Nancy B. Munro

Drinking-water standards

This paper discussed the revising of the primary and secondary drinking-water regulations by EPA in accordance with the Safe Drinking Water Act. Since consideration of risk is playing an increasing role in setting environmental standards, questions were raised regarding the adequacy of human health protection afforded by some of the existing and proposed standards. 1 table.

Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease

Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimers disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls (NC), 16 early MCI, and 17 early stage AD-are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.

Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease.

Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimers disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) — 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of pathophysiological changes in early-stage AD.

Early detection of Alzheimer's disease using nonlinear analysis of EEG via Tsallis entropy

A preliminary study by Sneddon et al. (2005) using visual working memory tasks coupled with quantified EEG (qEEG) analysis distinguished mild dementia subjects from normal aging ones with a high degree of accuracy. The present study hypothesizes that a simpler task such as having a subject count backwards mentally by ones can be coupled with qEEG to yield a similar degree of accuracy for classifying early dementia. The study focuses on participants with mild cognitive impairment (MCI) and includes both a delayed visual match-to-sample (working memory) task and a counting backwards task (eyes closed) for comparison. The counting backwards protocol included 15 normal aging and 11 MCI participants, and the working memory task included 9 normal aging and 7 MCI individuals. The EEG data were quantified using Tsallis entropy, and the brain regions analyzed included the prefrontal cortex, occipital lobe, and the posterior parietal cortex. The counting backwards task had a sensitivity of 82%, a specificity of 73%, and an overall accuracy of 77% whereas the working memory task had a sensitivity of 100%, a specificity of 89%, and an overall accuracy of 94%. The results suggest that simple tasks such as having a subject count backwards may distinguish MCI (p<;0.05) sufficiently to use as a rough screening tool, but psychophysical tasks such as working memory tests appear a potentially much more useful approach for diagnosing either MCI or very early Alzheimers disease.

Enhancements in epilepsy forewarning via phase-space dissimilarity

Summary: The authors extend the recent application of phase-space dissimilarity measures for scalp EEG data in two directions. First, a forewarning window of up to 8 hours was used, thereby providing more forewarning time of the seizure event. This window was limited to a maximum of 1 hour in their previous work. Second, they combined information from two channels via a multichannel phase-space to improve the quality and confidence limits of the forewarning. Combining these two enhancements, they obtained two-channel results that were superior to the single-channel ones.

Scalp EEG-Based Discrimination of Cognitive Deficits After Traumatic Brain Injury Using Event-Related Tsallis Entropy Analysis

Traumatic brain injury (TBI) is the leading cause of death and disability in children and adolescents in the U.S. This is a pilot study, which explores the discrimination of chronic TBI from normal controls using scalp EEG during a memory task. Tsallis entropies are computed for responses during an old-new memory recognition task. A support vector machine model is constructed to discriminate between normal and moderate/severe TBI individuals using Tsallis entropies as features. Numerical analyses of 30 records (15 normal and 15 TBI) show a maximum discrimination accuracy of 93% (p-value = 7.8557E-5) using four features. These results suggest the potential of scalp EEG as an efficacious method for noninvasive diagnosis of TBI.

Discrimination of Mild Cognitive Impairment and Alzheimer's Disease Using Transfer Entropy Measures of Scalp EEG

Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimers disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls, 16 MCI, and 17 early AD-are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7- 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.

A cognitive electrophysiological signature differentiates amnestic mild cognitive impairment from normal aging

BackgroundNoninvasive and effective biomarkers for early detection of amnestic mild cognitive impairment (aMCI) before measurable changes in behavioral performance remain scarce. Cognitive event-related potentials (ERPs) measure synchronized synaptic neural activity associated with a cognitive event. Loss of synapses is a hallmark of the neuropathology of early Alzheimer’s disease (AD). In the present study, we tested the hypothesis that ERP responses during working memory retrieval discriminate aMCI from cognitively normal controls (NC) matched in age and education.MethodsEighteen NC, 17 subjects with aMCI, and 13 subjects with AD performed a delayed match-to-sample task specially designed not only to be easy enough for impaired participants to complete but also to generate comparable performance between subjects with NC and those with aMCI. Scalp electroencephalography, memory accuracy, and reaction times were measured.ResultsWhereas memory performance separated the AD group from the others, the performance of NC and subjects with aMCI was similar. In contrast, left frontal cognitive ERP patterns differentiated subjects with aMCI from NC. Enhanced P3 responses at left frontal sites were associated with nonmatching relative to matching stimuli during working memory tasks in patients with aMCI and AD, but not in NC. The accuracy of discriminating aMCI from NC was 85% by using left frontal match/nonmatch effect combined with nonmatch reaction time.ConclusionsThe left frontal cognitive ERP indicator holds promise as a sensitive, simple, affordable, and noninvasive biomarker for detection of early cognitive impairment.

Forewarning of epileptic events from scalp EEG

This paper addresses epileptic event forewarning. One novel contribution is the use of graph theoretic measures to detect condition change from time-delay-embedding states. Another novel contribution is better forewarning of the epileptic events from two channels of scalp EEG, with a total true rate of 58/60 (sensitivity = 39/40, specificity = 19/20). Challenges include statistical validation in terms of true positives and true negatives; actionable forewarning in terms of time before the event; detection of the event to reset the forewarning algorithm; and implementation in a practical device.

Virtual Human: a diagnostic tool for human studies and health effects in the 21st century

The virtual human will be a research/simulation environment having an integrated system of biophysical models, data, and advanced computational algorithms. It will have a Web-based interface for easy, rapid access from several points of entry. The virtual human will serve as a platform for national and international users from governments, academia and industry to investigate the widest range of human biological and physical response to stimuli, be they biological, chemical, or physical. This effort will go far beyond the modeling of anatomy to incorporate refined computational models of whole-body processes, using mechanical and electrical tissue properties, and biology from physiology to biochemical information. The platform will respond mechanistically to varied and potentially iterative stimuli that can be visualized multi- dimensionally. This effort is in the formative stage of a several-year process that will lead to a program that is of similar proportion to the human genome, but will be much more computationally intensive. The main purpose of this paper is to communicate our early ideas about the philosophic basis of the program, to identify some of the applications for which the virtual human would be used, to elicit comments, and to provide a basis to identify prospective collaborators.