| Classification of neurocognitive impairment in pediatric drug-resistant focal epilepsy by quantifying seizure-affected brain network abnormalities in clinical diffusion-weighted imaging connectome |
Jeong-Won Jeong1,2,3,4 
, Min-Hee Lee1,2,
, Yoonho Hwang1,2
, Michael Behen1,3
, Aimee Luat3,5
, Csaba Juhász1,2,3,4
, Eishi Asano1,2,3,4,5
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1Department of Pediatrics, Wayne State University, Detroit, MI, USA
2Translational Imaging Laboratory, University Health Center, Detroit, MI, USA
3Department of Neurology, Wayne State University, Detroit, MI, USA
4Translational Neuroscience Program, Wayne State University, Detroit, MI, USA
5Department of Pediatrics, Central Michigan University, Mt. Pleasant, MI, USA |
Correspondence:
Jeong-Won Jeong, Email: jjeong@med.wayne.edu
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Received: 18 December 2025 • Revised: 30 January 2026 • Accepted: 6 February 2026 |
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| Abstract |
Background
Diverse factors including seizure onset age, seizure frequency, epilepsy duration, total number of antiseizure medications trialed are considered as seizures-related neurocognitive loads in children with drug-resistant focal epilepsy (DRE). However, their associations with the structural integrity of neurocognitive networks remain largely unknown.
Purpose
This study investigates a novel diffusion-weighted imaging (DWI) connectome methodology that can extract seizure-associated structural abnormality biomarkers from clinical DWI tractography, use them to classify neurocognitive impairments prior to surgery, and unveil the relationship between epilepsy-related factors and neurocognitive impairments.
Methods
Thirty-three DRE children (age: 11.8±3.3 years, 17 boys) and 29 age-matched healthy controls were enrolled to create seizure-affected networks whose edges connect epileptogenic regions to key brain regions of 6 neurocognitive networks. The deviations of local efficiency values were averaged across the seizure-affected brain regions and used as new imaging-based biomarkers quantifying the degrees of seizure-associated structural abnormalities accumulated on individual neurocognitive networks and classifying the neurocognitive impairments along with the epilepsy-related factors.
Results
Effect sizes of the proposed biomarkers for differentiating DRE from healthy controls were consistently very large across various subgroups defined by lesion types, lobar locations of epileptogenic foci, seizure frequency categories, and seizure types (i.e., Cohen d value >1.8). Compared with the epilepsy-related factors, the proposed biomarkers demonstrated superior classification accuracy for identifying neurocognitive impairments in general, verbal, and nonverbal domains. When combined with the epilepsy-related factors, the classification performance further improved, achieving an accuracy range of 90%–98% in the independent test patients. The subsequent association analysis using the proposed biomarkers as seizure-associated structural abnormality indicators demonstrated that the inclusion of such imaging indicators significantly enhances the strength of associations between epilepsy factors and neurocognitive impairments.
Conclusion
These findings offer strong potential for objectively identifying neurocognitive impairments in DRE children, supporting early, data-driven decisions for personalized interventions to mitigate long-term effects. |
| Key Words:
Pediatrics, Drug-resistant epilepsy, Diffusion magnetic resonance imaging, Cognitive dysfunction, Seizure-affected brain network |
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