Spatial spectra of scalp EEG and EMG from awake humans.

OBJECTIVE: Evaluate spectral scaling properties of scalp electroencephalogram (EEG) and electromyogram (EMG), optimal spacing of electrodes, and strategies for mitigating EMG. METHODS: EEG was recorded referentially from 9 subjects with a 64 channel linear array (electrodes 3mm apart) placed parasagittally or transversely on forehead or occiput, at rest with eyes open or closed, or with deliberate EMG. Temporal (PSD(t)) and spatial (PSD(x)) power spectral densities were calculated with one-dimensional fast Fourier transform (FFT) for comparison with earlier analyses of intracranial EEG. RESULTS: Scaling of PSD(t) from scalp resembled that from pia: near-linear decrease in log power with increasing log frequency (1/f(alpha)). Scalp PSD(x) decreased non-linearly and more rapidly than PSD(x) from pia. Peaks in PSD(t) (especially 4-12Hz) and PSD(x) (especially 0.1-0.4 cycles/cm) revealed departures from 1/f(alpha). EMG power in PSD(t) was more "white" than 1/f(alpha). CONCLUSIONS: Smearing by dura-skull-scalp distorts PSD(x) more than PSD(t) of scalp EEG from 1/f(alpha) scaling at the pia. Spatial spectral peaks suggest that optimal scalp electrode spacing might be approximately 1cm to capture non-local EEG components having the texture of gyri. Mitigation of EMG by filtering is unsatisfactory. A criterion for measuring EMG may support biofeedback for training subjects to reduce their EMG. SIGNIFICANCE: High-density recording and log-log spectral display of EEG provide a foundation for holist studies of global human brain function, as an alternative to network approaches that decompose EEG into localized, modular signals for correlation and coherence.     

Delirium: phenomenology and diagnosis--a neurobiologic view.

"Delirium" is a reversible confusional state. It results from widespread but reversible interference with the function of cortical neurons, as documented by diffuse slowing on EEG and decreases in cerebral metabolic rate. Delirium can be due to impairments in neuronal metabolism, in neurotransmission (notably cholinergic), or in input from subcortical structures. Engel and Romano (1959) formulated delirium and dementia as the two poles of a spectrum of "cerebral insufficiency," with delirium resulting from reversible functional impairment and dementia from irreversible anatomic damage. So many disorders can precipitate delirium that the differential diagnosis tests every facet of one's knowledge of medicine. With aging, both normative changes in the brain and the increasing incidence of brain diseases predispose to the development of delirium. The brain damage responsible for a dementia can sensitize to the development of a superimposed delirium.     

Psychophysiological correlates of the practice of Tantric Yoga meditation.

Autonomic and electroencephalographic (EEG) correlates of Tantric Yoga meditation were studied in three groups of subjects as they progressed from normal consciousness into meditation. Groups differed in their level of meditation proficiency. Measures of skin resistance, heart rate, respiration, autonomic orienting responses, resting EEG, EEG alpha and theta frequencies, sleep-scored EEG, averaged evoked responses, and subjective experience were employed. Unlike most previously reported meditation studies, proficient meditators demonstrated increased autonomic activation during meditation while unexperienced meditators demonstrated autonomic relaxation. During meditation, proficient meditators demonstrated increased alpha and theta power, minimal evidence of EEG-defined sleep, and decreased autonomic orienting to external stimulation. An episode of sudden autonomic activation was observed that was characterized by the meditator as an approach to the Yogic ecstatic state of intense concentration. These findings challenge the current "relaxation" model of meditative states.     

Full-band EEG (FbEEG): an emerging standard in electroencephalography.

While enormous resources have been recently invested into the development of a variety of neuroimaging techniques, the bandwidth of the clinical EEG, originally set by trivial technical limitations, has remained practically unaltered for over 50 years. An increasing amount of evidence shows that salient EEG signals are observed beyond the bandwidth of the routine clinical EEG, which is typically around 0.5-50 Hz. Physiological and pathological EEG activity ranges at least from 0.01 Hz to several hundred Hz, as demonstrated in recordings of spontaneous activity in the immature human brain, as well as during epileptic seizures, or various kinds of cognitive tasks and states in the adult brain. In the present paper, we will review several arguments leading to the conclusion that elimination of the lower (infraslow) or higher (ultrafast) bands of the EEG frequency spectrum in routine EEG leads to situations where salient and physiologically meaningful features of brain activity are ignored. Recording the full, physiologically relevant range of frequencies is readily attained with commercially available direct-current (DC) coupled amplifiers, which have a wide dynamic range and a high sampling rate. Such amplifiers, combined with appropriate DC-stable electrode-skin interface, provide a genuine full-band EEG (FbEEG). FbEEG is mandatory for a faithful, non-distorted and non-attenuated recording, and it does not have trade-offs that would favor any frequency band at the expense of another. With the currently available electrode, amplifier and data acquisition technology, FbEEG is likely to become the standard approach for a wide range of applications in both basic science and in the clinic.     

