66 research outputs found
Comment on Performance of Different Synchronization Measures in Real Data: A Case Study on Electroencephalographic Signals
Quian Quiroga [Phys. Rev. E 65, 041903 (2002)] reported a similar performance of several linear and nonlinear measures of synchronization when applied to the rat electrocorticogram (ECoG). However, they found that the mutual information measure did not produce robust estimates of synchronization when compared to other measures. We reexamined their data using a histogram method with adaptive partitioning and found the mutual information to be a useful measure of regional ECoG interdependence
Reply to âComment on âPerformance of different synchronization measures in real data: A case study on electroencephalographic signalsâ â
Acute Ethanol Effects on Focal Cerebral Ischemia in Nonfasted Rats
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66417/1/j.1530-0277.1997.tb03832.x.pd
Aprendizagem de procedimentos e efeitos ansiolĂticos: medidas eletrencefalogrĂĄficas, motora e atencional
Radial columns in autoradiographs generated from tracer methods for measuring cerebral cortical blood flow
Intracranial EEG Evaluation of a Resting State Network
The study of brain networks is based, to a large extent, on pairwise measurements of relationships. Such pairwise relationships can be estimated from brain electrical activity measured from the scalp with routine EEG, from intracranial electrodes during monitoring for epilepsy surgery, or with functional MRI (fMRI) measurements. Here we focus on the study of brain networks through the measurement of pairwise relationships in brain electrical activity as measured by intracranial EEGs. Intracranial EEG data from 11 patients were tested for the presence of relationships which would confi rm or disconfi rm the presence of a resting state network which has previously been observed with fMRI. Mutual information was used as a measure of relationship
Artificial neural network trained on smartphone behavior can trace epileptiform activity in epilepsy
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Spectroscopic Imaging of Human Medial Temporal Lobe Epilepsy At 7T
The presence of unilateral hippocampal atrophy in the evaluation of medial temporal lobe epilepsy (MTLE) is often a defining factor that if concordant with the EEG and PET and not contraindicated from neuropsychological data, will typically result in candidacy for surgical resection. However, it is possible to have hippocampal atrophy (HA) without intractable seizures particularly in familial MTLE. We used ultra-high field MR spectroscopic imaging at 7T to assess MTLE with patients who are all HA positive. We studied n=12 patients who were medically intractable and n=2 patients who were very well controlled (seizure free for >2years on medication)
elective homonuclear polarization transfer for spectroscopic imaging of GABA at 7T
We develop and implement a selective homonuclear polarization transfer method for the detection of 3.0 ppm Câ4 GABA resonance by spectroscopic imaging in the human brain at 7T. This single shot method is demonstrated with simulations and phantoms, which achieves comparable efficiency of detection to that of Jâdifference editing. The macromolecule resonance that commonly coâedits with GABA is suppressed at 7T through use of a narrow band preacquisition suppression pulse. This technique is implemented in humans with an eight channel transceiver array and high degree B0 shimming to measure supplementary motor area and thalamic GABA in controls (n = 8) and epilepsy patients (n = 8 total). We find that the GABA/Nâacetyl aspartate ratio in the thalamus of control volunteers, well controlled and poorly controlled epilepsy patients are 0.053 ± 0.012 (n = 8), 0.090 ± 0.012 (n = 2), and 0.038 ± 0.009 (n = 6), respectively
Temporal fluctuations in coherence of brain waves.
As a measure of dynamical structure, short-term fluctuations of coherence between 0.3 and 100 Hz in the electroencephalogram (EEG) of humans were studied from recordings made by chronic subdural macroelectrodes 5-10 mm apart, on temporal, frontal, and parietal lobes, and from intracranial probes deep in the temporal lobe, including the hippocampus, during sleep, alert, and seizure states. The time series of coherence between adjacent sites calculated every second or less often varies widely in stability over time; sometimes it is stable for half a minute or more. Within 2-min samples, coherence commonly fluctuates by a factor up to 2-3, in all bands, within the time scale of seconds to tens of seconds. The power spectrum of the time series of these fluctuations is broad, extending to 0.02 Hz or slower, and is weighted toward the slower frequencies; little power is faster than 0.5 Hz. Some records show conspicuous swings with a preferred duration of 5-15s, either irregularly or quasirhythmically with a broad peak around 0.1 Hz. Periodicity is not statistically significant in most records. In our sampling, we have not found a consistent difference between lobes of the brain, subdural and depth electrodes, or sleeping and waking states. Seizures generally raise the mean coherence in all frequencies and may reduce the fluctuations by a ceiling effect. The coherence time series of different bands is positively correlated (0.45 overall); significant nonindependence extends for at least two octaves. Coherence fluctuations are quite local; the time series of adjacent electrodes is correlated with that of the nearest neighbor pairs (10 mm) to a coefficient averaging approximately 0.4, falling to approximately 0.2 for neighbors-but-one (20 mm) and to < 0.1 for neighbors-but-two (30 mm). The evidence indicates fine structure in time and space, a dynamic and local determination of this measure of cooperativity. Widely separated frequencies tending to fluctuate together exclude independent oscillators as the general or usual basis of the EEG, although a few rhythms are well known under special conditions. Broad-band events may be the more usual generators. Loci only a few millimeters apart can fluctuate widely in seconds, either in parallel or independently. Scalp EEG coherence cannot be predicted from subdural or deep recordings, or vice versa, and intracortical microelectrodes show still greater coherence fluctuation in space and time. Widely used computations of chaos and dimensionality made upon data from scalp or even subdural or depth electrodes, even when reproducible in successive samples, cannot be considered representative of the brain or the given structure or brain state but only of the scale or view (receptive field) of the electrodes used. Relevant to the evolution of more complex brains, which is an outstanding fact of animal evolution, we believe that measures of cooperativity are likely to be among the dynamic features by which major evolutionary grades of brains differ
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