University of Oregon Libraries Statistical Abstract for Fiscal Year 2011-2012

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Discrimination of Mild Cognitive Impairment and Alzheimer\u27s Disease Using Transfer Entropy Measures of Scalp EEG

Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer\u27s 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

Maternity Waiting Homes as an Intervention to Increase Facility Delivery in Rural Zambia

Graduate or above research in rural Zambiahttps://deepblue.lib.umich.edu/bitstream/2027.42/148301/1/BeckPeroskyMunroKramerLockhartMusondaNaggayiLori.pd

Maternity waiting homes as an intervention to increase facility delivery in rural Zambia

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150534/1/ijgo12864_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150534/2/ijgo12864.pd

Postpartum physical intimate partner violence among women in rural Zambia

ObjectiveTo examine the demographic characteristics and mental health of women in rural Zambia who experienced physical intimate partner violence (IPV) postpartum.MethodsThe present secondary analysis was conducted using baseline data from an impact evaluation of a maternity waiting home intervention in rural Zambia. A quantitative household survey was conducted over 6 weeks, from midâ April to late May, 2016, at 40 rural health facility catchment areas among 2381 postpartum women (13 months after delivery; age â ¥15 years).ResultsA total of 192 (8.1%) women reported experiencing any type of physical IPV in the preceding 2 weeks; 126 had experienced severe physical IPV (had been kicked, dragged, beat, and/or choked by a husband or partner). High levels of depression were recorded for 174 (7.3%) women in the preceding 2 weeks. Being a female head of household was associated with an increased likelihood of experiencing severe physical IPV (aOR 2.64, 95% CI 1.70â 4.10). Women with high depression scores were also at an increased risk of experiencing any physical IPV (aOR 17.1, 95% CI 8.44â 34.9) and severe physical IPV (aOR 15.4, 95% CI 5.17â 45.9).ConclusionFuture work should consider the implications of government and educational policies that could impact the screening and treatment of pregnant women affected by all forms of physical IPV and depression in rural Zambia.Postpartum physical intimate partner violence among women in rural Zambia was associated with being a female head of household and high levels of depression.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146407/1/ijgo12654.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146407/2/ijgo12654_am.pd

Breaking down barriers to consistent, climate-smart regulation of invasive plants - a case study of northeast states

Efforts to prevent the introduction and spread of new invasive plants are most effective when regulated species are consistent across jurisdictional boundaries and proactively prohibit species before they arrive or in the earliest stages of invasion. Consistent and proactive regulation is particularly important in the northeast U.S. which is susceptible to many new invasive plants due to climate change. Unfortunately, recent analyses of state regulated plant lists show that regulated species are neither consistent nor proactive. To understand why, we focus on two steps leading to invasive plant regulation across six northeast states (Connecticut, Maine, Massachusetts, New Hampshire, New York, and Vermont): which sets of species are evaluated and how risk is assessed. Our analysis confirms previous findings that invasive plant regulations are inconsistent and reactive. Of the 128 plants regulated by one or more states, 54 were regulated by a single state and only 16 were regulated by all six states; regulated species tended to be widespread across the region (not proactive). These outcomes are largely driven by different sets of evaluated species. For example, neighboring states Vermont and New Hampshire evaluated 92 species in total, but only 26 overlapped. In addition, states rarely evaluated species that were absent from the state. Risk assessment protocols varied considerably across states, but consistently included criteria related to ecological impact, potential to establish, dispersal mechanisms, and life history traits. While none of the assessments explicitly consider climate change, they also did not contain language that would preclude regulating species that have not yet arrived in the state. To increase consistency and proactivity, states would benefit from 1) evaluating species identified as high risk by neighboring states as well as high risk, range-shifting invasives, both of which we compiled here and 2) explicitly considering climate change when assessing ‘potential distribution’ or ‘potential impact’ of target species. Additionally, a mechanism for sharing knowledge and risk assessments regionally would benefit states with fewer resources to address invasive species threats. Presenting a unified defense against current and future threats is critical for reducing impacts from invasive species and is achievable with better state-to-state coordination

Sugihara Causality Analysis of Scalp EEG for Detection of Early Alzheimer\u27s 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 Alzheimer\u27s 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

A Cognitive Electrophysiological Signature Differentiates Amnestic Mild Cognitive Impairment from Normal Aging

Background: Noninvasive 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. Methods: Eighteen 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. Results: Whereas 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. Conclusions: The left frontal cognitive ERP indicator holds promise as a sensitive, simple, affordable, and noninvasive biomarker for detection of early cognitive impairment