In this edition of FOCUS In Sound, we focus on a dynamic scientist from UCLA who has been recognized in the past by the Burroughs Wellcome Fund, and we’ll see how that recognition has had a profound effect on her work, her career, and her scientific contributions.
Transcription of “Interview with Ariana Anderson”
00;00;02;02 – 00;00;32;26
Ernie Hood
Welcome to Focus In Sound, the podcast series from the Focus newsletter published by the Burroughs Wellcome Fund. I’m your host, science writer Ernie Hood. In this edition of Focus In Sound, we focus on a dynamic young scientist from UCLA who has been recognized in the past by the Burroughs Wellcome Fund, and we’ll see how that recognition has had a profound effect on her work, her career and her scientific contributions.
00;00;33;15 – 00;01;06;07
Ernie Hood
Dr. Ariana Anderson is an assistant professor in the Department of Psychiatry and Biobehavioral Sciences at UCLA, where she also received herbs and Ph.D. degrees. She is also principal investigator and director of the UCLA Laboratory of Computational Neuropsychology. In 2014, she received the Burroughs Wellcome Fund career Award at the Scientific Interface, a $500,000 grant to fund her work over a five year period.
00;01;06;26 – 00;01;29;14
Ernie Hood
She was given the Cassie specifically to support her research on the placebo effect. We will hear all about that. But Dr. Anderson has used the funding along with her K 25 career award from the National Institute on Aging to pursue a variety of important scientific endeavors. Ariana Anderson, welcome to Focus In Sound.
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Ariana Anderson
Thank you. It’s a pleasure to be here with you.
00;01;31;23 – 00;01;52;27
Ernie Hood
I know you are the proud mother of four children, but I’d like to start our conversation with the venture you have referred to as your fifth child, the free app you’ve developed and released called Chatter Baby, which is available at Chatter Baby Dawg. Tell us about this app designed to measure and interpret infants Cries.
00;01;53;15 – 00;02;15;26
Ariana Anderson
Chatter Babies is an app that we developed for two purposes. The first purpose of the app is to help parents understand what their baby needs. Now, there’s a long history of scientific literature going back about 50 years that looks at the differences in infant cries associated with not just with different states. So, for example, a baby in pain cries differently, but also looking at markers of neurodevelopmental disorders.
00;02;16;15 – 00;02;36;03
Ariana Anderson
So, for example, some of the earliest work found that babies with bacterial meningitis, babies with Down’s syndrome, babies with epilepsy may show different patterns of their cries than babies who are neurocognitive bleed intact. What we wanted to do do was to see whether or not we could develop an app that would, first of all, help parents predict what was wrong with their child at that moment.
00;02;36;03 – 00;02;49;19
Ariana Anderson
Is baby fussy, hungry or in pain? But second of all, collect infant cry data for another purpose, which is to see whether long term babies with abnormal cry patterns become more likely to get a later developmental disorder such as autism.
00;02;50;09 – 00;02;55;12
Ernie Hood
So how did you develop and train the artificial intelligence powered algorithm?
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Ariana Anderson
Well, like any algorithm, what we needed was lots of data. So we collected almost 2000 total audio samples of babies. Now, these babies were either laughing neutral, or they were crying from a stimulus, which was labeled by the mother and also an expert mom panel who went checked it over. So, for example, we got painful cries from babies who were either getting vaccinated or getting their ears pierced.
00;03;18;17 – 00;03;34;22
Ariana Anderson
The other cries like hungry or fussy or scared or tired were things that were nominated by the parents. And then we had a mom panel go through all of those other cries and say, No, that baby doesn’t sound very hungry to me. So if all of the mom panel did not agree on the label, the cry, it was excluded from our study.
00;03;35;11 – 00;03;56;27
Ariana Anderson
With those labeled cries, we use standard speech recognition technology. So we extracted about 6000 different acoustic features from each cry. So these were things like the energy, the frequency, the different melodies and prosodic patterns that were existing. And we use these to classify and predict on new cries what the baby’s cry reason was AI fever.
00;03;56;27 – 00;04;09;20
Ernie Hood
Very good. That must have been a very exciting undertaking. How can both deaf and hearing parents use Chatter baby, to help understand what their babies are trying to tell them in their vocalizations?
