Hao Cheng
ABSTRACT
The absence of visible indicators in MRI-negative temporal lobe epilepsy (NTLE) presents challenges for analysis. Given its dynamic nature, NTLE could potentially be detected using dynamic functional biomarkers. For more precise, an algorithm jointing graph theory of brain network and dynamic complexity computation was proposed based on resting-state functional MRI (rfMRI). Machine learning of relevance vector machine (RVM) validated this proposal. 40 TLE patients, equal in MRI-negative and MRI-positive, and 20 healthy people underwent rfMRI; after estimated by time-varying parameter regression model at every time-point of rfMRI; The dynamic brain network formed by sparsity topology is quantified by graph theory local index, whose time series were evaluated by complexity of sample entropy (SE) model; by SE, specific or unique brain regions were successively extracted for NTLE, respectively relative to health or MRI-positive TLE, whose SEs were input into RVM, and effectiveness was validated by average classification accuracy. By integrating algorithm, 4 specific brain regions for NTLE were extracted, e.g., caudal superior parietal lobule, ventral agranular insula, nucleus accumbens and lateral prefrontal thalamus. Interestingly, lateral prefrontal thalamus was unique for NTLE. By the RVM, average specific classification accuracy gained 87.50%/75.00% respectively.
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Index Terms
- Dynamic fMRI specific complexity classification of MRI-negative temporal lobe epilepsy combined with machine learning: Specific classification of epilepsySpecific complexity classification of MRI-negative temporal lobe epilepsy
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