Abstract

Fasol M, Escudero J, Gonzalez-Sulser A (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Epilepsia Wiley-Blackwell

Book Volume: 65

Pages Range: 1-508

Issue: S1

DOI: 10.1111/epi.18151

Abstract

Purpose: Mutations in SYNGAP1, a gene responsible for regulating synaptic function, are reported to account for up to 1% of neurodevelopmental disorders. Most patients with pathogenic SYNGAP1 variants experience absence seizures, which coincide with developmental delay. The identification of non-invasive biomarkers which assess synaptic function could be invaluable to diagnose patients, track disease progression and determine treatment efficacy. We recently reported that a rat model of SYNGAP1 haploinsufficiency displays spontaneous seizures, abnormal social interactions, lack of extinction of fear learning, irregular sleep dynamics, and reduced connectivity between EEG electrodes.


Method: We analysed EEG recordings from SYNGAP1 heterozygous mutant rats and littermate controls, as well as overnight recordings from human patients with SYNGAP1 mutations and sibling controls. For individual EEG recording epochs excluding seizures (5 s in rats, 30 s in humans), we calculated feature values for signal complexity, spectral analysis and functional connectivity. We trained an extreme gradient boosting (XGBoost) machine learning classifier to differentiate between SYNGAP1 mutants and controls. We then applied the SHapley Additive exPlanations (SHAP) analysis to the classifier's predictions to determine which features were critical for identification.


Results: Using the XGBoost classifier we obtained out-of-the-box accuracy, precision, recall and F1 scores of 75%, 76%, 76% and 71% in rats and, 82%, 98%, 72% and 83% in humans respectively. The SHAP analysis revealed that functional connectivity metrics derived from somatosensory regions played a prominent role in detecting SYNGAP1 haploinsufficiency.


Conclusion: EEG machine learning analysis can efficiently segregate data from mutants and controls efficiently in both rats and humans. Connectivity parameters are most critical for machine identification in both species, suggesting animal model validity and potential clinical applicability.

Additional Organisation(s)

Sonderforschungsbereich 1540/1 - Erforschung der Mechanik des Gehirns (EBM): Verständnis, Engineering und Nutzung mechanischer Eigenschaften und Signale in der Entwicklung, Physiologie und Pathologie des zentralen Nervensystems

Involved external institutions

University of Edinburgh GB United Kingdom (GB)

How to cite

APA:

Fasol, M., Escudero, J., & Gonzalez-Sulser, A. (2024). Abstract. Epilepsia, 65, 1-508. https://doi.org/10.1111/epi.18151

MLA:

Fasol, M., Javier Escudero, and A Gonzalez-Sulser. "Abstract." Epilepsia 65 (2024): 1-508.

BibTeX: Download