Magnet mistakes

This is just a short post on the many ways in which films and telly often get MRI wrong, and one thing that they tend to get right. Also, it is a good excuse to post a few interesting MRI videos.

1. The magnet is ALWAYS on. You don’t turn on an MRI. Nor do you turn it off. The machine uses a magnetic field which is always on as long as the machine is operational (whether scans are taken or not). This field can be pretty strong, and will trap ferrous metal objects in the bore, even if there is no scan running. Actually turning off the magnetic field (quenching it) is only done if the scanner is being decommissioned or in life-threatening situations, as puts it out of action for at least a week and cost a lot to restart (>£20,000), even if it has not been damaged by the quench.

2. Pressing the red button is usually bad news. There are two types of big red button. One is an emergency stop which does not turn off the magnet per se, but turns off power to consoles, lights (not emergency lights) and so on. The other quenches the magnet (rarely done, see the above point) and it looks like this:

The video above is a magnet being quenched at 1% helium capacity, which is to say that it is not nearly as big an event as it could be.

3. The magnet is as strong as it is. Variable field strength is not a thing. You cannot turn up the field, and you cannot turn it down.

4. Scans typically take time to acquire and interpret. If it was possible to put a person in and read out the data within seconds, that would be great. However, a good structural scan takes minutes, and a functional scan often even longer plus it requires additional processing steps that can take hours or days. There are also setup scans typically run before the main scan, and these also take time. It is unfortunately not plug and play. Caveat: real-time MRI is a thing, but it is mostly used for cardiac imaging and rare cases of functional MRI neurofeedback sequences. Typically, these are not the ones portrayed in the offending films.

5. Colours? Scans usually don’t come automatically in pretty colours. Structural scans are in more-or-less grainy black and white, and while functional scans can be presented in colour, this requires a lot of processing after the scan has been completed (see above). And what you get out is typically a statistical map of the signal, not the actual measurements themselves. In short: colours usually means lots of stats, stats usually means lots of time.

6. It’s noisy! And not simply high-tech whirr either: it can sound like a construction site in there.

7. There’s often a coil. At least with neuroimaging, where the coil is a cage-like structure placed around the head. This mistake can be forgiven if the scan in question would use a body coil, which can be pretty invisible and look like a part of the table.

There are plenty of films getting MRI wrong, for example Die Another Day (although the MRI bloopers are arguably not the biggest problem with that film) and Terminator Genisys that manage to get not only the turn it on and off again wrong, but also the variable field strength, plus introducing a conveniently appalling lack of shielding (meaning that the fringe field (the magnetic field that surrounds the magnet) is so large it reaches the control room). Go watch the Terminator clip over on youtube (from 1 min) to see for yourself. I feel for that poor MRI scanner.

+1. It’s strong. This is the one most get right. The magnetic field is strong – it will pull ferrous items into the bore of the magnet, wreck the item (and sometimes itself), and you’re probably not strong enough to stop it.

That being said, there are plenty of films where metal props are far too close to the magnet to be believable, and the ‘patients’ are allowed to keep on items of clothing such as underwired bras and watches, and even bring handbags or other personal items into the scan room. Even if such items are not ferrous, they can still cause image artefacts, and are typically removed. I’ve been told there is a Grey’s Anatomy episode where an MRI was requested for a patient with a fork stuck in the neck – the less said about that the better.

Advertisements

Neurocomic

Following my list of resources for fresh neuroscientists, I figured I’d share something for those interested in exploring neuroscience but not quite ready to pick up a textbook, namely neurocomic.

Neurocomic is a Wellcome Trust supported project that aims to explain neuroscience ideas to a lay audience using comics. The brainchild of neuroscientists Hana Ros (UCL) and Matteo Farinella (who is also the artist), the story follows a man as he is trapped inside a brain and journeys to escape. The quest takes him through neuron forests, distinct brain regions and visual metaphors of common concepts in neuroscience and psychology, encountering various beasts and scientists along the way. Judging by its reviews, most people find it accessible and accurate, if a bit short (and short on women). Personally, I am particularly enthused by the medium.

The process and ideas behind the project are explained in the video below, and might be of interest to potential readers and to researchers considering tools for effective science communication.

Using illustrations to communicate science (or any information, really) can be powerful. I tend to draw quite a lot in my work, and find that visualising problems helps me work through them faster and see connections that might not be immediately apparent. It works great as a study method too (see this paper for example), helping students remember content better. As for communicating information, it is superb. I have a list of favourite science comics that have introduced me to new concepts more than once (I am particularly looking at you, xkcd, and your lovely comic explanation wiki). While three-panel strips rarely provide a full picture of a scientific concept, they can certainly offer a brief and memorable introduction. Similarly, neurocomic’s 150 pages are not enough to encompass the entire history and science of brain research, but it is a good place to start to get a flavour for some of the ideas in the field.

Neurocomic is on twitter @neurocomic

Smoking in the scanner?

There is a new paper out in Scientific Reports, titled “Investigating the neural correlates of smoking: Feasibility and results of combining electronic cigarettes with fMRI”. This is a study that have managed to combine actual smoking with functional MRI (fMRI).

Most studies looking at brain processing of smoking run into trouble with MRI. This is because smoking and scanning do not go well together. Hospitals don’t allow smoking, things should generally not be on fire in the MRI scanner, and ventilation is an issue when you’re lying in a narrow bore. Because of this, we haven’t been able to properly look at the sensations and behaviour of smoking alongside the effects of nicotine (and other active products in cigarette smoke). This study tries to get around these practical problems and also look at the brain response to real-time smoking.

For the practical part, the study used e-cigarettes. E-cigarettes solve some of the problems with smoking in the scanner (fire and ventilation to some extent), but can cause image artifacts and may also contain metal. The paper shows how smaller types of e-cigarettes did not cause image artifacts plus were safe to use in the scanner from a metal point of view. E-cigarette smoking is a good mimic for traditional smoking, so this is a workable model of ‘the real thing’ that fits with MRI.

