available, the NeuroVEP mfVEP results were in good agreement with Humphrey Automated Perimetry visual field analysis. The lesion locations deduced from the mfVEP data were consistent with Magnetic Resonance Imaging (MRI) and Optical Coherence Tomography (OCT) findings. Conclusion: This pilot study indicates that NeuroVEP has the potential to be a reliable, portable, and objective diagnostic device for electrophysiology and visual field analysis for neuro-visual disorders.

Indexed as

Full-Field Visual Evoked Potential (ffVEP)Machine LearningMulti-Focal Visual Evoked Potential (mfVEP)Multiple Sclerosis (MS)Optic NeuritisPortable DiagnosticsSignal ProcessingSupport Vector Machine (SVM)

Identifiers

PMID38234795
PMCPMC10793525
OpenAlexW4390245140

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.