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Find video protocols related to scientific articles indexed in Pubmed.
Pre-clinical Cognitive Phenotypes for Alzheimer Disease: A Latent Profile Approach.
Am J Geriatr Psychiatry
PUBLISHED: 07-29-2013
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Cognitive profiles for pre-clinical Alzheimer disease (AD) can be used to identify groups of individuals at risk for disease and better characterize pre-clinical disease. Profiles or patterns of performance as pre-clinical phenotypes may be more useful than individual test scores or measures of global decline.
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Identification of MCI individuals using structural and functional connectivity networks.
Neuroimage
PUBLISHED: 07-08-2011
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Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimers disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.
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Factor structure of the National Alzheimers Coordinating Centers uniform dataset neuropsychological battery: an evaluation of invariance between and within groups over time.
Alzheimer Dis Assoc Disord
PUBLISHED: 05-25-2011
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The neuropsychological battery from the National Alzheimers Disease Coordinating Center is designed to provide a sensitive assessment of mild cognitive disorders for multicenter investigations. Comprising 8 common neuropsychological tests (12 measures), the battery assesses cognitive domains affected early in the course of Alzheimer disease. We examined the factor structure of the battery across levels of cognition [normal, mild cognitive impairment, dementia] based on Clinical Dementia Rating scores to determine cognitive domains tapped by the battery. Using data pooled from 29 Alzheimers Disease Centers funded by National Institute on Aging, exploratory factor analysis was used to derive a general model using half of the sample; 4 factors representing memory, attention, executive function, and language were identified. Confirmatory factor analysis was used on the second half of the sample to evaluate invariance between groups and within groups over 1 year. Factorial invariance testing included systematic addition of constraints and comparisons of nested models. The general confirmatory factor analysis model had a good fit. As constraints were added, model fit deteriorated slightly. Comparisons within groups showed stability over 1 year. In a range of cognition from normal to dementia, factor structures and factor loadings will vary little. Further work is needed to determine whether domains become more or less distinct in severely cognitively compromised individuals.
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Enriched white matter connectivity networks for accurate identification of MCI patients.
Neuroimage
PUBLISHED: 07-11-2010
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Mild cognitive impairment (MCI), often a prodromal phase of Alzheimers disease (AD), is frequently considered to be a good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques has made it possible to understand neurological disorders at a whole-brain connectivity level. Accordingly, we propose an effective network-based multivariate classification algorithm, using a collection of measures derived from white matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities(?(1), ?(2), and ?(3)), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), these properties can be quantified for each pair of regions with common traversing fibers. For building an MCI classifier, clustering coefficient of each ROI in relation to the remaining ROIs is extracted as feature for classification. These features are then ranked according to their Pearson correlation with respect to the clinical labels, and are further sieved to select the most discriminant subset of features using an SVM-based feature selection algorithm. Finally, support vector machines (SVMs) are trained using the selected subset of features. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy given by our enriched description of WM connections is 88.9%, which is an increase of at least 14.8% from that using simple WM connectivity description with any single physiological parameter. A cross-validation estimation of the generalization performance shows an area of 0.929 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. It was also found, based on the selected features, that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies. Our MCI classification framework, especially the enriched description of WM connections, allows accurate early detection of brain abnormalities, which is of paramount importance for treatment management of potential AD patients.
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Validation of the coin rotation test: a simple, inexpensive, and convenient screening tool for impaired psychomotor processing speed.
Neurologist
PUBLISHED: 07-02-2010
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The Coin Rotation Test is a simple, convenient, and cost-effective measure of psychomotor processing speed that has been used in neurologic examinations at the Louisiana State University Health Sciences system for almost 20 years. On the Coin Rotation Test, participants rotate a coin through serial 180 degree turns using the thumb, index, and middle fingers for 10 seconds. In the current study, we sought to validate the Coin Rotation Test on a hospital-based sample by determining the tasks sensitivity and specificity in detecting psychomotor processing speed impairment on a well-established criterion measure, the Grooved Pegboard Test.
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Effects of the dietary approaches to stop hypertension diet, exercise, and caloric restriction on neurocognition in overweight adults with high blood pressure.
Hypertension
PUBLISHED: 03-19-2010
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High blood pressure increases the risks of stroke, dementia, and neurocognitive dysfunction. Although aerobic exercise and dietary modifications have been shown to reduce blood pressure, no randomized trials have examined the effects of aerobic exercise combined with dietary modification on neurocognitive functioning in individuals with high blood pressure (ie, prehypertension and stage 1 hypertension). As part of a larger investigation, 124 participants with elevated blood pressure (systolic blood pressure 130 to 159 mm Hg or diastolic blood pressure 85 to 99 mm Hg) who were sedentary and overweight or obese (body mass index: 25 to 40 kg/m(2)) were randomized to the Dietary Approaches to Stop Hypertension (DASH) diet alone, DASH combined with a behavioral weight management program including exercise and caloric restriction, or a usual diet control group. Participants completed a battery of neurocognitive tests of executive function-memory-learning and psychomotor speed at baseline and again after the 4-month intervention. Participants on the DASH diet combined with a behavioral weight management program exhibited greater improvements in executive function-memory-learning (Cohens D=0.562; P=0.008) and psychomotor speed (Cohens D=0.480; P=0.023), and DASH diet alone participants exhibited better psychomotor speed (Cohens D=0.440; P=0.036) compared with the usual diet control. Neurocognitive improvements appeared to be mediated by increased aerobic fitness and weight loss. Also, participants with greater intima-medial thickness and higher systolic blood pressure showed greater improvements in executive function-memory-learning in the group on the DASH diet combined with a behavioral weight management program. In conclusion, combining aerobic exercise with the DASH diet and caloric restriction improves neurocognitive function among sedentary and overweight/obese individuals with prehypertension and hypertension.
