Research Roundtable: Perspectives on a New Technology – TRUUST Neuroimaging
Scientist
on
April 9, 2020
The Background
In this blog series, we’ll be exploring new technologies offered on Scientist.com by some of our most innovative suppliers. While these tools may already have a defined track, applying different perspectives can radically change the way we view technology and innovation. In this installment of Research Roundtable, we’ll be discussing a new offering from TRUUST Neuroimaging.
The Tech
TRUUST Super Resolution is a breakthrough method for analyzing neurological data. Whether it’s gathered from EEG or MEA data, TRUUST can help researchers see brainwave data in a novel way. The solution utilizes pioneering technology developed in Near-Field Electromagnetic Holography analysis, providing for 10+x more accurate evaluation of brain wave activity. This new form of multi-level, electrophysiological, dynamic analysis can better interpret what is occurring inside the brain when a given subject is exposed to various stimuli.
The Roundtable
Meaghan Loy, MS, ALAT (The Interviewer) Category Director, in vivo Services
With a background in the vivarium and animal handling, Meaghan supports in vivo requests on the Scientist.com platform and works with clients to refine their Animal Welfare policies and procedures.
Brian Dalby, PhD Senior Director/Research Concierge
Brian specializes in supporting client requests and has a deep background in genetics and cell biology. He has been with Scientist.com for over five years.
Romila Mukerjea, PhD Director of Research Concierge and Discovery Sciences
With a background in protein biochemistry and recombinant antibody development, Romila handles technical aspects of requests on Scientist.com as well as managing the discovery services research areas.
Maria Negron Category Director, HEOR & RWE
Maria has a background in biomedical engineering and spends her time at Scientist.com supporting client requests and expanding our RWE/HEOR service offerings.
The Discussion
What’s the first thing that strikes you about this technology?
Romila: There are quite a few medical programs that are using patient information and modeling to get a better understanding of how the brain will respond to drugs. You can’t prove somebody has Alzheimer’s until you see their brain after death. This technology could also lead to better titers for drugs to provide an accurate response. There are some medications where this is critical, such as those for bipolar disorder. You could also determine dosages and avoid severe side effects.
Brian: Would there be applications in looking at cognitive decline in patients? You could gather data on people who have undergone cognitive testing and see whether they go on to develop Alzheimer’s or dementia. This could potentially help to define care and slow progression of disease. We see a lot of work on the platform relating to neurodegenerative diseases, so this is a hot area to work in right now.
Maria: The concept of knowing what’s going on inside the brain is really interesting. I think the bioethics of this could be explored more deeply for certain applications, but for medical research I think the benefits could be quite impactful.
How would you apply this in your field of expertise?
Romila: This would fit into the field of data sciences very well. There’s a huge amount of data that could come from this technology and possibly be used for artificial intelligence or machine learning. The more data sets you get, the better you could calculate drug titers and dosages. It would be really interesting to see if you look at off-target effects and then look at a corresponding in vitro assay to compliment it. We don’t actually know what all the neural networks are doing in patients and what’s activated and deactivated. This could give us a window into those networks.
Brian: My field of expertise is very similar to Romila’s. I think the ability to gather important information about neurological conditions could be critical to understanding the disease progression and arresting the symptoms early, if not reversing them.
Maria: My area of expertise involves research surrounding in vitro diagnostics. Biomedical engineering has several different routes including patient research or academic research. Within the medical device field, you could utilize this technology in testing drug efficacy, or the technology could be incorporated into a stand-alone medical device. It would also be possible to use this for sales and marketing where you could see large marketing gains from commercialization. It could predict how people react to a particular stimulus by anticipating how the brain reacts to a range of different things like medications, elections, advertisements and more.
Do you have any predictions for this technology in the next 5-10 years?
Romila: There’s a terrible feeling of hopelessness amongst patients with Alzheimer’s and their families. If there’s an opportunity here to get more information on what’s going on, backtrack pathways and figure out what’s happening, you could come up with a new class of drugs. You could use this technology to help find that drug or even a cure.
Brian: I could see this technology being used in the political arena. Polling voters may not give you accurate results, but you could correlate this data with responses to show what’s really happening and find interesting correlations.
Maria: I would push for research use but the most lucrative would be marketing and data. If the technology is powerful in that sense, I could envision it taking off.
The Source
TRUUST gives us their perspective on their technology.
What is so interesting about this technology?
Data quality is really the key aspect. TRUUST greatly improves data quality from existing data collection equipment, first in terms of spatial resolution, and second in terms of looking at the most revealing measures (neural electromagnetic energy flow). These two aspects combine to provide insights that would otherwise be invisible.
How can this technology be applied?
The great thing about TRUUST’s measures is that they are translational across the dish, the lab and human trials. This means great improvements in, for instance, cohort selection for trials, making sure participants are homogeneous, that the same indication is actually present, and that MEA or EEG drug-response predictions are more accurate. It also means that more uncertainty can be eliminated earlier in the pre-clinical stage with more cost-effective data collection methods like MEA before moving to trials.
Do you have any predictions for this technology in the next 5-10 years?
The great promise of TRUUST’s technology is really greater understanding of the brain and brain-related health problems. We predict that enhanced visibility into brain data will provide a paradigm-shift in our understanding of how the brain works — with many important applications from brain health (diagnosis, drug development and brain stimulation) to artificial intelligence (prediction, neuromorphic computing and brain-computer interfaces).
The Wrap Up
Differing perspectives has always been one of the things I’m most interested in when it comes to new ideas and technologies. My first thought upon learning about TRUUST’s technology was how it could be applied to animal studies. Would it be possible to monitor animals as they undergo abuse and dependency studies and use that data to reduce animal numbers? Or, could this technology be utilized early on to spot neurological side-effects or efficacy and thus halt compound testing early if indicated?
My colleagues, however, had different initial reactions that varied based on their background and day-to-day activities. All of these ideas were slightly different from TRUUST’s intended application. Whether all of these applications are possible remains to be discovered, but if you are interested in learning more about TRUUST, please visit their profile on Scientist.com to start a discussion. — Meaghan Loy