there is an unmet need for better treatment options. Cannabidiol (CBD), a major phytocannabinoid constituent of cannabis, has shown antiseizure activity and behavioral benefits in preclinical and clinical studies for some disorders associated with epilepsy, suggesting that the same could be true for AS. Here, we show that acute CBD (100 mg/kg) treatment attenuated hyperthermia- and acoustically induced seizures in a mouse model of AS. However, neither acute CBD nor a 2-week-long course of CBD administered immediately after a kindling protocol could halt the proepileptogenic plasticity observed in AS model mice. CBD had a dose-dependent sedative effect but did not have an impact on motor performance. CBD abrogated the enhanced intracortical local field potential power, including the delta and theta rhythms observed in AS model mice, indicating that CBD administration could also help normalize the EEG deficits observed in individuals with AS. We believe our results provide critical preclinical evidence supporting CBD treatment of seizures and alleviation of EEG abnormalities in AS and will thus help guide the rational development of CBD as a treatment for AS.
used in resting-state EEG studies. This calculation is a product of complex power spectrum decomposition, and it is sensitive to both amplitude and phase relationships between two signals. Strong power modulation at single sources can be detected by multiple nearby electrodes, inflating local connectivity measurements between these electrodes without reflecting the true coherence between separate but adjacent neural sources . This induces the confounding factor of local source strength, limiting the certainty of the real cause of the relationship when concurrent power modulations are detected (although phase relationship usually has a larger contribution than amplitude ). To overcome this limitation, phase syn- chrony analysis methods represent an approach to assess the phase relationship independently. Lachaux and col- leagues  proposed calculating a ‘phase-locking value’ (PLV) to measure phase synchrony. In their method, two EEG signals were first narrow band-passed (target frequency ± 2 Hz), then convolved with the Gabor wavelet function. Finally, the phase outputs from wavelet de- composition between two signals were compared. PLV is a metric bound between 0 and 1, with 1 indicating that phase difference varies little between trials (ERP) or segments (resting EEG), and 0 indicating a complete lack of phase synchrony. In addition to measuring phase relationships independently, another important feature is that PLV does not rest on the assumption of stationarity (between trials or segments) as in classic coherence calcu- lation. Stationarity refers to the similarity of spectral prop- erties between measurements, which can be more easily assumed with multiple trials that have identical stimula- tion periods, as in ERP tasks. In resting EEG, there are no clear breakpoints between segments of continuous data, and segment lengths are often based on the best tradeoff between frequency and temporal resolution, that is, how short each measurement segment can be while still affording accurate coverage of a number of oscillations in the frequency bands of interest. In the case of traditional coherence, non-stationarity of the power in a frequency band across time with no change in phase may present as changes in coherence values. Taking the confound between power and coherence into account is particularly important in studies of ASD, given reports of differences in resting-state spectral power in this population.
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I solemnly declare that this dissertation entitled “Correlation of severity of autism with risk factors and EEG abnormalities in children aged 3-12 years attending child guidance clinic at Institute of child health and hospital for children” has been prepared by me at Madras Medical College and Institute of child health, during 2012-2015. This dissertation is submitted to the Tamil Nadu Dr.M.G.R. Medical University towards the partial fulfilment of requirements for the award of M.D. Degree in Pediatrics (Branch-VII).
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From the results of serial EEG recordings, the tim- ing of injury can be judged postpartum, if EEG re- sults were normal during the early neonatal period but afterward acute stage EEG abnormalities ap- peared, followed by chronic stage abnormalities ap- peared; just before or around birth, if EEG results revealed acute stage abnormalities only during the early neonatal period; and sometime before birth, if EEG results revealed chronic stage abnormalities during the early neonatal period. One limitation of this study is that the first EEG was recorded within 72 hours of life, not immediately after birth. Al- though the first EEG should be obtained as early as possible, EEG recordings immediately after birth are often not practical because preterm infants usually need to be stabilized by intensive care. We acknowl- edge that it is difficult to distinguish perfectly be- tween a brain injury that occurred several hours after birth from one that occurred just before birth. We mean that around birth can include the first few hours after birth.
