FMRI data modulators 1: Heart rate

It's 2027 and you are preparing to run a new fMRI experiment. Since 2023 you've been working on a custom 7 T scanner that was developed to mitigate several issues which plagued the early decades of fMRI. Long gone are the thermal shim and gradient drifts of yesteryear, courtesy of an intelligent water cooling system that maintains all hardware at near constant temperature even when the scanner is run flat out. Your scanner also has a custom gradient set with active shielding over the subject's chest. It means the rise time of the gradients is limited only by peripheral nerve stimulation in the subject's face and scalp, not by the possibility of causing fibrillation in the heart. You can use a slew rate four times faster than on the scanner you had back in 2017, meaning distortions of your 1 mm cubic voxels, acquired over the entire brain (including cerebellum!) are minuscule. What's more, your images no longer suffer from translations and shearing because of the subject's chest motion. Your scanner tracks the magnetic field across the subject's head and actively compensates for the effects of breathing. When used with the comfortable head restraint system that mates directly to the receiver electronics - which itself monitors changes in coil loading to ensure the 128-channel array coil doesn't impart its own bias field onto your images - you have finally got to the point in your career where you no longer worry about head motion.

Almost. There's no doubt the hardware of the future could be remarkable compared to today's scanners. Our current scanners are clinical products being used for science rather than scientific instruments per se. However, even if we were to supersede BOLD with a non-vascular "neural current" contrast mechanism, the basic physics of MRI suggests that we will have to consider real brain motion in the future, just as we do today. Perhaps we can differentiate this brain motion from the contrast of interest using multiple echoes or some other trick, but I don't envisage being able to ignore the brain's vasculature entirely, whereas I am optimistic that improved scanner engineering might one day ameliorate the mechanical and thermal instabilities. Real brain motion and regional variation in pulsatility are likely to be biological limits that must be accommodated rather than eliminated.


What are the mechanisms of concern?

We can restrain the subject's skull quite well using a bite bar or a printed case. Inside the skull, however, is a gelatinous blob of brain, highly vascularized, under a small positive pressure (the intracranial pressure, ICP). The brain will tend to throb with the heart rate (HR) as blood is pumped into the brain through the arteries. The arterial network is spatially heterogeneous and so we see heterogeneous motion across the brain. The arteries enter at the base of the brain, causing the entire midbrain and brainstem to move relative to the cortex. Locally, tissue close to large vessels can demonstrate greater displacements than tissue just a few millimeters away. These regional perturbations will arise with a range of delays relative to the cardiac output, as the blood pressure wave migrates from the heart. The greater the distance from the heart, the longer the lag. We'll see in a later post how this phenomenon can be used to estimate blood pressure.

There are also cardiac driven pulsations in the cerebrospinal fluid (CSF). These can be visualized as small displacements of tissue adjacent to the ventricular system as well as in sulci of the cortex. Pulsation in CSF and the changing velocity of blood in large vessels also tend to produce image contrast changes. This isn't real brain motion, of course, but it is a consideration if one is attempting to use local signal properties or overall image contrast to ameliorate regional pulsatility. A new paper by Viessmann et al. provides a timely investigation of the issues, concluding that fluctuations in partial volumes of blood and CSF/interstitial fluid give rise to local T2* changes over the cardiac cycle. So the final complexity is again temporal. The cardiac cycle is itself non-stationary, leading to dynamic changes in the locations of blood, CSF and brain tissue.

How is heart rate measured inside the scanner?

Outside the MRI environment it is possible to obtain detailed information on cardiac function through the electrocardiogram (ECG). Inside the MRI, the typical ECG response shape is significantly altered by magnetohydrodynamic and cardioballistic (or Hall) effects. (These effects also cause a significant source of artifacts in EEG recorded inside an MRI.) ECG also requires electrodes be placed on the chest, adding extra setup complications and privacy issues. Breasts and body fat can make it very difficult to get a good ECG signal from many subjects.

A convenient in-scanner measure of HR can be obtained from a photoplethysmograph, more commonly referred to as a pulse oximeter, albeit with less precision than when using the ECG outside the MRI. The signal processing used in pulse oximetry varies with the manufacture. Typically, the signal is filtered to optimize the instantaneous HR plus the (arterial) oxygenated hemoglobin fraction of total hemoglobin, termed SpO2. A detailed comparison found that pulse oximetry correlated strongly with heart rate variability (HRV) measured using ECG and we may thus consider oximetry an acceptable option for HR monitoring during fMRI.

While simple to use, pulse oximetry does have limitations. Motion of the sensor can be a problem, leading to erratic traces and the possibility that the signal is lost entirely. The sensor should be secured properly and subjects instructed on what not to move in order to maintain good data. Most often a finger is used for oximetry but if a study requires bimanual responses then a toe might be considered. Note, however, that certain subjects with low peripheral circulation can be hard to record from. The scanner suite temperature can be a major factor; consider subject comfort and warmth.

When might heart rate vary?

