In vivo laser speckle contrast imaging of 4-aminopyridine- or pentylenetetrazole-induced seizures.
Yuhling WangVassiliy TsytsarevLun-De LiaoPublished in: APL bioengineering (2023)
Clinical and preclinical studies on epileptic seizures are closely linked to the study of neurovascular coupling. Obtaining reliable information about cerebral blood flow (CBF) in the area of epileptic activity through minimally invasive techniques is crucial for research in this field. In our studies, we used laser speckle contrast imaging (LSCI) to gather information about the local blood circulation in the area of epileptic activity. We used two models of epileptic seizures: one based on 4-aminopyridine (4-AP) and another based on pentylenetetrazole (PTZ). We verified the duration of an epileptic seizure using electrocorticography (ECoG). We applied the antiepileptic drug topiramate (TPM) to both models, but its effect was different in each case. However, in both models, TPM had an effect on neurovascular coupling in the area of epileptic activity, as shown by both LSCI and ECoG data. We demonstrated that TPM significantly reduced the amplitude of 4-AP-induced epileptic seizures (4-AP+TPM: 0.61 ± 0.13 mV vs 4-AP: 1.08 ± 0.19 mV; p < 0.05), and it also reduced gamma power in ECoG in PTZ-induced epileptic seizures (PTZ+TPM: 38.5% ± 11.9% of the peak value vs PTZ: 59.2% ± 3.0% of peak value; p < 0.05). We also captured the pattern of CBF changes during focal epileptic seizures induced by 4-AP. Our data confirm that the system of simultaneous cortical LSCI and registration of ECoG makes it possible to evaluate the effectiveness of pharmacological agents in various types of epileptic seizures in in vivo models and provides spatial and temporal information on the process of ictogenesis.
Keyphrases
- transcription factor
- high glucose
- high resolution
- magnetic resonance
- randomized controlled trial
- temporal lobe epilepsy
- systematic review
- healthcare
- electronic health record
- emergency department
- cerebral blood flow
- stem cells
- mesenchymal stem cells
- big data
- bone marrow
- health information
- machine learning
- ionic liquid