Synchronous 4D magnitude and phase fMRI reveals sleep induced changes in respiratory brain
Poster No:
2583
Submission Type:
Abstract Submission
Authors:
Saara Syväoja1, Lauri Raitamaa2, Vesa Kiviniemi3
Institutions:
1University of Oulu, Oulu, Finland, 2University of Oulu, Oulu, Pohjois-Pohjanmaa, 3Oulu University Hospital, Oulu, Finland
First Author:
Co-Author(s):
Introduction:
Sleep is crucial for the proper functioning of the brain, including memory consolidation, emotional regulation, restoration and repair, and overall brain health. Physiological brain pulsations are thought to have a role in sleep, which however is not fully understood. To study pulsations in awake and sleep stages we used fMRI magnetic resonance encephalography (MREG) imaging sequence to simultaneously capture both phase and magnitude 4D brain data at ultrafast 10 Hz sampling rate, ergo 10 whole brain images per second. This approach allowed us to visualize physiological pulsations across the entire brain and obtain information that would otherwise be overlooked using traditional methods. In this study, our focus is on analyzing phase signal due to the phase fMRI signal increased sensitivity to physiological phenomena as compared to the magnitude signal. Our findings revealed a significant increase in respiratory amplitudes, as observed in both phase and magnitude maps during sleep compared to wakefulness.
Methods:
The study involved twelve healthy volunteers (age 26.2 ± 4.3 years, 5 females). During the awake scan session, the participants were asked to rest with their eyes open and focus on a cross displayed on the screen for 10 minutes. Three days later, the sleep scans were conducted.
Subjects were scanned using a Siemens 3T SKYRA scanner with a 32-channel head coil and a 3D whole brain MREG sequence (TR 100ms, TE 3.6ms, flip angle 25, matrix=643, FOV=192mm). Data preprocessing involved motion spike removal with AFNI's 3dDespike, standard FSL pipeline with high-pass filtering at 0.008 Hz, motion correction using FSL MCFLIRT, and brain extraction with FSL BET. Anatomical MPRAGE images aided in registering MREG data to MNI152 standard space.
We used a modified ALFF method to study amplitude of fluctuation (AF) of very low frequency (AFVLF), respiratory (AFresp), and cardiac (AFcard) pulsations. The frequency band for AFVLF was 0.01-0.1 Hz, corresponding to the classical ALFF, while the bands for AFresp and AFcard were 0.1 Hz wide, centered around the previously defined individual peaks (i.e., peak ± 0.05 Hz).
For every subject, the time courses of each voxel from whole brain temporal MREG data were transformed using AFNI 3dPeriodogram to the frequency domain via a fast Fourier transformation, which yielded the voxel-wise power spectrum. The square root of the power spectral density was calculated, and amplitudes calculated over the frequency bands of interest were summed to obtain a corresponding AF map [5].
Voxel-wise comparisons of the AF maps between awake and sleep states were performed by a two-sample t-test using a paired non-parametric threshold-free permutation test (5000 permutations) implemented in randomise from FSL. The tests were corrected for the family-wise error rate (FWER) at a significance level of p<0.05.
Subjects were scanned using a Siemens 3T SKYRA scanner with a 32-channel head coil and a 3D whole brain MREG sequence (TR 100ms, TE 3.6ms, flip angle 25, matrix=643, FOV=192mm). Data preprocessing involved motion spike removal with AFNI's 3dDespike, standard FSL pipeline with high-pass filtering at 0.008 Hz, motion correction using FSL MCFLIRT, and brain extraction with FSL BET. Anatomical MPRAGE images aided in registering MREG data to MNI152 standard space.
We used a modified ALFF method to study amplitude of fluctuation (AF) of very low frequency (AFVLF), respiratory (AFresp), and cardiac (AFcard) pulsations. The frequency band for AFVLF was 0.01-0.1 Hz, corresponding to the classical ALFF, while the bands for AFresp and AFcard were 0.1 Hz wide, centered around the previously defined individual peaks (i.e., peak ± 0.05 Hz).
For every subject, the time courses of each voxel from whole brain temporal MREG data were transformed using AFNI 3dPeriodogram to the frequency domain via a fast Fourier transformation, which yielded the voxel-wise power spectrum. The square root of the power spectral density was calculated, and amplitudes calculated over the frequency bands of interest were summed to obtain a corresponding AF map [5].
Voxel-wise comparisons of the AF maps between awake and sleep states were performed by a two-sample t-test using a paired non-parametric threshold-free permutation test (5000 permutations) implemented in randomise from FSL. The tests were corrected for the family-wise error rate (FWER) at a significance level of p<0.05.
Results:
Our findings revealed a significant increase in respiratory amplitudes, as observed in both phase and magnitude maps during sleep compared to wakefulness. Notably, the brainstem, cerebellum, and temporal lobe exhibited the highest amplitudes in the phase maps, while the frontal lobe, parietal lobe, and temporal lobe exhibited the strongest amplitudes in the magnitude maps. Comparison of the phase and magnitude maps revealed both differences and similarities, highlighting the potential for expanding the amount of information obtained in a single imaging session of the brain.
·Mean values of amplitude of fluctuation (AF) from phase and magnitude maps in VLF, respiratory and cardiovascular bands.
Conclusions:
The present findings imply that the concurrent utilization of phase and magnitude information using MREG scanning offers a more comprehensive insight into the dynamics of the brain physiological pulsations during sleep compared to solely analyzing the magnitude maps.
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Non-BOLD fMRI
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 1
Keywords:
FUNCTIONAL MRI
Sleep
1|2Indicates the priority used for review