In terms of brain responses, the authors found activation in several brain regions associated with smoking e-cigarettes. These regions included motor cortex, insula, cingulate, amygdala, putamen, thalamus, globus pallidus and cerebellum. There were also (relative) deactivations in the ventral striatum and orbitofrontal cortex associated with smoking.

wall_paper

Image from the paper showing brain responses when participants were instructed to smoke. Red-yellow is activation and blue is deactivation.

Some of this activation is (unsurprisingly) linked to movement. The motor cortex activation (stronger on the left hand side, which correspond to right-hand side motion) is most likely due to movements associated with smoking. Similarly, cerebellar activation is often related to motion. Other regions are associated more with the effects of smoking. The putamen is part of a brain region called the striatum, which plays a role in reward and in supporting addiction. The ventral striatum (and orbitofrontal cortex) are associated with drug craving.

From a personal point of view, having worked a great deal with breathing, I am excited that the paper showed activation in the insula and cingulate. Both are structures involved in breathing and breathlessness tasks. However, without behavioural measures to link the findings to, it is hard to say what this activation means in this setting. It is important to remember that just because a similar activation pattern occurs with two different tasks, it doesn’t necessarily follow that the activation means the same. Each region of the brain typically handle more than one thing, particularly cortical regions.

The authors also found that activation patterns was similar both if the participants were told when to smoke and when to stop (first scan), and if they could smoke at will (second scan). However, in the second scan, the activation was weaker. The authors suggest that this could be because this task was more variable, meaning more between-subject variance and poorer timing (from a fMRI point of view). It could also be an order effect, as the subjects had more nicotine in their system in the second scan. This fits with lower activation in reward-related brain regions in the second scan. Or it could simply be because to smoke on command or whenever one wants to are different situations. Again, it is hard to tell why without other measures.

Nevertheless, this is an interesting paper, both from a methods point of view and for those interested in smoking processing and effects on the brain. It’s also written in a nice and easily accessible way. I’d recommend looking it up:

Reference: Matthew B. Wall, Alexander Mentink, Georgina Lyons, Oliwia S. Kowalczyk, Lysia Demetriou & Rexford D. Newbould. Investigating the neural correlates of smoking: Feasibility and results of combining electronic cigarettes with fMRI. Scientific Reports 7, Article number: 11352 (2017)
DOI: 10.1038/s41598-017-11872-z
Website: https://www.nature.com/articles/s41598-017-11872-z 

Pulmonary rehab: changing the signal

Pulmonary rehabilitation is one of the most effective treatments for breathlessness in chronic obstructive pulmonary disease (COPD), yet its effect is variable. While up to 60% of patients who complete a course of treatment see an improvement, that leaves 40% that do not. Understanding why it works for some and not for others can help personalise and improve treatment for COPD. This is what we’ve focused on in our most recent paper (preprint here) that will be published in the European Respiratory Journal. UPDATE: final published paper here.

A bit of background on how sensations are perceived. When we feel a sensation, our brains often both register and modulate the sensory information from the body. In fact, our sensory perception is probably quite dependent on how the brain processes incoming sensory information. This is influenced by what the brain thinks will happen and why. It is thought that previous experiences (called priors) create expectations in the brain about sensations, and that these are updated whenever the brain receives actual sensory information.

Below is a quote from a paper on how priors influence pain perception by Geuter et al. [1], explaining the concept so clearly I decided to reproduce it in its entirety:

All over the human body, there are receptors that help to alert the brain to potential harm. For example, intense heat on the skin elicits a signal that travels to the brain and activates many parts of the brain. Some of the same brain regions that are switched on by signals of potential bodily harm also help the brain to form expectations about events. A person’s expectations may have a strong influence on how they experience pain. For example, if a person expects that taking a pill will reduce their pain, they may feel less pain even if the pill is a fake.

Exactly how the brain processes pain signals and expectations remains unclear. Does the brain activity simply reflect how intense the heat is? Some scientists think there may be two separate processes going on: one that predicts what will happen and another that calculates the difference between the prediction and what the receptors actually detect. This difference is called a prediction error. If every unpredicted sensory signal elicits a calculation of the prediction error that would help improve the brain’s future predictions.

This system is open to manipulation. There are many factors that can adjust these priors or weight the incoming sensory information, causing the person to over- or under-perceive sensations. For example, anxiety and attentional bias may cause over-perception of sensations.

But how? How does this relate to COPD? In COPD, a prior may be formed linking shortness of breath to physical activity, for example climbing stairs. This prior, if bolstered by for example anxiety and attentional bias, may begin to dominate and cause over-perception of breathlessness. This means that breathlessness perception would be governed more by the prior and the anxiety/fear than by the input from the body. In this example, a simple flight of stairs become a cue for the brain to access its priors, which means generating an expectation of breathlessness and anxiety, all because that is what previous experiences have demonstrated will happen.

Pulmonary rehabilitation, however, challenges these priors. Rehabilitation makes the patient face their breathlessness, but in a safe healthcare setting. This may change the patient’s priors and how they process breathlessness-related cues. If this is the case, we may expect that patients with different priors show different treatment outcome, and we may expect that patients show a different response to cues after treatment than before.

But where? We know that predictions about bodily state and emotion (i.e. priors) are typically generated in a stimulus valuation network. This network consists of many brain regions, including the anterior insula, anterior cingulate cortex (ACC), orbitofrontal cortex and ventromedial prefrontal cortex. There are also more ‘downstream’ regions associated with breathing, including the posterior insula, which process incoming respiratory sensory information. These are responsible for sending sensory information from the body to other parts of the brain (both those dealing with the physical sensation and those processing the emotional impact such as the stimulus valuation network). The posterior insula, along with regions such as the angular gyrus and the supramarginal gyrus, are involved with how much attention a physical sensation gets. All of these regions might be likely places where pulmonary rehabilitation would change activation patterns.

