So there’s loads of different neuroimaging methods out there that are used depending on what it is you’re looking for! I’ve had the privilege of actually studying it and there’s so so many different types more than just functional MRI that people don’t really know about so here are a few and what they’re used for an how they work.
MRI - Magnetic Resonance Imaging
The most commonly used form of neuroimaging and for good reason. MRI uses the body’s tissue density and magnetic properties of water to visualise structures within the body. It has really incredible spatial and temporal quality and is predominantly used in neuroscience/neurology for looking for any structural abnormalities such as tumours, tissue degeneration etc. It’s fantastic a fantastic form of imaging and is used in numerous amounts of research.
Functional MRI (fMRI)
These images are captured the same way as MRI but the quality is a little bit lower because the aim is to capture function (those blobs you can see) as quickly and accurately as possible so the quality is compromised a little bit. Nonetheless, fMRI usually uses the BOLD response to measure function. It measures the amount of activity in different areas of the brain when doing certain things, so during a memory test for example, and it does that by measuring the amount oxygen that a certain area requires. The increased oxygen is believed to be sent to an area where there is more neuronal activity, so it’s not a direct measurement but rather we’re looking at a byproduct. There are numerous studies trying to find the direct link between the haemodynamic response and neuronal activity, particularly at TUoS (where I’m doing my masters!) but for the moment this is all we have. This sort of imaging is used a lot for research and checking the general function of the brain, so if you were to have had surgery on your brain, they may run one of these just to see which areas might be affected from it and how, or in research we’ve used this a lot to research cognition - which areas are affected during certain cognitive tasks (ie my MSc thesis - Cognition in schizophrenia and consanguinity).
Diffusion Tensor Imaging (DTI)
This is my current favourite type of NI right now! DTI is beautiful, unique and revolutionary in this day and age, it’s almost like sci-fi stuff! DTI measures the rate of water diffusion along white matter tracts and with that calculates the directions and structural integrity of them to create these gorgeous white matter brain maps. They are FANTASTIC for finding structural damage in white matter - something that is making breakthroughs in research lately ie. schizophrenia, genetics and epilepsy. It measures the rate of diffusion which tells you about possible myelin/axonal damage and anisotropy, so the directions and if they are “tightly wound” or loosely put together - think of it like rope, good FA is a good strong rope, poor FA is when it starts to fray and go off in different directions - like your white matter tracts. My current research used DTI and it was honestly surreal to work with, the images are also acquired through an MRI scanner so you can actually get these images the same time you’re getting MRI’s done, functional or otherwise!
Positron Emission Tomography (PET)
One of the “controversies” (if you could call it that) is the use of radioactive substances in PET scanning. It requires the injection of a nuclear medicine to have the metabolic processes in your brain light up like Christmas! It uses a similar functional hypothesis to BOLD fMRI, in that it is based on the assumption that higher functional areas would have higher radioactivity and that’s why it lights up in a certain way. It depends on glucose or oxygen metabolism, so high amounts of glucose/oxygen metabolism would show up red and less active areas would show up blue, perfect for showing any functional abnormalities in the overall brain. However it has incredibly poor temporal resolution and due to it’s invasive nature, MRI is chosen more often. (The pictures are gorgeous though!)
These are not “imaging” types in the stereotypical sense. They create a series of waves that you can physically see (think of the lines you get on a lie detector!). Electrodes/Tiny magnets are placed on the scalp/head in specific areas corresponding to certain brain structures. EEG picks up on electrical activity which is the basis of neuronal function, whereas MEG picks up on magnetic fields - the same property that is utilised by MRI. One of the biggest issues with EEG is that deeper structures passing through tissues get distorted, whereas MEG doesn’t because it only measures the magnetic properties. I’ve not had a lot of experience with either of these but I do know EEG is used in a lot of medical procedures to measure brain activity, from measuring seizures and sleep disorders to measuring brain activity in a coma. It’s fantastic and if you can actually figure out how to conduct and interpret results it’s an invaluable tool into looking at electrical activity.
Brain Waves Can be Used to Detect Potentially Harmful Personal Information
Cyber security and authentication have been under attack in recent months as, seemingly every other day, a new report of hackers gaining access to private or sensitive information comes to light. Just recently, more than 500 million passwords were stolen when Yahoo revealed its security was compromised.
Securing systems has gone beyond simply coming up with a clever password that could prevent nefarious computer experts from hacking into your Facebook account. The more sophisticated the system, or the more critical, private information that system holds, the more advanced the identification system protecting it becomes.
Fingerprint scans and iris identification are just two types of authentication methods, once thought of as science fiction, that are in wide use by the most secure systems.