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Ariana Anderson
Chatter Babies A free app that’s available on Google Play and also on the Apple Store. When you download Chatter, baby, you send a five second audio sample to our servers where we run our machine learning algorithms on it. It returns to you a probability of fuzzy, hungrier pain. Much like a weather report. And it allows you then to interpret based on the results the most likely reason for your baby’s cry.
00;04;32;28 – 00;04;37;17
Ernie Hood
I see. And what’s the application for particularly for deaf parents?
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Ariana Anderson
Deaf parents and hearing parents both have the same need. Why is my baby crying? So it actually works exactly the same. Now we’d like to continue in the future to make this something that we can use for remote monitoring. We’re trying to figure out now how to, for example, set this up similar to an Alexa, where it hears a baby crying.
00;04;55;27 – 00;05;11;01
Ariana Anderson
It will be able to notify the parent in a different area of the house. So we’re working now on expanding our algorithms and also integrating them into remote monitoring systems so that deaf parents can be notified, for example, when their baby’s in another room, whether or not their baby’s crying, and if so, why?
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Ernie Hood
Ariana, you’ve had this project going since 2013. Is the artificial intelligence learning more and more as it goes?
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Ariana Anderson
Absolutely. So what we do with this is that our app is also a method of collecting data. So before when we were collecting data manually, we only had a smaller sample to work with. But now that we have large data sample, we have a few hundred thousand baby cries to work on. We’re using deep learning algorithms to identify what the patterns really are.
00;05;38;00 – 00;05;54;14
Ariana Anderson
The deep learning just gives us a better idea of how to classify these babies and whether or not whether or not the baby cries depends on things like the age or the nationality or any other underlying medical condition that the baby might have. So because we have a big data set, we’re able to identify better what the baby needs.
00;05;54;14 – 00;05;59;03
Ariana Anderson
And we’re also able to use more sophisticated deep learning algorithms to pursue these objectives.
00;05;59;17 – 00;06;08;29
Ernie Hood
Tell us about the application you’ve been working on to use Chatter Baby to help identify infants who may be at risk of being on the autism spectrum.
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Ariana Anderson
We’ve seen some really wonderful small sample. Studies show that babies who are at risk for autism, so babies who have an older sibling with autism are they show different cry patterns. So if a baby is a year old or even 18 months old, someone can listen to that baby cry and see that that doesn’t sound right. It just sounds a regular sounds a little bit off.
00;06;28;04 – 00;06;52;12
Ariana Anderson
It doesn’t have the same tone as a typical baby. Now, these are wonderful studies. They’re very strong evidence, but they’re based on small samples. What we’re doing with chatter, baby, is we’re not just collecting infant cries, but works, collecting extensive developmental history. So we ask parents questions about the pregnancy, about any sort of genetic risk, whether or not there’s a sibling, the family’s autism, whether or not the baby had a difficult delivery.
00;06;52;12 – 00;07;10;01
Ariana Anderson
A number of risk factors that we know may be associated with increased autism risk. After that, we follow the babies for six years, starting at age two, we send them screeners for autism using standard instruments that calculate whether or not their child is at higher risk for autism, or we continue to follow them until they’re six years old.
00;07;10;26 – 00;07;33;21
Ariana Anderson
Then we’ll be able to go back and look at all this wealth of data we’ve collected and identify whether or not the children who got later diagnosed with autism had the abnormal vocal patterns early. Now, we don’t just have to look at vocal patterns. We’re also looking at other things. So, for example, whether or not there was a problem with the baby’s delivery, whether or not the baby was premature, whether or not they spent time in the nick, whether mom use drugs during pregnancy.
00;07;34;03 – 00;07;41;20
Ariana Anderson
We’re collecting a variety of risk factors that we can use later on, and the vocal patterns are going to just be one of the many clues we’re able to assess.
00;07;42;11 – 00;07;51;27
Ernie Hood
That sounds very exciting, and I’m sure it’s going to yield some important information going forward. How successful has Chatter, baby been up to this point?
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Ariana Anderson
We’ve been very successful in attracting a wide user base. We have been featured in medium major media outlets in all countries around the world. So, for example, just recently we’re in the biggest newspaper in Lebanon. Now, because of this, we’re able to get a variety of data sources which allow people to get a variety of participants across the world that we wouldn’t be able to get if we were running a local study within our lab at UCLA.
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Ariana Anderson
We think the main advantage of being able to implement the study by launching an app is attracting a large user base. We’re providing a free service for people across the world who would never have access to come into UCLA laboratory otherwise.
