Leslie S. Prichep

Decreased EEG synchronization in Alzheimer's disease and mild cognitive impairment.

The hypothesis of a functional disconnection of neuro-cognitive networks in patients with mild cognitive impairment (MCI) and Alzheimer Dementia was investigated using baseline resting EEG data. EEG databases from New York (264 subjects) and Stockholm (155 subjects), including healthy controls and patients with varying degrees of cognitive decline or Alzheimer Dementia were analyzed using Global Field Synchronization (GFS), a novel measure of global EEG synchronization. GFS reflects the global amount of phase-locked activity at a given frequency by a single number; it is independent of the recording reference and of implicit source models. Patients showed decreased GFS values in Alpha, Beta, and Gamma frequency bands, and increased GFS values in the Delta band, confirming the hypothesized disconnection syndrome. The results are discussed within the framework of current knowledge about the functional significance of the affected frequency bands.

Millisecond by millisecond, year by year: normative EEG microstates and developmental stages.

Most studies of continuous EEG data have used frequency transformation, which allows the quantification of brain states that vary over seconds. For the analysis of shorter, transient EEG events, it is possible to identify and quantify brain electric microstates as subsecond time epochs with stable field topography. These microstates may correspond to basic building blocks of human information processing. Microstate analysis yields a compact and comprehensive repertoire of short lasting classes of brain topographic maps, which may be considered to reflect global functional states. Each microstate class is described by topography, mean duration, frequency of occurrence and percentage analysis time occupied. This paper presents normative microstate data for resting EEG obtained from a database of 496 subjects between the age of 6 and 80 years. The extracted microstate variables showed a lawful, complex evolution with age. The pattern of changes with age is compatible with the existence of developmental stages as claimed by developmental psychologists. The results are discussed in the framework of state dependent information processing and suggest the existence of biologically predetermined top-down processes that bias brain electric activity to functional states appropriate for age-specific learning and behavior.

The anesthetic cascade: A theory of how anesthesia suppresses consciousness

ADEQUATE surgical anesthesia must achieve three goals: immobility, amnesia, and absence of awareness. After the evidence of anesthetic lipophilicity was presented by Meyer and Overton, it was widely assumed that all these actions were accomplished at some unitary site. A body of evidence has now accumulated demonstrating that for many anesthetic agents, the dose required to suppress consciousness exceeds the amnestic dose but is substantially less then that required for surgical immobility during noxious stimuli. This suggests that these three dimensions may be mediated by different regions of the central nervous system. As has been pointed out by Rampil, the variability among anesthetics of the ratios of concentrations needed to suppress consciousness, to block memory, and to achieve surgical immobility further invalidate the unitary hypothesis. A comprehensive explanation of the mechanism by which anesthetics cause loss of consciousness (LOC) has not yet been developed. Abundant in vitro and in vivo evidence has been provided of effects of anesthetics on a wide variety of molecular and cellular processes. Campagna et al. have recently provided a review of current understanding of the molecular mechanisms of anesthesia, summarizing evidence showing that inhaled anesthetics achieve immobilization by depressing the spinal cord, whereas amnesic actions are mediated within the brain. They document research indicating that subtle differences in the clinical actions of inhaled anesthetics may be attributed to distinct actions on a number of critical molecular targets. Although this evidence makes it clear that neuronal actions and interactions at many different levels and in many different brain tissues are altered or disrupted by anesthetic drugs, it does not explain why these different more or less discrete effects have the common global effect of causing LOC, which we define as suppression of awareness. This article attempts to provide such an explanation in terms of the alteration of neurophysiologic processes that are essential for the mediation of consciousness. Before undertaking that effort, it is appropriate to provide a brief overview of the results of research in related fields.

Invariant reversible QEEG effects of anesthetics

Continuous recordings of brain electrical activity were obtained from a group of 176 patients throughout surgical procedures using general anesthesia. Artifact-free data from the 19 electrodes of the International 10/20 System were subjected to quantitative analysis of the electroencephalogram (QEEG). Induction was variously accomplished with etomidate, propofol or thiopental. Anesthesia was maintained throughout the procedures by isoflurane, desflurane or sevoflurane (N = 68), total intravenous anesthesia using propofol (N = 49), or nitrous oxide plus narcotics (N = 59). A set of QEEG measures were found which reversibly displayed high heterogeneity of variance between four states as follows: (1) during induction; (2) just after loss of consciousness (LOC); (3) just before return of consciousness (ROC); (4) just after ROC. Homogeneity of variance across all agents within states was found. Topographic statistical probability images were compared between states. At LOC, power increased in all frequency bands in the power spectrum with the exception of a decrease in gamma activity, and there was a marked anteriorization of power. Additionally, a significant change occurred in hemispheric relationships, with prefrontal and frontal regions of each hemisphere becoming more closely coupled, and anterior and posterior regions on each hemisphere, as well as homologous regions between the two hemispheres, uncoupling. All of these changes reversed upon ROC. Variable resolution electromagnetic tomography (VARETA) was performed to localize salient features of power anteriorization in three dimensions. A common set of neuroanatomical regions appeared to be the locus of the most probable generators of the observed EEG changes.