but the patient did have extensor plantar responses bilat- erally. Mini-Mental State Examination score was 19/30 (temporal and spatial disorientation with verbal learning impairment) and extensive neuropsychological assess- ment confirmed impairment of language (verbal fluency and naming), memory, and selective attention. The patient was also found to have fluctuations of conscious- ness as well as hallucinations. EEG demonstrated a background pattern of 7 – 8 Hz and slow biphasic and triphasic waves with higher amplitude in frontal regions (figure, A). A few days after admission, the patient deve- loped faciobrachial tonic seizures and generalized tonic- clonic seizures. The severity and frequency of seizures, as well as the EEG abnormalities (figure, B), improved after treatment with levetiracetam. Neuropsychological features, however, remained unchanged. Standard blood examinations were normal. Blood tests were also nega- tive for lupus anticoagulant, antinuclear antibodies, anti- bodies to extractable nuclear antigens, antineutrophil cytoplasmic autoantibodies, anticardiolipin antibodies, and cryoglobulins. Serum neoplastic markers and angiotensin-converting enzyme were within nor- mal range.
All patients had normal baseline EEG measures, and ten patients (38.5%) later showed EEG abnormalities. The daily dose of clozapine at the occurrence of EEG abnormalities varied from 125 to 600 mg (mean 305 mg/day). There were no significant differences between the normal and abnormal EEG groups in terms of sex, mean dose of clozapine, or length of clozapine treatment (Table 1). In the abnormal EEG group, the mean age and illness duration were significantly lower and shorter, respectively, than the corresponding values in the nor- mal EEG group (Table 1). The time to the occurrence of EEG abnormalities varied from 4 to 52 weeks (mean 14.6 weeks). The clinical courses of the patients with EEG abnormality are summarized in Table 2. The numbers of patients with EEG abnormalities were as follows: spikes in one patient, spike and wave complexes in ten patients, and slow waves in four patients (Table 2). No patients withdrew from the study because of the appearance of EEG abnormalities.
AUDS patients present with rapidly fluctuating mood symptoms, motor agitation when depressed and relative lack of insight and concern. These patients have more often a history of seizures, diagnose of epilepsy and EEG abnormalities than MDE patients. Whereas atypical depressive disorders are frequently encountered in patients in tertiary epilepsy centers , it seems that seizures, epilepsy and EEG abnormalities are also over- represented in AUDS patients in psychiatric emergency units. Obviously, further studies are needed to corrobo- rate these results. In a sentinel paper on epilepsy and depression, Kanner complains that “neurologists and psychiatrists [have] stopped talking to each other” . It is time that both specialties join forces again. We sug- gest that the study of AUDS patients may offer a new approach to better understanding epilepsy and its asso- ciation with depressive disorders.
Since the 1980s, a high EEG abnormality (15-30%) has been reported for patients with PD [14-17]. We carried out this study to investigate how the EEG abnormalities of PD patients are related to the clinical features and pathology of these patients. The risk of diagnosing panic disorder as epilepsy has been pointed out by some spe- cialists. There are a few case studies in which patients who had been initially diagnosed with panic disorder later proved to have been suffering from epilepsy[18,19]. This misdiagnosis risk could be attributed to the fact that of the 13 symptoms in the diagnosis criteria of panic attack in the Diagnostic and Statistical Manual of Mental Disorders(DSM)-IV, 12 symptoms are also observed in partial epilepsy[20,21]. Feeling of chocking is the only exception. It should be noted that epilepsy is diagnosed operationally, while an EEG check is only supplementary.
