Laminar analysis of 7 T BOLD using an imposed spatial activation pattern in human V1

By Jonathan R. Polimeni, Bruce Fischl, Douglas N. Greve, Lawrence L. Wald. NeuroImage, 2010

423 citations recorded by the source corpus.

With sufficient image encoding, high-resolution fMRI studies are limited by the biological point-spread of the hemodynamic signal. The extent of this spread is determined by the local vascular distribution and by the spatial specificity of blood flow regulation, as well as by measurement parameters that (i) alter the relative sensitivity of the acquisition to activation-induced hemodynamic changes and (ii) determine the image contrast as a function of vessel size. In particular, large draining vessels on the cortical surface are a major contributor to both the BOLD signal change and to the spatial bias of the BOLD activation away from the site of neuronal activity. In this work, we introduce a laminar surface-based analysis method and study the relationship between spatial localization and activation strength as a function of laminar depth by acquiring 1mm isotropic, single-shot EPI at 7 T and sampling the BOLD signal exclusively from the superficial, middle, or deep cortical laminae. We show that highly-accelerated EPI can limit image distortions to the point where a boundary-based registration algorithm accurately aligns the EPI data to the surface reconstruction. The spatial spread of the BOLD response tangential to the cortical surface was analyzed as a function of cortical depth using our surface-based analysis. Although sampling near the pial surface provided the highest signal strength, it also introduced the most spatial error. Thus, avoiding surface laminae improved spatial localization by about 40% at a cost of 36% in z-statistic, implying that optimal spatial resolution in functional imaging of the cortex can be achieved using anatomically-informed spatial sampling to avoid large pial vessels.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e5c8a5fd7d8d5dc2b550ca38d7359022ebf12ba9

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Citations in the corpus

Anatomically motivated modeling of cortical laminae (2014, background): “Polimeni et al. (2010a) calculated the curvature of the primary visual cortex from an image of a post-mortem sample, and related the curvature values to the distance between the stria of Gennari and the inner cortical boundary.”
Neuroimaging of the Periaqueductal Gray: State of the Field (2011, background): “The effective spatial and temporal resolution of fMRI is increasing, with one example being the development of 7 T fMRI with sub-millimeter resolution (Polimeni et al., 2010) (Fig.”
Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping (2014, methodology): “Modern scanners at 7T or higher allow researchers to resolve functional data at ever finer spatial scales, even to the point of resolving individual gray matter layers (Polimeni et al., 2010; Trampel et al., 2011).”
Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project (2013, methodology): “These gains have been used to generate 7 T functional maps of cortical columns and/or layers (e.g. Chaimow et al., 2011; Koopmans et al., 2011; Olman et al., 2012; Polimeni et al., 2010; Yacoub et al., 2007, 2008; Zimmermann et al., 2011) aswell as otherfine scale organizations, such as digits of th”
Evaluation of slice accelerations using multiband echo planar imaging at 3 Tesla (2013, positive): “…high and ultrahigh magnetic fields has enabled acquisitions of functional magnetic resonance imaging (fMRI) data with increasingly higher spatial resolution and finer detail (e.g., De Martino et al., 2011; Heidemann et al., 2012; Polimeni et al., 2010; Yacoub et al., 2008; Zimmermann et al., 2011).”
Multi‐echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals (2017, methodology): “For studies close to the level of cortical layers (Koopmans et al., 2011; Polimeni et al., 2010), higher resolution is needed, and so these applications necessitate major changes in hardware or acquisition strategy in order to utilize ME-fMRI.”