An Investigation of Functional and Anatomical Connectivity Using Magnetic Resonance Imaging

By M. Koch, David G. Norris, Margret Hund-Georgiadis. NeuroImage, 2002

548 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d50e83ead96f51f6bf3be739d2dbad6f99595d7c

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements (2003, methodology): “For example, a grid-based random walk process may be simulated (15), in which a particle is allowed to diffuse at a rate determined by a function of the magnitude of the diffusion coefficient along one of the possible inter-grid point (inter-voxel) directions chosen at random.”
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI (2013, methodology): “The technique, usually referred to as probabilistic tractography (Behrens et al., 2003; Koch et al., 2002), yields maps”
Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue (2005, methodology): “…voxel p is crossed by a streamline is used to define a map of the probability j of connection to the start point, in a similar fashion to that used by Koch et al. (2002): jð pÞ Z lim N/N jðp; NÞ z mðp; NÞ N : (2.7) Maps of j are generated for each voxel within a userdefined tracking start region.”
Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity (2009, background): “Furthermore, both Koch et al. (2002) and Honey et al. (2009) found that increased structural connectivity predicts increased functional connectivity , but increased functional connectivity does not as consistently predict increased structural connectivity.”
A method for using blocked and event-related fMRI data to study "resting state" functional connectivity (2007, background): “First, some consider them to reflect human anatomical connectivity (Koch et al., 2002; Quigley et al., 2003).”
Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS) (2012, methodology): “DTI, a noninvasive measure of anatomical connectivity, has been shown to relate well to both functional connectivity measured with resting state fcMRI (De Luca et al., 2006; Greicius et al., 2009; Honey et al., 2009; Koch et al., 2002; Lowe et al., 2008; Skudlarski et al., 2008; van den Heuvel et al”
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging (2007, background): “Other studies have examined the relationship between spontaneous BOLD correlations and anatomical connectivity using diffusion tensor imaging (DTI”
Diffusion MRI at 25: Exploring brain tissue structure and function (2012, background): “The combined effects of the axon packing in the fiber bundles and the thickness of the myelin sheath on the degree of anisotropy have still to be described in detail, but DTI already represents an outstanding tool to study brain development in animals and humans (Dubois et al., 2008).”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, background): “Indeed, concerning a general relationship between functional and structural connectivity, local regions on either side of a sulcus that are structurally connected by short-range fibers were found to also show a high level of functional connectivity (Koch et al., 2002).”
Network-Level Structure-Function Relationships in Human Neocortex (2016, background): “dicts the magnitude of the corresponding functional connection, the reverse is not necessarily true (Koch et al. 2002; Skudlarski et al. 2008; Honey et al. 2009; Shen et al. 2012; Hermundstad et al. 2014, but see also Hermundstad et al. 2013).”
Characterizing brain anatomical connections using diffusion weighted MRI and graph theory (2007, methodology): “On the other hand, although probability of connection between seed and target voxels has been previously used (Tuch, 2002; Parker et al., 2002; Koch et al., 2002; Parker et al., 2003; Behrens et al., 2003; Staempfli et al., 2006), the generalization of this concept to characterize anatomical connect”
Emerging concepts for the dynamical organization of resting-state activity in the brain (2010, unknown): “35 ).”
Diffusion Tensor Imaging and Beyond (2011, unknown): “Other probabilistic methods have also been proposed, including: the use of fast marching algorithms (179), front evolution methods (176), random-walk models (180), and graphtheoretic approaches (181).”
MRtrix: Diffusion tractography in crossing fiber regions (2012, background): “‘‘Probabilistic’’ tractography algorithms were developed to address this issue, and a number of different approaches have been proposed (Koch et al., 2002; Parker et al., 2002, 2003; Behrens et al., 2003; Tournier et al., 2003; Jones and Pierpaoli, 2005; Lazar and Alexander, 2005).”
Measuring Brain Connectivity: Diffusion Tensor Imaging Validates Resting State Temporal Correlations (2008, unknown): “The related work (Koch et al., 2002) was limited to neighboring gyri and found only weak trends towards agreement between techniques; so far no one has been able to compare them globally.”
Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions (2009, methodology): “[34] in which we use the multidirectional fibre ODF.”
Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, unknown): “tion of a few studies [10].”