Neuropsychological aspects of type of epilepsy and etiological factors in adults

Jokeit, Hennric; Schacher, M.

doi:10.1016/j.yebeh.2003.11.003

Cited by 70 publications

(37 citation statements)

References 38 publications

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“…19,64 Parietal lobe epilepsy is associated with visual perceptual and spatial-constructional impairments, agnosia, decreased tactile discrimination ability contralateral to the side of surgery, left-right orientation confusion, hemineglect, and visual illusions. 12,34,52 Resection of the parietal lobe can also lead to anomia, agraphia, alexia, apraxia, acalculia, and face-perception difficulties in a small percentage of patients. 39,52 One study demonstrated that resec- Graph showing the reduction in naming ability (rounded to closest number of words) from before to after a left ATR stratified by age group (age at which patients began to have seizures).…”

Section: Deficits After Occipital and Parietal Lobe Resectionmentioning

confidence: 99%

Prediction of neuropsychological outcome after resection of temporal and extratemporal seizure foci

Dulay

Busch

2012

FOC

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Resection of seizure foci is an effective treatment for the control of medically intractable epilepsy. However, cognitive morbidity can occur as a result of surgical intervention. This morbidity is dependent on several factors, including location and extent of resection, disease characteristics, patient demographic characteristics, and functional status of the tissue to be resected. In this review article, the authors provide a summary of the neurocognitive outcomes of epilepsy surgery with an emphasis on presurgical predictors of postsurgical cognitive decline.

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Section: Deficits After Occipital and Parietal Lobe Resectionmentioning

confidence: 99%

Prediction of neuropsychological outcome after resection of temporal and extratemporal seizure foci

Dulay

Busch

2012

FOC

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“…Temporal lobe epilepsy (TLE) is the most frequent form of epilepsy in adults (Jokeit & Schacher, 2004), characterized by recurrent seizures (Acharya et al, 2008). Some patients with TLE are resistant to current medical therapies (Freitas et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Antiepileptogenic effect of curcumin on kainate-induced model of temporal lobe epilepsy

Kiasalari

Roghani

Khalili

et al. 2013

Pharmaceutical Biology

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Context: Temporal lobe epilepsy (TLE) is an intractable neurological disorder. Curcumin is the bioactive component of turmeric with anti-epileptic and neuroprotective potential. Objective: The beneficial effect of curcumin on the intrahippocampal kainate-induced model of TLE was investigated. Materials and methods: Rats were divided into sham, curcumin-pretreated sham, kainate and curcumin-pretreated kainate groups. The rat model of TLE was induced by unilateral intrahippocampal injection of 4 mg of kainate. Rats received curcumin p.o. at a dose of 100 mg/kg/d starting 1 week before the surgery. Seizure activity (SE) and oxidative stressrelated markers were measured. Furthermore, the Timm index for evaluation of mossy fiber sprouting (MFS) and number of Nissl-stained neurons were quantified. Results: All rats in the kainate group had SE, while 28.5% of rats showed seizures in the curcumin-pretreated kainate group. Malondialdehyde and nitrite and nitrate levels significantly increased in the kainate group (p50.01 and p50.05, respectively), and curcumin significantly lowered these parameters (p50.05). Superoxide dismutase activity significantly decreased in the kainate group (p50.05) and curcumin did not improve it. Rats in the kainate group showed a significant reduction of neurons in Cornu Ammonis 1 (CA1) (p50.05), CA3 (p50.005) and hilar (p50.01) regions, and curcumin significantly prevented these changes (p50.05-0.005). The Timm index significantly increased in the kainate group (p50.005), and curcumin significantly lowered this index (p50.01). Discussion and conclusion: Curcumin pretreatment can attenuate seizures, lower some oxidative stress markers, and prevent hippocampal neuronal loss and MFS in the kainate-induced model of TLE.

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“…It is defined as an underlying tendency of the brain to produce sudden bursts of abnormal electrical activity that disrupt other brain functions. Well-known causes of epilepsy may include: genetic disorders, traumatic brain injury, metabolic disturbances, alcohol or drug abuse, brain tumour, stroke, infection, and cortical malformations (Jokeit and Schacher, 2004). …”

Section: Epilepsymentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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Type of publicationDoctoral thesis Rights AbstractThe electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of-use and mobility are important.Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours.However, as typical EEG signals are of the order of microvolts (µV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance.This thesis therefore, contributes to the further improvement of automated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science.The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning i classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG.Subsequently, additional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head-movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement artefact detection.The artefact detecti...

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Neuropsychological aspects of type of epilepsy and etiological factors in adults

Cited by 70 publications

References 38 publications

Prediction of neuropsychological outcome after resection of temporal and extratemporal seizure foci

Prediction of neuropsychological outcome after resection of temporal and extratemporal seizure foci

Antiepileptogenic effect of curcumin on kainate-induced model of temporal lobe epilepsy

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

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