Measuring Brain Connectivity: Diffusion Tensor Imaging Validates Resting State Temporal Correlations

By Pawel Skudlarski, Kanchana Jagannathan, Vince D. Calhoun, Michelle Hampson, Beata A. Skudlarska, Godfrey D. Pearlson. NeuroImage, 2008

492 citations recorded by the source corpus.

Diffusion tensor imaging (DTI) and resting state temporal correlations (RSTC) are two leading techniques for investigating the connectivity of the human brain. They have been widely used to investigate the strength of anatomical and functional connections between distant brain regions in healthy subjects, and in clinical populations. Though they are both based on magnetic resonance imaging (MRI) they have not yet been compared directly. In this work both techniques were employed to create global connectivity matrices covering the whole brain gray matter. This allowed for direct comparisons between functional connectivity measured by RSTC with anatomical connectivity quantified using DTI tractography. We found that connectivity matrices obtained using both techniques showed significant agreement. Connectivity maps created for a priori defined anatomical regions showed significant correlation, and furthermore agreement was especially high in regions showing strong overall connectivity, such as those belonging to the default mode network. Direct comparison between functional RSTC and anatomical DTI connectivity, presented here for the first time, links two powerful approaches for investigating brain connectivity and shows their strong agreement. It provides a crucial multi-modal validation for resting state correlations as representing neuronal connectivity. The combination of both techniques presented here allows for further combining them to provide richer representation of brain connectivity both in the healthy brain and in clinical conditions.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d8112fed6b5da7b46ece667c7f8a5d5d0069a8f5

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Citations in the corpus

Graph-Based Network Analysis of Resting-State Functional MRI (2010, unknown): “In particular, by focusing on the whole brain connectivity pattern, previous studies have demonstrated overall, though imperfect, agreement between functional and structural connectivity at different spatial levels (Skudlarski et al., 2008; Honey et al., 2009).”
The structural-functional connectome and the default mode network of the human brain (2014, unknown): “To the best of our knowledge, only few studies have used such an approach and only two have focused on the relationship between structure and function in a voxel-wise (Skudlarski et al., 2008) or more coarsely parcellated fashion (Honey et al.”
The resting brain: unconstrained yet reliable. (2009, background): “ing) and the validity of negative correlations (Skudlarski et al. 2008), others studies suggest global signal correction as a reasonable alternative to direct measurement and subsequent”
Schizophrenia, neuroimaging and connectomics (2012, background): “…such conditions recapitulates well-known task-evoked co-activation patterns (Smith et al., 2009) and is correlated with underlying anatomical connectivity (Honey et al., 2009; Skudlarski et al., 2008; Zalesky and Fornito, 2009) and synchronized oscillations in neuronal activity (He et al., 2008).”
Network-based statistic: Identifying differences in brain networks (2010, methodology): “, 1999), hundreds of thousands of streamlines are generated to etch out the trajectories of these fiber tracts and the association between a pair of nodes is measured by the number of streamlines via which they are interconnected (Gong et al., 2009; Hagmann et al., 2007, 2008; Itturia-Medina et al.,”
Modeling the Impact of Lesions in the Human Brain (2009, background): “Comparative studies of structural and functional connectivity indicate that the presence of structural links between pairs of cortical regions is predictive of the occurrence of endogenously driven (resting-state) functional connectivity [4,8,9].”
Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, methodology): “tivity measured by MRI ([11–19]; [5] for review).”
Inter-Individual Differences in Resting State Functional Connectivity Predict Task-Induced BOLD Activity (2010, background): “Still, given that our understanding of structure/function relationships in resting-state fMRI is only just emerging, this issue merits further examination (Greicius et al., 2009; Hagmann et al., 2008; Honey et al., 2009; Skudlarski et al., 2008; van den Heuvel et al., 2009).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, unknown): “These analyses where then extended to the first in vivo structuralmaps of the human connectome generated using diffusion imaging (Hagmann et al., 2007, 2008; Skudlarski et al., 2008; Zalesky and Fornito, 2009) and found rapid applications in the clinical neurosciences (Bassett et al.”
The human connectome: Origins and challenges (2013, background): “Other studies (e.g. Greicius et al., 2009; Hagmann et al., 2010; Skudlarski et al., 2008; van den Heuvel et al., 2009), including some in nonhuman primates (Adachi et al., 2012), have confirmed a robust, albeit complex, relationship between structural connections and spontaneous dynamic…”
Development of functional and structural connectivity within the default mode network in young children (2010, background): “This association between function and structure has been observed to bemost significant within the DMN (Hagmann et al., 2008; Skudlarski et al., 2008).”