Numerous factors can alter HR. Recent exercise is an obvious one. Even at rest, however, the instantaneous HR is a consequence of complex interactions between the sympathetic and parasympathetic nervous systems, together referred to as the autonomic nervous system. For example, a degree of arrhythmia (usually referred to as the respiratory sinus arrhythmia, RSA) is a natural fluctuation of HR produced during the normal breathing cycle. Instantaneous HR increases slightly during inspiration and decreases slightly during expiration. The RSA produces modulation of around 0.15-0.4 Hz on top of typical resting heart rates in the range 0.5-1.5 Hz. A slower modulation, typically 0.04-0.15 Hz, may also occur. The precise mechanisms generating the slow modulation are less well understood but are generally considered to involve changes in arterial tone, and/or cerebral autoregulation to maintain a constant cerebral blood flow in spite of changes in overall blood pressure. I will be doing a separate post on these vascular low frequency oscillations; they can be detected in the periphery with suitable modifications to the pulse oximetry.

Cardiac output is also affected by arousal. It is is common to find components of HRV used as a measure of autonomic activity, e.g. anxiety, in psychology experiments, but given the broad spectrum of potential influences on HRV it is obviously important to apply rigorous control to extraneous factors, including exercise, pharmaceutical and other compounds including caffeine and alcohol, and some disease states, particularly diseases affecting the vasculature.


Using heart rate data

The HR is generally rapid compared to a typical fMRI sampling rate of 0.5 Hz for TR = 2000 ms. In this case the effects of HR will be aliased, making it easy to mistake cardiac for low frequency neurovascular fluctuations, or a task effect. For task-based fMRI, a convenient tactic is to ensure experimental power is placed well away from aliased cardiac (and respiratory) frequencies. This is usually assumed to be sufficient, especially if physiological noise regressors are used in the analysis. The situation is more complicated in resting-state fMRI because there is no external driving function against which to evaluate brain activity.

The issue of HRV as a potential confound in resting-state fMRI data was first addressed by Shmueli et al. in 2007 using cross-correlation of the cardiac waveform with the fMRI data, allowing for different delays between the two data sets. The generality of HRV analysis was extended by Chang, Cunningham & Glover (2009) using the concept of a canonical cardiac response function (CRF) convolved with the HR time series data, by analogy with the hemodynamic response function (HRF) used in event-related fMRI analyses. Using this analysis method, Chang and colleagues were able to delineate transient changes of autonomic nervous system states manifest in brain network connectivity during rest, suggesting that the specificity of their method is sufficient to discriminate confounding signal changes when arousal may not be under experimental control. More recent work by Falahpour et al. explored the utility of subject-specific CRF, while very recently de la Cruz et al. suggested that it might be better to separate subjects into groups based on HR, and use a separate mean group CRF for low (48-68 bpm) and high (68-96 bpm) heart rates. The differing explanatory power seems to be related to slight differences in the dynamics of HR variation, raising the possibility that subjects with higher HR may be more highly aroused, perhaps because of greater scanner anxiety. Alternatively, the HR grouping might be a consequence of underlying cardiovascular health, or differing starting conditions such as recent exercise or caffeine use. These findings are a timely warning that even when attempting to remove HR effects from resting fMRI data, group-wise differences in HR could lead to significant residual effects dependent on mean HR.

There are now several comprehensive reviews that consider cardiac "nuisance signals" and "data cleaning" or "de-noising," especially for resting-state fMRI where physiological confounds are of great concern. If you are interested in applying such methods then I strongly urge you to read all the reviews and then the primary references before doing anything. I suggest you start with the excellent review by Caballero-Gaudes & Reynolds. If you're not already convinced of the complications, this paper should get you over that particular hump. As yet, there is no consensus on a best approach to dealing with nuisance signals. One reason for this is prosaic: different studies investigating physiological signals have different independent data to utilize. Some consider HR alone, some consider HR plus one or more further independent traces, such as chest motion and expired CO2. What you do depends on what you've got available to you. Then there is the vast parameter space to consider, with TR, voxel size, echo train length and many other parameters likely to contribute to the relative efficacy of one "nuisance signal" reduction method over another for a particular application. And finally there are statistical implications, as Caballero-Gaudes & Reynolds highlight:

"...similar to other approaches based on nuisance regression, adding more physiological noise regressors does not necessarily lead to improvement in BOLD sensitivity and higher statistical significance due to the loss in degrees of freedoms and possible correlations of the physiological regressors with the BOLD fluctuations generated by the experimental paradigm in task-based fMRI or the intrinsic neuronal fluctuations in the resting state. Hence, the optimal set of regressors will depend on the sequence and parameters of acquisition, as well as the regions of interest."

Final thoughts

Until someone proves otherwise, I advocate acquiring a separate pulse oximetry signal for all resting state fMRI scans, and it is likely prudent for all task fMRI experiments. Even if you can address your own questions without resorting to physiological data, having independent physiological measures available may make subsequent use of data by others more powerful.