Buchman A.S.,Rush University Medical Center |
Yu L.,Rush University Medical Center |
Wilson R.S.,Rush University Medical Center |
Shulman J.M.,Baylor College of Medicine |
And 3 more authors.
BMC Geriatrics | Year: 2014
Background: We tested the hypothesis that harm avoidance, a trait associated with behavioral inhibition, is associated with the rate of change in parkinsonism in older adults. Methods. At baseline harm avoidance was assessed with a standard self-report instrument in 969 older people without dementia participating in the Rush Memory and Aging Project, a longitudinal community-based cohort study. Parkinsonism was assessed annually with a modified version of the motor section of the Unified Parkinson's Disease Rating Scale (mUPDRS). Results: Average follow-up was 5 years. A linear mixed-effects model controlling for age, sex and education showed that for an average participant (female, 80 years old at baseline, with 14 years of education and a harm avoidance score of 10), the overall severity of parkinsonism increased by about 0.05 unit/ year (Estimate, 0.054, S.E., 0.007, p <0.001) and that the level of harm avoidance was associated with the progression of parkinsonism (Estimate, 0.004, S.E., 0.001, p <0.001). Thus, for an average participant, every 6 point (∼1 SD) increase in harm avoidance score at baseline, the rate of progression of parkinsonism increased about 50% compared to an individual with an average harm avoidance score. This amount of change in parkinsonism over the course of the study was associated with about a 5% increased risk of death. The association between harm avoidance and progression of parkinsonism persisted when controlling for cognitive function, depressive symptoms, loneliness, neuroticism, late-life cognitive, social and physical activities and chronic health conditions. Conclusion: A higher level of the harm avoidance trait is associated with a more rapid progression of parkinsonism in older adults. © 2014 Buchman et al.; licensee BioMed Central Ltd.
Tomson S.N.,University of California at Los Angeles |
Schreiner M.J.,University of California at Los Angeles |
Narayan M.,Rice University |
Rosser T.,Childrens Hospital Los Angeles |
And 7 more authors.
Human Brain Mapping | Year: 2015
Neurofibromatosis type I (NF1) is a genetic disorder caused by mutations in the neurofibromin 1 gene at locus 17q11.2. Individuals with NF1 have an increased incidence of learning disabilities, attention deficits, and autism spectrum disorders. As a single-gene disorder, NF1 represents a valuable model for understanding gene-brain-behavior relationships. While mouse models have elucidated molecular and cellular mechanisms underlying learning deficits associated with this mutation, little is known about functional brain architecture in human subjects with NF1. To address this question, we used resting state functional connectivity magnetic resonance imaging (rs-fcMRI) to elucidate the intrinsic network structure of 30 NF1 participants compared with 30 healthy demographically matched controls during an eyes-open rs-fcMRI scan. Novel statistical methods were employed to quantify differences in local connectivity (edge strength) and modularity structure, in combination with traditional global graph theory applications. Our findings suggest that individuals with NF1 have reduced anterior-posterior connectivity, weaker bilateral edges, and altered modularity clustering relative to healthy controls. Further, edge strength and modular clustering indices were correlated with IQ and internalizing symptoms. These findings suggest that Ras signaling disruption may lead to abnormal functional brain connectivity; further investigation into the functional consequences of these alterations in both humans and in animal models is warranted. © 2015 Wiley Periodicals, Inc.
Cuddapah V.A.,University of Alabama at Birmingham |
Pillai R.B.,University of Alabama at Birmingham |
Shekar K.V.,University of Alabama at Birmingham |
Lane J.B.,University of Alabama at Birmingham |
And 10 more authors.
Journal of Medical Genetics | Year: 2014
Background: Rett syndrome (RTT), a neurodevelopmental disorder that primarily affects girls, is characterised by a period of apparently normal development until 6-18 months of age when motor and communication abilities regress. More than 95% of individuals with RTT have mutations in methyl-CpG-binding protein 2 (MECP2), whose protein product modulates gene transcription. Surprisingly, although the disorder is caused by mutations in a single gene, disease severity in affected individuals can be quite variable. To explore the source of this phenotypic variability, we propose that specific MECP2 mutations lead to different degrees of disease severity. Methods: Using a database of 1052 participants assessed over 4940 unique visits, the largest cohort of both typical and atypical RTT patients studied to date, we examined the relationship between MECP2 mutation status and various phenotypic measures over time. Results: In general agreement with previous studies, we found that particular mutations, such as p.Arg133Cys, p. Arg294X, p.Arg306Cys, 3° truncations and other point mutations, were relatively less severe in both typical and atypical RTT. In contrast, p.Arg106Trp, p.Arg168X, p. Arg255X, p.Arg270X, splice sites, deletions, insertions and deletions were significantly more severe. We also demonstrated that, for most mutation types, clinical severity increases with age. Furthermore, of the clinical features of RTT, ambulation, hand use and age at onset of stereotypies are strongly linked to overall disease severity. Conclusions: We have confirmed that MECP2 mutation type is a strong predictor of disease severity. These data also indicate that clinical severity continues to become progressively worse regardless of initial severity. These findings will allow clinicians and families to anticipate and prepare better for the needs of individuals with RTT.
Allen G.I.,Rice University |
Allen G.I.,Jan and Dan Duncan Neurological Research Institute
Proceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013 | Year: 2013
Inferring functional connectivity, or statistical dependencies between activity in different regions of the brain, is of great interest in the study of neurocognitive conditions. For example, studies - indicate that patterns in connectivity might yield potential biomarkers for conditions such as Alzheimer's and autism. We model functional connectivity using Markov Networks, which use conditional dependence to determine when brain regions are directly connected. In this paper, we show that standard large-scale two-sample testing that compares graphs from distinct populations using subject level estimates of functional connectivity, fails to detect differences in functional connections. We propose a novel procedure to conduct two-sample inference via resampling and randomized edge selection to detect differential connections, with substantial improvement in statistical power and error control. © 2013 IEEE.
Pang K.,Baylor College of Medicine |
Pang K.,Jan and Dan Duncan Neurological Research Institute |
Wan Y.-W.,Baylor College of Medicine |
Wan Y.-W.,Jan and Dan Duncan Neurological Research Institute |
And 7 more authors.
Bioinformatics | Year: 2014
Motivation: Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. Results: Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. © The Author 2014.