Nonlinear local electrovascular coupling. II: From data to neuronal masses

In the companion article a local electrovascular coupling (LEVC) model was proposed to explain the continuous dynamics of electrical and vascular states within a cortical unit. These states produce certain mesoscopic reflections whose discrete time series can be reconstructed from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). In this article we develop a recursive optimization algorithm based on the local linearization (LL) filter and an innovation method to make statistical inferences about the LEVC model from both EEG and fMRI data, i.e., to estimate the unobserved states and the unknown parameters of the model. For a better understanding, the LL filter is described from a Bayesian point of view, providing the particulars for the case of hybrid data (e.g., EEG and fMRI), which could be sampled at different rates. The dynamics of the exogenous synaptic inputs going into the cortical unit are also estimated by introducing a set of Gaussian radial basis functions. In order to study the dynamics of the electrical and vascular states in the striate cortex of humans as well as their local interrelationships, we applied this algorithm to EEG and fMRI recordings obtained concurrently from two subjects while passively observing a radial checkerboard with a white/black pattern reversal. The EEG and fMRI data from the first subject was used to estimate the electrical/vascular states and parameters of the LEVC model in V1 for a 4.0 Hz reversion frequency. We used the EEG data from the second subject to investigate the changes in the dynamics of the electrical states when the frequency of reversion is varied from 0.5-4.0 Hz. Then we made use of the estimated electrical states to predict the effects on the vasculature that such variations produce.     

The EEG in coma.

The EEG allows insight into thalamocortical function in comatose patients when this is inaccessible clinically. A single EEG can help with broad diagnostic categorization whereas continuous or serial EEG provides monitoring for unstable and potentially treatable conditions and for monitoring the effects of therapy. The EEG plays a supplemental role in establishing the prognosis in disease states that are capable of causing neuronal death. The most prevalent and problematic of these conditions involves survivors of cardiac arrest who are initially in coma with intact brainstem reflexes. In such patients single EEGs are of 100% specificity for no possibility of recovery of consciousness only for essentially complete generalized suppression (<10 microV) after the first day of the arrest. Several other generalized patterns, including less marked suppression, burst-suppression, epileptiform activity, periodic complexes, and alpha-theta coma patterns, usually but not invariably indicate a poor outcome. Serial EEGs, continuous raw and automated "trending," testing of reactivity, and the inclusion of multiple variables hold promise for an improved role in the prognostic determination in these patients.     

Alpha-like rhythmical activity of the temporal lobe

In addition to the classical posterior alpha rhythm and Rolandic mu rhythm, a third type of rhythmical activity in alpha- (or sub-alpha-) frequency can be recorded over the temporal lobe and especially over the midtemporal region. This rhythm usually escapes detection in the scalp EEG but is commonly seen over bone defects ("breach rhythm"). With the use of epidural electrodes, however, this rhythm becomes quite prominent and dominates the activity of the temporal lobe unless there is excessive local EEG abnormality and/or pathology. This temporal rhythm lies in the range of 6-11/sec and thus often extends into the theta frequency range. There is no proven blocking effect; this mere fact sets it apart from posterior alpha and Rolandic mu rhythm. It is essentially a rhythm of the waking state but may linger into drowsiness and even into light NREM sleep. It is also separable from the highly controversial frontotemporal "kappa rhythm" of bygone times. EEG recordings from the depth of the temporal lobe (limbic structures) do not demonstrate such a rhythm that is likely to originate from the neocortical portion. Its neurophysiological and psychophysiological significance is enigmatic.     

Comparison of human ictal, interictal and normal non-linear component analyses.