But fingerprints can be stolen and iris scans can be replicated. Nothing has proven
foolproof from being subject to computer hackers.
“The principal argument for behavioral, biometric authentication is that standard modes of authentication, like a password, authenticates you once before you access the service,” said Abdul Serwadda a cybersecurity expert and assistant professor in the Department of Computer Science at Texas Tech University.
“Now, once you’ve accessed the service, there is no other way for the system to still know it is you. The system is blind as to who is using the service. So the area of behavioral authentication looks at other user-identifying patterns that can keep the system aware of the person who is using it. Through such patterns, the system can keep track of some confidence metric about who might be using it and immediately prompt for reentry of the password whenever the confidence metric falls below a certain threshold.”
One of those patterns that is growing in popularity within the research community is the use of brain waves obtained from an electroencephalogram, or EEG. Several research groups around the country have recently showcased systems which use EEG to authenticate users with very high accuracy.
However, those brain waves can tell more about a person than just his or her identity. It could reveal medical, behavioral or emotional aspects of a person that, if brought to light, could be embarrassing or damaging to that person. And with EEG devices becoming much more affordable, accurate and portable and applications being designed that allows people to more readily read an EEG scan, the likelihood of that happening is dangerously
“The EEG has become a commodity application. For $100 you can buy an EEG device that fits on your head just like a pair of headphones,” Serwadda said. “Now there are apps on the market, brain-sensing apps where you can buy the gadget, download the app on your phone and begin to interact with the app using your brain signals. That led us
to think; now we have these brain signals that were traditionally accessed only by doctors being handled by regular people. Now anyone who can write an app can get access to users’ brain signals and try to manipulate them to discover what is going on.”
That’s where Serwadda and graduate student Richard Matovu focused their attention: attempting to see if certain traits could be gleaned from a person’s brain waves. They presented their findings recently to the Institute of Electrical and Electronics Engineers (IEEE) International Conference on Biometrics.
Brain waves and cybersecurity
Serwadda said the technology is still evolving in terms of being able to use a person’s brain waves for authentication purposes. But it is a heavily researched field that has drawn the attention of several federal organizations. The National Science Foundation (NSF), funds a three-year project on which Serwadda and others from Syracuse University and the University of Alabama-Birmingham are exploring how several behavioral modalities, including EEG brain patterns, could be leveraged to augment traditional user authentication mechanisms.
“There are no installations yet, but a lot of research is going on to see if EEG patterns could be incorporated into standard behavioral authentication procedures,” Serwadda said.
Assuming a system uses EEG as the modality for user authentication, typically for
such a system, all variables have been optimized to maximize authentication accuracy. A selection of such variables would include:
The features used to build user templates.
The signal frequency ranges from which features are extracted
The regions of the brain on which the electrodes are placed, among other variables.
Under this assumption of a finely tuned authentication system, Serwadda and his colleagues tackled the following questions:
If a malicious entity were to somehow access templates from this authentication-optimized system, would he or she be able to exploit these templates to infer non-authentication-centric
information about the users with high accuracy?
In the event that such inferences are possible, which attributes of template design could reduce or increase the threat?
Turns out, they indeed found EEG authentication systems to give away non-authentication-centric information. Using an authentication system from UC-Berkeley and a variant of another from a team at Binghamton University and the University of Buffalo, Serwadda and Matovu tested their hypothesis, using alcoholism as the sensitive private information which an adversary might want to infer from EEG authentication templates.
In a study involving 25 formally diagnosed alcoholics and 25 non-alcoholic subjects, the lowest error rate obtained when identifying alcoholics was 25 percent, meaning a classification accuracy of approximately 75 percent.
When they tweaked the system and changed several variables, they found that the ability to detect alcoholic behavior could be tremendously reduced at the cost of slightly reducing the performance of the EEG authentication system.
Motivation for discovery
Serwadda’s motivation for proving brain waves could be used to reveal potentially harmful personal information wasn’t to improve the methods for obtaining that information. It’s to prevent it.
To illustrate, he gives an analogy using fingerprint identification at an airport. Fingerprint scans read ridges and valleys on the finger to determine a person’s unique
identity, and that’s it.
In a hypothetical scenario where such systems could only function accurately if the
user’s finger was pricked and some blood drawn from it, this would be problematic because the blood drawn by the prick could be used to infer things other than the user’s identity, such as whether a person suffers from certain diseases, such as diabetes.
Given the amount of extra information that EEG authentication systems are able glean about the user, current EEG systems could be likened to the hypothetical fingerprint reader that pricks the user’s finger. Serwadda wants to drive research that develops EEG authentication systems that perform the intended purpose while revealing minimal information about traits other than the user’s identity in authentication terms.
