# Temporal and spatial characterization of physiological noise in rs-fMRI at high temporal resolution

Olga Kuldavletova, Marin Mauboussin, Mikael Naveau, Anaïs Vandevelde, Nicolas Delcroix, Marc Joliot, Olivier M Etard

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Olga Kuldavletova, Marin Mauboussin, Mikael Naveau, Anaïs Vandevelde, Nicolas Delcroix, et al. Temporal and spatial characterization of physiological noise in rs-fMRI at high temporal resolution. OHBM2022, Jun 2022, Glasgow, United Kingdom. . hal-03878764

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Submitted on 30 Nov 2022

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# TEMPORAL AND SPATIAL CHARACTERIZATION OF PHYSIOLOGICAL NOISE IN RS-FMRI AT HIGH TEMPORAL RESOLUTION

(TR = 125MS)

Kuldavletova O¹, Mauboussin M¹, Naveau M², Vandevelde A³, Delcroix N², Joliot M⁴, Etard O¹

¹ Normandie Université, UNICAEN, INSERM, COMETE, CYCERON, CHU Caen, Caen, France
² Normandie Université, UNICAEN, CNRS, UMS3408 CYCERON, Caen, France
³ CHRU de Tours, Tours, France
⁴ GIN, UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France

Cerebral fMRI is a useful imaging tool that allows to visualize neuronal activity based on blood oxygen levels. However, except the neuronal component of interest, fMRI signal contains "noise" components due to physiological processes (heartbeat, respiration...).

Litterature contains attempts to estimate this part of the signal, but not to measure it.

This study aims to measure the respective impact of cardiac pulse and respiratory cycle on MRI signal in human brain and its regions with short TR.

17 MRI exams; 9 subjects (1 to 3 sessions per subject)

<figure><img src="image_2.png" /></figure>

3T Philipps MRI scanner

2D single slice EPI-GE, TR = 125 ms, TE = 30 ms, flip angle = 22°, 5 min 12 sec per session

<figure><img src="image_3.png" /></figure>

PPG

<figure><img src="image_4.png" /></figure>

Pneumatic thoracic respiration transducer

<figure><img src="image_5.png" /><figcaption>The Grand Mean of an MRI signal time series in one of the 4 regions of interest over all cardiac cycles through all scans (15) and all subjects (9).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>The Grand Mean of an MRI signal time series in one of the 4 regions of interest over all respiratory cycles through all scans (17) and all subjects (9).</figcaption></figure>

## High pressure CSF circulation: the ventricles

<figure><img src="image_7.png" /></figure>

**In frequency domain:** we have computed the amplitudes of the spectral peaks of the MRI signal voxel-wise, at the respiratory and cardiac frequencies measured for each subject. The amplitudes of these peaks were plotted to depict the regions that are most sensible to cardiac or to respiratory oscillations.

<figure><img src="image_8.png" /><figcaption>The cardiac effect is most pronounced in the motion of the CSF in ventricles and in venous outflow.</figcaption></figure>

## Low pressure CSF circulation: the meninges

<figure><img src="image_9.png" /><figcaption>Amplitude of spectral peaks of the MRI signal or each ROI</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Cardiac frequency</figcaption></figure>

<caption>Respiratory frequency</caption>

LMM with (1) ROI and (2) cardiac/respiratory peak spectral amplitude as fixed factors, their interactions, and a subject as a random effect, Bonferroni correction

<figure><img src="image_11.png" /></figure>

The respiratory effect is most pronounced in the motion of the CSF in the contours of the brain, mostly the frontal part, as in the supine position gravity acts on the floating in the CSF brain, tearing it down to the back of the head.

<caption>The respiratory impact in the Global signal is significantly more important than the cardiac impact.</caption>