OBJECTIVES: The non-linear properties of EEG and filtered rhythms obtained from healthy subjects and epileptic patients with complex partial seizures were analyzed to investigate whether EEG in different neurological states can be generated by the mechanism that integrates several non-linear dynamic systems. METHODS: The control EEG (from 26 healthy subjects), interictal EEG and ictal EEG (from 25 patients) were digitally filtered into delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (30-40 Hz) components. The correlation dimension was calculated on each original signal and corresponding surrogate data. A new method was developed to accelerate the calculation of the correlation integral. Function P(m,r) was defined to visualize the meaning of the correlation dimension. The point critical to the estimation was determined by the P(m,r) function. RESULTS: The EEG in the control subjects and patients showed significantly lower correlation dimensions than the surrogate data. The delta, alpha, beta and gamma components from the control EEG exhibited similar complexity to the surrogate data, while only the alpha component from the interictal EEG presented the same dimension as the surrogate data. The correlation dimensions of the theta and alpha components remained the same when the neurological state changed from interictal EEG to ictal EEG. The complexity of the beta component was higher than the complexity of other components in both control subjects and patients. The correlation dimension of EEG was significantly correlated to the complexity of delta, theta, beta and gamma components. CONCLUSIONS: Our results suggest that EEG and filtered components in different neurological states demonstrate varied dynamic properties. The characteristics of neuronal networks can be differentiated by the dynamics of filtered components. Separating EEG into different dynamic systems may facilitate understanding of the mechanisms involved in the human EEG.     

Brain dynamics in the active vs. passive auditory oddball task: exploration of narrow-band EEG phase effects.

OBJECTIVE: We aimed to examine relationships between the phase of narrow-band electroencephalographic (EEG) activity at stimulus onset and the resultant event-related potentials (ERPs) in active vs. passive auditory oddball tasks, using a novel conceptualisation of orthogonal phase effects. METHODS: This study focused on the operation of three recently-reported phase-influenced mechanisms, and ERP responses to the standard stimuli were analysed. Prestimulus narrow-band EEG activity (in 1 Hz bands from 1 to 13 Hz) at Cz was assessed for each trial using digital filtering. For each frequency, the cycle at stimulus onset was used to sort trials into four phases, for which ERPs were derived from both the filtered and unfiltered EEG activity at Fz, Cz and Pz. RESULTS: Preferred brain states at various frequencies were indicated by approximately 20% differential occurrence within the orthogonal phase dimensions explored. CONCLUSIONS: The preferred states were associated with more efficient processing of the stimulus, as reflected in differences in latency and/or amplitude of various ERP components, and provided evidence for the operation of the three separate phase-influenced mechanisms. SIGNIFICANCE: Both the occurrence of preferred brain states, and the mechanisms linking them to ERP outcomes focused on here, appeared relatively invariant across tasks, suggesting that they largely reflect reflexive brain processes.     

Subacute sclerosing panencephalitis: serial electroencephalographic studies.

A total of 42 EEGs from five patients with subacute sclerosing panencephalitis were studied. Periodic complexes were noticed in 35 (83%) of these. The interval between the complexes shortened in all patients with progression of the illness. The gradual EEG changes may reflect the increasing number of infected cells as well as an on-going accumulation of immature virus structures. The records without complexes were either from the early onset (one record) or terminal stage (six records).     

Neurophysiological studies of auditory verbal hallucinations

We discuss 3 neurophysiological approaches to study auditory verbal hallucinations (AVH). First, we describe "state" (or symptom capture) studies where periods with and without hallucinations are compared "within" a patient. These studies take 2 forms: passive studies, where brain activity during these states is compared, and probe studies, where brain responses to sounds during these states are compared. EEG (electroencephalography) and MEG (magnetoencephalography) data point to frontal and temporal lobe activity, the latter resulting in competition with external sounds for auditory resources. Second, we discuss "trait" studies where EEG and MEG responses to sounds are recorded from patients who hallucinate and those who do not. They suggest a tendency to hallucinate is associated with competition for auditory processing resources. Third, we discuss studies addressing possible mechanisms of AVH, including spontaneous neural activity, abnormal self-monitoring, and dysfunctional interregional communication. While most studies show differences in EEG and MEG responses between patients and controls, far fewer show symptom relationships. We conclude that efforts to understand the pathophysiology of AVH using EEG and MEG have been hindered by poor anatomical resolution of the EEG and MEG measures, poor assessment of symptoms, poor understanding of the phenomenon, poor models of the phenomenon, decoupling of the symptoms from the neurophysiology due to medications and comorbidites, and the possibility that the schizophrenia diagnosis breeds truer than the symptoms it comprises. These problems are common to studies of other psychiatric symptoms and should be considered when attempting to understand the basic neural mechanisms responsible for them.     

