We continue our review of the key principles of NMR with another video courtesy of Paul Callaghan. In it, Prof Callaghan introduces the idea of bulk magnetization; the thing that you induce in your subject's brain (which is ~80% water) when you slide the subject into the magnet, and which you then manipulate to produce images.
In the video shown previously (see Part One), Prof Callaghan introduced the phenomenon of resonance and demonstrated it with a spinning wheel. In MRI the resonance frequency is governed by a simple proportionality, as given in the Larmor equation. We will use this equation later on to establish different frequencies across an object, thereby encoding spatial information and yielding, ultimately, an image of that object. We will also see how the Larmor equation is used in the k-space formalism, so make sure you have a good understanding of this deceptively simple yet intuitively valuable equation.
Wednesday, February 9, 2011
Sunday, February 6, 2011
Physics for understanding fMRI artifacts: Part One
This is the first in a series of posts in which I will attempt to provide you with the means to diagnose the manifold artifacts that plague fMRI. These artifacts can be inherent, e.g. distortion and dropout, or the consequences of a hardware issue, e.g. RF interference or gradient spiking, or may arise from your subject, e.g. cardiac pulsatility and head movement. However the artifacts arise, the aim is simple: by providing you the means to recognize what is going wrong in your experiment you may be able to discern the root cause, then remedy the problem and salvage your data.
It's a lot like plane spotting, only less fun.
The psychologists amongst you would be able to lecture me for days on category learning. Well, that's what this is. Except that before we can differentiate between one artifact and another we must first understand how EPI is designed to work in an ideal situation. (You don't need to know how planes fly to categorize them, apparently.) And to comprehend artifacts it's important for you to have a reasonable appreciation of the underlying physics, especially the concept of k-space. Here's the loose plan:
It's a lot like plane spotting, only less fun.
The psychologists amongst you would be able to lecture me for days on category learning. Well, that's what this is. Except that before we can differentiate between one artifact and another we must first understand how EPI is designed to work in an ideal situation. (You don't need to know how planes fly to categorize them, apparently.) And to comprehend artifacts it's important for you to have a reasonable appreciation of the underlying physics, especially the concept of k-space. Here's the loose plan:
Saturday, January 22, 2011
Comparing fMRI protocols
In a December post I suggested a decision tree that can be used for deciding whether or not to adopt a new (or new to you) method or device for your next fMRI experiment. In essence it was a form of risk analysis. But it isn't only new methods that need to be evaluated carefully before you embark on an experiment. What about the plethora of parameters that characterize even the simplest combination of single-shot EPI with whatever passes for standard hardware on your scanner? RF coil selection, echo spacing, TE, slice thickness, slice gap, TR, RF flip angle... all can have profound effects on your data. In the absence of a compelling paper that strongly implicates a particular protocol for your experiment, how do you make an informed choice before you proceed?
Functional signal and physiologic noise
In an ideal world you would be able to run a pilot experiment that robustly activates all the brain regions you're interested in. This approach can work well if all of your regions of interest lie in primary cortex: responses to stimuli are typically robust, baselines are fairly easily established, and simple stimuli can often be used to assess regional responses. But many contemporary experiments don't lend themselves to extensive piloting; actually doing the entire experiment may be the only way to assess whether regions A, B and C are activated at all, let alone more or less with a particular parameter setting! Instead, we may have to focus our attention on the noise properties of the tissue.
Functional signal and physiologic noise
In an ideal world you would be able to run a pilot experiment that robustly activates all the brain regions you're interested in. This approach can work well if all of your regions of interest lie in primary cortex: responses to stimuli are typically robust, baselines are fairly easily established, and simple stimuli can often be used to assess regional responses. But many contemporary experiments don't lend themselves to extensive piloting; actually doing the entire experiment may be the only way to assess whether regions A, B and C are activated at all, let alone more or less with a particular parameter setting! Instead, we may have to focus our attention on the noise properties of the tissue.
