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Review
. 2025 Jun 18:15357597251332191.
doi: 10.1177/15357597251332191. Online ahead of print.

Imaging Epilepsy: Past, Passing, and to Come

Affiliations
Review

Imaging Epilepsy: Past, Passing, and to Come

William H Theodore et al. Epilepsy Curr. .

Abstract

New imaging techniques appearing over the last few decades have replaced procedures that were uncomfortable, of low specificity, and prone to adverse events. While computed tomography remains useful for imaging patients with seizures in acute settings, structural magnetic resonance imaging (MRI) has become the most important imaging modality for epilepsy evaluation, with adjunctive functional imaging also increasingly well established in presurgical evaluation, including positron emission tomography (PET), single photon ictal-interictal subtraction computed tomography co-registered to MRI and functional MRI for preoperative cognitive mapping. Neuroimaging in inherited metabolic epilepsies is integral to diagnosis, monitoring, and assessment of treatment response. Neurotransmitter receptor PET and magnetic resonance spectroscopy can help delineate the pathophysiology of these disorders. Machine learning and artificial intelligence analyses based on large MRI datasets composed of healthy volunteers and people with epilepsy have been initiated to detect lesions that are not found visually, particularly focal cortical dysplasia. These methods, not yet approved for patient care, depend on careful clinical correlation and training sets that fully sample broad populations.

Keywords: artificial intelligence; genetic epilepsy syndromes; image processing; imaging.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Imaging Drug-Resistant Epilepsy: Presurgical Evaluation.
Figure 2.
Figure 2.
Overview of MELD Graph and AID-HS Machine-Learning Algorithms for “Finding the Lesions You Cannot See.”
Figure 3.
Figure 3.
G-Ratio Image Map in SSADHD Subject (Left) and Age-Matched Healthy Control (Right). The Higher G-Ratio (Denser Red-Coloring) in SSADHD Corresponds to Myelin Thinning.
Figure 4.
Figure 4.
Global and Individual TLE Signature Derived from Latent Space. The Top Left Panel Displays the “Global” Signal of Epilepsy (SEE-GAAN) Derived from Latent Space (Blue Areas Indicate Greater Signal and Green Areas Indicate Less Signal). This Epilepsy Signal is Centered on the Limbic Circuit, with Subcortical Structures such as the Hippocampus and Thalamus, Along with the Lateral Temporal Neocortex and the Insula. The Top Right Panel Displays 3 Example Subjects Demonstrating the Epilepsy Signal at a Single-Subject Level (ie, the “Local” Signal) that have Patterns Seemingly Related to their Specific Epilepsy Characteristics, in this Case, the Side of Seizure Onset.

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