Abstract

A “carpet plot” is a 2-dimensional plot (time vs. voxel) of scaled fMRI voxel intensity values. Low frequency oscillations (LFOs) can be successfully identified from BOLD fMRI and used to study characteristics of neuronal and physiological activity. Here, we evaluate the use of carpet plots paired with a developed slope-detection algorithm as a means to study LFOs in resting state fMRI (rs-fMRI) data with the help of dynamic susceptibility contrast (DSC) MRI data. Carpet plots were constructed by ordering voxels according to signal delay time for each voxel. The slope-detection algorithm was used to identify and calculate propagation times, or “transit times”, of tilted vertical edges across which a sudden signal change was observed. We aim to show that this metric has applications in understanding LFOs in fMRI data, possibly reflecting changes in blood flow speed during the scan, and for evaluating alternative blood-tracking contrast agents such as inhaled CO2. We demonstrate that the propagations of LFOs can be visualized and automatically identified in a carpet plot as tilted lines of sudden intensity change. Resting state carpet plots produce edges with transit times similar to those of DSC carpet plots. Additionally, resting state carpet plots indicate that edge transit times vary at different time points during the scan.

Highlights

  • A “carpet plot” is a 2-dimensional plot of scaled fMRI voxel intensity values

  • dynamic susceptibility contrast (DSC)-MRI scans and Resting state functional magnetic resonance imaging (rs-fMRI) scans of eight healthy subjects (1F, 7 M, mean ± s.d., 33 ± 12 years), all acquired on a 3 T Siemens MRI scanner, were used in this study

  • The voxel-specific delay for DSC-MRI data was represented by time to peak (TTP), which is defined as the time duration from the beginning of the scan to the dip of the DSC signal loss

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Summary

Introduction

A “carpet plot” is a 2-dimensional plot (time vs. voxel) of scaled fMRI voxel intensity values. Low frequency oscillations (LFOs) can be successfully identified from BOLD fMRI and used to study characteristics of neuronal and physiological activity. Resting state functional magnetic resonance imaging (rs-fMRI) records spontaneous fluctuations of the bloodoxygen-level-dependent (BOLD) signal from individuals lying quietly in the scanner without performing any specific ­task[1] It has become a powerful, widely adopted tool to study functional o­ rganization[2] and brain n­ etworks[3], as well as their changes related to health and d­ isease[4]. BOLD fMRI does not measure neuronal signals directly It is a blood-related signal, which reflects a dynamic interplay between cerebral blood flow (CBF), volume, and cerebral metabolic rate of oxygen ­consumption[5].

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