Assessing interactions in the brain with exact low-resolution electromagnetic tomography

Scalp electric potentials (electroencephalogram; EEG) are contingent to the impressed current density unleashed by cortical pyramidal neurons undergoing post-synaptic processes. EEG neuroimaging consists of estimating the cortical current density from scalp recordings. We report a solution to this inverse problem that attains exact localization: exact low-resolution brain electromagnetic tomography (eLORETA). This non-invasive method yields high time-resolution intracranial signals that can be used for assessing functional dynamic connectivity in the brain, quantified by coherence and phase synchronization. However, these measures are non-physiologically high because of volume conduction and low spatial resolution. We present a new method to solve this problem by decomposing them into instantaneous and lagged components, with the lagged part having almost pure physiological origin.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimers disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

Quantitative EEG correlates of cognitive deterioration in the elderly

We report on the quantitative analysis of the EEG (QEEG), using the Neurometric method, in large samples of normal elderly; normal subjectively impaired elderly; patients with mild cognitive impairment; patients presenting with a continuum of primary cognitive deterioration from mild to moderately severe as measured by the Global Deterioration Scale (GDS), compatible with dementia of the Alzheimers type (DAT). Neurometric QEEG measures were found to be a sensitive index of degree of cognitive impairment, especially reflected in increased absolute and relative power in the theta band, with delta increasing in later stages of deterioration. While these abnormalities were widespread, neither localized or lateralized, MANOVAs for GDS and relative power in theta reached highest significance in a bilateral temporo-parietal arc. A possible relationship between hippocampal dysfunction, cognitive deterioration, and theta abnormalities is discussed in relation to these findings. The results suggest that Neurometric QEEG features are sensitive to the earliest presence of subjective cognitive dysfunction and might be useful in the initial evaluation of patients with suspected dementia, as well as in estimating the degree of cognitive deterioration in DAT patients.

Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging

An extensive literature reports changes in quantitative electroencephalogram (QEEG) with aging and a relationship between magnitude of changes and degree of clinical deterioration in progressive dementia. Longitudinal studies have demonstrated QEEG differences between mild cognitively impaired (MCI) elderly who go on to decline and those who do not. This study focuses on normal elderly with subjective cognitive complaints to assess the utility of QEEG in predicting future decline within 7 years. Forty-four normal elderly received extensive clinical, neurocognitive and QEEG examinations at baseline. All study subjects (N = 44) had only subjective complaints but no objective evidence of cognitive deficit (evaluated using the Global Deterioration Scale [GDS] score, GDS stage = 2) at baseline and were re-evaluated during 7-9 year follow-up. Baseline QEEGs of Decliners differed significantly (p < 0.0001, by MANOVA) from Non-Decliners, characterized by increases in theta power, slowing of mean frequency, and changes in covariance among regions, especially on the right hemisphere. Using logistic regression, an R2 of 0.93 (p < 0.001) was obtained between baseline QEEG features and probability of future decline, with an overall predictive accuracy of 90%. These data indicate high sensitivity and specificity for baseline QEEG as a differential predictor of future cognitive state in normal, subjectively impaired elderly.

The pre–mild cognitive impairment, subjective cognitive impairment stage of Alzheimer’s disease

Subjective cognitive impairment (SCI) has been a common, but poorly understood condition, frequently occurring in older persons.

Acute effects and recovery after sport-related concussion: a neurocognitive and quantitative brain electrical activity study.

ObjectiveTo investigate the clinical utility and sensitivity of a portable, automatic, frontal quantitative electroencephalographic (QEEG) acquisition device currently in development in detecting abnormal brain electrical activity after sport-related concussion. DesignThis was a prospective, non-randomized study of 396 high school and college football players, including cohorts of 28 athletes with concussion and 28 matched controls. All subjects underwent preseason baseline testing on measures of postconcussive symptoms, postural stability, and cognitive functioning, as well as QEEG. Clinical testing and QEEG were repeated on day of injury and days 8 and 45 postinjury for the concussion and control groups. Main Outcomes and ResultsThe injured group reported more significant postconcussive symptoms during the first 3 days postinjury, which resolved by days 5 and 8. Injured subjects also performed poorer than controls on neurocognitive testing on the day of injury, but no differences were evident on day 8 or day 45. QEEG studies revealed significant abnormalities in electrical brain activity in the injured group on day of injury and day 8 postinjury, but not on day 45. ConclusionsResults from the current study on clinical recovery after sport-related concussion are consistent with early reports indicating a typical course of full recovery in symptoms and cognitive dysfunction within the first week of injury. QEEG results, however, suggest that the duration of physiological recovery after concussion may extend longer than observed clinical recovery. Further study is required to replicate and extend these findings in a larger clinical sample, and further demonstrate the utility of QEEG as a marker of recovery after sport-related concussion.