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Aerobic exercise and neurocognitive performance: a meta-analytic review of randomized controlled trials.
Psychosom Med
PUBLISHED: 03-11-2010
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To assess the effects of aerobic exercise training on neurocognitive performance. Although the effects of exercise on neurocognition have been the subject of several previous reviews and meta-analyses, they have been hampered by methodological shortcomings and are now outdated as a result of the recent publication of several large-scale, randomized, controlled trials (RCTs).
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Genetic regulation of alpha-synuclein mRNA expression in various human brain tissues.
PLoS ONE
PUBLISHED: 07-03-2009
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Genetic variability across the SNCA locus has been repeatedly associated with susceptibility to sporadic Parkinsons disease (PD). Accumulated evidence emphasizes the importance of SNCA dosage and expression levels in PD pathogenesis. However whether genetic variability in the SNCA gene modulates the risk to develop sporadic PD via regulation of SNCA expression remained elusive. We studied the effect of PD risk-associated variants at SNCA 5 and 3regions on SNCA-mRNA levels in vivo in 228 human brain samples from three structures differentially vulnerable to PD pathology (substantia-nigra, temporal- and frontal-cortex) obtained from 144 neurologically normal cadavers. The extensively characterized PD-associated promoter polymorphism, Rep1, had an effect on SNCA-mRNA levels. Homozygous genotype of the protective, Rep1-259 bp allele, was associated with lower levels of SNCA-mRNA relative to individuals that carried at least one copy of the PD-risk associated alleles, amounting to an average decrease of approximately 40% and >50% in temporal-cortex and substantia-nigra, respectively. Furthermore, SNPs tagging the SNCA 3-untranslated-region also showed effects on SNCA-mRNA levels in both the temporal-cortex and the substantia-nigra, although, in contrast to Rep1, the decreased-risk alleles were correlated with increased SNCA-mRNA levels. Similar to Rep1 findings, no difference in SNCA-mRNA level was seen with different SNCA 3SNP alleles in the frontal-cortex, indicating there is brain-region specificity of the genetic regulation of SNCA expression. We provide evidence for functional consequences of PD-associated SNCA gene variants in disease relevant brain tissues, suggesting that genetic regulation of SNCA expression plays an important role in the development of the disease.
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Can lifestyle modification improve neurocognition? Rationale and design of the ENLIGHTEN clinical trial.
Contemp Clin Trials
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Risk factors for cardiovascular disease (CVD) not only increase the risk for clinical CVD events, but also are associated with a cascade of neurophysiologic and neuroanatomic changes that increase the risk of cognitive impairment and dementia. Although epidemiological studies have shown that exercise and diet are associated with lower CVD risk and reduced incidence of dementia, no randomized controlled trial (RCT) has examined the independent effects of exercise and diet on neurocognitive function among individuals at risk for dementia. The ENLIGHTEN trial is a RCT of patients with CVD risk factors who also are characterized by subjective cognitive complaints and objective evidence of neurocognitive impairment without dementia (CIND) STUDY DESIGN: A 2 by 2 design will examine the independent and combined effects of diet and exercise on neurocognition. 160 participants diagnosed with CIND will be randomly assigned to 6 months of aerobic exercise, the DASH diet, or a combination of both exercise and diet; a (control) group will receive health education but otherwise will maintain their usual dietary and activity habits. Participants will complete comprehensive assessments of neurocognitive functioning along with biomarkers of CVD risk including measures of blood pressure, glucose, endothelial function, and arterial stiffness.
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Resting-state multi-spectrum functional connectivity networks for identification of MCI patients.
PLoS ONE
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In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered (0.025 ? ƒ ? 0.100 Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients.
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Event-related functional magnetic resonance imaging changes during relational retrieval in normal aging and amnestic mild cognitive impairment.
J Int Neuropsychol Soc
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The earliest cognitive deficits observed in amnestic mild cognitive impairment (aMCI) appear to center on memory tasks that require relational memory (RM), the ability to link or integrate unrelated pieces of information. RM impairments in aMCI likely reflect neural changes in the medial temporal lobe (MTL) and posterior parietal cortex (PPC). We tested the hypothesis that individuals with aMCI, as compared to cognitively normal (CN) controls, would recruit neural regions outside of the MTL and PPC to support relational memory. To this end, we directly compared the neural underpinnings of successful relational retrieval in aMCI and CN groups, using event-related functional magnetic resonance imaging (fMRI), holding constant the stimuli and encoding task. The fMRI data showed that the CN, compared to the aMCI, group activated left precuneus, left angular gyrus, right posterior cingulate, and right parahippocampal cortex during relational retrieval, while the aMCI group, relative to the CN group, activated superior temporal gyrus and supramarginal gyrus for this comparison. Such findings indicate an early shift in the functional neural architecture of relational retrieval in aMCI, and may prove useful in future studies aimed at capitalizing on functionally intact neural regions as targets for treatment and slowing of the disease course. (JINS, 2012, 18, 1-12).
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.