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Your search keyword '"Eleftherios Garyfallidis"' showing total 3 results
Start Over Author "Eleftherios Garyfallidis" Journal frontiers in neuroinformatics
3 results on '"Eleftherios Garyfallidis"'

1. Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer’s Disease

Author

Alexandra Badea, Wenlin Wu, Jordan Shuff, Michele Wang, Robert J. Anderson, Yi Qi, G. Allan Johnson, Joan G. Wilson, Serge Koudoro, Eleftherios Garyfallidis, Carol A. Colton, and David B. Dunson

Subjects
mouse model, Alzheimer’s disease, neurodegeneration, magnetic resonance imaging, tractography, tract based analysis, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

The major genetic risk for late onset Alzheimer’s disease has been associated with the presence of APOE4 alleles. However, the impact of different APOE alleles on the brain aging trajectory, and how they interact with the brain local environment in a sex specific manner is not entirely clear. We sought to identify vulnerable brain circuits in novel mouse models with homozygous targeted replacement of the mouse ApoE gene with either human APOE3 or APOE4 gene alleles. These genes are expressed in mice that also model the human immune response to age and disease-associated challenges by expressing the human NOS2 gene in place of the mouse mNos2 gene. These mice had impaired learning and memory when assessed with the Morris water maze (MWM) and novel object recognition (NOR) tests. Ex vivo MRI-DTI analyses revealed global and local atrophy, and areas of reduced fractional anisotropy (FA). Using tensor network principal component analyses for structural connectomes, we inferred the pairwise connections which best separate APOE4 from APOE3 carriers. These involved primarily interhemispheric connections among regions of olfactory areas, the hippocampus, and the cerebellum. Our results also suggest that pairwise connections may be subdivided and clustered spatially to reveal local changes on a finer scale. These analyses revealed not just genotype, but also sex specific differences. Identifying vulnerable networks may provide targets for interventions, and a means to stratify patients.

Published
2019
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2. Dipy, a library for the analysis of diffusion MRI data

Author

Ariel Rokem, Ian Nimmo-Smith, Maxime Descoteaux, Stefan van der Walt, Eleftherios Garyfallidis, Matthew Brett, and Bagrat Amirbekian

Subjects
Computer science, diffusion tensor, Biomedical Engineering, Neuroscience (miscellaneous), medical imaging, tractography, computer.software_genre, 030218 nuclear medicine & medical imaging, lcsh:RC321-571, diffusion MRI, 03 medical and health sciences, 0302 clinical medicine, Probabilistic Tractography, DSI, HARDI, Methods Article, Cluster analysis, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, computer.programming_language, Signal reconstruction, dMRI, Python (programming language), Computer Science Applications, Visualization, python, DTI, Data analysis, free open source software, Data mining, Deconvolution, computer, 030217 neurology & neurosurgery, Neuroscience, Tractography, Diffusion MRI, clustering
Abstract

Diffusion Imaging in Python (Dipy) is a free and open source software projectfor the analysis of data from diffusion magnetic resonance imaging (dMRI)experiments. dMRI is an application of MRI that can be used to measurestructural features of brain white matter. Many methods have been developed touse dMRI data to model the local configuration of white matter nerve fiberbundles and infer the trajectory of bundles connecting different parts of thebrain.Dipy gathers implementations of many different methods in dMRI, including:diffusion signal pre-processing; reconstruction of diffusion distributions inindividual voxels; fiber tractography and fiber track post-processing, analysisand visualization. Dipy aims to provide transparent implementations forall the different steps of dMRI analysis with a uniform programming interface.We have implemented classical signal reconstruction techniques, such as thediffusion tensor model and deterministic fiber tractography. In addition,cutting edge novel reconstruction techniques are implemented, such asconstrained spherical deconvolution and diffusion spectrum imaging withdeconvolution, as well as methods for probabilistic tracking and originalmethods for tractography clustering. Many additional utility functions areprovided to calculate various statistics, informative visualizations, as wellas file-handling routines to assist in the development and use of noveltechniques.In contrast to many other scientific software projects, Dipy is not beingdeveloped by a single research group. Rather, it is an open project thatencourages contributions from any scientist/developer through GitHub and opendiscussions on the project mailing list. Consequently, Dipy today has aninternational team of contributors, spanning seven different academic institutionsin five countries and three continents, which is still growing.

Published
2014
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