Capillary level imaging of local cerebral blood flow in bicuculline-induced epileptic foci

Local hemodynamics of the cerebral cortex is the basis of modern functional imaging techniques, such as fMRIand PET. Despite the importance of local regulation of the blood flow, capillary level quantification of cerebral blood flow has been limited by the spatial resolution of functional imaging techniques and the depth penetration of conventional optical microscopy. Two-photon laser scanning microscopic imaging technique has the necessary spatial resolution and can image capillaries in the depth of the cortex. We have loaded the serum with fluorescein isothiocyanate dextran and quantified the flow of red blood cells (RBCs) in capillaries in layers 2/3 of the mouse somatosensory cortex in vivo. Basal capillary flux was quantified as approximately 28.9+/-13.6 RBCs/s (n=50, mean+/-S.D.) under ketamine-xylazine anesthesia and 26.7+/-16.0 RBCs/s (n=31) under urethane anesthesia. Focal interictal (epileptiform) activity was induced by local infusion of bicuculline methochloride in the cortex. We have observed that capillary blood flow increased as the cortical local field events developed into epileptiform in the vicinity of GABA receptor blockade (<300 microm from the administration site). Local blood flow in the interictal focus increased significantly (42.5+/-18.5RBCs/s, n=52) relative to the control conditions or to blood flow measured in capillaries at distant (>1mm from the focus) sites from the epileptic focus (27.8+/-12.9 RBCs/s, n=30). These results show that hyper-synchronized neural activity is associated with increased capillary perfusion in a localized cortical area. This volume is significantly smaller than the currently available resolution of the fMRI signal.

Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings

EEG signals obtained during tonic-clonic epileptic seizures can be severely contaminated by muscle and physiological noise. Heavily contaminated EEG signals are hard to analyse quantitatively and also are usually rejected for visual inspection by physicians, resulting in a considerable loss of collected information. The aim of this work was to develop a computer-based method of time series analysis for such EEGs. A method is presented for filtering those frequencies associated with muscle activity using a wavelet transform. One of the advantages of this method over traditional filtering is that wavelet filtering of some frequency bands does not modify the pattern of the remaining ones. In consequence, the dynamics associated with them do not change. After generation of a 'noise free' signal by removal of the muscle artifacts using wavelets, a dynamic analysis was performed using non-linear dynamics metric tools. The characteristic parameters evaluated (correlation dimension D2 and largest Lyapunov exponent lambda1) were compatible with those obtained in previous works. The average values obtained were: D2=4.25 and lambda1=3.27 for the pre-ictal stage; D2=4.03 and lambda1=2.68 for the tonic seizure stage; D2=4.11 and lambda1=2.46 for the clonic seizure stage.