Sleep in spousally bereaved elders with subsyndromal depressive symptoms.

Spousal bereavement in late life frequently leads to major depression. However, many people suffer from "minor" depressive symptoms that entail considerable suffering even in the absence of syndromal major depression. We describe longitudinal electroencephalographic (EEG) sleep and clinical evaluations in 14 elderly, recently spousally bereaved subjects who were experiencing subsyndromal depressive symptoms. While subjects did not meet diagnostic criteria for syndromal major depression, they did have mildly elevated scores on the Hamilton Rating Scale for Depression (mean = 10.6, range = 8-16) at the time of initial sleep studies (T1), which were carried out, on average, 5.5 months after loss of the spouse. Entry into the study was limited to volunteers who did not have a personal history of major depression or psychiatric disorder. Twelve subjects underwent followup clinical and EEG sleep evaluations (T2), 9.9 months after spousal loss. Fifty percent continued to show depressive symptoms at 6-month followup. Test-retest comparisons of sleep and clinical measures were made with a group of sex- and age-matched control subjects who were neither bereaved nor depressed. EEG sleep measures did not significantly correlate with time from loss of spouse, severity of depressive symptoms, or subjective sleep quality. Analysis of variance with repeated measures detected a significant group X time interaction effect for delta sleep ratio (decreasing in controls but increasing in the bereaved).     

EEG sleep and affective psychoses: I. Schizoaffective disorders.

The sleep electroencephalogram (EEG) was studied in 41 depressed inpatients. EEG sleep records were compared for two diagnostic subgroups; patients with psychotic depression (n = 29) or with schizoaffective disorders (n = 12). As was true in the previous pilot study, no major EEG sleep variables distinguished the patients with psychotic depression from those with schizoaffective disorders. These data are consistent with the theory that all psychotic depressive states may have certain common psychobiologic features such as shortened rapid eye movement (REM) sleep latency.     

Full-band EEG (fbEEG): a new standard for clinical electroencephalography.

A variety of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and magnetoencephalography (MEG), have been established during the last few decades, with progressive improvements continuously taking place in the underlying technologies. In contrast to this, the recording bandwidth of the routine clinical EEG (typically around 0.5-50 Hz) that was originally set by trivial technical limitations has remained practically unaltered for over half a decade. An increasing amount of evidence shows that salient EEG signals take place and can be recorded beyond the conventional clinical EEG bandwidth. These physiological and pathological EEG activities range from 0.01 Hz to several hundred Hz, and they have been demonstrated in recordings of spontaneous activity in the preterm human brain, and during epileptic seizures, sleep, as well as in various kinds of cognitive tasks and states in the adult brain. In the present paper, we will describe the practical aspects of recording the full physiological frequency band of the EEG (Full-band EEG; FbEEG), and we review the currently available data on the clinical applications of FbEEG. Recording the FbEEG is readily attained with commercially available direct-current (DC) coupled amplifiers if the recording setup includes electrodes providing a DC-stable electrode-skin interface. FbEEG does not have trade-offs that would favor any frequency band at the expense of another. We present several arguments showing that elimination of the lower (infraslow) or higher (ultrafast) bands of the EEG frequency spectrum in routine EEG has led, and will lead, to situations where salient and physiologically meaningful features of brain activity remain undetected or become seriously attenuated and distorted. With the currently available electrode, amplifier and data acquisition technology, it is to be expected that FbEEG will become the standard approach in both clinical and basic science.     

Interictal EEG as a physiological adaptation. Part II. Topographic variability of composition of brain oscillations in interictal EEG.

