Segmented Echo Planar Imaging Improves Detection of Subcortical Functional Connectivity Networks in the Rat Brain View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Stefano Tambalo, Giulia Scuppa, Angelo Bifone

ABSTRACT

Susceptibility artifacts in the vicinity of aural and nasal cavities result in significant signal drop-out and image distortion in echo planar imaging of the rat brain. These effects may limit the study of resting state functional connectivity in deep brain regions. Here, we explore the use of segmented EPI for resting state fMRI studies in the rat, and assess the relative merits of this method compared to single shot EPI. Sequences were evaluated in terms of signal-to-noise ratio, geometric distortions, data driven detection of resting state networks and group level correlations of time series. Multishot imaging provided improved SNR, temporal SNR and reduced geometric distortion in deep areas, while maintaining acceptable overall image quality in cortical regions. Resting state networks identified by independent component analysis were consistent across methods, but multishot EPI provided a more robust and accurate delineation of connectivity patterns involving deep regions typically affected by susceptibility artifacts. Importantly, segmented EPI showed reduced between-subject variability and stronger statistical significance of pairwise correlations at group level over the whole brain and in particular in subcortical regions. Multishot EPI may represent a valid alternative to snapshot methods in functional connectivity studies, particularly for the investigation of subcortical regions and deep gray matter nuclei. More... »

PAGES

1397

References to SciGraph publications

  • 2008-03. High resolution single-shot EPI at 7T in MAGNETIC RESONANCE MATERIALS IN PHYSICS, BIOLOGY AND MEDICINE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-018-37863-2

    DOI

    http://dx.doi.org/10.1038/s41598-018-37863-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111911857

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30718628


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