Naim Haddad

Non-invasive detection and identification of brain activity patterns in the developing fetus

OBJECTIVE Utilizing a MEG-based device specifically designed to study the fetus, we investigated the presence of salient patterns in spontaneous fetal brain activity. METHODS We performed 91 MEG recordings from 30 fetuses at various gestational ages. The tracings were evaluated and compared to the well-established electroencephalographic (EEG) features in premature infants. Also, we looked at the correlation of the gestational age (GA) on the occurrence of these patterns and complexes. RESULTS We were able to identify specific patterns and track changes in fetal brain activity starting at 28 weeks of gestation. The patterns and trends were similar to the established EEG features in premature infants at comparable ages. Of the 30 fetuses, 18 (60%) had at least one recording with discontinuity, 7 (23%) had sharp transients, and 8 (27%) had delta brush activity. Further there was a decrease in the presence of discontinuous patterns after 35 weeks. CONCLUSIONS We have shown that fetal spontaneous brain activity features can be recorded and identified using MEG technique. The observation of more discontinuity at early gestational ages is consistent with the overall pattern of maturation seen in EEGs of premature infants. SIGNIFICANCE With refinements, this method can aid in understanding the maturation process of fetal brain activity and further develop as a tool for fetal neurological evaluation.

Correlation between fetal brain activity patterns and behavioral states: An exploratory fetal magnetoencephalography study

The fetal brain remains inaccessible to neurophysiological studies. Magnetoencephalography (MEG) is being assessed to fill this gap. We performed 40 fetal MEG (fMEG) recordings with gestational ages (GA) ranging from 30 to 37 weeks. The data from each recording were divided into 15 second epochs which in turn were classified as continuous (CO), discontinuous (DC), or artifact. The fetal behavioral state, quiet or active sleep, was determined using previously defined criteria based on fetal movements and heart rate variability. We studied the correlation between the fetal state, the GA and the percentage of CO and DC epochs. We also analyzed the spectral edge frequency (SEF) and studied its relation with state and GA. We found that the odds of a DC epoch decreased by 6% per week as the GA increased (P = 0.0036). This decrease was mainly generated by changes during quiet sleep, which showed 52% DC epochs before a 35 week GA versus 38% after 35 weeks (P = 0.0006). Active sleep did not show a significant change in DC epochs with GA. When both states were compared for MEG patterns within each GA group (before and after 35 weeks), the early group was found to have more DC epochs in quiet sleep (54%) compared to active sleep (42%) (P = 0.036). No significant difference in DC epochs between the two states was noted in the late GA group. Analysis of SEF showed a significant difference (P = 0.0014) before and after a 35 week GA, with higher SEF noted at late GA. However, when both quiet and active sleep states were compared within each GA group, the SEF did not show a significant difference. We conclude that fMEG shows reproducible variations in gross features and frequency content, depending on GA and behavioral state. Fetal MEG is a promising tool to investigate fetal brain physiology and maturation.

Postoperative nonconvulsive encephalopathic status: identification of a syndrome responsible for delayed progressive deterioration of neurological status after skull base surgery. Clinical article.

OBJECT Over a 10-year period, the authors have observed a rare but recurring syndrome manifested by a delayed, postoperative, progressive decline in the level of consciousness to deep coma that is time-limited to several days with abrupt awakening. Extensive evaluation and workup demonstrated an abnormality on continuous electroencephalographic monitoring that implied nonconvulsive status epilepticus after the exclusion of structural, perfusion, infectious, or metabolic causes. This state has been very refractory to treatment with antiepileptic medication. In this article, the authors raise the awareness of this syndrome and its diagnosis, management, and outcome. METHODS The authors reviewed the medical records of a cohort of 7 patients who exemplified this syndrome who were treated during the last 5 years. RESULTS All 7 patients were women with a mean (+/- SD) age of 55 +/- 15 years. The mean duration of surgery was 8.9 +/- 1.8 hours. All patients had a stereotypical course of delayed progressive decline in their level of consciousness after surgery (average 3.3 +/- 4.3 days) leading to deep coma. The unconscious state was time-limited, lasting on average 17.3 +/- 13.7 days. Continuous electroencephalographic monitoring demonstrated a generalized abnormality with periodic discharges and abundant slow delta activity. A rather abrupt awakening occurred a few days after cessation of electrographic seizure activity. Structural, vascular, infectious, or metabolic causes were excluded based on an extensive workup. CONCLUSIONS In this study, the authors delineate and raise the awareness of an unusual syndrome. Recognition of this syndrome is important as a cause for delayed coma after surgery. The authors stress the need for respiratory, hemodynamic, and nutritional support for these patients until recovery. The origin of this syndrome remains enigmatic and is likely to be multifactorial with a prominent pharmacological role related to anesthetic agent or medication in a setting of craniotomy that is associated with alteration of the blood-brain barrier.