00;08;29;05 – 00;08;54;16
Ernie Hood
Ariana in your capacity as P.I. and director of the Laboratory of Computational Neuropsychology, you’ve led several other important projects over the last few years. I’d like to hear about all of them, starting with your research related to the placebo effect. That’s a fascinating area that has been crying out for elucidation. And I understand your research is designed to also aid the drug development process.
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Ernie Hood
Tell us more.
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Ariana Anderson
The placebo response is one of the biggest problems in developing new drugs. And the reason for this is that when drug trials are instituted, everyone gets a pill, but they don’t know what it is. So that means the placebo response is actually operating within people receiving a medication. So you have this very powerful effect that is trying to compete with the effects of an active drug.
00;09;16;10 – 00;09;35;01
Ariana Anderson
And it’s also very noisy. So it’s hard for us to tell whether a change is due to a drug or the placebo effect within people. And it’s hard to discriminate between active and inactive medications because of that. What we are trying to do is we are trying to use multiple measurements to assess and identify and control the effects of the placebo.
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Ariana Anderson
Now we’re doing this in a few ways. The first way we’re doing it is we’re using brain imaging. We’re you’re looking at drug studies of people who have received medication before and after, and the medication they might have received might be an active medication or it might be a sham one. And we’re trying to identify whether or not they’re these brain changes that are specific to receiving a placebo pill and whether or not there’s what changes look like they happen just because someone’s getting treated in general.
00;09;59;11 – 00;10;10;14
Ariana Anderson
If we can measure these different components of the placebo response, then we can identify whether or not these placebo components are affecting the drug outcome. And that’s what we were trying to do in our brain imaging research.
00;10;10;27 – 00;10;22;05
Ernie Hood
You’ve also had great success in using electronic medical records and data mining to create new detection algorithms for diabetes screening. How does that work?
00;10;22;21 – 00;10;40;16
Ariana Anderson
Normally, diabetes risk assessment only looks at a few different pieces of information to identify whether or not people might be high risk. So these may be things like age. It might be your BMI, it might be your gender and perhaps ethnicity. However, we know that there is a wealth of information that’s collected when you go to the doctor.
00;10;40;24 – 00;11;00;29
Ariana Anderson
We have for example, how long you’ve been a patient, how many medications you’re taking, what other diagnoses you might already have, whether or not you have hypertension, the variety of information that we believe can help better assess and better predict whether or not someone’s likely to have diabetes. Now, this is an important problem because one in four people with diabetes don’t know that they have it.
00;11;00;29 – 00;11;18;12
Ariana Anderson
And oftentimes they don’t figure out they have it until they have some horrible complication of it. So, for example, someone might have just blurry eyes and tingling skin and they will completely ignore it. Then they might go to the doctor later because they have a sore on their foot that doesn’t heal. And they’ll figure out that they have some form of gangrene because it’s a complication of diabetes.
00;11;18;25 – 00;11;48;02
Ariana Anderson
So oftentimes people with diabetes don’t find out they have it until they have some major complication that requires hospitalization. One of the most expensive parts of diabetes isn’t actually treating the disease, is treating the complications that come from it, especially when it’s not managed. And you can’t manage a disease that you don’t know that you have. What we are trying to do is trying to use electronic medical records to automatically calculate risk of diabetes so clinicians can know whether or not that person needs to be screened using all the available information.
00;11;48;21 – 00;12;16;14
Ariana Anderson
Now, our past work showed that people who are at risk for diabetes have different features and electronic medical records that go far beyond this basic information. So, for example, if you have a history of high blood pressure, that is a risk factor. But then there’s also a bunch of other risk factors that you wouldn’t expect. So, for example, if you have bacterial infections, that might be another risk factor for diabetes that we didn’t know about before that we could use to increase your risk of meeting a diabetes screening.
00;12;17;11 – 00;12;42;20
Ariana Anderson
Now, what we’re actually doing with this project, since we published our first papers, we’re trying to extend this to major psychiatric disorders. So, for example, people with schizophrenia are more likely to have diabetes based on both genetic risk and the medications they’re taking. So what we’re trying to do with our medical records here at UCLA and throughout the UC system is we’re creating new screening algorithms for diabetes that are intended for people with psychiatric disorders.
