Characteristics of respiratory measures in young adults scanned at rest, including systematic changes and “missed” deep breaths

Breathing rate and depth influence the concentration of carbon dioxide in the blood, altering cerebral blood flow and thus functional magnetic resonance imaging (fMRI) signals. Such respiratory fluctuations can have substantial influence in studies of fMRI signal covariance in subjects at rest, the so-called "resting state functional connectivity" technique. If respiration is monitored during fMRI scanning, it is typically done using a belt about the subject's abdomen to record abdominal circumference. Several measures have been derived from these belt records, including the windowed envelope of the waveform (ENV), the windowed variance in the waveform (respiration variation, RV), and a measure of the amplitude of each breath divided by the cycle time of the breath (respiration volume per time, RVT). Any attempt to gauge respiratory contributions to fMRI signals requires a respiratory measure, but little is known about how these measures compare to each other, or how they perform beyond the small studies in which they were initially proposed. Here, we examine the properties of these measures in hundreds of healthy young adults scanned for an hour each at rest, a subset of the Human Connectome Project chosen for having high-quality physiological records. We find: 1) ENV, RV, and RVT are all correlated, and ENV and RV are more highly correlated to each other than to RVT; 2) respiratory events like deep breaths exhibit characteristic heart rate elevations, fMRI signal changes, head motions, and image quality abnormalities time-locked to large deflections in the belt traces; 3) all measures can "miss" deep breaths; 4) RVT "misses" deep breaths more than ENV or RV; 5) all respiratory measures change systematically over the course of a 14.4-min scan. We discuss the implication of these findings for the literature and ways to move forward in modeling respiratory influences on fMRI scans.

[1]  S. A. Khonsary Guyton and Hall: Textbook of Medical Physiology , 2017, Surgical Neurology International.

[2]  Peter A. Bandettini,et al.  The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration , 2008, NeuroImage.

[3]  Jonathan D. Power,et al.  Reply to Spreng et al.: Multiecho fMRI denoising does not remove global motion-associated respiratory signals , 2019, Proceedings of the National Academy of Sciences.

[4]  Timothy O. Laumann,et al.  Data Quality Influences Observed Links Between Functional Connectivity and Behavior , 2017, Cerebral cortex.

[5]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[6]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[7]  A. Anderson,et al.  Respiratory effects in human functional magnetic resonance imaging due to bulk susceptibility changes. , 2001, Physics in medicine and biology.

[8]  Jonathan D. Power,et al.  Distinctions among real and apparent respiratory motions in human fMRI data , 2019, NeuroImage.

[9]  A. Snyder,et al.  Longitudinal analysis of neural network development in preterm infants. , 2010, Cerebral cortex.

[10]  Alex Martin,et al.  Fractionation of social brain circuits in autism spectrum disorders. , 2012, Brain : a journal of neurology.

[11]  Jack L. Feldman,et al.  The peptidergic control circuit for sighing , 2016, Nature.

[12]  Catie Chang,et al.  Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test–retest reliability and effect of fMRI sampling rate , 2015, NeuroImage.

[13]  H. Laufs,et al.  Decoding Wakefulness Levels from Typical fMRI Resting-State Data Reveals Reliable Drifts between Wakefulness and Sleep , 2014, Neuron.

[14]  M. Poulin,et al.  Dynamics of the cerebral blood flow response to step changes in end-tidal PCO2 and PO2 in humans. , 1996, Journal of applied physiology.

[15]  G. Muehllehner,et al.  Positron emission tomography , 2006, Physics in medicine and biology.

[16]  Catie Chang,et al.  Corrigendum to “Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test-retest reliability and effect of fMRI sampling rate.” , 2018, NeuroImage.

[17]  Jonathan D. Power,et al.  Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data , 2018, Proceedings of the National Academy of Sciences.

