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Human Sciences: Translational Neuroscience

INTRODUCTION

The Panel on Human Sciences at the Army Research Laboratory (ARL) conducted its review of ARL’s translational neuroscience program at Aberdeen, Maryland, on June 11-13, 2013. This chapter provides an evaluation of that work, recognizing that it represents only a portion of ARL’s human sciences core technology competency portfolio.

The goal of the translational neuroscience (TN) program is to integrate modern neuroscience with human factors, psychology, and engineering to enhance the understanding of soldier function and behavior in complex operational settings. TN, as it is defined, is a unique and important effort whose objectives, if successfully accomplished, could be a game changer for soldier and mission effectiveness.

TN has concentrated its efforts on three research thrusts:

1. Brain–computer interaction (BCI) technologies. Enable mutually adaptive brain–computer interaction technologies for improving soldier-system performance.

2. Real-world neuroimaging. Assess those aspects of brain function that can be usefully monitored outside of the laboratory setting and assess and/or develop the technologies that are best adapted for this purpose.

3. Brain structure–function couplings. Translate knowledge of differences in individuals’ brain structure and function to understand and predict differences in task performance.

From 2009 through 2013, the TN group made significant gains in publication rates and quality (from 16 publications in 2009 to 44 in 2013, including an increase in publications in peer-reviewed journals from 6 in 2009 to 17 in 2013); numbers of postdoctoral researchers (from 2 in 2009 to 11 in 2013); outreach and mentoring activities (from none in 2009 to 7 in 2013); and level of external funding (from $730,000 in 2009 to $10,750,000 in 2013). On these measures, the group has attained a level found in neuroscience groups at many first-tier academic institutions.

ACCOMPLISHMENTS AND ADVANCEMENTS

Over the past 6 years the TN group has received consistently high marks from ARLTAB and has been repeatedly cited as a model for how a new group can effectively be developed at a government research laboratory.

Publication rates and journal quality have continued to rise to impressive levels and represent a very significant accomplishment. While further improvement in the quality of journal publication is urged, the TN group’s productivity is on a par with what might be expected from recognized academic institutions working in the domain.

The TN group has successfully attracted higher levels of outside funding and now enjoys a level of external support that matches that of most first-tier academic institutional groups in neuroscience. This is an outstanding accomplishment.



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4 Human Sciences: Translational Neuroscience INTRODUCTION The Panel on Human Sciences at the Army Research Laboratory (ARL) conducted its review of ARL’s translational neuroscience program at Aberdeen, Maryland, on June 11-13, 2013. This chapter provides an evaluation of that work, recognizing that it represents only a portion of ARL’s human sciences core technology competency portfolio. The goal of the translational neuroscience (TN) program is to integrate modern neuroscience with human factors, psychology, and engineering to enhance the understanding of soldier function and behavior in complex operational settings. TN, as it is defined, is a unique and important effort whose objectives, if successfully accomplished, could be a game changer for soldier and mission effectiveness. TN has concentrated its efforts on three research thrusts: 1. Brain–computer interaction (BCI) technologies. Enable mutually adaptive brain–computer interaction technologies for improving soldier-system performance. 2. Real-world neuroimaging. Assess those aspects of brain function that can be usefully monitored outside of the laboratory setting and assess and/or develop the technologies that are best adapted for this purpose. 3. Brain structure–function couplings. Translate knowledge of differences in individuals’ brain structure and function to understand and predict differences in task performance. From 2009 through 2013, the TN group made significant gains in publication rates and quality (from 16 publications in 2009 to 44 in 2013, including an increase in publications in peer-reviewed journals from 6 in 2009 to 17 in 2013); numbers of postdoctoral researchers (from 2 in 2009 to 11 in 2013); outreach and mentoring activities (from none in 2009 to 7 in 2013); and level of external funding (from $730,000 in 2009 to $10,750,000 in 2013). On these measures, the group has attained a level found in neuroscience groups at many first-tier academic institutions. ACCOMPLISHMENTS AND ADVANCEMENTS Over the past 6 years the TN group has received consistently high marks from ARLTAB and has been repeatedly cited as a model for how a new group can effectively be developed at a government research laboratory. Publication rates and journal quality have continued to rise to impressive levels and represent a very significant accomplishment. While further improvement in the quality of journal publication is urged, the TN group’s productivity is on a par with what might be expected from recognized academic institutions working in the domain. The TN group has successfully attracted higher levels of outside funding and now enjoys a level of external support that matches that of most first-tier academic institutional groups in neuroscience. This is an outstanding accomplishment. 45