Telemedicine for patients with epilepsy: A pilot experience☆☆☆

We aimed to describe our preliminary experience with the telemedicine (TM) seizure clinic. A retrospective database analysis for the TM seizure visits between January 2009 and January 2012 was performed. Each subjects age, gender, epilepsy syndrome, seizure types, and outcome were identified. The antiepileptic drug (AED) regimen at each visit was noted, as well as instances where surgical therapies and admission for monitoring were discussed. We identified a total of 74 encounters with 24 patients. Fifteen subjects (62.5%) had focal epilepsy, whereas seven (29%) had generalized epilepsy. Seven patients (29%) experienced one seizure type, 14 (58.5%) had two seizure types, and five (12.5%) had three or more seizure types. Thirty-two out of the 74 encounters (43%) resulted in some change in the AED regimen. Surgical therapeutic options were discussed in 35% of the visits. Two-thirds of subjects were either seizure-free or improved by the last encounter. The no-show rate was only 11%. We were able to deliver follow-up care through a TM model to patients with epilepsy with a wide spectrum of syndromes and severity. We believe that TM improves access to specialized care for patients with epilepsy living in rural areas.

Improved spindle detection through intuitive pre-processing of electroencephalogram.

BACKGROUND Numerous signal processing techniques have been proposed for automated spindle detection on EEG recordings with varying degrees of success. While the latest techniques usually introduce computational complexity and/or vagueness, the conventional techniques attempted in literature have led to poor results. This study presents a spindle detection approach which relies on intuitive pre-processing of the EEG prior to spindle detection, thus resulting in higher accuracy even with standard techniques. NEW METHOD The pre-processing techniques proposed include applying the derivative operator on the EEG, suppressing the background activity using Empirical Mode Decomposition and shortlisting candidate EEG segments based on eye-movements on the EOG. RESULTS/COMPARISON Results show that standard signal processing tools such as wavelets and Fourier transforms perform much better when coupled with apt pre-processing techniques. The developed algorithm also relies on data-driven thresholds ensuring its adaptability to inter-subject and inter-scorer variability. When tested on sample EEG segments scored by multiple experts, the algorithm identified spindles with average sensitivities of 96.14 and 92.85% and specificities of 87.59 and 84.85% for Fourier transform and wavelets respectively. These results are found to be on par with results obtained by other recent studies in this area.

Spectral power differences in the brain activity of growth-restricted and normal fetuses

Using non-invasive fetal magnetoencephalography (fMEG), we investigated spontaneous brain activity in 28 fetuses diagnosed with intrauterine growth restriction (IUGR) and compared the results to 47 normal-growth fetuses. The fetal gestational age ranged from 28 to 39 weeks with post-natal recordings obtained on 17 of the IUGR fetuses. Power spectrum was computed and was divided into four frequency bands. A significant difference in the relative spectral power in delta, theta and beta bands (P<0.01) was observed only in the 28-32 week gestation age group with alpha band showing a similar trend (P=0.054). This observation suggests that growth restriction may have a more pronounced effect on the fetal brain in early gestation. Larger population studies could reveal the potential value of fMEG as an additional surveillance tool for growth-restricted fetuses.