OBJECTIVE: In the present experimental study, we examined topographic variability of composition of brain oscillations and their temporal behavior in frequencies from 0.5 to 30 Hz of interictal EEG without epileptiform abnormalities and healthy EEG. METHODS: Spatio-temporal variability of brain oscillations (indexed by short-term EEG spectral patterns (SPs)) was assessed by the probability-classification analysis of SPs. As a result, multi-dimensional SP-vector for each analysis EEG epoch was obtained. RESULTS: It was demonstrated that interictal EEG was characterized (a) by a significant decrease of spatio-temporal variability of brain oscillations, (b) by longer periods of temporal stabilization for operational modules which comprise larger number of cortical areas, and (c) by significantly more intermittent recurrence when compared with EEG of control subjects. Generally it was shown that EEG channels display different states of coordination independently on their correlation and coherence using brain oscillations at multiple frequencies. CONCLUSIONS: Results of this study suggested that EEG correlate of chronic epileptogenesis in the brain is a particular metastable state of biopotential field, which can be estimated by SP-vector. The fact that all results were significantly different from surrogate EEGs reflects a nonoccasional and thus, most likely, an adaptive nature of spatio-temporal reorganization in interictal EEG. SIGNIFICANCE: Parameters of spatio-temporal organization of interictal EEG without the signs of epileptiform activity can be considered as additional information in premorbid diagnostics of status epilepticus, and may also provide insights into basic laws that govern brain oscillations in general.     

Deblurring.

In most instances, traditional EEG methodology provides insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. This article describes a method called Deblurring for increasing the spatial detail of the EEG and for fusing neurophysiologic and neuroanatomic data. Deblurring estimates potentials near the outer convexity of the cortex using a realistic finite element model of the structure of a subject's head determined from their magnetic resonance images. Deblurring is not a source localization technique and thus makes no assumptions about the number or type of generator sources. The validity of Deblurring has been initially tested by comparing deblurred data with potentials measured with subdural grid recordings. Results suggest that deblurred topographic maps, registered with a subject's magnetic resonance imaging and rendered in three dimensions, provide better spatial detail than has heretofore been obtained with scalp EEG recordings. Example results are presented from research studies of somatosensory stimulation, movement, language, attention and working memory. Deblurred ictal EEG data are also presented, indicating that this technique may have future clinical application as an aid to seizure localization and surgical planning.     

Nonlinear dynamical analysis of the neonatal EEG time series: The relationship between sleep state and complexity

OBJECTIVE: The complexity of the EEG time series during stages of neonatal sleep states is investigated. The relationship between these sleep states, birth status (i.e. preterm and full-term), and the complexity of the EEG is assessed. METHODS: Dimensional complexity, an estimate of the correlation dimension (D(2)) of the EEG time series, is used as a novel index for quantifying the complexity of the EEG time series during different neonatal sleep states. The dimensional complexity is estimated by using both the Grassberger-Procaccia algorithm and Theiler's modified algorithm. Also, the hypothesis that the neonatal EEG time series contains nonlinear features is investigated and verified in certain sleep states using surrogate data testing. RESULTS: Dimensional complexity of the neonatal EEG time series during active (REM) sleep tends to be higher than during quiet (NREM) sleep; while dimensional complexity of the neonatal EEG time series during indeterminate sleep is virtually at the midpoint of the dimensional complexity range between the active and quiet sleep states. Consequently, there are statistically significant differences between the neonatal EEG time series during different sleep states as measured by dimensional complexity. Also, the birth status (preterm or full-term) of the neonate has an influence on dimensional complexity of the neonatal EEG time series. Further, the surrogate data testing null hypothesis for dimensional complexity cannot be rejected during active sleep. CONCLUSIONS: The neonatal EEG time series tends to have statistically different complexities corresponding to different sleep states. Given that the neonatal EEG time series during active sleep is more complex than during quiet sleep, one can suggest that there is greater activity among cortical circuit elements during active as compared to quiet sleep. Further, active sleep neuronal dynamics are best modeled by a linear stochastic process, while in quiet sleep a nonlinear deterministic model may be more appropriate. SIGNIFICANCE: There has been considerable controversy associated with measures of complexity, such as dimensional analysis, as applied to neonatal EEG data. This paper confirms that there are statistically significant differences in dimensional complexity associated with different states of sleep and that the origin of this complexity shifts from linear stochastic to nonlinear deterministic with transitions from active to quiet sleep, and is further influenced by maturation. This may provide important insight into the organization and structure of neuronal networks during sleep and with brain maturation in the neonatal period.     

