Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted.
The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit https://cc.nih.gov.
Updates regarding government operating status and resumption of normal operations can be found at https://opm.gov.

Ante la falta de fondos del gobierno federal, no se actualizará este sitio web y la organización no responderá a transacciones ni consultas hasta que se aprueben los fondos.
 El Centro Clínico de los Institutos Nacionales de la Salud  (el hospital de investigación) permanecerá abierto. Consulte https://cc.nih.gov(en inglés)
Infórmese sobre el funcionamiento del gobierno federal y el reinicio de las actividades en https://opm.gov.

Year of Publication: 2024
Project: Layer Specific fMRI
FIM Authors:
Authors:
  • Yuhui Chai
  • Tyler Morgan
  • Daniel Handwerker
  • Linqing Li
  • Laurentius Huber
  • Bradley P. Sutton
  • Peter Bandettini
Abstract:

Functional MRI (fMRI) time series are inherently susceptible to the influence of respiratory variations. While many studies treat respiration as a source of noise in fMRI, this study employs natural respiratory variations during high resolution (0.8 mm) fMRI at 7T to formulate a respiration effect related map and then use this map to reduce macrovascular bias for a more laminar-specific fMRI measurement. Our results indicate that respiratory-related signal changes are modulated by breath phase (breathing in/out or in the transition between breath in and out) during fMRI acquisition, with distinct patterns across various brain regions. We demonstrate that respiration maps generated from normal fMRI runs, such as task-oriented sessions, closely resemble those from deep-breath and breath-hold experiments. These maps show a significant correlation with the macro-vasculature automatically segmented based on susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) images. Most crucially, by removing voxels most responsive to respiratory variations, we can refine high-resolution fMRI measurements to be more layer-specific, improving the accuracy of laminar fMRI analysis.


Code
Journal: Imaging Neuroscience
Volume: 2
URL: https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00249/123637
DOI: https://doi.org/10.1162/imag_a_00249