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# Best Practices for Collecting Physiological Data
This document provides a succinct introduction to what physiological
data are typically recorded during an fMRI experiment, how these signals
are recorded, and how these signals can improve our modeling of fMRI
time series data. This is an active field of research, and we encourage
all users to get the latest recommendations prior to initiating a new
study. For a more in-depth article on similar subject matter, please see
[Bulte and Wartolowska (2017) Neuroimage
154:81-91](https://www.sciencedirect.com/science/article/pii/S1053811916306929).
## Why collect physiological data?
Physiological monitoring is a key component of understanding
physiological sources of signal variance in fMRI data. Monitoring
physiology during scanning is critical to enable the characterization of
a given subject's physiologic state at the time of the scan, and to
track variations in physiology throughout the scan. With these data, we
can more accurately model how these factors manifest in the fMRI signal
time series.
Physiological fluctuations can be identified as "noise" or as
"signals of interest", depending on the research question of the
imaging experiment. For most fMRI experiments, the goal is to isolate
signal fluctuations that are associated with a neural stimulus and the
resulting hemodynamic response (Caballero-Gaudes et al. 2016). In these
data, it is important to model and remove signals with a non-neural
origin, such as breathing or cardiac related signal variance. Removing
these confounds will improve the sensitivity and confidence of the fMRI
analysis. In some fMRI experiments, the goal is to characterize a
physiologic effect (for example, studies that map cerebrovascular
reactivity aim to quantify the dilation of blood vessels during certain
non-neural stimuli) (Caballero-Gaudes et al. 2016). In these studies it
is essential that the relevant physiologic parameters are recorded so
that the analysis produces robust, quantitative physiological parameter
maps.
Another benefit of collecting physiological data is that it provides a
method to monitor the subject and/or patient during the scan in
real-time. Any sudden changes in the different aspects being monitored
can help those in the control room identify if the person is under
duress or complying with the scan protocol. Looking out for these
changes is particularly helpful during an individual's first MRI scan,
when they may react poorly to the scan environment. In some protocols,
tracking physiology in real-time can ensure that values stay within
safe, IRB-approved limits.
Although current modeling of physiology is imperfect, and fMRI signal
processing techniques do not yet accurately factor in all physiologic
signals, the field continues to develop and our modeling continues to
improve. We encourage all fMRI researchers to collect these data to more
fully capture the variable human physiology inherent to imaging
experiments.
## How are typical physiological data collected?
The most common types of physiological data acquired in fMRI analysis
are cardiac pulsation (pulse), breathing (chest position), and expired
gas concentrations (particularly CO2 and O2).
**Cardiac pulsation** can be collected via a sensor on the fingertip (or
ear lobe, toe, or other pulse point). These devices are often known as a
photoplethysmograph, and typically are included in the MRI scanner
infrastructure. The data can thus be collected by the scanner, or
recorded by a separate device. Typically the peaks of the cardiac
pulsation trace are identified (e.g., the timing of each heart beat).
Cardiac pulsation causes local movement artifacts, particularly around
large arterial vessels, the edges of the ventricles, and in brainstem
areas (Dagli et al. 1999). RETROICOR (Glover et al. 2000) is one cardiac
denoising example, where the timing of each fMRI acquisition relative to
the nearest cardiac peak is used to model and remove these local
pulsation artifacts. Low frequency changes in heart rate have also been
modeled and shown to influence the fMRI time series (Shmueli et al.
2007, Chang et al. 2009).
**Breathing** is typically monitored using a "respiratory belt" around
the participant's chest/diaphragm. The belt may be rigid or elastic,
using MR compatible force or pressure transducers to generate a signal
proportional to the chest diameter. The optimal positioning of the belt
depends on the device being used, however it is best to be fairly
consistent in how the belt is worn throughout a study. In some labs,
multiple belts are used to better capture different types of breathing
styles (e.g., "chest breathing" versus "belly breathing"). Often a
belt is incorporated into the MRI scanner infrastructure, and these data
can be collected by the scanner or recorded by a separate device. The
peaks and troughs of the breathing trace are identified, which can
provide information about breathing rate as well as breathing depth.
