The laminar pattern of resting state in human cerebral cortex

Harmonia Group

Research output: Contribution to journalArticlepeer-review

Abstract

Resting state functional Magnetic Resonance Imaging (RS-fMRI) provides the means to measure neuronal activity. One of the most commonly used methods to explore the RS-fMRI signal is the Probabilistic Independent Component Analysis (PICA). PICA allows to depict brain functional connectivity (FC) networks. Yet most of the IC maps obtained with this method do not represent any particular FC network. Consequently, those IC maps are classified as artifacts or noise of an unknown source. We hypothesized that the unexplained RS-fMRI signal patterns that are picked up by the PICA can be related to the differences in oxygen metabolism and blood flow in cortical layers. This study aimed at (1) providing preliminary evidence to the effects of laminar organization of neocortex on the RS-fMRI signal, and (2) evaluating the application of laminar maps to aid the classification of IC maps. We created laminar maps 1–4 that depict relative cortical thickness of layers IV and VI. Our data show that the RS-fMRI signal is significantly related to the relative thickness of the cortical layer VI but not layer IV. Importantly, the laminar maps 1–4 overlap with four separate IC maps. Thus, the laminar maps 1–4 improve classification and interpretation of the IC maps. Moreover, the laminar maps 1–4 may be considered as FC networks that are the bridging piece between particular cognitive functions. Together, these data provide preliminary evidence to the fundamental questions about the role of cortical layering in the RS-fMRI signal and brain FC networks.

Original languageEnglish (US)
Pages (from-to)8-16
Number of pages9
JournalMagnetic Resonance Imaging
Volume76
DOIs
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Keywords

  • Brain spontaneous activity
  • Brodmann
  • Cytoarchitecture
  • Functional connectivity
  • fMRI

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