The DNA of most vertebrates is depleted in CpG dinucleotide: a C followed by a G in the 5' to 3' direction. CpGs are the target for DNA methylation, a chemical modification of cytosine (C) heritable during cell division and the most well-characterized epigenetic mechanism. The remaining CpGs tend to cluster in regions referred to as CpG islands (CGI). Knowing CGI locations is important because they mark functionally relevant epigenetic loci in development and disease. For various mammals, including human, a readily available and widely used list of CGI is available from the UCSC Genome Browser. This list was derived using algorithms that search for regions satisfying a definition of CGI proposed by Gardiner-Garden and Frommer more than 20 years ago. Recent findings, enabled by advances in technology that permit direct measurement of epigenetic endpoints at a whole-genome scale, motivate the need to adapt the current CGI definition. In this paper, we propose a procedure, guided by hidden Markov models, that permits an extensible approach to detecting CGI. The main advantage of our approach over others is that it summarizes the evidence for CGI status as probability scores. This provides flexibility in the definition of a CGI and facilitates the creation of CGI lists for other species. The utility of this approach is demonstrated by generating the first CGI lists for invertebrates, and the fact that we can create CGI lists that substantially increases overlap with recently discovered epigenetic marks. A CGI list and the probability scores, as a function of genome location, for each species are available at http://www.rafalab.org.
While a link between body mass index (BMI) and brain volume has been established in several cross-sectional studies, evidence of the association between change in BMI over time and changes in brain structure is limited. Using data from a cohort of 347 former lead workers and community controls with two MRI scans over an approximately 5-year period, we estimated cross-sectional and longitudinal associations of BMI and brain volume using both region-of-interest (ROI) and voxel-based morpho-metric (VBM) methods. We found that associations of BMI and brain volume were not significantly different in former lead workers as compared to community controls. In the cross-sectional analysis, higher BMIs were associated with smaller brain volumes in gray matter (GM) using both ROI and VBM approaches. No associations with white matter (WM) were observed. In the longitudinal analysis, higher baseline BMI was associated with greater decline in temporal and occipital GM ROI volumes. Change in BMI over the five-year period was only associated with change in hippocampal volume and was not associated with change in any of the GM ROIs. Overall, higher BMI was associated with lower GM volume in several ROIs and with declines in volume in temporal and occipital GM over time. These results suggest that sustained high body mass may contribute to progressive temporal and occipital atrophy.
We describe and analyze a longitudinal diffusion tensor imaging (DTI) study relating changes in the microstructure of intracranial white matter tracts to cognitive disability in multiple sclerosis patients. In this application the scalar outcome and the functional exposure are measured longitudinally. This data structure is new and raises challenges that cannot be addressed with current methods and software. To analyze the data, we introduce a penalized functional regression model and inferential tools designed specifically for these emerging types of data. Our proposed model extends the Generalized Linear Mixed Model by adding functional predictors; this method is computationally feasible and is applicable when the functional predictors are measured densely, sparsely or with error. An online appendix compares two implementations, one likelihood-based and the other Bayesian, and provides the software used in simulations; the likelihood-based implementation is included as the lpfr() function in the R package refund available on CRAN.
Background and purpose Data from both humans and animal models suggest that most recovery from motor impairment occurs in a sensitive period that lasts only weeks after stroke and is mediated in part by an increased responsiveness to training. Here we used a mouse model of focal cortical stroke to test two hypotheses. First we investigated if responsiveness to training decreases over time after stroke. Second, we tested whether fluoxetine, which can influence synaptic plasticity and stroke recovery, can prolong the period over which large training-related gains can be elicited after stroke. Methods Mice were trained to perform a skilled prehension task to an asymptotic level of performance after which they underwent stroke induction in the caudal forelimb area (CFA). The mice were then retrained after a 1-day or 7-day delay with and without fluoxetine. Results Recovery of prehension after a CFA stroke was complete if training was initiated one day after stroke but incomplete if it was delayed by 7 days. In contrast, if fluoxetine was administered at 24 hours after stroke, then complete recovery of prehension was observed even with the 7-day training delay. Fluoxetine appeared to mediate its beneficial effect by reducing inhibitory interneuron expression in intact premotor cortex rather than through effects on infarct volume or cell death. Conclusions There is a gradient of diminishing responsiveness to motor training over the first week after stroke. Fluoxetine can overcome this gradient and maintain maximal levels of responsiveness to training even 7 days after stroke.
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