Currently, in the vast majority of studies on the EEG authentication problem, researchers primarily seek to outdo each other in terms of the system error rates. They work with the central objective of designing a system having error rates which are much lower than the state-of-the-art. Whenever a research group develops or publishes an EEG authentication system that attains the lowest error rates, such a system is immediately installed as the reference point.
A critical question that has not seen much attention up to this point is how certain design attributes of these systems, in other words the kinds of features used to formulate the user template, might relate to their potential to leak sensitive personal information. If, for example, a system with the lowest authentication error rates comes with the added baggage of leaking a significantly higher amount of private information, then such a system might, in practice, not be as useful as its low error rates suggest.
Users would only accept, and get the full utility of the system, if the potential privacy breaches associated with the system are well understood and appropriate mitigations undertaken.
But, Serwadda said, while the EEG is still being studied, the next wave of invention is already beginning.
“In light of the privacy challenges seen with the EEG, it is noteworthy that the next
wave of technology after the EEG is already being developed,” Serwadda said. “One
of those technologies is functional near-infrared spectroscopy (fNIRS), which has a much higher signal-to-noise ratio than an EEG. It gives a more accurate picture of brain activity given its ability to focus on a particular region of the brain.”
The good news, for now, is fNIRS technology is still quite expensive; however there is every likelihood that the prices will drop over time, potentially leading to a
civilian application to this technology. Thanks to the efforts of researchers like Serwadda, minimizing the leakage of sensitive personal information through these technologies is beginning to gain attention in the research community.
“The basic idea behind this research is to motivate a direction of research which
selects design parameters in such a way that we not only care about recognizing users
very accurately but also care about minimizing the amount of sensitive personal information it can read,” Serwadda said.
EEGs use electrodes outside the brain, and look like this (image source):
The electrodes mean that EEGs give us good time resolution but poor spatial resolution; by contrast, MRIs give us good spatial resolution but poor time resolution, so they’re useful for investigating different questions.
A very un-glam hospitalglam from my sleep deprived EEG. I stayed up all night and then went to have lights strobed in my face and the results weren’t pretty. I’ll have to wait for an appointment with my neurologist for them to confirm whether the symptoms I experienced were epileptic seizures or something else but let’s just say the sleep deprivation, hyperventilation and photic stimulation all did a good job of making me feel extremely out of control of my body and disoriented. So this goofy photo is all I have whilst I process the fears that I had until this morning been able to dismiss with “well the neuro doesn’t really believe I have epilepsy so I must not have it”.
The electroencephalogram (EEG) is based on electrical recordings taken from the scalp. It was first used by Hans Berger over 65 years ago. Electrodes placed on the scalp pick up very small changes in electrical activity within the brain. These changes are shown on a computer screen and can also be printed out. The pattern of changes is sometimes referred to as “brain waves”.
The EEG has proved useful in many ways. For example, it has been found that there are five stages of sleep, varying in terms of the depth of sleep and the presence or absence of dream activity. These stages differ in terms of the EEG record, and EEG research was crucial in identifying these stages. It has also proved useful in the detection of epilepsy, damaged brain tissue, and the location of tumors by abnormal changes in brain wave patterns.
The EEG has further been of value in identifying the functions of the two hemispheres of the brain. There is more activity in the left hemisphere than in the right hemisphere when someone is carrying out a language-based task. However, the opposite is the case during the performance of a spatial task.
However, there are also various limitations to the use of the EEG, such as the fact that it measures electrical activity in several areas of the brain at once, and so it is hard to work out which parts of the brain are more active, and which are less active. It is also an indirect measure of brain activity because the recording electrodes are on the scalp. The EEG has been compared to trying to hear what people are saying in the next room by putting your ear to the wall.
“…. which would then make the initial velocity 40 meters per second, at theta equals 30 degrees…” Your physics professor rambles on and on, scribbling diagrams here and there on the smart-board. You sit all the way back in the lecture hall, avoiding the cluster of future Issac Newtons up ahead. Your empty notebook is withering away on the desk.
The doors behind you open, and a boy sneaks in, slowly shutting the door as it creaks. You check the time on your phone and try not to give him a judgmental look when he settles down next to you. How is he 45 minutes late? He might as well have stayed home… You try to mind your own business and stare at your blank notebook.
Well into the lessons, you feel a tap on your lower arm.
Where in the brain is language located, and how do we know? In this week’s episode, The Ling Space talks about neurolinguistics, the two main areas in the brain that are in charge of language, and two different neuroimaging techniques we use to look at where and when the brain does all its linguistic magic.
We tried a bunch of new things for this one, so we’re looking forward to seeing what people think! Happy Halloween. ^_^