RESUMEN
OBJECTIVE: With the advent of ultra-long-term recordings for monitoring of epilepsies, the interpretation of results of isolated electroencephalographic (EEG) recordings covering only selected brain regions attracts considerable interest. In this context, the question arises of whether detected ictal EEG patterns correspond to clinically manifest seizures or rather to purely electrographic events, that is, subclinical events. METHODS: EEG patterns from 268 clinical seizures and 252 subclinical electrographic events from 50 patients undergoing video-EEG monitoring were analyzed. Features extracted included predominant frequency band, duration, association with rhythmic muscle artifacts, spatial extent, and propagation patterns. Classification using logistic regression was performed based on data from the whole dataset of 10-20 system EEG recordings and from a subset of two temporal electrode contacts. RESULTS: Correct separation of clinically manifest and purely electrographic events based on 10-20 system EEG recordings was possible in up to 83.8% of events, depending on the combination of features included. Correct classification based on two-channel recordings was only slightly inferior, achieving 78.6% accuracy; 74.4% and 74.8%, respectively, of events could be correctly classified when using duration alone with either electrode set, although classification accuracies were lower for some subgroups of seizures, particularly focal aware seizures and epileptic arousals. SIGNIFICANCE: A correct classification of subclinical versus clinical EEG events was possible in 74%-83% of events based on full EEG recordings, and in 74%-78% when considering only a subset of two electrodes, matching the channel number available from new implantable diagnostic devices. This is a promising outcome, suggesting that ultra-long-term low-channel EEG recordings may provide sufficient information for objective seizure diaries. Intraindividual optimization using high numbers of ictal events may further improve separation, provided that supervised learning with external validation is feasible.
RESUMEN
Myelin loss with consecutive axon degeneration and impaired remyelination are the underlying causes of progressive disease in patients with multiple sclerosis. Astrocytes are suggested to play a major role in these processes. The unmasking of distinct astrocyte identities in health and disease would help to understand the pathophysiological mechanisms in which astrocytes are involved. However, the number of specific astrocyte markers is limited. Therefore, we performed immunohistochemical studies and analyzed various markers including GFAP, vimentin, S100B, ALDH1L1, and LCN2 during de- and remyelination using the toxic murine cuprizone animal model. Applying this animal model, we were able to confirm overlapping expression of vimentin and GFAP and highlighted the potential of ALDH1L1 as a pan-astrocytic marker, in agreement with previous data. Only a small population of GFAP-positive astrocytes in the corpus callosum highly up-regulated LCN2 at the peak of demyelination and S100B expression was found in a subset of oligodendroglia as well, thus S100B turned out to have a limited use as a particular astroglial marker. Additionally, numerous GFAP-positive astrocytes in the lateral corpus callosum did not express S100B, further strengthening findings of heterogeneity in the astrocytic population. In conclusion, our results acknowledged that GFAP, vimentin, LCN2, and ALDH1L1 serve as reliable marker to identify activated astrocytes during cuprizone-induced de- and remyelination. Moreover, there were clear regional and temporal differences in protein and mRNA expression levels and patterns of the studied markers, generally between gray and white matter structures.