00;12;43;06 – 00;13;08;04
Ariana Anderson
These people are the ones who are at highest risk for psychiatric disorders from many different factors, but also they’re the people who are most likely to not have reliable contact with the medical community other than seeing a psychiatrist. So we’re making a tool that psychiatrists can use to automatically assess whether or not the person needs to be screened for diabetes, given that they’re on a variety of medications for mood and given that they probably have a genetic likelihood of already having it.
00;13;08;18 – 00;13;37;07
Ernie Hood
Well, Arianna, it’s so interesting that your work in computational neuropsychology seems to focus on being able to detect and predict various conditions early on. Another example is your work on early prediction of cognitive decline in Alzheimer’s disease, vascular dementia and other neurocognitive disorders. Fill us in on how you’ve been developing that aspect, which is what actually helped you get the NIA Grant I mentioned earlier.
00;13;37;19 – 00;13;58;27
Ariana Anderson
When people think of dementia, they often think of Alzheimer’s disease. However, there’s vascular dementia, which is the second leading cause of memory impairment in older adults. Now, vascular dementia can mean that you have risk factors for strokes, but it also means that there is problems with your vascular system, your vascular compliance that leads to you basically being a bit slower in processing and responding to information.
00;13;59;15 – 00;14;23;05
Ariana Anderson
So what we are doing is we’re using functional MRI to look at the hemodynamic response. How does your blood flow respond when you, for example, are thinking of something, when you’re seeing an image of something? And we’re finding out that the pattern of US response can predict whether or not you’re having memory issues above and beyond, for example, how many years you went to school or your age, your ethnicity or socioeconomic status.
00;14;23;20 – 00;14;42;10
Ariana Anderson
So these actual patterns you see in vascular responses may indicate that you’re having already some sort of cognitive problems that are caused not by, for example, the typical plaques and tangles, but just caused by vascular issues. So vascular help can determine cognitive ability early on. It’s an early marker of cognitive problems.
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Ernie Hood
Tell us about your project related to prison violence at UCLA.
00;14;47;20 – 00;15;07;14
Ariana Anderson
We’re also interested in the social outcomes. So, for example, many people who have mental health issues might end up in the prison system. We’re interested in finding out how we can look at different interventions and whether or not they might be effective, for example, for reducing violence in prison, for reducing recidivism. So I work closely with an organization called Data.gov.
00;15;07;24 – 00;15;29;07
Ariana Anderson
It’s a collaboration between NYU and UCLA, where we’re looking at how to do these real time interventions. How do you implement these, uhm, these trials to judge whether or not these interventions that are being implemented in prisons actually are effective in reducing violence and helping outcomes in reducing stress among prison staff, for example?
00;15;29;27 – 00;15;42;07
Ernie Hood
Last but not least, Ariana, I wanted to be sure to ask you about the Cathy Award from the Burroughs Wellcome Fund. What has been the lasting impact of receiving that award back in 2014?
00;15;42;21 – 00;16;05;14
Ariana Anderson
The Cassie Award for me has been the freedom to pursue projects that I believe are high impact that might not yet have funding implemented. So for example, there are many projects that we have to do or they want to do as scientists, but when we want to do it, we have to write a grant to do it. It’ll take two years or three years to not just write the grant, but get accepted and have funding in the bank because the grant cycle is so slow.
00;16;05;29 – 00;16;31;21
Ariana Anderson
Those are three years we could have spent writing the paper. We could have had the work done in the first year if we only had the funding to begin it. Because of the Burroughs funding. I’ve been able to hire staff to help me with these projects to get out all of these studies and these ideas that I have and be one of the first to actually implement them without this funding, we wouldn’t have the flexibility to pursue the variety of research projects and we would have a backlog because we’d be waiting for the projects to get funded before the work could actually begin.
00;16;32;02 – 00;16;34;10
Ernie Hood
So it’s been kind of an accelerator or then.
00;16;34;19 – 00;16;48;06
Ariana Anderson
Absolutely. I’m not spending three years trying to get money to start the work. I can go ahead and do it right away. It’s been a much more efficient use of my time to pursue these projects this way, and that’s why we’re able to get this work done quickly.
00;16;48;22 – 00;16;58;11
Ernie Hood
Ariana, it’s been great speaking to you and please keep up the very impressive body of work you are engaged in. Thanks for joining us here on Focus In Sound.
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Ariana Anderson
Thank you very much for having me. It’s been a pleasure.
00;17;02;16 – 00;17;12;14
Ernie Hood
We hope you’ve enjoyed this edition of the Focus In Sound podcast. Until next time. This is really good. Thanks for listening.
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