What we did. We recruited 31 people with COPD and studied them before and after pulmonary rehabilitation. On each visit, we did the same tests: we collected a set of behavioural questionnaires (of which we used one, the Dyspnoea-12 [2], as our main measure of breathlessness); we did a lung function and an exercise test; and we did a functional brain scan (FMRI) to test their brain activity while they were looking at (and rating) breathlessness-related cues for anxiety (How anxious would this make you feel?”) and breathlessness (“How breathless would this make you feel?”). *

Behavioural changes. The ratings of the patients were overall much lower for anxiety after rehabilitation, and this correlated with the main measure of breathlessness (Dyspnoea-12). The correlation was influenced by changes in depression in our patients, although we don’t know whether it is the depression that influences anxiety and breathlessness, the anxiety that influences depression and breathlessness, or the breathlessness that influences anxiety and depression. It may easily be that all of these factors influence each other. We do, however, know that they are linked. The figure below (Fig 1) shows how all the behavioural and physiological measures are correlated.

prepost1bFig 1. Correlation matrices of the measured behavioural variables. Abbreviations: wA, cue ratings of anxiety; wB, cue rating of breathlessness; StG, St Georges Respiratory score; Cat, Catastrophising score; Vig, Vigilance/Awareness score; Dep, Depression score; T Anx, Trait anxiety; S Anx, State Anxiety; Fat, Fatigue; BisBas, inhibition/activation scale; Spir, lung function (FEV1/FVC); ISWT, exercise ability (incremental shuttle walk test).**

While rehabilitation worked for the group as a whole, we saw that there was variability in the treatment response between patients. There was also no improvement in breathlessness ratings, nor was there any change in lung function in the group. Lung function was not linked to any of the behavioural measures, meaning that it isn’t a good measure of the impact of breathlessness in COPD.

Brain changes. Then we looked at how variation in brain activity explained the variation in our patients’ ratings of the cues over the course of their treatment. By looking at how variation in brain activity follows variation in ratings, we could make sure that even the patients that didn’t respond normally to treatment were included in the analysis. In other words, if a patient didn’t respond it is likely that their brain activation would not change either, and if a patient got worse we might see that their brain activation went in a different direction from those that got better. This gives us a much stronger idea of which areas get upregulated and downregulated (or stays the same) with successful treatment.

Looking at this variation, we saw that reduced breathlessness was linked with less activation in some brain regions (the anterior insula, ACC, posterior insula and supramarginal gyrus). This is a dampening in activity in brain areas handling expectations of breathlessness, and it could mean that successful treatment works by making patients re-evaluate their priors. Reduced anxiety was linked with greater activation in a slightly different set of brain regions (the posterior cingulate cortex, angular gyrus, primary motor cortex and supramarginal gyrus). As a set, these are involved with how much attention a physical sensation gets, and may be dampened by anxiety. In other words, if you are anxious, it is difficult to regulate how much attention you give a thing (i.e. if you are scared of spiders, you can’t just ignore one if you see one). So when we see an increase in these regions, this may mean that the patients are less anxious and more able to regulate attention. Taken together, this suggests that our patients had a more objective processing of breathlessness cues and were less dominated by their priors after rehabilitation.

prepost1.jpgFig 2. Change in brain activity that fits with rehabilitation-induced changes in response to breathlessness cues (both for anxiety and for breathlessness). Blue colours mean lower brain activity, and red/yellow colours mean higher brain activity. **

Predicting treatment outcome. We also looked at whether brain activation before the treatment could predict who would benefit from the treatment and who would not. Several regions showed higher activation in those patients who went on to improve with treatment. These included the stimulus valuation network plus the primary motor cortex. Improvements in anxiety ratings were predicted by high activation in the ACC and ventromedial prefrontal cortex, which overlaps with one of our previous studies looking at breathlessness and anxiety in COPD patients versus healthy controls [3]. These findings are also supported by a study that showed how higher fear levels before pulmonary rehabilitation tends to mean a greater response to treatment [4].

prepost3Fig 3. Brain activity before treatment that is linked with treatment outcome, both in terms of breathlessness (top) and anxiety (bottom). **

To conclude. Pulmonary rehabilitation seems to lead to reduced activity in the brain’s stimulus valuation network and increased activity in attention regulating networks. Those with strong responses in the stimulus valuation network before pulmonary rehabilitation typically see a bigger reduction in their responses to breathlessness cues after treatment. It may be that pulmonary rehabilitation works both by updating breathlessness-related priors and by reducing feelings of depression and anxiety that typically influence sensory processing. *** If this is the case, then we could improve treatment by focusing on re-learning priors, either by using drugs or alternative behavioural therapies. We could also use MRI as a way of developing behavioural tests (questionnaires, computerised tasks) that can be used to figure out who will benefit the most and in which way from the treatment.

References:
[1] Geuter, S. et al. eLife 2017; 6:e24770
[2] Yorke, J. et al. Thorax 2010; 65: 21-26
[3] Herigstad, M. et al. Chest 2015; 148(4): 953-961
[4] Janssens, T. et al. Chest 2011; 140: 618-625

Footnotes:
*The FMRI analysis used standard significant thresholds (cluster Z = 2.3, corrected cluster p = 0.05 corrected for multiple comparisons across the whole brain).
**Adapted from Herigstad et al, 2017, biorxiv: https://doi.org/10.1101/117390. The copyright holder for this preprint is the author/funder. It is made available under a CC-BY 4.0 International license
***In addition to potential improvements in fitness. We did see an increase in exercise capacity, even if none of the measured baseline physiological variables were changed, and it is possible that the rehabilitation causes the patients to become healthier and stronger.

Link: 
The paper is available from here: http://biorxiv.org/content/early/2017/03/23/117390
DOI: https://doi.org/10.1101/117390

The published paper is out here

Full citation: Mari Herigstad, Olivia K. Faull, Anja Hayen, Eleanor Evans, F. Maxine Hardinge, Katja Wiech, Kyle T.S.Pattinson. Treating breathlessness via the brain: changes in brain activity over a course of pulmonary rehabilitation. 

