Evidence map›Paper›PMID 42259832›Full record

ArticleScientific data2026

A multimodal epilepsy dataset of paired 3-Tesla and 7-Tesla MRI and intracranial EEG.

Tamjid Imtiaz, Alfredo Lucas, Mariam Josyula, Nina Petillo, Joost Wagenaar, Joel M Stein, Sandhitsu Das, Nishant Sinha, Kathryn A Davis

Abstract readDataset
In one paragraph

Article in Scientific data, 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
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.

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

9 authors.

Tamjid Imtiaz *Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Alfredo Lucas *Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Mariam JosyulaCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Nina PetilloCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Joost WagenaarDepartment of Biostatistics, Epidemiology and Informatics, Philadelphia, USA.
Joel M SteinCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Sandhitsu DasCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Nishant SinhaCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Kathryn A DavisCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA. katedavis@pennmedicine.upenn.edu.

Funding

Biomarkers to Predict Outcome from Responsive Brain Stimulation for EpilepsyR61NS125568 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVIS, KATHRYN ADAMIAK · 2023 to 2025
$3.6M
Optimized Intracranial EEG Targeting in Focal Epilepsy based upon Neuroimaging ConnectomicsR01NS116504 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVIS, KATHRYN ADAMIAK · 2021 to 2025
$3.3M
Guiding epilepsy surgery using network models and Stereo EEGR01NS125137 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2022 to 2025
$2.8M
Pennsieve: Impactful Multimodal Data Sharing for Epilepsy ResearchU24NS134536 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Joost B Wagenaar · 2024 to 2026
$2.6M
Scalable methods to quantify epileptic network and guide epilepsy surgeryR00NS138680 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Nishant Sinha · 2026 to 2026
$249k
NINDS NIH HHS R00 NS138680NINDS NIH HHS R00NS138680NINDS NIH HHS R01 NS116504NINDS NIH HHS R01NS116504NINDS NIH HHS R01 NS125137NINDS NIH HHS R61 NS125568NINDS NIH HHS U24 NS134536NINDS NIH HHS U24NS134536U.S. Department of Defense W81XWH2210593
6 · The paper itself

Abstract

There is an increasing need to integrate multimodal datasets in epilepsy research, particularly to correlate electrophysiology with imaging in patients with refractory epilepsy. We present a multimodal paired 3T and 7T MRI dataset acquired from 30 drug-resistant focal epilepsy patients (18 females, 38.8 ± 11.7 years) who underwent T1-weighted (T1w), T2-weighted (T2w), Fluid Attenuated Inversion Recovery (FLAIR), and resting-state functional MRI (rs-fMRI). In addition to the raw data, we release preprocessed anatomical and functional data, along with various quality control and clinical metadata files. For participants who subsequently underwent intracranial EEG (iEEG) (n = 15), curated ictal and interictal epochs are also included. We demonstrate a potential application of this paired 3T and 7T data by training a deep learning model capable of synthesizing high-field 7T T1w MR images from the 3T equivalents. We anticipate that this dataset will facilitate future multiscale analyses in epilepsy.

Indexed as

Drug Resistant EpilepsyElectroencephalographyEpilepsyMagnetic Resonance ImagingAdultFemaleHumansMale

Identifiers

PMID42259832
PMCPMC13582879

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.