Evidence map›Paper›PMID 42238897›Full record

ArticleFrontiers in network physiology2026

Quantifying cognitive effort's impact on suppression of epilepsy-associated after discharges.

Sai Pavan Beeram, Matthew Farris, Samir Hossain, Nicholas Rethans, Joon Y Kang, Emily A Pereira

Abstract read
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Article in Frontiers in network physiology, 2026. 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
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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Sai Pavan BeeramDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, United States.
Matthew FarrisDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, United States.
Samir HossainDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, United States.
Nicholas RethansDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, United States.
Joon Y KangDepartment of Neurology, Johns Hopkins University, Baltimore, MD, United States.
Emily A PereiraDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epilepsy affects over 50 million people worldwide, with approximately 30% of patients experiencing seizures that are resistant to medication, significantly impacting their quality of life. In this study, we analyze a unique dataset of intracranial electroencephalography (iEEG) recordings from patients undergoing functional brain mapping prior to epilepsy surgery. During this procedure, electrodes are implanted to identify critical brain regions, and brief pulse electrical stimulation is applied. Often, abnormal brain activity, known as epilepsy-associated after discharges (EAADs), are triggered by stimulation. During these events, patients are engaged in cognitive tasks to exert mental effort, such as solving arithmetic and spelling problems, as a potential method to suppress EAADs. We present evidence that exerting mental effort to perform cognitive tasks can suppress EAADs. Using detrended fluctuation analysis (DFA), multifractal DFA (MFDFA), and fractional-order dynamical network models (FODN), we characterize the temporal and spatial dynamics of iEEG data during these events to identify conditions and classify the trials under which cognitive effort is most effective to suppress EAADs. Our logistic regression model achieves an average accuracy of 77% using leave-one-out cross-validation. Our findings pave the way for a promising, non-invasive therapeutic avenue for managing epileptic activity through targeted cognitive activation. Our work lays the groundwork for novel brain-state modulation strategies for the treatment of epilepsy.

Indexed as

brain-state modulationcognitive effortdetrended fluctuation analysisepilepsyfractional-order dynamical networksintracranial EEGmultifractal DFAnetwork physiology

Identifiers

PMID42238897
PMCPMC13226027

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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.