Wednesday, December 22, 2010
Resting state fMRI - part III
The story so far...
Finally, here is the third part of a three-part series of posts that have sought to determine a general protocol for resting state fMRI (rs-fMRI). In the first post I reviewed a paper by van Dijk et al. that showed that spatial and temporal resolution didn't make a huge difference to the way resting state networks could be detected using current methods (i.e. seed cross correlation or ICA).
In the second post I presented the results of some simple tests that aimed to determine what sort of spatial coverage could be attained with parameters in accordance with the conclusions of the van Dijk paper. Temporal SNR (TSNR) was used as a simple proxy for data quality. It was found that TSNR for 3.5 mm in-plane resolution was fairly consistent across a range of axial and axial-oblique slice orientations, as well as for sagittal slices.
One question remained, however: given the tolerance to a longish TR (compared to event-related fMRI) for detecting resting networks, would it be beneficial to acquire many thinner slices in a longer TR, or fewer thicker slices in a shorter TR? Following van Dijk et al. we wouldn't expect any huge penalty from extending the TR a bit, but there might be a gain of signal in regions suffering extensive dropout which would suggest that thinner slices might be useful.
Finally, here is the third part of a three-part series of posts that have sought to determine a general protocol for resting state fMRI (rs-fMRI). In the first post I reviewed a paper by van Dijk et al. that showed that spatial and temporal resolution didn't make a huge difference to the way resting state networks could be detected using current methods (i.e. seed cross correlation or ICA).
In the second post I presented the results of some simple tests that aimed to determine what sort of spatial coverage could be attained with parameters in accordance with the conclusions of the van Dijk paper. Temporal SNR (TSNR) was used as a simple proxy for data quality. It was found that TSNR for 3.5 mm in-plane resolution was fairly consistent across a range of axial and axial-oblique slice orientations, as well as for sagittal slices.
One question remained, however: given the tolerance to a longish TR (compared to event-related fMRI) for detecting resting networks, would it be beneficial to acquire many thinner slices in a longer TR, or fewer thicker slices in a shorter TR? Following van Dijk et al. we wouldn't expect any huge penalty from extending the TR a bit, but there might be a gain of signal in regions suffering extensive dropout which would suggest that thinner slices might be useful.
Saturday, December 4, 2010
Beware of physicists bearing gifts!
A decision tree to evaluate new methods for fMRI.
Sooner or later, someone – probably a physicist - is going to suggest you adopt some revolutionary new method for your fMRI experiment. (See note [1].) The method will promise to make your fMRI simultaneously faster and better! Or, perhaps you’ll see a new publication from a famous group – probably a bunch of physicists – that shows stunning images with their latest development and you’ll rush off to your own facility’s physicist with the challenge, “Why aren’t we doing this here?”
On the other hand, inertia can become close to insurmountable in fMRI, too. Many studies proceed with particular scan protocols for no better reason than it worked last time, or it worked for so-and-so and he got his study published in the Journal of Excitable Findings and then got tenure at the University of Big Scanners. Historical precedent can be helpful, no doubt, but there ought to be a more principled basis for selecting a particular setup for your next experiment. At a minimum, selection of your equipment, pulse sequence and parameters should be conscious decisions!
But what to do if your understanding of k-space can be reasonably described as ‘fleeting’ and you couldn’t tell a Nyquist ghost from the Phantom of the Opera? Do you blindly trust the paper? Trust your physicist? Succumb to your innate fear of the unknown and resist all attempts at change…? You need a mechanism whereby you can remain the central part of the decision-making process, even if you don’t have the background physics knowledge to critically evaluate the new method. It is, after all, your experiment that will benefit or suffer as a result.
Skepticism is healthy
Let’s begin by considering the psychology surrounding what you see in papers and at conferences. Human nature is to put one’s best foot forward. So start by recognizing that whatever you see in the public domain is almost certainly not what you can expect to get on an average day. Consider published results to be the best-case scenario, and don’t expect to be able to match them without effort and experience. There may be all kinds of practical tricks needed to get consistently good data.