Localization of spontaneous magnetoencephalographic activity of neonates and fetuses using independent component and Hilbert phase analysis

The fetal magnetoencephalogram (fMEG) is measured in the presence of large interference from maternal and fetal magnetocardiograms (mMCG and fMCG). These cardiac interferences can be attenuated by orthogonal projection (OP) technique of the corresponding spatial vectors. However, the OP technique redistributes the fMEG signal among the channels and also leaves some cardiac residuals (partially attenuated mMCG and fMCG) due to loss of stationarity in the signal. In this paper, we propose a novel way to extract and localize the neonatal and fetal spontaneous brain activity by using independent component analysis (ICA) technique. In this approach, we perform ICA on a small subset of sensors for 1-min duration. The independent components obtained are further investigated for the presence of discontinuous patterns as identified by the Hilbert phase analysis and are used as decision criteria for localizing the spontaneous brain activity. In order to locate the region of highest spontaneous brain activity content, this analysis is performed on the sensor subsets, which are traversed across the entire sensor space. The region of the spontaneous brain activity as identified by the proposed approach correlated well with the neonatal and fetal head location. In addition, the burst duration and the inter-burst interval computed for the identified discontinuous brain patterns are in agreement with the reported values.

Recurrent status epilepticus as the primary neurological manifestation of CADASIL: A case report.

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) often presents with a history of migraine with aura and eventual manifestations of dementia with unrelenting, repeated cerebral vascular insults. Only 6–10% of patients with CADASIL have been reported to develop seizures, and status epilepticus (SE) is exceedingly rare. Here, we describe a patient who presented with recurrent SE, with eventual biopsy diagnosis of CADASIL. An 80-year-old woman presented to our hospital three times in two years with decreased level of consciousness and subtle intermittent right-sided upper extremity and facial twitching. There was no known significant family history and no past medical history for seizures, stroke, migraine headache, or overt dementia. Electroencephalography revealed recurrent focal seizures with left hemispheric onset and evolution, fulfilling the criteria for focal SE each time. All three admissions required sedation with midazolam to control seizure activity, in addition to high doses of multiple antiepileptic drugs. Brain MRI repeatedly showed extensive abnormalities in the periventricular and deep white matter, subcortical white matter, and bilateral basal ganglia. Skin biopsy was obtained on the third admission, and electron microscopy showed numerous deposits of granular osmiophilic material, which are pathognomonic for CADASIL. Detailed investigations failed to reveal any other etiology for the patients condition. This case illustrates the potential for nonconvulsive SE to be the sole manifestation of CADASIL. With the appropriate brain MRI findings, CADASIL should be added to the list of rare causes of SE.

Quantification of fetal magnetoencephalographic activity in low-risk fetuses using burst duration and interburst interval.

OBJECTIVE To identify quantitative MEG indices of spontaneous brain activity for fetal neurological maturation in normal pregnancies and examine the effect of fetal state on these indices. METHODS Spontaneous MEG brain activity was examined in 22 low-risk fetal recordings with gestational age (GA) ranging from 30 to 37 weeks. As major quantitative characteristics of spontaneous activity, burst duration (BD) and interburst interval (IBI) were studied in correlation with GA and fetal state. RESULTS IBI showed a decrease with gestational age (-0.21 s/week, P=0.0031). This trend was only maintained in the quiet-sleep state. With respect to BD, no significant trends were detected with GA and state. CONCLUSION IBI can be quantified as a fetal brain maturational parameter. The decrease in IBI over gestation was similar to the trend reported in the preterm neonatal EEG studies. Quiet sleep could be the optimal state to study such MEG maturational indices. SIGNIFICANCE With further investigation, indices extracted from spontaneous fetal brain activity may serve as an early warning for fetal neurological distress.

Localizing the neonatal and fetal spontaneous brain activity by hilbert phase analysis

We propose a novel method to characterize the spontaneous brain signals using Hilbert phases. The Hilbert phase of a signal exhibits phase slips when the magnitude of the successive phase difference exceeds π. To this end we use standard deviation (σΔτ) of the time (Aτ) between successive phase slips to characterize the signals. We demonstrate the application of this approach to neonatal and fetal magnetoencephalographic signals recorded using a 151-sensor array to identify the sensors containing the neonatal and fetal brain signals. To this end we propose a spatial filter using σ(Ατ) as weights to reconstruct the brain signals.