[18]  T. Similowski,et al.  Measuring Ventilatory Activity with Structured Light Plethysmography (SLP) Reduces Instrumental Observer Effect and Preserves Tidal Breathing Variability in Healthy and COPD , 2017, Front. Physiol..

[19]  G. Glover,et al.  Regional Variability of Cerebral Blood Oxygenation Response to Hypercapnia , 1999, NeuroImage.

[20]  Timothy O. Laumann,et al.  Sources and implications of whole-brain fMRI signals in humans , 2017, NeuroImage.

[21]  Catie Chang,et al.  Influence of heart rate on the BOLD signal: The cardiac response function , 2009, NeuroImage.

[22]  Irene Tracey,et al.  Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal , 2004, NeuroImage.

[23]  M. Dylan Tisdall,et al.  Correction of respiratory artifacts in MRI head motion estimates , 2018, NeuroImage.

[24]  Jonathan D. Power,et al.  Customized head molds reduce motion during resting state fMRI scans , 2018, NeuroImage.

[25]  G. Natalini,et al.  Variations in Arterial Blood Pressure and Photoplethysmography During Mechanical Ventilation , 2006, Anesthesia and analgesia.

[26]  Mark W. Woolrich,et al.  Investigations into within- and between-subject resting-state amplitude variations , 2017, NeuroImage.

[27]  H. Laufs,et al.  Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep , 2013, Proceedings of the National Academy of Sciences.

[28]  Lisa Byrge,et al.  Identifying and characterizing systematic temporally-lagged BOLD artifacts , 2017, NeuroImage.

[29]  Timothy O. Laumann,et al.  On Global fMRI Signals and Simulations , 2017, Trends in Cognitive Sciences.

[30]  J. Dempsey,et al.  Mechanisms of hypoxia‐induced periodic breathing during sleep in humans. , 1983, The Journal of physiology.

[31]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[32]  Jeff H. Duyn,et al.  Characterization of regional heterogeneity in cerebrovascular reactivity dynamics using novel hypocapnia task and BOLD fMRI , 2009, NeuroImage.

[33]  Catie Chang,et al.  Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI , 2009, NeuroImage.

[34]  Klaas E. Stephan,et al.  Analysis and correction of field fluctuations in fMRI data using field monitoring , 2017, NeuroImage.

[35]  Iwao Kanno,et al.  Changes in Human Cerebral Blood Flow and Cerebral Blood Volume during Hypercapnia and Hypocapnia Measured by Positron Emission Tomography , 2003, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[36]  Stephen M. Smith,et al.  Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data , 2017, NeuroImage.

[37]  Thomas M. Talavage,et al.  Simulation of human respiration in fMRI with a mechanical model , 2002, IEEE Transactions on Biomedical Engineering.

[38]  A Eberhard,et al.  Evaluation of respiratory inductive plethysmography: accuracy for analysis of respiratory waveforms. , 1997, Chest.

[39]  Jonathan D. Power Temporal ICA has not properly separated global fMRI signals: A comment on Glasser et al. (2018) , 2019, NeuroImage.

[40]  M. Fukunaga,et al.  Low frequency BOLD fluctuations during resting wakefulness and light sleep: A simultaneous EEG‐fMRI study , 2008, Human brain mapping.

[41]  Han Yuan,et al.  Correlated slow fluctuations in respiration, EEG, and BOLD fMRI , 2013, NeuroImage.

[42]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[43]  Jonathan D. Power A simple but useful way to assess fMRI scan qualities , 2017, NeuroImage.

[44]  M. Preisig,et al.  Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study. , 2015, The Lancet. Respiratory medicine.

[45]  Maxime Cannesson,et al.  Relation between respiratory variations in pulse oximetry plethysmographic waveform amplitude and arterial pulse pressure in ventilated patients , 2005, Critical care.

[46]  T Douglas Bradley,et al.  Central sleep apnea and Cheyne-Stokes respiration. , 2008, Proceedings of the American Thoracic Society.