There are three primary ways by which breathing can influence the fMRI
signals. First, breathing often leads to bulk motion of the body and
head (Brosch et al. 2002). These effects are typically modeled using
volume registration and motion correction algorithms. Second, breathing
changes the chest position which can influence the success of the shim,
continuously changing B0 homogeneity throughout the scan and in turn
affecting signal amplitude (Brosch et al. 2002, Raj et al. 2001). These
effects are also modeled using techniques like RETROICOR. Thirdly,
changing breathing rate and depth can influence blood gases, which can
drive vasodilation or vasoconstriction, and thus substantially influence
the fMRI signal amplitude (Chang and Glover 2009). RVT correction (Birn
et al. 2008) estimates the change in breathing rate/depth to model these
effects.
**Blood gases** It is also possible (and recommended!) to directly
record changes in blood gas levels, rather than infer them from a chest
position measurement. Most commonly we measure carbon dioxide levels
(CO2), which is a known vasodilator and can drive large variability in
blood flow and the BOLD signal (Birn et al. 2006, Wise et al. 2004). We
can also measure oxygen (O2) levels; O2 only has a mild vasoconstrictive
effect on the cerebrovasculature, but O2 levels can directly influence
BOLD signal contrast (Bulte et al. 2007). These two blood gases are
typically strongly anticorrelated with each other in most scans, but can
also be manipulated independently and influence the fMRI signals through
distinct mechanisms (Floyd et al. 2003). Best practice would be to
record both. Although the most accurate recordings of blood gas levels
would be achieved through arterial sampling, this is not recommended for
most imaging experiments. Instead, the concentrations of CO2 and O2 in
arterial blood can be approximated by the partial pressure of each gas
at the end of an exhalation, or the end-tidal partial pressure (commonly
abbreviated as PETCO2 and PETO2) (Bengtsson et al. 2001, McSwain et al.
2010). The person being scanned wears a nasal cannula (soft plastic tube
that rests just below the nostrils) or face mask that is connected to a
gas analyzer in the control room. The resulting data shows the
fluctuations in CO2 and O2 across every breath; an algorithm must
extract the "end-tidal" values.
## What equipment is needed?
Peripheral devices:
- finger photoplethysmograph (pulse-oximeter)
- respiratory belt
- disposable nasal cannula (or face mask)
- long sample line to connect from the scan room to the control room
Some peripheral devices can be passed through a void in the penetration
panel from the control room to the scan room (e.g., gas sampling line);
others must be plugged into the penetration panel for noise filtering
(e.g., some pulse sensors). Devices native to the MRI scanner may
communicate wirelessly with the scanner. When adding non-native
peripheral devices to the scanner environment, we recommend that you
check that you are not bringing any outside noise into the scan room or
bringing too much scanner noise into the physiological recordings. It
may be necessary to develop additional devices or mechanisms to shield
these connections.
Recording devices:
- CO2 and O2 analyzer
- analog-to-digital converter (ADC) or other data acquisition (DAQ)
device
- associated signal recording/analysis software
For example, ADInstruments sells the Powerlab and uses LabChart
software; Biopac sells the MP160 and uses AcqKnowledge software. It is
also important to sync the physiological recordings with the fMRI scan
triggers. To do this, it will be necessary to extract the trigger pulses
from your MRI scanner, typically inputting these analog signals via BNC
into the same ADC that is recording the physiological information.
## What to do with physiological data once they has been collected?
Ideally you have recorded physiological data throughout the entire scan
session, and have trigger data to identify when scanning occurred.
Phys2bids can be used to organize the various physiological data traces
that you have collected. With this program, your data will have the
appropriate BIDS labels to describe physiological information. As a
sanity check, you should quickly plot each trace to ensure that it
matches the type of information you think you collected.
After this restructuring of the data, there are numerous tools available
to process each type of physiological trace, identifying end-tidal
values for O2 and CO2, and phases of the cardiac and respiratory cycles.
These data are then further processed via smoothing or convolution to
create physiological regressors, which can be incorporated into a
generalized linear model framework to explain portions of your fMRI
signal attributed to physiological effects.
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