School of Behavioral and Brain Sciences
Permanent URI for this communityhttps://hdl.handle.net/10735.1/1526
The mission of the School of Behavioral and Brain Sciences is to understand the intersection of mind, brain and behavior; enhance the health, education, and quality of life of children and families; and create and implement technologies and therapies that repair and strengthen human abilities. We accomplish these goals by recruiting and supporting outstanding faculty to conduct innovative research and student training in a climate that fosters collaboration across
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Browsing School of Behavioral and Brain Sciences by Author "0000 0001 1648 3631 (Abdi, H.)"
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Item Bios2mds: An R Package for Comparing Orthologous Protein Families by Metric Multidimensional ScalingPele, Julien; Becu, Jean-Michel; Abdi, Hervé; Chabbert, Marie; 0000 0001 1648 3631 (Abdi, H.); 94060324 (Abdi)Background: The distance matrix computed from multiple alignments of homologous sequences is widely used by distance-based phylogenetic methods to provide information on the evolution of protein families. This matrix can also be visualized in a low dimensional space by metric multidimensional scaling (MDS). Applied to protein families, MDS provides information complementary to the information derived from tree-based methods. Moreover, MDS gives a unique opportunity to compare orthologous sequence sets because it can add supplementary elements to a reference space.Results: The R package bios2mds (from BIOlogical Sequences to MultiDimensional Scaling) has been designed to analyze multiple sequence alignments by MDS. Bios2mds starts with a sequence alignment, builds a matrix of distances between the aligned sequences, and represents this matrix by MDS to visualize a sequence space. This package also offers the possibility of performing K-means clustering in the MDS derived sequence space. Most importantly, bios2mds includes a function that projects supplementary elements (a.k.a. " out of sample" elements) onto the space defined by reference or " active" elements. Orthologous sequence sets can thus be compared in a straightforward way. The data analysis and visualization tools have been specifically designed for an easy monitoring of the evolutionary drift of protein sub-families.Conclusions: The bios2mds package provides the tools for a complete integrated pipeline aimed at the MDS analysis of multiple sets of orthologous sequences in the R statistical environment. In addition, as the analysis can be carried out from user provided matrices, the projection function can be widely used on any kind of data.Item Effect of Age on Variability in the Production of Text-Based Global InferencesWilliams, Lynne J.; Dunlop, Joseph P; Abdi, Hervé; 0000 0001 1648 3631 (Abdi, H.); 94060324 (Abdi)As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one's world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation-a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging. © 2012 Williams et al.Item Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to Assign Scans to Categories without Using Spatial NormalizationAbdi, Hervé; Williams, Lynne J.; Connolly, Andrew C.; Gobbini, M. Ida; Dunlop, Joseph P.; Haxby, James V.; 0000 0001 1648 3631 (Abdi, H.); 94060324 (Abdi)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 analyze datasets with different voxel numbers per participant and, so does not require spatial normalization. MUSUBADA statistical inferences are implemented with cross-validation techniques (e.g., jackknife and bootstrap), its performance is evaluated with confusion matrices (for fixed and random models) and represented with prediction, tolerance, and confidence intervals. We present an example where we predict the image categories (houses, shoes, chairs, and human, monkey, dog, faces, ) of images watched by participants whose brains were scanned. This example corresponds to a DA question in which the data table is made of subtables (one per subject) and with more variables than observations.