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The number of postdoctoral fellows in the program originating from first-tier academic institutions has grown considerably. Overall, the program now employs an impressive group of early- career scientists. The consistent investment in the growth of access to intellectual capital is exemplary. In 2012, HRED completed the renovation of its Aberdeen headquarters. This renovation included the construction of the new MIND laboratory for the neuroscience group. Although somewhat smaller than would be ideal, this lab is an excellent state-of-the-art facility that is well built and well equipped. It is likely to prove to be an excellent facility for the group in the years to come. Brain–Computer Interaction Technologies While ARL’s program in brain–computer interaction (BCI) technologies is only a few years old, it has made significant strides in fundamental research and in the development of applications. ARL has carved out an appropriate niche in the BCI community and is well-positioned with a clear emphasis on enabling practical BCI systems for soldier support. The decision to explore the integration of other sensing modalities—for example, electrocardiography (ECG), electromyography (EMG), galvanic skin response (GSR), and eye movements—into EEG-based BCI applications is conceptually strong and innovative. Overall, BCI has the potential to lead theoretical and practical breakthroughs in achieving maximum application performance with minimum invasiveness. The applications and demonstrations shown indicated clear evidence of innovative fundamental and systems-based research. In particular, the integration of rapid serial visual presentation (RSVP) with the RAVEN system (RSVP-based adaptive virtual environment with neural processing) represents a significant fundamental advance. The cross-validation of performance estimates from two tasks (driving and RSVP) is a significant achievement, showing the generalizability of the work across multiple BCI applications. The use of transfer learning to train EEG classifiers using data from previous subjects is innovative and appropriate and has the potential to significantly reduce individual-specific training time needed for BCI systems. The use of sliding windows in hierarchical discriminant component analysis (HDCA) to deal with temporal variability in BCI responses is well-considered and solves a significant practical problem in the performance of these systems. Real-World Neuroimaging The real-world neuroscience group outlined a project to develop novel EEG measurement technologies coupled with the development of supporting algorithms. The project goal is to design systems for specific tasks rather than a single system that can work in all contexts. Some substantive directions of the work were described, among them to overcome real-world limitations for use of the electrode system so that it (1) works with hair, (2) slips on and off easily without significant setup, and (3) has high enough sensitivity to capture the signals necessary for the specific task. Overall, project goals and potential applications were clearly articulated and progress to date was illustrated by demonstrations. Brain Structure–Function Couplings Finite-Element Head Model The Finite-Element Head Model project involves a rigorous method for incorporating anisotropic properties of biological tissue into a finite-element model for use in the development of head protective systems. This is an important goal for which the neuroscience group appears to be well qualified. The group evaluated this model using real brain pressure datasets from a cadaver study conducted several years ago. The results demonstrated that the model was considerably more accurate than had been 46

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expected. A suggested future direction would be to model the diffusion tensor imaging (DTI) abnormalities from the postmortem brains of individuals with blast or concussive injuries. The effects of an intact, living vascular system, cerebrospinal fluid channels, and interstitial pressure gradients might yield a very different outcome than is seen in impact-damaged cadavers. Functional Connectivity Project The TN group appears to have made significant headway in assessing several tools for causal modeling in EEG data. These are state-of-the-art tools that have not been well tested or validated for EEG use anywhere else, and the TN group is doing a solid job of identifying the strengths and limitations of these approaches. Given the central role EEG plays in HRED’s translational portfolio, undertaking this validation is an excellent use of resources. The approach is sound, and the tools seem to be undergoing reasonable validation. Phase Synchronization Tools in Electroencephalography The TN group has made good progress in assessing tools for measuring and identifying phase synchronization in EEG data. These are tools that have not been well tested or validated anywhere else. Given the central role EEG plays in the TN portfolio, this project reflects an excellent use of resources. The approach is sound, limitations of the tools seem to be well identified by the research, and methods for maximizing EEG signal in a fieldable device are clearly being developed. Decision Making in the Field Project Using advanced psychological models of decision making in a simulated checkpoint screening task, the TN group is assessing the degree to which mission or task biases can be adjusted by instruction and incentives. The work is of very high quality and clearly Army-relevant. It will likely provide important data about how soldiers and officers take mission instructions into consideration and how effectively they can adapt their behavior to the needs at hand, and it might even offer insight into training effectiveness. In the future, the project needs to be expanded to disentangle expertise effects from task difficulty effects, and it also needs to allow for assessing the difference between soldiers and civilians in these kinds of tasks. OPPORTUNITIES AND CHALLENGES In the case of the TN program, many of the challenges represent opportunities to do more of what the group is already doing. Brain–Computer Interaction Technologies BCI technology based on neural recordings has emerged over the past 15 years as an important subfield of both translational neuroscience and bioengineering. This is evidenced by the dramatic increase in the number of publications and presentations related to BCI techniques in neuroscience journals and conferences. ARL’s mission focus on healthy soldiers poses an interesting challenge for BCI research, since the common goal of most BCI research is to treat or assist patients with sensory, motor, or cognitive 47

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disabilities. Hence, instead of using BCI technology for control as most clinically relevant BCIs do, the TN group has focused its efforts on the detection of mental states such as fatigue (state-based BCIs) or of external events such as relevant targets in a visual scene (event-based BCIs). Because their goal is to assist healthy soldiers in the field, the ARL researchers have resorted to the only currently viable noninvasive technology, namely, EEG electrodes placed on the scalp. The BCI program is largely predicated on the assumption that computers and humans have complementary strengths (e.g., processing throughput versus higher-level reasoning and reliability and objectivity versus flexibility and situational awareness) and that hybrid systems leveraging BCI have the potential to achieve performance levels that neither a computer nor a human could achieve individually. This is a compelling notion, but it is not fully clear how the ongoing efforts and future plans specifically leverage, demonstrate, and validate it. There is the opportunity to do so, for example, by comparing the RAVEN system performance using RSVP-based BCI against state-of-the-art machine vision and automatic target recognition (ATR) algorithms. Such a comparison would show that the flexibility and situational awareness of humans greatly contribute to a computer system’s ability to detect and recognize targets and other items of interest. While the performance estimate cross-validation work is technically impressive, it is important to note that these systems are effectively detecting performance (often with significant delay) rather than predicting it in a temporal context. This may not be inherently problematic, given that some applications may benefit from BCI-based performance detection if performance assessment is not possible through other means. The group’s claims to have the ability to predict poor performance before it begins to set in (e.g., early detection of fatigue that may lead to poor performance) need to be tempered. In point of fact, the current BCI system cannot determine if the brain is in a state that leads to poor performance or if the brain is just reacting to the poor performance (but see Baldassarre et al., 2012). 1 The goal of this research is ambitious and laudable and will necessitate a deeper understanding of the brain response and/or a demonstration of true temporal performance prediction. Given that the BCI technology program is still in its infancy, the advancements in individual projects are impressive. However, the emphasis of the presentations on such projects made it difficult to appreciate the long-term vision of the program and the nested fundamental research questions and application goals that will be addressed as the program matures. Reliability has become an important concern in the BCI field as is evidenced by DARPA’s program in reliable neural interfaces. For BCI systems to become widely used in clinical and nonclinical applications, it is crucial that electrodes continue to record stable signals from relevant brain areas for at least months and ideally for many years. This is a particularly serious problem for invasive electrodes where foreign body reactions and the intracranial environment can affect signal quality and stability. For noninvasive EEG electrodes, the exact electrode placement can vary slightly every time the EEG cap is removed and reattached. The TN group needs to investigate whether its EEG-based BCI systems that have been calibrated for a particular subject can continue to detect relevant states and events over weeks to months without resorting to recalibration of the decoding algorithm. It is widely assumed that field potentials recorded from different EEG electrodes provide partially redundant information. Redundancy is important because it can buffer these BCI systems from catastrophic failure and allow for graceful degradation. On the other hand, redundancy may allow BCI systems to transmit data from a smaller number of electrodes, thereby reducing the bandwidth requirements, which may be particularly relevant for wireless transmission. The ARL needs to explore the degree of redundancy of its systems by examining state- and event-detection performance as a function of the number of electrodes used, much as researchers who use invasive electrodes perform “neuron- dropping” analyses. On a related note, it would be useful to explore the spatial organization of information content 1 A. Baldassarre, Christopher M. Lewis, Giorgia Committeri, Abraham Z. Snyder, Gian Luca Romani, and Maurizio Corbetta, 2012, Individual variability in functional connectivity predicts performance of a perceptual task. PNAS 109 (9): 3516-3521. 48

