424 research outputs found
Global Analysis of Gene Expression and Projection Target Correlations in the Mouse Brain
Recent studies have shown that projection targets in the mouse neocortex are correlated with their gene expression patterns. However, a brain-wide quantitative analysis of the relationship between voxel genetic composition and their projection targets is lacking to date. Here we extended those studies to perform a global, integrative analysis of gene expression and projection target correlations in the mouse brain. By using the Allen Brain Atlas data, we analyzed the relationship between gene expression and projection targets. We first visualized and clustered the two data sets separately and showed that they both exhibit strong spatial autocorrelation. Building upon this initial analysis, we conducted an integrative correlation analysis of the two data sets while correcting for their spatial autocorrelation. This resulted in a correlation of 0.19 with significant p value. We further identified the top genes responsible for this correlation using two greedy gene ranking techniques. Using only the top genes identified by those techniques, we recomputed the correlation between these two data sets. This led to correlation values up to 0.49 with significant p values. Our results illustrated that although the target specificity of neurons is in fact complex and diverse, yet they are strongly affected by their genetic and molecular compositions
Understanding Huntington\u27s disease using Machine Learning Approaches
Huntingtonâs disease (HD) is a debilitating neurodegenerative disorder with a complex pathophysiology. Despite extensive studies to study the disease, the sequence of events through which mutant Huntingtin (mHtt) protein executes its action still remains elusive. The phenotype of HD is an outcome of numerous processes initiated by the mHtt protein along with other proteins that act as either suppressors or enhancers of the effects of mHtt protein and PolyQ aggregates. Utilizing an integrative systems biology approach, I construct and analyze a Huntingtonâs disease integrome using human orthologs of protein interactors of wild type and mHtt protein. Analysis of this integrome using unsupervised machine learning methods reveals a novel connection linking mHtt protein with chromosome condensation and DNA repair. I generate a list of candidate genes that upon validation in a yeast and drosophila model of HD are shown to affect the mHtt phenotype and provide an in-vivo evidence of our hypothesis. A separate supervised machine learning approach is applied to build a classifier model that predicts protein interactors of wild type and mHtt protein. Both the machine learning models that I employ, have important applications for Huntingtonâs disease in predicting both protein and genetic interactions of huntingtin protein and can be easily extended to other PolyQ and neurodegenerative disorders such as Alzheimerâs and Parkinsonâs disease
Extreme Graphical Models with Applications to Functional Neuronal Connectivity
With modern calcium imaging technology, the activities of thousands of
neurons can be recorded simultaneously in vivo. These experiments can
potentially provide new insights into functional connectivity, defined as the
statistical relationships between the spiking activity of neurons in the brain.
As a commonly used tool for estimating conditional dependencies in
high-dimensional settings, graphical models are a natural choice for analyzing
calcium imaging data. However, raw neuronal activity recording data presents a
unique challenge: the important information lies in the rare extreme value
observations that indicate neuronal firing, as opposed to the non-extreme
observations associated with inactivity. To address this issue, we develop a
novel class of graphical models, called the extreme graphical model, which
focuses on finding relationships between features with respect to the extreme
values. Our model assumes the conditional distributions a subclass of the
generalized normal or Subbotin distribution, and yields a form of a curved
exponential family graphical model. We first derive the form of the joint
multivariate distribution of the extreme graphical model and show the
conditions under which it is normalizable. We then demonstrate the model
selection consistency of our estimation method. Lastly, we study the empirical
performance of the extreme graphical model through several simulation studies
as well as through a real data example, in which we apply our method to a
real-world calcium imaging data set
Characterization and Inference of Graph Diffusion Processes from Observations of Stationary Signals
Many tools from the field of graph signal processing exploit knowledge of the
underlying graph's structure (e.g., as encoded in the Laplacian matrix) to
process signals on the graph. Therefore, in the case when no graph is
available, graph signal processing tools cannot be used anymore. Researchers
have proposed approaches to infer a graph topology from observations of signals
on its nodes. Since the problem is ill-posed, these approaches make
assumptions, such as smoothness of the signals on the graph, or sparsity
priors. In this paper, we propose a characterization of the space of valid
graphs, in the sense that they can explain stationary signals. To simplify the
exposition in this paper, we focus here on the case where signals were i.i.d.
at some point back in time and were observed after diffusion on a graph. We
show that the set of graphs verifying this assumption has a strong connection
with the eigenvectors of the covariance matrix, and forms a convex set. Along
with a theoretical study in which these eigenvectors are assumed to be known,
we consider the practical case when the observations are noisy, and
experimentally observe how fast the set of valid graphs converges to the set
obtained when the exact eigenvectors are known, as the number of observations
grows. To illustrate how this characterization can be used for graph recovery,
we present two methods for selecting a particular point in this set under
chosen criteria, namely graph simplicity and sparsity. Additionally, we
introduce a measure to evaluate how much a graph is adapted to signals under a
stationarity assumption. Finally, we evaluate how state-of-the-art methods
relate to this framework through experiments on a dataset of temperatures.Comment: Submitted to IEEE Transactions on Signal and Information Processing
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Computer-based cognitive intervention for aphasia: Behavioural and neurobiological outcomes
Aphasia, an acquired impairment of language that commonly occurs after stroke, can have significant consequences on all aspects of functioning of affected individuals. Some have proposed that the language deficits observed in aphasia are due to underlying limitations in cognitive processes that support language1-3. This âcognitiveâ theory of aphasia is gaining increased attention in the research literature4, and is the impetus for the study of treatments for aphasia that target these underlying cognitive processes5-8. Indeed, studies of cognitive interventions in healthy populations have reported positive outcomes in behavioural (i.e. language and overall cognitive functioning9, 10) as well as neurobiological (i.e., brain function and/or structure11-13) domains, offering promise for the application of these types of interventions to aphasia.
Recently, computer-based âbrain trainingâ programs have become increasingly prevalent. BrainFitness (BF) is one such commercially available program; it has been used to show improvement in auditory processing speed, attention and working memory in typically aging adults14, 15. This program has the potential to be a useful intervention for individuals with aphasia, but questions regarding the clinical utility of the program and neural correlates of training-related behavioural changes remain. The purpose of this study was to investigate the effect of BF training in people with aphasia using behavioural and neurobiological outcome measures
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