MRI and motion correction

Magnetic resonance imaging is sensitive to motion. Just like with other images, movement may cause blurring and distortion (‘artefacts’). To counteract this, motion correction methods are often used. These include devices that track motion as well as software that can correct some of the artefacts after the images have been collected. We have just published a paper on a potential new way to do this, using a wireless accelerometer (link, open access [1]), so here is a quick blog post about motion and MRI, explaining some of our findings along the way.

technologies-05-00006-g001The GE Firefly scanner, 3T

One of the reasons for doing this work is that we are using a new scanner for newborn babies. Motion is always an issue in MRI, even for adults, but the scanning of newborns may be particularly vulnerable. It is not always easy to convince a newborn baby to remain still. Newborns may move several centimetres where adults only shift a few millimetres. As the newborn is smaller in size, this movement also has greater impact in that it can completely displace the (much smaller) structure of interest. Newborn babies also show differences in physiology compared to adults, which can affect the scan. For example, they breathe faster and less regular, and the resulting motion is transmitted to the head to a greater degree (due to the smaller distance between head and chest) [2].

Types of motion
Motion comes in many types. There is microscopic motion, related to for example circulation of blood or water diffusion, and there is macroscopic motion, related to whole-body movement and physiological functions, for example breathing movement. It may be periodic (e.g. breathing movement), intermittent (e.g. yawns, hiccups) or continuous (e.g. general unsettledness, scanner vibrations). In research settings, noise and motion may be induced by experimental procedures [3]. Motion causes artefacts such as blurring, signal loss, loss of contrast and even the replication of signal in wrong places (‘ghosting’) – all lowering the quality of the image. An example of a motion artefact can be seen in the image below.

wireless_fig2Fast Spin Echo image. Left: no motion artefact; Right: artefact due to in-plane rotational head movement. Image from Paley et al. 2017. Technologies, 5(1); 6. (CC BY 4.0)

In the figure above, there are lines on the right-hand scan (red arrow), which are distortions. These distortions were created because the head rotated slightly whilst it was being scanned. Too much distortion and the image will become less useful for clinical and experimental purposes.

Types of motion correction
There are many types of MRI motion correction. The simplest may often be to prevent and minimise movement using coaching of the patient, sedation and fast and/or motion-resistant imaging protocols. A fast scan with a still individual will usually give very little motion. However, this may not always be possible. Patients do not always lie still, sedation may not always be a good idea, and even our best imaging sequences can be vulnerable to movement to some extent. Large movement is therefore often best tackled through different means: it is detected and corrected for. Correction can be done during the scan (real-time) or after the scan (creation of the image from the raw data and/or post-processing of the image).

There are limits to this type of large movement correction. For example, we can use so-called navigator pulses during the scan to correct for movement in real time, but they tend to make scans take much longer. We can also use tracking devices to correct for motion both during and after a scan, but such devices are limited by the level of motion they can detect and require a fair bit of extra equipment to work inside or interact with the scanner. Finally, we can correct for motion in reconstruction or post-processing, but this too usually takes a lot of time and effort. Which type(s) of correction method is best may differ between different types of scan, patients, and experimental protocols and so on.

In our paper, we used an external motion measuring device – a wireless accelerometer, similar to one that you may buy for fitness purposes – to measure motion of the head. The nice thing about this is that it can give us full real-time 3D motion information about how the head moves. It is not like a visual device, which needs a clear line of sight to be able to ‘observe’ the head at all times. The accelerometer gave us continuous, wireless feedback on the angle of the object being scanned. We could then use this information to adjust the MR data, using a motion correction algorithm. The algorithm, using movement data from the accelerometer, adjusted how the MR signal was recorded at each given time point. We were in short using the signal from the accelerometer to shift k-space.

This meant that shifts in signal due to movement could in theory be recorded and, at least partly, fixed. Conversely, it also meant that motion could be introduced in a motion-free image. To introduce motion, we first made a motion data file with the accelerometer, simply by manually rotating it and recording the angles. We then applied this motion data file to the raw data of a motion-free scan. The motion file was used to shift signal in k-space for each affected phase encode step. Doing this, we could distort the image in the same way that real motion would cause distortions, despite there being no original motion in the MR data. We could ramp this up as we pleased, adding more and more ‘motion’, as shown in the figure below.

wireless_fig3.jpg
MR images incorporating increasing amounts of motion. (a) Original no-motion image, (b-f) motion applied, starting with 2 × 10−2 radians (b) and doubled for each successive image. Image from Paley et al. 2017. Technologies, 5(1); 6. (CC BY 4.0)

In principle, reversal of the motion effects should be possible. The motion in the figure above was introduced using a standard rotation matrix which multiplied the k-space locations by the measured angle, and if we reverse this process (i.e. counter-rotate the k-space data according to the measured angles), removing the noise should be possible. As with most things, it is easier to break than fix, yet we did see a subtle reversal of motion artefacts for a simple side-to-side rotation. This means that a wireless accelerometer may eventually be used to retrospectively correct for motion in neonatal MRI scans. It is also possible that it could be used for guiding real-time correction methods.

References: 
1. Paley, M., Reynolds, S., Ismail, N., Herigstad, M., Jarvis, D. & Griffiths, P. Wireless Accelerometer for Neonatal MRI Motion Artifact Correction. Technologies. 2016; 5(1): 6. doi:10.3390/technologies5010006
2. Malamatenioua, C., Malika, S., Counsella, S., Allsopa, J., McGuinnessa, A., Hayata, T., Broadhousea, K., Nunesa, R., Ederiesc, A., Hajnala, J. & Rutherford, M. Motion-compensation techniques in neonatal and fetal MR imaging. Am J Neuroradiol. 2013; 34(6):1124-36.
3. Hayen, A., Herigstad, M., Kelly, M., Okell, T., Murphy, K., Wise, R., & Pattinson, K. The effects of altered intrathoracic pressure on resting cerebral blood flow and its response to visual stimulation. NeuroImage. 2012; 66: 479-488. doi: 10.1016/j.neuroimage.2012.10.049.

What happens during an MRI?

