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Big Data Era in Magnetic Resonance Imaging of the Human Brain
Published in Ervin Sejdić, Tiago H. Falk, Signal Processing and Machine Learning for Biomedical Big Data, 2018
Xiaoyu Ding, Elisabeth de Castro Caparelli, Thomas J. Ross
The 1000 FCP is part of the International Neuroimaging Data-sharing Initiative (INDI), which is now sponsored by the Child Mind Institute [27]. This project was launched in December 2009 and was initially designed to gather and share fMRI human data in order to establish the comprehensive mapping of the functional connectome. In a retrospective data sharing, rsfMRI data from over 1400 healthy adult volunteers collected from 35 sites around the world were publicly released [28]. Additionally, the data processing steps used to evaluate the feasibility of this project were also made available on Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) [29]. As a result, by combining datasets from different centers, the influence of different data acquisition protocols and scanners on resting-state fMRI measures were investigated [30], and the use of the intersubject variance-based method to identify presumed limits between functional networks was explored, finally establishing the presence of a universal functional architecture in the human brain [28].
Axon-Inspired Communication Systems
Published in James E. Morris, Krzysztof Iniewski, Nanoelectronic Device Applications Handbook, 2017
Valeriu Beiu, Liren Zhang, Azam Beg, Walid Ibrahim, Mihai Tache
All of these results are supporting the scarcity of the long interconnects in the brain. The detailed map of the full set of neurons and synapses within the nervous system of an organism is known as a connectome (http://en.wikipedia.org/wiki/Connectome), and the National Institutes of Health is supporting the Human Connectome Project (http://www.humanconnectomeproject.org/), which started in 2011. In fact, the first comprehensive attempt to reverse-engineer the mammalian brain was started in 2005 as the EPFL/Blue Brain Project (http://bluebrain.epfl.ch/), and impressive simulation results of neocortical columns (about 10,000 biologically accurate individual neurons) have already been obtained (see Figure 15.2). The expectation is that the Blue Brain Project will be expanded and continued under the EU’s FET Flagship program as the Human Brain Project (http://www.humanbrainproject.eu/), a fact which has just been announced in January 2013 (http://www.nature.com/news/brain-simulation-and-graphene-projects-win-billion-euro-competition-1.12291).
Bayesian Generalized Sparse Symmetric Tensor-on-Vector Regression
Published in Technometrics, 2021
Sharmistha Guha, Rajarshi Guhaniyogi
In recent times, multidimensional arrays or tensors, which are higher order extensions of two-dimensional matrices, are being encountered in datasets emerging from different disciplines. Similar to the rows and columns of a matrix, the dimensions or axes of a tensor are known as tensor modes. A tensor is known to be symmetric if interchanging the modes results in the same tensor. Therefore, a symmetric matrix is a special case of a symmetric tensor in two dimensions. The indices of any tensor mode in a symmetric tensor are often referred to as tensor nodes. This article is motivated by a variety of brain related data applications, where comprehensive maps of neural connections in the brain, also known as brain connectomes, are available for multiple subjects. These brain connectomes are often expressed in the form of symmetric tensors. Our focus is mainly on datasets in which a sample of symmetric tensors is available from multiple subjects of interest, along with a few subject specific observable attributes, often referred to as phenotypes. In these applications, there is a symmetric tensor corresponding to every subject, and the tensor nodes are labeled and shared across all subject specific tensors through a common map.