Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline

By I. Dinov, J. Horn, K. Lozev, R. Magsipoc, P. Petrosyan, Zhizhong Liu, A. MacKenzie-Graham, Paul R. Eggert, D. S. Parker, Arthur W. Toga. 2009

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Citations in the corpus

Cognitive Network Neuroscience (2015, background): “It is important to note that all parcellation schemes can be defined across a range of spatial resolutions, enabling the exploration of multiresolution structure in the human brain (Bassett, Brown, et al., 2011; Bassett et al., 2010; Zalesky et al., 2010; Meunier et al., 2009).”
Continual Learning in Neural Networks (2019, background): “It seems that complex biological systems have hierarchical organization [102], or the so called modules within modules suggesting that neural modular composition might be necessary for achieving real continual learning of different tasks and problems.”
Functional network organization of the human brain (2011, background): “…Tomasi and Volkow, 2011; van den Heuvel et al., 2008), large parcels from anatomically based brain atlases (Hartman et al., 2011; He et al., 2009; Meunier et al., 2009a; Spoormaker et al., 2010; Tian et al., 2011), or random interpolations between voxels and parcels (Hayasaka and Laurienti,…”
A New Measure of Centrality for Brain Networks (2010, methodology): “well-studied method [21,24,38] that does not rely solely on the”
Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits (2010, result): “Even the smallest and least precisely estimated connectome, that derived from the human MRI datasets, also generated networks with some hierarchical modular properties (across 2 topological scales) and this observation is compatible with other data demonstrating hierarchical modularity in human brai”
Structural and Functional Brain Networks: From Connections to Cognition (2013, background): “Although the rich-club phenomenon is consistent with hierarchical brain architectures (21), it largely has its basis in “strong” structural connectivity, measured with neuroimaging.”
Weight-conserving characterization of complex functional brain networks (2011, result): “The nature of negative correlations in resting-state functional MRI remains controversial (Murphy et al., 2009; Fox et al., 2009) although recent studies unambiguously demonstrate the neurophysiological origin of strong negative correlations (e.g. Chang and Glover, 2009).”
Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture (2014, background): “Recorded under similar conditions over an extended period of time, the human brain possesses a stable set of functionally coupled networks that echo many known features of anatomical organization [1–13].”
Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, methodology): “A couple of groups recently applied some data-driven techniques to derive a large number of brain regions and conduct a region-based analysis (Bellec et al., 2006; Thirion et al., 2006; Meunier et al., 2009).”
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python (2011, background): “A much more extensive and feature rich solution is the LONI Pipeline (Rex et al., 2003; Dinov et al., 2009, 2010).”
Brain graphs: graphical models of the human brain connectome. (2011, background): “We also know from recent work that brain networks are not only modular at a global scale, they also demonstrate a hierarchical modularity (Meunier et al. 2009) or nested arrangement of modules within modules, which may be advantageous in terms of stability, evolvability, and efficiency of physical…”
Schizophrenia, neuroimaging and connectomics (2012, background): “An important property of many complex networks, the brain included, is that they can be decomposed into subsets of nodes that have greater connectivity with each other than with other nodes; i.e., they are modular (Meunier et al., 2009, 2011; Newman, 2006).”