MRI is complex, but the basic events in the scanner are quite straight-forward. Below is a short, simple guide to what happens during an MRI scan without too much physics to complicate matters. It explains the actual events in the scanner and a simplified overview of the parameters we can use to change the images we collect.

technologies-05-00006-g001

  1. First we align the protons in the object we want to scan with the magnetic field of the scanner (B0). This happens naturally when the object is placed into the scanner, as the slightly magnetic protons adjust themselves to match the magnetic field of the scanner.
  2. Second, we use a radio frequency (RF) pulse to ‘tip’ these aligned protons out of their alignment with the magnetic field. This pulse is sometimes called B1 and is applied at a 90° angle to B0. This causes the protons to rotate, and we can measure this rotation with RF measurement coils. RF coils are essentially loops of wire, and the changing magnetic flux of the protons induces an electric current through these loops. This is because changes in electrical currents generate magnetic fields, and changes in magnetic fields generate electrical currents. RF coils may often be designed to both deliver the RF pulse (through an applied electrical current) and receive the signal (through the resulting change in magnetic flux). During the rotation, two things happen.
    1. Protons begin to align themselves again with B0. The speed of this realignment is called the T1 relaxation time. For any given type of object (or tissue, if we are doing medical imaging), the composition of the object will cause its protons to realign at different rates. Faster realignment means brighter signal.
    2. Protons become out of phase with eachother. This reduces the signal we can measure with our coil (as the rotation of the photons are no longer ‘pulling in the same direction’). The speed at which this happens is called T2 relaxation time. Protons remaining in phase for longer means brighter signal.
  3. The RF pulse is applied again, to repeat the procedure. The average of all these repeats gives us a clear MR image.

The contrasts of the scan (T1, T2) are determined by two parameters: relaxation time (TR) and echo time (TE).

TR is the time between the RF pulses. If we have a long TR, all protons in the object have time to realign with B0. If we have a short TR, some protons may not have fully realigned by the time the next RF pulse arrives. In terms of medical imaging, some tissues will need longer to have all their protons realign than other tissues. If the ‘slow’ tissues have not realigned within the TR, the signal from these tissues will be less than the ‘fast’ tissues. This way, we can tell the difference between different tissues.

TE is the time we use to measure the signal induced by the rotating protons. Some types of tissue will have protons that fall out of phase (‘dephase’) faster than protons in other types of tissue. For example, protons that are in fluids have less obstacles, and will remain in phase for quite a long time. Protons that are constrained by structures may not remain in phase that long. Longer TEs means that the protons have more time to dephase, and this will reduce the signal from tissues that dephase quickly more than from tissues that dephase slowly.

Generally speaking, we have three types of contrast: T1-weighted, T2-weighted and proton-density (PD) weighted

A scan sequence with a short TR and short TE is usually called T1-weighted. By ‘a short TE’ we usually mean that the TE is shorter than the T2. In other words, there is not enough time for the protons dephasing properly, and the T2 effects are masked. The shorter TR, on the other hand, means we can easily differentiate between tissues with longer and shorter T1. The scan is therefore T1-weighted. Tissues that are bright in T1-weighted scans are fat and white brain matter. Muscle and grey brain matter are less bright (grey in colour), and fluids tend to be black.

A long TR and long TE scan sequence is usually called T2-weighted. Longer TE means that we get differentiation based on protons dephasing at different rates, and the T2 effects are visible. The longer TR, however, means all tissues have time to have all their protons realigned to B0, so we get no differentiation based on T1 times. The scan is therefore T2-weighted. Tissues that are bright in T2-weighted scans are fat and fluids. Muscle and grey brain matter are grey, and white brain matter is almost black.

A long TR and a short TE means we get both T1 differentiation and T2 differentiation, and we call this proton-density weighted. This gives us the actual density of protons in the tissues. A short TR and long TE means we get neither T1 nor T2 differentiation. We don’t use this type of scan, as it doesn’t yield any useful information.

T1t2PD.jpg

In this post, I have summarised some of the basics about MR imaging. In my next post, I will move on to outline some of the basics about raw MR data processing, covering k-space and Fourier transforms.

Breathlessness and opioids

We’ve recently published a paper on how opioids can modulate breathlessness. (The whole manuscript is open access here). Low-dose opioids can be used for treating chronic breathlessness, but we don’t know exactly how they work.

Opioid receptors exist across the brain. These are part of the internal opioid system (endogenous opioid system) for natural pain relief. When opioids are used in the clinical setting to treat negative stimuli, such as pain, they influence the unpleasantness and the intensity of the stimulus in different ways(1). In terms of breathing, opioids lower breathing by influencing brainstem respiratory centres(2), causing breathing to stop completely in high doses, and they can also affect higher brain centres(3).

Opioids also have behavioural effects, and may, amongst other things, influence associative learning. Associative learning is when an association between two stimuli is learnt by pairing these together. For example, in chronic breathlessness this could be previously neutral stimuli (e.g. a flight of stairs) and breathlessness. This could create an anticipatory threat response, which means that simply seeing a flight of stairs is enough to bring about breathlessness or the fear associated with breathlessness. This can worsen the breathlessness for the patient in the long run.

In this study, we hypothesized that opioids improve breathlessness in part through changing the anticipatory response to breathlessness. We focused on two brain regions in particular: the amygdala and hippocampus. Both are strongly involved in associative learning(4) and are rich in opioid receptors(5,6).

What did we do? First, we asked healthy volunteers to do a breathing test where we paired three different degrees of breathlessness and three symbols. The symbols were presented immediately before the volunteer was made breathless and were matched to the level of breathlessness that they signified. We invited those volunteers who learnt to associate each different symbol and its corresponding breathlessness to undertake two MRI scans in random order. Before one scan they received a low-level opioid (remifentanil) infusion, and before the other they received a control (saline) infusion. During the scans, the volunteers repeated the breathlessness/symbol task. Below is a schematic of the breathing circuit used for the study, showing how different levels of breathlessness was induced.

breathingsystemFigure 1. Breathing circuit for inducing breathlessness

What did we find? We were able to show that breathlessness anticipation in the control condition was processed in the right anterior insula and operculum, and that the breathlessness itself was processed in the insula, operculum, dorsolateral prefrontal cortex, anterior cingulate cortex, primary sensory cortices and motor cortices. These regions have been identified in other studies on breathlessness (as discussed in a previous blogpost).

However, in the opioid condition, we saw the following:
1. Opioids reduced breathlessness unpleasantness (Figure 2)
2. This reduction correlated with reduced activity in the amygdala and hippocampus during anticipation of breathlessness (Figure 3)
3. This reduction also correlated with increased activity in the anterior cingulate cortex and nucleus accumbens during the actual breathlessness (Figure 3)
4. During the actual breathlessness, the opioid infusion directly reduced activity in the anterior insula, anterior cingulate cortex and sensory motor cortices (Figure 4).

opioid_res1Figure 2. Ratings of breathlessness and intensity. Abbreviation: Remi=remifentanil

Reduction in unpleasantness. The reduction in unpleasantness with opioids was expected – the different effect of opioids on intensity and unpleasantness has been shown in many other negative conditions, including pain(1). Interestingly, we could confirm that this lowered unpleasantness correlated with reduced activity in brain regions linked with associative learning and memory (amygdala and hippocampus) before the breathlessness began (Figure 3, bottom). This reduced activation in the amygdala and hippocampus, regions that are needed for formation of unpleasant memories, may explain how low-dose opioids gradually become more efficient as a therapy over the first week of administration. The reduction in amygdala/hippocampus activation may mean that fewer new negative memories and reaction patterns are formed.

opioid_res2.jpgFigure 3. Brain activity linked to lowered unpleasantness, relating to breathlessness (top) and anticipation of breathlessness (bottom). NA=nucleus accumbens, paraCC=paracingulate cortex, ACC=anterior cingulate cortex, PC=precuneus, ant hipp=anterior hippocampus, amyg=amygdala. 

We also see that the reduced unpleasantness correlates with activation during the actual breathlessness in the anterior cingulate cortex and nucleus accumbens (Figure 3, top). These are parts of the endogenous opioid system which reduces the perception of negative stimuli. This means that the reduced unpleasantness our volunteers felt is linked with these regions being more active. In other words, they may act to further dampen the negative sensation. Less unpleasantness during the breathlessness means that even less negative memories are likely to be formed.

Reduction in breathlessness activation. Finally, opioids directly reduced activity in the anterior insula, anterior cingulate cortex, sensory motor cortices and brainstem (Figure 4). The activation during control (saline) in the figure below is typical of breathlessness, and has been found in several other studies using breathing challenges.

opioid_res3.jpg

Figure 4. Brain activity during breathlessness in the control (saline) condition (top) and where it is reduced by the opioid (remifentanil, bottom). Increased activation is shown in red-yellow, and decreased in blue. M1/S1=primary motor & sensory cortices, OP=operculum, dlPFC=dorsolateral prefrontal cortex, Thal=thalamus, ACC=anterior cingulate cortex, vmPFC=ventromedial prefrontal cortex, PAG=periaqueductal grey, SMG=supramarginal gyrus.

The areas that are reduced in activation with opioids are commonly activated in breathlessness and central to respiratory sensation. For example, the anterior insula, the most commonly activated brain region during breathlessness, is believed to assess the quality of the stimulus and help control interpretation. The anterior cingulate cortex, which is also commonly activated in breathlessness, is similarly involved in control of negative emotions. These regions may be part of an interpretation process that is shaped by expectation and learning(7) similar to other control systems in the body (e.g.(8)).

Summary: Opioids manipulate brain regions associated with learning, negative memory formation and negative stimulus control. We have shown that the opioid remifentanil may alter breathlessness perception and the brain regions associated both with anticipation of and actual breathlessness. This suggests that opioids work to reduce breathlessness in part through direct effects on respiratory control mechanisms in the brainstem, insula and anterior cingulate cortex, and in part through changes in how breathlessness is anticipated, by changing associative learning processes in the amygdala/hippocampus.

References:
1. Pain, 22 (1985), pp. 261–269
2. Br J Anaesth, 100 (2008), pp. 747–758
3. J Neurosci, 29 (2009), pp. 8177–8186
4. Curr Opin Neurobiol, 14 (2004), pp. 198–202
5. Life Sci, 83 (2008), pp. 644–650
6. Pain, 96 (2002), pp. 153–162
7. Nat Rev Neurosci, 16 (2015), pp. 419-429
8. Exp Physiol, 92 (2007), pp. 695-704

DOI: http://dx.doi.org/10.1016/j.neuroimage.2017.01.005
Link: https://www.ncbi.nlm.nih.gov/pubmed?term=10.1016%2Fj.neuroimage.2017.01.005
All images presented with permission, creative commons licence.

Breathlessness & the Brain

Breathlessness can be many things. For example, it can be the shortness of breath after exercise – short-lived and laced with endorphins – or it can be the frightening gasping for breath experienced by patients with a range of diseases from cardiac failure and cancer to respiratory disease. From a physiological point of view, these may look quite similar, but they are not really the same thing. Breathlessness is the sensation produced by sensory input evaluated within the context of psychological and environmental factors. In short, the quality of breathlessness depends on why, who and where it is experienced – it’s situational and subjective. This means it cannot be approached, from a medical point of view, with a one size fits all solution. It’s important to understand its nuances.

So, which parts of the brain are involved in processing breathlessness? The answer is quite simple: we don’t know yet. To date, studies are few and far between, with the majority being on breathlessness in healthy controls which may or may not translate to various patient groups. While we don’t know anything for certain, particularly for patients, we do however have a few likely suspects.

usual

The usual suspects. The first is the insular cortex. This is a region of the brain that has been identified in the majority of neuroimaging studies on breathlessness. It plays a role in the conscious awareness of body state and is involved in the perception of other unpleasant sensations, such as pain. The role of the insula is two-fold, with the posterior (towards the back) part processing the physical aspects and the anterior (towards the front) part processing the affective (relating to moods and feelings) aspects of the sensation 1. This anterior-posterior division may also be present in breathlessness, as the anterior insula appears to be more associated with the unpleasantness of breathlessness.

The next on the list of likely suspects are somatosensory and motor regions (e.g. 2), which are also activated in a number of breathlessness studies. This is not surprising, given the added work of breathing harder than normal when you’re breathless. In addition to cortical somatosensory/motor regions, we also see activation in the brainstem (particularly in the brainstem respiratory centres) and in the cerebellum (a region associated with motor function). Some of these areas may be mostly involved with breathing itself and not with breathlessness specifically, as they appear frequently in studies looking at simple breathing responses without any breathlessness.

Then there are regions that sometimes appear and sometimes don’t: the prefrontal cortex, the periaqueductal grey (PAG), the amygdala and the anterior cingulate cortex (ACC). In the case of the PAG, which is a structure in the midbrain involved in pain processing and fight-or-flight responses, this may simply be because it is small and hard to image. Recent studies using high resolution imaging have found that the PAG is involved with unpleasant respiratory sensations, and that parts of the PAG have different roles in processing respiratory threat (see Figure 1 below) 3. Looking further into its sub-divisions, the lateral PAG is downregulated during restricted breathing, and the ventrolateral PAG is upregulated. This is interesting as the lateral PAG has been associated with active coping strategies for stressful situations that can be escaped, and the ventrolateral PAG with passive coping strategies for stressful situations that cannot be escaped. In short, it seems as if different parts of the PAG are involved in different aspects of breathlessness.

elife-12047-fig2-v3-480wFig 1. 7T FMRI of respiratory threat in the PAG. Faull et al. 2016.

The prefrontal cortex has been implicated in the processing of breathlessness cues (i.e. a non-physical stimulus) in COPD patients (medial prefrontal cortex, mPFC), as has the ACC (see Figure 2 below) 4. The latter has also been identified in many studies on breathlessness in healthy volunteers, and the former is an area of the prefrontal cortex involved in emotion processing, and particularly associated with responses to fear and threat. In the above study, both patients and controls showed activation in the insula (labelled ‘Conjunction’). Opioids, which can cause make breathlessness less unpleasant, dampens brain responses to breath holding in the prefrontal and anterior cingulate cortices as well as in the insula 5. So, it’s a good guess that the prefrontal cortex and ACC are involved in the modulation of breathlessness.

chestpaperFig 2. COPD patients and control. Response to breathlessness cues. From Herigstad et al. 2015.

Similarly, the amygdala, which is frequently identified in a whole range of studies relating to emotion, threat appraisal and fight-or-flight responses, is also activated in some studies of breathlessness. The amygdala is strongly connected to the anterior insula, and breathlessness studies that have seen activation in the amygdala also show large activation in the insula.

There are also another few regions that are occasionally found and we don’t quite know how they fit in. These include the before-mentioned cerebellum, the dorsolateral prefrontal cortex and the precuneus.

The cerebellum is probably mostly involved in the motoric response to breathlessness, as it is a centre for coordination of motor function, but as the cerebellum has also been associated with cognitive function, we can’t yet determine its role in breathlessness. The dorsolateral prefrontal cortex may be part of the cognitive evaluation of breathlessness, as this is a structure that is associated with attention and working memory. The precuneus could be involved in both sensory and cognitive aspects of breathlessness, depending on which part of the precuneus is active. It is a poorly mapped structure, but in general it has a sensorimotor anterior region which links to sensory/motor areas of the brain and the insula, and a cognitive central region which links to prefrontal regions, including the dorsolateral prefrontal cortex. It also links to the thalamus, which is a subcortical structure that relays a vast range of information between the brainstem and the higher brain areas.

Linking it all together. We don’t know how this all ties together. However, one could speculate ways in which breathlessness is processed in the brain:

Brainstem breathing control. We know that respiratory centres in the brainstem receive peripheral input (input from the rest of the body) and adjust the breathing pattern in response to this. The brainstem is crucial for breathing – without it, no breathing occurs – and it almost certainly plays a part in the actual breathing response to breathlessness. The cerebellum, which is probably involved in the motor response to breathlessness, connects to the higher brain areas through the brainstem. So far so good.

PAG as a gateway. Peripheral input is also believed to be received in the PAG, which could act as a gateway of sorts. The lateral PAG relays signal directly to primary motor/sensory cortices and the (posterior?) insula, and this path may be processing breathlessness intensity. The ventrolateral PAG relays signal directly to the prefrontal cortex, (anterior?) insula and also to motor regions of the brain, and this path may be processing the threat and/or possible responses to the breathlessness. The PAG probably also connects with the thalamus, which in turn acts as a hub (see below).

Thalamus as a hub. Finally, signal could also be relayed via the PAG (or directly) to the thalamus, which has been identified in a range of breathlessness studies and is a common hub for cortical communication. From the thalamus, signal is transmitted to a range of cortical areas (including prefrontal regions and the amygdala via the medial/frontal thalamus, and somatosensory and motor regions via the ventroposterior thalamus). Breathlessness is likely modulated by several of these cortical areas and how they interact.

The complexity of the interactions may be best explained by an example:

Anxiety relating to threat is modulated by activation in both the medial prefrontal cortex (mPFC) and the amygdala. The mPFC possibly influences threat by dampening activation in the amygdala. So far, so good. The prefrontal cortex receives signal from the PAG, and may also receive input from the ACC. Both the ACC and PAG are in turn linked to the anterior insula. The anterior insula also receives input from the thalamus, which relays signal to the amygdala. The thalamus also receives input from the PAG. So now we have a network of interconnected regions which may all influence each other and work to fine-tune the anxiety response. Furthermore, the anterior insula may also receive and integrate information on the physical sensation from the posterior insula, which is linked to the ventroposterior thalamus. This part of the thalamus is connected with somatosensory and motor cortices. These are also influenced by the PAG. The thalamus and somatosensory/motor cortices also relay signal to the precuneus, which in turn could connect to the dorsolateral prefrontal cortex, thus incorporating cognitive processing. And so on. It can get a bit complex.

A suggested network is presented in the figure below.

brain3 Fig 3. Possible breathlessness network. Purple=connected cortical regions, black= signal to/from periphery, blue=signal from PAG. ‘Hubs’ are shown in green. PFC = prefrontal cortex (encompasses both medial (emotional/threat processing) and dorsolateral (cognitive processing) PFC)

In short, various studies have shown that a range of brain regions are activated during breathlessness, but we don’t yet know exactly how they are involved in processing the sensation. We may still only guess at the full picture, based on breathlessness work as well as studies on other unpleasant sensations (pain, mostly), threat or emotional processing. Much of the same processing mechanisms are likely to be found in these similar sensations. While the above figure outlines some possible networks for the processing of breathlessness, based on our current understanding of breathlessness and related conditions, (much) more information is needed.

References:
1. Oertel, B., Preibisch, C., et al. Clin Pharmacol Ther 2008; 83:577–588.
2. Hayen, A., Herigstad, M., et al. NeuroImage 2012; 66: 479-488.
3. Faull, O., Jenkinson, M., et al. eLife 2016; 5: e12047
4. Herigstad, M., Hayen, A., et al. Chest 2015; 148(4): 953-61
5. Harvey, A., Pattinson, K., et al. J Magn Reson Imaging 2008; 28:1337–1344.

Predicting trouble: EEG, NO and stroke

We’ve recently published a paper titled “Electroencephalographic Response to Sodium Nitrite May Predict Delayed Cerebral Ischemia After Severe Subarachnoid Hemorrhage” on how electroencephalography (EEG for short) can be used to figure out which patients with a certain type of stroke (subarachnoid hemorrhage) will develop a complication after the initial brain bleed and which will not.

Subarachnoid hemorrhage is a type of stroke that can happen at any age. It is a bleed on the surface of the brain, in a space between the arachnoid membrane and the pia mater surrounding the brain (see figure below).

Meninges-en

The bleed is usually caused by an aneurysm (a bulging, weak section of a blood vessel) bursting, causing blood to escape into the subarachnoid space. Here, it puts pressure on the brain tissue (causing tissue damage) which can also reduce blood flow to other parts of the brain (causing lack of oxygen and cell death). The released blood may be toxic to the brain tissue and could cause inflammation. At worst, a subarachnoid hemorrhage results in death or severe brain damage.

Some of the damage is caused directly by the bleed (e.g. the pressure on the brain), and some is caused by how the bleed disrupts the normal control of blood flow in the brain. In particular, it is bad when the nitric oxide pathway stops functioning properly. Nitric oxide helps preserve the circulation in small blood vessels in the brain, partly through enlarging the blood vessels (vasodilation) and lowering the gathering of blood-clot forming platelets. After a bleed, nitric oxide levels in the brain are often reduced. This could be because hemoglobin in the blood stops enzymes responsible for producing nitric oxide from working properly. Another contributing factor is that the nitric oxide that is already present in the brain reacts with superoxide (part of the immune response) which leads to the levels of nitric oxide being even further lowered.

Nitric oxide disruption appears to be involved in delayed cerebral ischemia, which is the most common complication after a subarachnoid hemorrhage. For example, people with genetically lower activity in an enzyme that produces nitric oxide have a higher risk of this complication. Delayed cerebral ischemia is the unpredictable lack of oxygen to the brain leading to severe, even fatal, brain damage. This typically happens 3-14 days after the initial subarachnoid hemorrhage.

Which brings us to our question:

Patients who have the same clinical severity, no measurable genetic differences in nitric oxide production, and who seem exactly the same can go on to show very different outcomes. One can develop ischemia and suffer devastating new brain injuries, and the other return to normal without complications. How can we tell who will get it and who will not?

EEG measures neuronal (electrical) activity using electrodes placed on your scalp. It picks up fluctuations in voltage from the electric currents in the neurons, and in the clinical setting it is used to measure the spontaneous electrical activity in a brain over time. Different parts of the brain generate different signals. Some parts show signal with low frequencies (long waves, delta and theta frequencies) and some with short frequencies (rapid waves, alpha frequencies). The signal is linked to blood flow. In subarachnoid hemorrhage, it can be used to see cerebral ischemia develop naturally at an early stage, because as the blood flow is reduced, short frequencies begin to fade and long frequencies steadily increase. In short, the ratio of alpha to delta (for example), will be reduced in the ischemic patients. However, it can take several days of recording to get a result with this method. It is possible, but not practical.

However, we know that nitric oxide disruption seems to be critically involved in delayed cerebral ischemia. This means that we can give patients a nitric oxide donor (sodium nitrite) to stimulate the nitric oxide pathway, and use EEG to measure how well they respond. By doing this, we can speed the process up. We can see if the patient responds well to the sodium nitrite, or not. This means, in short, that we can see which patients show nitric oxide pathway disruption.

Using this method, we showed in our paper that patients who later went on to develop delayed cerebral ischemia showed no change (or a decrease) in the EEG signal whilst infused with sodium nitrite. Those that did not develop delayed cerebral ischemia did the exact opposite, and showed a strong increase in EEG signal.

temp3EEG spectrograms from a patient that did not develop delayed cerebral ischemia, and one who did, plus a scatter plot with the group differences in spectrogram ratio (alpha delta ratio (ADR))

So we have not only shown how the nitric oxide pathway is important in the development of delayed cerebral ischemia, but this also means we now quite possibly may be able to relatively quickly determine who is at risk for this life-threatening complication, and who is not.

Reference: Garry, P.S., Rowland, M.J., Ezra, M., Herigstad, M., Hayen, A., Sleigh, J.W., Westbrook. J., Warnaby, C.E. and Pattinson, K.T.S. (2016) Electroencephalographic Response to Sodium Nitrite May Predict Delayed Cerebral Ischemia After Severe Subarachnoid Hemorrhage. doi: 10.1097/CCM.0000000000001950

Brainstem? Brainstem!

Revisiting brain regions, courtesy  of Pinky and the Brain.

 

(Incidentally, I named my first neuroscience review paper “Dyspnoea and the Brain” partly in honour of this comic, fully expecting the title to be dismissed by colleagues and journal alike. It wasn’t. Nobody has spotted the connection yet, sadly.)