Next, recognize that there is usually no correlation between the difficulty of implementing a method as described in a paper’s experimental section and the actual amount of time and energy it took to get the results. For all you know it may have taken six research assistants working sixty hours a week for six months to get the analysis done. That said, do spend a few moments reviewing the experimental description and look for clues as to the amount of legwork involved. If the method used standard software packages, for example, that’s usually a sign that you could implement the method yourself without hiring a full-time programmer. Custom code? A flag that advanced computing skills and resources may be required.
Okay, at this point we are now in a fit mental state to pour cold, hard logic into this decision-making process. We’re ready to ask some questions of the new method, and to make a direct comparison to the standard alternatives we have available (where ‘standard’ means something that has been well tested and used extensively by your own and other labs).
Sooner or later, someone – probably a physicist - is going to suggest you adopt some revolutionary new method for your fMRI experiment. (See note [1].) The method will promise to make your fMRI simultaneously faster and better! Or, perhaps you’ll see a new publication from a famous group – probably a bunch of physicists – that shows stunning images with their latest development and you’ll rush off to your own facility’s physicist with the challenge, “Why aren’t we doing this here?”
On the other hand, inertia can become close to insurmountable in fMRI, too. Many studies proceed with particular scan protocols for no better reason than it worked last time, or it worked for so-and-so and he got his study published in the Journal of Excitable Findings and then got tenure at the University of Big Scanners. Historical precedent can be helpful, no doubt, but there ought to be a more principled basis for selecting a particular setup for your next experiment. At a minimum, selection of your equipment, pulse sequence and parameters should be conscious decisions!
But what to do if your understanding of k-space can be reasonably described as ‘fleeting’ and you couldn’t tell a Nyquist ghost from the Phantom of the Opera? Do you blindly trust the paper? Trust your physicist? Succumb to your innate fear of the unknown and resist all attempts at change…? You need a mechanism whereby you can remain the central part of the decision-making process, even if you don’t have the background physics knowledge to critically evaluate the new method. It is, after all, your experiment that will benefit or suffer as a result.
Skepticism is healthy
Let’s begin by considering the psychology surrounding what you see in papers and at conferences. Human nature is to put one’s best foot forward. So start by recognizing that whatever you see in the public domain is almost certainly not what you can expect to get on an average day. Consider published results to be the best-case scenario, and don’t expect to be able to match them without effort and experience. There may be all kinds of practical tricks needed to get consistently good data.
Next, recognize that there is usually no correlation between the difficulty of implementing a method as described in a paper’s experimental section and the actual amount of time and energy it took to get the results. For all you know it may have taken six research assistants working sixty hours a week for six months to get the analysis done. That said, do spend a few moments reviewing the experimental description and look for clues as to the amount of legwork involved. If the method used standard software packages, for example, that’s usually a sign that you could implement the method yourself without hiring a full-time programmer. Custom code? A flag that advanced computing skills and resources may be required.
Okay, at this point we are now in a fit mental state to pour cold, hard logic into this decision-making process. We’re ready to ask some questions of the new method, and to make a direct comparison to the standard alternatives we have available (where ‘standard’ means something that has been well tested and used extensively by your own and other labs).
Sunday, November 21, 2010
A call to publish negative results?
The Journal of Cerebral Blood Flow and Metabolism has taken the brave and, I would argue, constructive step of actively soliciting manuscripts that present negative results. In their words:
If any field could use a forum for negative results it is neuroimaging, and fMRI in particular. We seem to have a bias towards positive results that is second only to pharmaceuticals research. Of course, it is perfectly natural for scientists to want to find something rather than not find it. Not finding an earth-like planet orbiting another solar system isn't nearly as exciting as finding one. And one wonders whether Ponce de Leon would have got tenure at a modern university if his most cited work was entitled "On not finding the Fountain of Youth."