So far most of the studies of PSS were based on clin- ical semiology. EEG was performed when indicated by clinically obvious seizures. This would miss many electro- graphic (subclinical/non-convulsive) seizures in patients with altered mental status. EEG is the best neurodiag- nostic technique for detecting epileptic activity, especially in patients with non-convulsive PSS. EEG abnormalities in patients with stroke have been well described for many years and can be divided into three types: (1) non-spe- cific abnormalities (diffused and focal polymorphic delta slowing, ipsilateral attenuation or loss of alpha and beta activities, as well as sleep spindle) 48 49 ; (2) interictal
This patient’s overall clinical picture is most consistent with cortical myoclonus progressing to EPC. His focal, time-locked EEG abnormalities and myoclonic jerks are consistent with cortical myoclonus. Given that his myoclonus is conﬁned to one part of his body and has been lasting for hours to days, he meets criteria for EPC. A focal viral encephalitis as the cause of his EPC is supported by his time course of symptoms, MRI brain ﬁndings, and neutrophil-predominant pleocytosis. A speciﬁc viral pathogen was not identiﬁed, but up to 60% of encephalitis cases remain undiagnosed. 4
Computed tomography (CT), magnetic resonance im- aging (MRI), and electroencephalography (EEG) have all emerged as tools to assist in the diagnosis and assess- ment of neurological disorders; however, EEG is perhaps the most scalable and affordable of these tools . Using EEG, it is possible to study neural oscillations, or the rhythmic fluctuations in excitability of large popula- tions of neurons. The generation of these neural oscilla- tions is thought to be dependent on the excitatory and inhibitory balance of the cortex, and these oscillations have been linked to many processes in the brain, includ- ing sensory perception, memory, and cognition . Various characteristics of the background EEG and power spectrum have been shown to be abnormal in nu- merous neurological and psychiatric disorders, and EEG abnormalities have been described in both mouse models of Rett syndrome and in girls with the disorder [13 – 16] However, for a number of conditions, including Rett syndrome, the power spectral characteristics as well as the association between disease severity and EEG findings, particularly power spectral data, are unknown.
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We have shown significant reductions in FSIQ, VIQ, PIQ, PSQ, and GLC at 5 years in children who had mild HIE at birth compared with a contemporaneous comparison group. Although children with mild HIE had higher rates of overall intact survival, on detailed IQ assessments, children with a history of mild HIE did no better than children with moderate HIE at birth. Greater differences were evident between the mild and moderate grades when a detailed analysis of outcome was examined. Intact survival after mild EEG abnormalities at 24 hours was 65% and 57% with moderate HIE. Both groups compared poorly to the comparison groups, in which 97% had an intact survival.
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Among many other techniques, Artificial Neural Network (ANN) is one of the famous non-linear techniques that is used for the classification of EEG parameters for the drivers drowsiness detection (Correa, Orosco and Laciar, 2014), (Hashemi, Saba and Resalat, 2014), (Kaur and Singh, 2013), (Kurt et al ., 2009), (Tan and Halim, 2015). One of the main advantages of using ANN is that, it can classify the EEG data without prior information about it. It can extract the patterns from EEG signals even if there is no relation between input and output. This is a very important characteristic, as in many realistic cases the input and output relation is very difficult to establish. But, the accuracy of the ANN is very low as compared to the other techniques (Kaur and Singh, 2013).
Considering the serious outcome caused by epileptic seizure for the patients and the large population affected by epileptic seizure, a device that can quickly detect the onset of seizure and deliver therapy can be of great help. In recent years, the surge in brain-computer interface (BCI) technology introduces tremendous opportunities to applying physiological signals to biomedical applications. According to previous studies, the electroencephalogram signals (EEG) are closely related to brain activities and can be used to detect neural diseases [4, 5]. Therefore, analy- sis on the EEG signals is a powerful and enabling way to detect the onset of seizures. While we can collect EEG sig- nals from different parts of body, the scalp EEG is most widely adopted, which is a non-invasive, multi-channel recording of the brain’s electrical activities.
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A. Data Acquisition: The EEG database for processing is extracted by the University of Bonn , . This collection contains EEG information originating from different interval, to be specific, healthy subjects and epileptic subjects .The gathering these data contains five datasets recognized as: O, Z, F, N and S; each set have 100 sections of EEG signals of 23.6 seconds. Sets O and Z were gotten from healthy subjects with eyes open and shut individually; sets F and N were gotten among seizure free states in various zones of the mind and set S was gotten from a subject among seizure state . Sets Z and S were utilized just for the outcomes reported here.
