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Full-Text Articles in Physical Sciences and Mathematics
Modeling Neurovascular Coupling From Clustered Parameter Sets For Multimodal Eeg-Nirs, M. Tanveer Talukdar, H. Robert Frost, Solomon G. G. Diamond
Modeling Neurovascular Coupling From Clustered Parameter Sets For Multimodal Eeg-Nirs, M. Tanveer Talukdar, H. Robert Frost, Solomon G. G. Diamond
Dartmouth Scholarship
Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from …
Multiple Subject Barycentric Discriminant Analysis (Musubada): How To Assign Scans To Categories Without Using Spatial Normalization, Hervé Abdi, Lynne J. Williams, Andrew C. Connolly, M. Ida Gobbini
Multiple Subject Barycentric Discriminant Analysis (Musubada): How To Assign Scans To Categories Without Using Spatial Normalization, Hervé Abdi, Lynne J. Williams, Andrew C. Connolly, M. Ida Gobbini
Dartmouth Scholarship
We present a new discriminant analysis (DA) method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA) suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels) that are themselves grouped into regions of interest (ROIs). Like DA, MUSUBADA (1) assigns observations to predefined categories, (2) gives factorial maps displaying observations and categories, and (3) optimally assigns observations to categories. MUSUBADA handles cases with more variables than observations and can project portions of the data table (e.g., subtables, which can represent participants or ROIs) on the factorial maps. Therefore MUSUBADA can …
Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit
Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit
Dartmouth Scholarship
There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.
Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross
Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross
Dartmouth Scholarship
The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach.
Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota
Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota
Dartmouth Scholarship
The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD …