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BRAIN Initiative 2.0 RFI due May 15th

posted Apr 30, 2019, 2:26 PM by Bionic Lab   [ updated May 2, 2019, 6:13 AM ]

What you need to know about Phase 2 BRAIN RFI (Request for Information).

The BRAIN Initiative was a 2013 White House initiative to support better understanding the brain. The Budget for the BRAIN Initiative has also expanded with recent bipartisan legislation securing funding to at least 2026 with an operational budget greater than NIBIB. There were some politics involved, but ultimately a decision was made to use the money in phase 1 to largely support brain related proposals that struggled in traditional mechanisms and traditional review panels. These ended up being grants that were engineering based (design driven proposals) as opposed to classical neuroscience hypothesis driven proposals that are funded through traditional mechanisms under NIBIB, NINDS, NIMH, NIA, NIE, etc (eg. parent R01s)

BRAIN Initiative Phase 1 is closing to an end, they the committee/working group is trying to pick a direction for Phase 2. There is considerable internal pushback against technology development and a push for funding “circuit function” research (ie research that is already funded through traditional mechanisms). However, thanks to your feedback during the first RFI, the ACD has recommended continued funding for neurotechnologies summarized here: https://www.braininitiative.nih.gov/strategic-planning/acd-working-group/brain-research-through-advancing-innovative-neurotechnologies

That said, there remains a major gap between where we stand and supporting "Technology Development", which is not addressed under current BRAIN Initiative Mechanisms and not acknowledged in the current update. For example, there is very little "Basic Science of Interface Biology" research to help guide/inform technology development.

Why?

1)      Neural Interface research is not represented on the working group panel nor the leadership of the BRAIN Initiative. The major representation is made up of neuroscientists despite “The overarching vision of the BRAIN Initiative is best captured by Goal #7 (New Technology)”

2)      Effective increases to pay lines: by removing support for grants that struggle in normal review/study sections (ie technology development), it frees up money. Then, by supporting BRAIN grants that already have standard mechanisms (circuit function parent R01s) through the BRAIN Initiative, it frees up money in the traditional mechanisms (ie parent R01s). This effectively raises the pay line for standard circuit function grants.

Lessons from 2017 GSI

The NIH announced the “Grant Support Index (GSI)” on May 2, 2017 to support new investigators (average age of 1st R01 has shifted from 35 (1980) to 47 (2017)). While I’ve been fortunate to receive my R01 at 32, this is a huge issue. The GSI was designed to help decrease that first R01 age, by capping funding to each investigator at a maximum of 3 R01s. However, the NIH received overwhelming feedback that led to abandoning the GSI on June 17, 2017. This is because graduate students, postdocs, and assistant professors that would benefit from it never responded to RFI/request for feedback, where as STRONG and overwhelming feedback was provided by a few directors and PIs with 5+ R01 equivalents.

Lesson: This is your opportunity to have your voices heard. You don’t have to be a PI or a faculty. You can be a postdoc, student, industry, an artist, or not in the USA. You just need to submit your opinions https://www.braininitiative.nih.gov/strategic-planning/acd-working-group/brain-initiative-request-information-rfi-2019 by MAY 15.

Here are some talking points that I think are important and within the scope of the BRAIN 2025 roadmap and new priorities from the ACD, but are overlooked missing in current RFAs and funding opportunities:

Acknowledge and Agree with the ACD

·       The importance of continued neurotechnology development

·       Power of integrated technologies

·       Support of neuronal and non-neuronal research on neural activity and neurotechnologies

