Quantitative longitudinal imaging of activated microglia as a marker of inflammation in the pilocarpine rat model of epilepsy using [11C]-( R)-PK11195 PET and MRI.

Inflammation may play a role in the development of epilepsy after brain insults. [11C]-( R)-PK11195 binds to TSPO, expressed by activated microglia. We quantified [11C]-( R)-PK11195 binding during epileptogenesis after pilocarpine-induced status epilepticus (SE), a model of temporal lobe epilepsy. Nine male rats were studied thrice (D0-1, D0 + 6, D0 + 35, D0 = SE induction). In the same session, 7T T2-weighted images and DTI for mean diffusivity (MD) and fractional anisotropy (FA) maps were acquired, followed by dynamic PET/CT. On D0 + 35, femoral arterial blood was sampled for rat-specific metabolite-corrected arterial plasma input functions (AIFs). In multiple MR-derived ROIs, we assessed four kinetic models (two with AIFs; two using a reference region), standard uptake values (SUVs), and a model with a mean AIF. All models showed large (up to two-fold) and significant TSPO binding increases in regions expected to be affected, and comparatively little change in the brainstem, at D0 + 6. Some individuals showed increases at D0 + 35. AIF models yielded more consistent increases at D0 + 6. FA values were decreased at D0 + 6 and had recovered by D0 + 35. MD was increased at D0 + 6 and more so at D0 + 35. [11C]-( R)-PK11195 PET binding and MR biomarker changes could be detected with only nine rats, highlighting the potential of longitudinal imaging studies.

Advanced [(18)F]FDG and [(11)C]flumazenil PET analysis for individual outcome prediction after temporal lobe epilepsy surgery for hippocampal sclerosis.

PURPOSE: We have previously shown that an imaging marker, increased periventricular [(11)C]flumazenil ([(11)C]FMZ) binding, is associated with failure to become seizure free (SF) after surgery for temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS). Here, we investigated whether increased preoperative periventricular white matter (WM) signal can be detected on clinical [(18)F]FDG-PET images. We then explored the potential of periventricular FDG WM increases, as well as whole-brain [(11)C]FMZ and [(18)F]FDG images analysed with random forest classifiers, for predicting surgery outcome. METHODS: Sixteen patients with MRI-defined HS had preoperative [(18)F]FDG and [(11)C]FMZ-PET. Fifty controls had [(18)F]FDG-PET (30), [(11)C]FMZ-PET (41), or both (21). Periventricular WM signal was analysed using Statistical Parametric Mapping (SPM8), and whole-brain image classification was performed using random forests implemented in R (http://www.r-project.org). Surgery outcome was predicted at the group and individual levels. RESULTS: At the group level, non-seizure free (NSF) versus SF patients had periventricular increases with both tracers. Against controls, NSF patients showed more prominent periventricular [(11)C]FMZ and [(18)F]FDG signal increases than SF patients. All differences were more marked for [(11)C]FMZ. For individuals, periventricular WM signal increases were seen at optimized thresholds in 5/8 NSF patients for both tracers. For SF patients, 1/8 showed periventricular signal increases for [(11)C]FMZ, and 4/8 for [(18)F]FDG. Hence, [(18)F]FDG had relatively poor sensitivity and specificity. Random forest classification accurately identified 7/8 SF and 7/8 NSF patients using [(11)C]FMZ images, but only 4/8 SF and 6/8 NSF patients with [(18)F]FDG. CONCLUSION: This study extends the association between periventricular WM increases and NSF outcome to clinical [(18)F]FDG-PET, but only at the group level. Whole-brain random forest classification increases [(11)C]FMZ-PET's performance for predicting surgery outcome.