Advances in the Application of Technology to Epilepsy: The CIMIT/NIO Epilepsy Innovation Summit
Center for Integration of Medicine and Innovative Technology, Boston, MA, USA. Epilepsy & Behavior
(Impact Factor: 2.26).
09/2009; 16(1):3-46. DOI: 10.1016/j.yebeh.2009.06.028
In 2008, a group of clinicians, scientists, engineers, and industry representatives met to discuss advances in the application of engineering technologies to the diagnosis and treatment of patients with epilepsy. The presentations also provided a guide for further technological development, specifically in the evaluation of patients for epilepsy surgery, seizure onset detection and seizure prediction, intracranial treatment systems, and extracranial treatment systems. This article summarizes the discussions and demonstrates that cross-disciplinary interactions can catalyze collaborations between physicians and engineers to address and solve many of the pressing unmet needs in epilepsy.
Available from: Antonio Dourado
- "In general, fundamentally new therapeutic options like closed-loop electrical stimulation       and automatic local administration of antiepileptic medication   would be facilitated if sufficient prediction performance could be achieved (for overviews see   ). This would be of considerable interest especially for patients with pharmacoresistant epilepsies  . "
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ABSTRACT: Subclinical seizures (SCS) have rarely been considered in the diagnosis and therapy of epilepsy and have not been systematically analyzed in studies on seizure prediction. Here, we investigate whether predictions of subclinical seizures are feasible and how their occurrence may affect the performance of prediction algorithms. Using the European database of long-term recordings of surface and invasive electroencephalography data, we analyzed the data from 21 patients with SCS, including in total 413 clinically manifest seizures (CS) and 3341 SCS. Based on the mean phase coherence we investigated the predictive performance of CS and SCS. The two types of seizures had similar prediction sensitivities. Significant performance was found considerably more often for SCS than for CS, especially for patients with invasive recordings. When analyzing false alarms triggered by predicting CS, a significant number of these false predictions were followed by SCS for 9 of 21 patients. Although currently observed prediction performance may not be deemed sufficient for clinical applications for the majority of the patients, it can be concluded that the prediction of SCS is feasible on a similar level as for CS and allows a prediction of more of the seizures impairing patients, possibly also reducing the number of false alarms that were in fact correct predictions of CS. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
Available from: Agatha Lenartowicz
- "No sleep inducing medication was administered. EEG was recorded with an fMRI-compatible device (fEEG, Kappametrics, Inc., Virginia, US) that reduces fMRI and ballistocardiographic noise to allow visualization of cerebrally generated signals (Schachter et al., 2009). Each imaging session included multiple EEGfMRI recordings with durations that ranged from 3.5 to 15 minutes. "
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ABSTRACT: Sleep spindles and K-complexes are EEG hallmarks of non-REM sleep. However, the brain regions generating these discharges and the functional connections of their generators to other regions are not fully known. We investigated the neuroanatomical correlates of spindles and K-complexes using simultaneous EEG and fMRI.
EEGs recorded during EEG-fMRI studies of 7 individuals were used for fMRI analysis. Higher-level group analyses were performed, and images were thresholded at Z ≥ 2.3.
fMRI of 106 spindles and 60 K-complexes was analyzed. Spindles corresponded to increased signal in thalami and posterior cingulate, and right precuneus, putamen, paracentral cortex, and temporal lobe. K-complexes corresponded to increased signal in thalami, superior temporal lobes, paracentral gyri, and medial regions of the occipital, parietal and frontal lobes. Neither corresponded to regions of decreased signal.
fMRI of both spindles and K-complexes depicts signal subjacent to the vertex, which likely indicates each discharges' source. The thalamic signal is consistent with thalamic involvement in sleep homeostasis. The limbic region's signal is consistent with roles in memory consolidation. Unlike the spindle, the K-complex corresponds to extensive signal in primary sensory cortices.
Identification of these active regions contributes to the understanding of sleep networks and the physiology of awareness and memory during sleep.
Available from: Jessie Hsiang
- "EEG was recorded with an fMRI-compatible device (fEEG, Kappametrics, Inc., Virginia, US) that reduces imaging associated noise to allow visualization of cerebrally generated signals. (Schachter et al., 2009) Each imaging session included multiple simultaneous EEG and fMRI recordings with durations that ranged from 3.5 to 15 minutes. The total EEGfMRI recording time for a participant typically was 45 to 60 minutes. "
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ABSTRACT: The vertex sharp transient (VST) is an electroencephalographic (EEG) discharge that is an early marker of non-REM sleep. It has been recognized since the beginning of sleep physiology research, but its source and function remain mostly unexplained. We investigated VST generation using functional MRI (fMRI).
Simultaneous EEG and fMRI were recorded from seven individuals in drowsiness and light sleep. VST occurrences on EEG were modeled with fMRI using an impulse function convolved with a hemodynamic response function to identify cerebral regions correlating to the VSTs. A resulting statistical image was thresholded at Z>2.3.
Two hundred VSTs were identified. Significantly increased signal was present bilaterally in medial central, lateral precentral, posterior superior temporal, and medial occipital cortex. No regions of decreased signal were present.
The regions are consistent with electrophysiologic evidence from animal models and functional imaging of human sleep, but the results are specific to VSTs. The regions principally encompass the primary sensorimotor cortical regions for vision, hearing, and touch.
The results depict a network comprising the presumed VST generator and its associated regions. The associated regions functional similarity for primary sensation suggests a role for VSTs in sensory experience during sleep.
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