One of the challenges facing fMRI is that it demands extensive statistics or modeling to coax meaning out of tiny signals in an ocean of noise. The convoluted analyses provide skeptics with plenty of ammunition that we are basically making it all up by establishing the test that yields the answer we were looking for. (I have a former colleague - a 'real' MR scientist - who claims dismissively that fMRI stands for fictional MRI.) Without rigorous stats/models, though, it is easy to fall into the trap of false positive errors, a problem that has led to accusations of all sorts of voodoo of its own. How, then, should we treat negative fMRI results? Are there any caveats to encouraging their publication?
A negative result or a bad experiment: what's the difference?
"In addition to original research articles, authors are welcomed to submit Negative Results. The Negative Results article intends to provide a forum for data that did not substantiate the alternative hypothesis and/or did not reproduce published findings."Good for them! With one of the biggest physiology journals getting its act together, what field might be next? How about neuroimaging?
If any field could use a forum for negative results it is neuroimaging, and fMRI in particular. We seem to have a bias towards positive results that is second only to pharmaceuticals research. Of course, it is perfectly natural for scientists to want to find something rather than not find it. Not finding an earth-like planet orbiting another solar system isn't nearly as exciting as finding one. And one wonders whether Ponce de Leon would have got tenure at a modern university if his most cited work was entitled "On not finding the Fountain of Youth."
One of the challenges facing fMRI is that it demands extensive statistics or modeling to coax meaning out of tiny signals in an ocean of noise. The convoluted analyses provide skeptics with plenty of ammunition that we are basically making it all up by establishing the test that yields the answer we were looking for. (I have a former colleague - a 'real' MR scientist - who claims dismissively that fMRI stands for fictional MRI.) Without rigorous stats/models, though, it is easy to fall into the trap of false positive errors, a problem that has led to accusations of all sorts of voodoo of its own. How, then, should we treat negative fMRI results? Are there any caveats to encouraging their publication?
A negative result or a bad experiment: what's the difference?
Friday, November 12, 2010
Towards an optimal protocol for resting state fMRI – part II
A couple of weeks ago I used the results in a paper by van Dijk et al. to provide guidance towards a possible optimal/general protocol for resting state fMRI using EPI. That review concluded with the following rough criteria: whole brain coverage, spatial resolution around 3 mm and temporal resolution in the 2-3 seconds range. The largest of the open questions pertained to the interplay between these three specifications, in particular the ability to obtain whole brain (cortex and cerebellum) coverage in the time available, whilst minimizing (we hope) the dropout and distortion that are ever-present features of EPI.
Experimental details:
In what must be considered a disposable experiment on a single subject (medical types might call this a case study), I acquired test data sets with the following parameters:
Siemens 3 T Trio/TIM running VB15, 12-channel HEAD MATRIX coil, ep2d_bold pulse sequence, TR=2500 ms, TE=25 ms, slice thickness=3 mm, gap=0.3 mm, 43 interleaved slices, matrix=64x64, FOV=224x224 mm (except for one test with 192x192 mm), bandwidth=2056 Hz/pixel, echo spacing=0.55 ms, number of volumes=144, fatsat=ON, MoCo=ON, no spatial filters. (See note 1.)
Experimental details:
In what must be considered a disposable experiment on a single subject (medical types might call this a case study), I acquired test data sets with the following parameters:
Siemens 3 T Trio/TIM running VB15, 12-channel HEAD MATRIX coil, ep2d_bold pulse sequence, TR=2500 ms, TE=25 ms, slice thickness=3 mm, gap=0.3 mm, 43 interleaved slices, matrix=64x64, FOV=224x224 mm (except for one test with 192x192 mm), bandwidth=2056 Hz/pixel, echo spacing=0.55 ms, number of volumes=144, fatsat=ON, MoCo=ON, no spatial filters. (See note 1.)
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