Behavior-dependent evoked potentials in the hippocampal CA1 region of the rat. II. Effect of eserine, atropine, ether and pentobarbital

The correlations of the rat's behaviors and the hippocampal EEG with the averaged evoked potentials (AEPs) evoked by the Schaffer collaterals in the hippocampal CA1 region of the rat were studied after intraperitoneal injections of several drugs known to affect hippocampal EEG.Ether and eserine induced continuous train of rhythmical slow activity (RSA) of 5–6/sec in the hippocampal EEG, during which the AEPs showed waveforms intermediate between those elicited during large irregular activity (LIA) in the awake, immobile control and those elicited during walking in the control. Low dose nembutal and atropine induced high amplitude LIA during immobility, resembling EEG during slow-wave sleep (SWS). The AEPs during these LIA states, and that during LIA of wake-immobility were of similar waveforms. The AEP waveforms are proposed to form a continuum which corresponds to the continuum of EEG from high amplitude LIA to RSA of increasing frequency. AEP waveforms do not depend only on walking or similar movements which correlate with high frequency RSA. Atropine sulfate (25–50 mg/kg i.p.) severely dampened the oscillations in the AEPs of rats during walking or similar movements, even though the high frequency RSA was essentially unaffected. The difference between AEPs during immobility and those during walking was markedly reduced after atropine, even though the EEG-behavior relationship persisted. The effect of atropine on AEPs may be interpreted as a direct effect on the hippocampus which is apparently inconsistent with present knowledge. If the effect was on inputs from the brain stem or the septum to the hippocampus, the hypothesis that there are two pharmacological types of RSA (atropine-sensitive and -resistant) requires re-definition and re-examination.     

Neural networks involving the medial temporal structures in temporal lobe epilepsy.

OBJECTIVES: In a previous study using the averaged coherence technique to study interactions between medial/limbic and lateral/neocortical regions, we observed that epileptogenic networks in temporal lobe epilepsy seizures (TLES) could be divided into 4 subtypes, i.e. medial (M), medial-lateral (ML), lateral-medial (LM), and lateral (L). In the ML and LM subtypes, medial structures and the anterior temporal neocortex are co-activated at the onset of seizures. However, using this approach, we were unable to determine the direction of coupling and may have overlooked non-linear variations in interdependency. The purpose of the present study using non-linear regression for analysis of stereoelectroencephalographic (SEEG) signal pairs was to measure the degree and direction of coupling between medial and neocortical areas during TLES in patients with the M, ML, and LM subtypes. METHODS: Eighteen patients with drug-resistant TLEs who underwent SEEG recording were studied. We used a non-linear correlation method as a measure of the degree and the direction of coupling on SEEG signal pairs. Patients with pure lateral TLEs were not studied. We analyzed the functional coupling between 3 regions of the temporal lobe: the anterior temporal neocortex, the amygdala, and the anterior hippocampus. A physiological model of EEG generation was used to validate the non-linear quantification method and assess its applicability to real SEEG signals. RESULTS: Results are first based on a physiological model of EEG data in which both degree and direction of coupling are explicitly represented, thus allowing construction of the neural systems inside which causality relationships are controlled and generation of multichannel EEG signals from these systems. These signals provide an objective way of studying the performance of non-linear regression analysis on real signals.In medial networks (10 patients), the ictal discharge is limited to the medial limbic structures and may propagate secondarily to the cortex. Quantified results demonstrated no significant coupling between medial and lateral structures at the beginning of the seizures. Conversely, almost constant unidirectional or bidirectional coupling was observed between hippocampus and amygdala.In medial-lateral (5 patients) and lateral-medial (3 patients) networks, the initial ictal discharge includes both limbic and neocortical regions. A rapid "tonic" discharge is observed over the temporal neocortex at the onset of seizure. Quantitative analysis showed an initial increase in the non-linear correlation coefficient between neocortex and medial structures. Quantification of the coupling direction demonstrated influence of medial over lateral structures (medial-lateral) or of the lateral neocortex over medial structures (lateral-medial). CONCLUSIONS: These results confirm the existence of several generic and organized networks involving the medial structures during TLE seizures.     

A Polygraphic Study of Bioelectrical Brain Maturation In Preterm Infants

Bioelectrical brain maturation was studied in 26 low-risk preterm infants by serial polygraphic recordings. A modified method of visual EEG analysis was used, based on Parmelee's coding system, which allowed quantification. It gave the following results: (1) The main characteristics of EEG maturation in preterm infants were a progressive spatio-temporal differentiation, with an increase of rhythmic activities and a decrease of discontinuity. (2) A strong relationship was found between post-menstrual age of the infants and EEG maturity, but there were exceptions to this rule. (3) longer duration of extra-uterine life had a small accelerating influence on EEG maturation. (4) Certain basic types of EEG patterns were closely related to behavioural states, whereas EEG maturity was not state-dependent. (5) The relationship between EEG pattern types and behavioural states becomes more stable with increasing age.