Neurovascular multiparametric MRI defines epileptogenic and seizure propagation regions in experimental mesiotemporal...

Improving the identification of the epileptogenic zone and associated seizure-spreading regions represents a significant challenge. Innovative brain-imaging modalities tracking neurovascular dynamics during seizures may provide new disease biomarkers.

With use of a multi-parametric magnetic resonance imaging (MRI) analysis at 9.4 Tesla, we examined, elaborated, and combined multiple cellular and cerebrovascular MRI read-outs as imaging biomarkers of the epileptogenic and seizure-propagating regions. Analyses were performed in an experimental model of mesial temporal lobe epilepsy (MTLE) generated by unilateral intra-hippocampal injection of kainic acid (KA).

In the ipsilateral epileptogenic hippocampi, tissue T₁ and blood-brain barrier (BBB) permeability to gadolinium were increased 48-72 hours post-KA, as compared to sham and contralateral hippocampi. BBB permeability endured during spontaneous focal seizures (4-6 weeks), along with a significant increase of apparent diffusion coefficient (ADC) and blood volume fraction (BVf). Simultaneously, ADC and BVf were augmented in the contralateral hippocampus, a region characterized by electroencephalographic seizure spreading, discrete histological neurovascular cell modifications, and no tissue sclerosis. We next asked whether combining all the acquired MRI parameters could deliver criteria to classify the epileptogenic from the seizure-spreading and sham hippocampi in these experimental conditions and over time. To differentiate sham from epileptogenic areas, the automatic multi-parametric classification provided a maximum accuracy of 97.5% (32 regions) 48-72 hours post-KA and of 100% (60 regions) at spontaneous seizures stage. To differentiate sham, epileptogenic, and seizure-spreading areas, the accuracies of the automatic classification were 93.1% (42 regions) 48-72 hours post-KA and 95% (80 regions) at spontaneous seizure stage.

Combining multi-parametric MRI acquisition and machine-learning analyses delivers specific imaging identifiers to segregate the epileptogenic from the contralateral seizure-spreading hippocampi in experimental MTLE. The potential clinical value of our findings is critically discussed.