The purpose of this randomized, controlled trial was to test the effect of SSC on 5 neonatal sleep organization features assessed with EEG/polysomnographic measures at postmenstrual age (PMA) of 32 weeks. The EEG/sleep measures represent different neural networks through- out the neural axis that contribute to sleep organization. Complex interconnections among multiple neuronal networks that subserve sleep allow phenotypic expres- sion of defined states of sleep, arousal, and wakefulness, punctuated by phasic activities such as motor activities (including REM). The effects of SSC on these measures have not been tested previously. We hypothesized that SSC would alter EEG/sleep organization.
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The experiments  were approved by the UBC Behav- ioral Research Ethics Board. We recruited seven able- bodied individuals, who did not wear glasses for this study. Their age ranges from 26 to 31. Participants gave an informed consent before participating in the experiment. Each individual was seated comfortably approximately 75 cm in front of a computer monitor and wore a 64-channel electrode cap. EEG signals were recorded from 15 elec- trodes placed over the motor cortex area of the brain as shown in Figure 2. Electrooculogram (EOG) signals were recorded by two pairs of electrodes placed around both eyes. Facial muscle activities were recorded by four pairs of electromyogram (EMG) surface electrodes placed sym- metrically on two related facial muscles from each side of the face: zygomaticus major and corrugator supercilii. All electrodes were referenced to the linked right and left
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Low spatial resolution on the scalp functional magnetic resonance imaging, for instance, will directly show areas of the brain that are active, whereas encephalogram needs intense interpretation simply to hypothesise what areas are activated by a specific response . EEG poorly determines neural activity that happens below the higher layers of the brain (the cortex). Unlike PET and MRS, cannot establish specific locations within the brain at that numerous neurotransmitters, drugs, etc. may be found . Often takes an extended time to attach a topic to electroencephalogram, because it needs precise placement of dozens of electrodes round the head and also the use of various gels, saline solutions, and/or pastes to stay them in place. Signal-to-noise ratio is poor, thus refined information analysis and comparatively large numbers of subjects are required to extract helpful data from EEG. Combining EEG with other Neuroimaging Techniques Simultaneous electroencephalogram recordings and functional magnetic resonance imaging scans are obtained successfully, although successful coincidental recording needs that many technical difficulties be overcome, like the presence of ballistocardiographic unit, magnetic resonance imaging pulse unit and also the induction of electrical currents in electroencephalogram wires that move inside the sturdy magnetic fields of the MRI. Whereas difficult, these are with success overcome during a variety of studies . Similarly, simultaneous recordings with meg and electroencephalogram have additionally been conducted, that has many benefits over using either technique alone:
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Vertical and horizontal head movements only caused statistical significant SNRD val- ues for ear electrodes in the alpha, beta and gamma bands. The scalp EEG was measured with active electrodes, and the ear-EEG was measured with passive electrodes. Based on this difference in electrode technology, it is likely that the ear-EEG recordings were more affected by capacitive coupled noise and noise related to cable motions. Thus, the artifacts, observed in the ear, could originate from cable motions rather than head move- ments as such. Cable motions can be reduced by mounting the EEG amplifier on the head, as demonstrated by Debener et al. .
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Occasionally children with epilepsy show a stagnation or decline in their cognitive performance. The EEG can be useful in investigating the possible role of epileptiform activity in causing this. In some children, especially those with idiopathic generalized epilepsies, frequent interictal and subtle ictal discharges are responsible and can be detected on prolonged EEG recordings, preferably with simultaneous video recording. In others, electrical status during slow wave sleep may be responsible for cognitive problems and this possibility should be investigated by a sleep recording. Finally, some children with apparent cognitive decline are in non-convulsive status epilepticus.This is usually obvious on a standard EEG.
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