Research Priorities

  • What is still lacking is the “basic science” research for neurotechnology. The use of technology causes very unique biological changes (whether reactive tissue response or plastic changes). While neurological diseases and brain injuries have robust basic science physiology research that guide treatments, there are very few mechanisms supporting the basic science physiology and neurobiology research at the neurotechnology-nervous system interface. An equal emphasis is necessary to research neural interface material science at the level of neurodegenerative diseases. Understanding these problems will be critical in strategically guiding next-generation technology development in a data driven manner. This is particularly the most important “Next step” of advancing “integrative efforts in BRAIN 2.0”
“We cannot solve our problems with the same thinking we used when we created them.” – Albert Einstein
“It isn’t that they cannot find the solution. It is that they cannot see the problem.” – G.K Chesterton
“If you are unable to understand the cause of a problem, it is impossible to solve it.” – Naoto Kan
“We fail more often because we solve the wrong problem than because we get the wrong solution to the right problem.” – Russell L. Ackoff
“We cannot solve our problems with the same thinking we used when we created them.” – Albert Einstein
“It isn’t that they cannot find the solution. It is that they cannot see the problem.” – G.K Chesterton
“If you are unable to understand the cause of a problem, it is impossible to solve it.” – Naoto Kan
“We fail more often because we solve the wrong problem than because we get the wrong solution to the right problem.” – Russell L. Ackoff
  • Supporting Technology Failure Analysis. In industry, the first step to technology development is to comprehensively and robustly study the modes and distribution of failures that can occur with the technology. While normally, these are risks that industrial partners take on, much of the technology is immature or have too long of a R&D cycle for commercial investment into these analyses. Therefore, there is a need for government support to facilitate failure analysis (biological and technological) to better inform and guide the development of future technologies.
  • Packaging: Failure analysis will likely reveal that packaging and usability are key aspects of device failure (failure to adopt and failure to perform) in technology development. Again, traditionally, these are R&D that commercial partners typically shoulder, but the R&D cycle for these technologies are too long for commercial investment. Their development needs to be supported by institutional partnership. This includes support at review panel and study section level to fund "un-sexy" packaging development which is critical to the technology dissemination pathway (and getting away from the standard R01 review and funding model)
  • Non-traditional RFA structure and Study Section/Review Panels: Much of the current inefficiencies in Brain technology in the US is due to the structure of review and funding. Even when RFAs are specifically written to support projects such as neuropixel and Grégoire Courtine's work, it is the review panels that tear into these as unfundable projects. For the US to take back the leadership in technology development, perhaps it is important to rethink the NIH funding pipeline for technology R&D (not just the RFA but also the review process). Not just treating these RFAs as standard R01s.
  • Along these lines, there are notable deficiencies in some BRAIN Initiative review panels or committees with respect to individuals that have both expertise at the interface of Technology and Neurobiology.
  • Biology and Biophysics of…[neurotechnology] are currently being reviewed by study sections that predominantly include people who are “non-biologists” and “non-biophysicist” which negatively impact these RFA mechanisms.
  • The above analysis are necessary in order to truly understand and apply these technologies for circuit function research and eliminate artifacts and false assumptions from contaminating the interpretation of the data collected using new technologies.
  • Neurobiology of Neurotechnology (Neural Interface Cybernetics: ie the science of communications and automatic control systems in both machines and living things): So much focus is on building “new” technology, there is very little effort to focus on the “science that governs functional communication between biology and interface”. This goes beyond biocompatibility and tissue scarring. How does biology govern communication with the technology and how does technology influence neural activity?
  • Too much emphasis on channel count and not enough on intelligent design: there are no RFAs currently available to study what makes a good interface and what makes a bad interface. Instead, current focus is on “increasing channel count”. But maximizing channel count likely means a greater “observer principle” alteration of the natural circuit. A better approach would be evaluate the science of the technology interface to inform better design and optimization of different design parameters. See https://doi.org/10.1002/adfm.201701269
  • Technology will always be obsolete, but the knowledge generated to guide technology development will be immortal. Therefore, there should be emphasis on understanding the biology of neural interfaces. see http://www.kozailab.com/links/basicscience
  • Cross-training: The importance of understanding both the language of neuroscience and neural engineering is emphasized elsewhere. It also explains why you can’t “just collaborate, you do the engineering and I’ll do the science” (closed-loop engineering): https://doi.org/10.3390/mi9090445
  • Observer Principle: Despite all the tools that are being developed, we still have very little idea how using these tools alter natural circuitry function. How can you use these tools to study circuit function if you don't understand the "side effects" on native neural circuits from using these tools? (eg, electrodes cause silencing of nearby neuron (alive but not firing properly) because oxygen and nutrient delivery is damaged. This means that instead of increasing channel count for next generation electrodes, but we should be focusing on technology that helps repair the neurovascular unit around implants. See https://doi.org/10.1088/1741-2552/aa9dae)
  • Assumptions: Many neurotechnologies are based on assumption. There is immerging research that suggests or proves that some of these assumptions are wrong (eg NeuN labeled neurons around electrodes are not silenced.). Instead, there should be RFAs that are designed to tease apart and test these assumptions.
  • Standard Technology: There is no emphasis on studying how standard technology influences neural circuit function. How can we evaluate if “new” technology from BRAIN is better or not if there are no efforts to evaluate these impacts on neural circuits with standard technologies?
  • Utilizing technology to understand neurodegeneration and regeneration/wound healing is not widely explored, especially their influence on neural (and gliovascular) circuit function.
  • Disease/Degeneration: Many degenerative diseases and brain injuries share activation of similar pathways. The key differences are where and what triggers onset. What makes these degenerative disease hard to study is that their onset is difficult to identify and the focal point of activation is hard to pinpoint (some have multiple focal initiation points). With technologies, the time and location is precisely known and you get a bonus of having a sensor or effector at the epicenter of the trigger injury. This not only informs technology development, but also understanding disease progression and potential platform for treatments.
  • Without underlying basic science to understand how to regulate “non-neuronal cells” and how “non-neuronal cells” regulate neural circuits, how can we create gliotechnologies and neurovasculartechnologies?

 

Dissemination and Training

  • Cross-training: The importance of understanding both the language of neuroscience and neural engineering is emphasized elsewhere. It also explains why you can’t “just collaborate, you do the engineering and I’ll do the science” (closed-loop engineering): https://doi.org/10.3390/mi9090445
  • While ACD has shown appreciation for integrated approach, technology, and training. There is considerable personal risk and inherently limited financial opportunity for trainees to pursue integrated training in neuroscience and neurotechnology. NIH should carefully consider what these challenges are and how NIH can support and de-risk integrated training for the individual. Otherwise, it will remain a pipe dream that would be "nice to have someday".

 

Other

  • Representation: No advocates for technology interface nor are there any individuals that appreciate the neural interface challenge serve on the working group. One technologist on the working group has, on multiple occasions, stated in talks, “I’m not really sure what happens to the brain [when this is implanted]. I’m not a biologist, it’s not important to me.”

Topics that do not do well in traditional NIH mechanisms

1)    1. Technology development

2)    2. Neural Interface Science/Neural Interface Biology: exploring the biological mechanisms that govern Neural Technology Interfaces at a level equivalent to neurodegenerative diseases, Stroke or TBI.

3)    3. Regulatory Neurotechnology science

a.     What makes technology safe

b.     What makes technology effective

c.     We don’t even know which experiments to carry out to evaluate the above questions. We need research to determine what experiments and metric standards inform safety and effectiveness at both pre-clinical and clinical trial levels