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across the scalp. Are there certain cortical regions that provide more accurate detection information than others? Is the information distributed evenly across the scalp? For example, there is evidence from single-cell recordings in nonhuman primates that neurons in the inferior temporal (IT) cortex modulate their firing rates to targets that are to be searched for in a complex scene in a visual search paradigm. Responses are enhanced in IT neurons that “prefer” the target, whereas responses are suppressed in neurons that do not prefer the target. Do EEG electrodes located over the temporal lobe provide better target detection capabilities than electrodes over the occipital, parietal, or frontal lobes? This suggestion to exploit the redundancy of multielectrode signals can be viewed as an alternative to source location, discussed above. The applications of BCI systems that were presented were limited to state- and target-detection requiring no more than 1 bit of information. It might be useful to explore opportunities to extract richer information content than can be available with EEG alone or together with other biosensor technologies. For example, would it be possible to detect multiple levels of fatigue, attention, and arousal? Could EEG systems be used to detect states associated with subjects’ ability to acquire information or to learn? Could the RSVP target detection system be expanded to detect more than one target class? For example, an operator may be looking for two different types of aggressors in a visual scene and respond differently to each. Although their target detection system based on Rapid Serial Visual Presentation (RSVP) is quite impressive, it will be important to validate it against several control conditions to ensure that the improvement in search speed is attributable to target detection from the EEG system. One control condition would be to compare search speed using randomly sorted images that are not sorted via the EEG system. Another control condition would be to compare search speed using different machine vision and automatic target recognition algorithms to perform the sorting of images with potential targets instead of the EEG system. Such comparisons would help validate the larger claim that hybrid human–computer systems, leveraging their complementary strengths, can perform better than either system alone. Real-World Neuroimaging The TN effort to develop nonproprietary dry electrodes is a very challenging area wherein a breakthrough could significantly impact medical EEG, human factors, neuroeconomics, and neuromarketing and likely to lead to important new applications. The integration of electrode technologies with thoughtful statistical analyses for the purpose of artifact detection and classification could bring important and valuable contributions. The TN group has designed a very sensible balance of projects in the portfolio and has organized a strong international collaboration to help achieve the group’s goals. Among these goals are the following: • Phantom head development. The work in support of the EEG phantom presents a good opportunity to perform standardized testing. The goal of having a real- world system requires the group to do viable real-world testing that extends to different levels of sweating and motion. • High-risk dry-electrode project. Given the focus on dry-electrode pads as a key impediment, it would be useful to conduct analyses of the computational and energy requirements for a real-world system. For example, how much energy, processing power, communications capability, and data storage will be required? Furthermore, will new algorithms be required to handle the additional challenges of the real-world environment? The current-generation dry-electrode system is in an early stage of development. The published time series from the electrode that was initially provided did not include measurements of brain waves. Fortunately, the panel was updated with sample brain wave recordings collected in real time. These indicated that while the overall stability of this dry electrode is impressive, the signals are very small. 49

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They detect large artifacts, and it is not evident that cortical potentials are being picked up. The TN group needs to compare the scalp measurements of these electrodes with other active dry EEG electrode systems (e.g., Gtec medical engineering) to ascertain the advantages and disadvantages of different approaches. Should there be significant differences in the scalp data, say in the signal-to-noise of averaged event-related potentials measured using the different dry electrodes and wet electrodes, the group might further make measurements of impedances and signals, humidity and perspiration testing (i.e., salt bridges), electromagnetic interference, and a standard 10-20 system for comparison of topography with the wet system to check for antenna effects from high-impedance electrodes. The group needs to continue to consider alternative electrode types and analysis and signal enhancement methods that can reduce artifacts from electrode movement. Measures for evaluating signal quality need to be developed, and the TN group needs to ensure it is aware of and understands the lessons learned from prior work in this area. Relevance to Protection from Traumatic Brain Injury While the ARL is focused on the performance and protection of healthy individuals and it has noted that medical conditions are outside its mission, the problems that the group does focus on are relevant to performance and life-threatening situations that are commonly encountered by Army personnel and that are poorly understood. For example, a major threat to the performance of military personnel, during peacetime as well as wartime, is traumatic brain injury (TBI), particularly mild traumatic brain injury. Reports of soldiers recently returned from combat in Iraq found that 22.8 percent had sustained a TBI and that most of these were mild. The TN group has the potential to model and predict which areas of the brain are most susceptible to various mild TBIs and can in turn use these data to help guide the design of protective gear to militate against these injuries. Obviously the work by TN could be of significant value with respect to identifying areas of TBI. For example, moving this work beyond impacts that might result from a blunt object striking a forehead could lead to techniques for identifying areas of brain damage when the injury is not detectable by routine imaging. OVERALL TECHNICAL QUALITY OF THE WORK Overall, the quality of the research presented, the capabilities of the leadership, the knowledge and abilities of the investigators, their scientific productivity, and proposed future directions are impressive. The work is well aligned with the clear and substantive mission to move neuroscience from the laboratory to real-world military settings—that is, from the bench to the battlefield. The TN group conducts high-quality neuroscience research that is routinely validated by its publication in recognized, peer-reviewed journals and is on a par with work at a good university neuroscience department. The group leadership is highly effective and qualified, and there is a palpable energy and enthusiasm in the strong mix of early-career and mid-career scientists. The facilities are, for the most part, state of the art, and the group demonstrated impressive leverage and collaboration with the broader scientific communities at universities, industry, and other government laboratories. 50