Jeffrey P. Sutton

Non-invasive neuroimaging using near-infrared light

This article reviews diffuse optical brain imaging, a technique that employs near-infrared light to non-invasively probe the brain for changes in parameters relating to brain function. We describe the general methodology, including types of measurements and instrumentation (including the tradeoffs inherent in the various instrument components), and the basic theory required to interpret the recorded data. A brief review of diffuse optical applications is included, with an emphasis on research that has been done with psychiatric populations. Finally, we discuss some practical issues and limitations that are relevant when conducting diffuse optical experiments. We find that, while diffuse optics can provide substantial advantages to the psychiatric researcher relative to the alternative brain imaging methods, the method remains substantially underutilized in this field.

Mars 520-d mission simulation reveals protracted crew hypokinesis and alterations of sleep duration and timing

The success of interplanetary human spaceflight will depend on many factors, including the behavioral activity levels, sleep, and circadian timing of crews exposed to prolonged microgravity and confinement. To address the effects of the latter, we used a high-fidelity ground simulation of a Mars mission to objectively track sleep–wake dynamics in a multinational crew of six during 520 d of confined isolation. Measurements included continuous recordings of wrist actigraphy and light exposure (4.396 million min) and weekly computer-based neurobehavioral assessments (n = 888) to identify changes in the crews activity levels, sleep quantity and quality, sleep–wake periodicity, vigilance performance, and workload throughout the record-long 17 mo of mission confinement. Actigraphy revealed that crew sedentariness increased across the mission as evident in decreased waking movement (i.e., hypokinesis) and increased sleep and rest times. Light exposure decreased during the mission. The majority of crewmembers also experienced one or more disturbances of sleep quality, vigilance deficits, or altered sleep–wake periodicity and timing, suggesting inadequate circadian entrainment. The results point to the need to identify markers of differential vulnerability to hypokinesis and sleep–wake changes during the prolonged isolation of exploration spaceflight and the need to ensure maintenance of circadian entrainment, sleep quantity and quality, and optimal activity levels during exploration missions. Therefore, successful adaptation to such missions will require crew to transit in spacecraft and live in surface habitats that instantiate aspects of Earths geophysical signals (appropriately timed light exposure, food intake, exercise) required for temporal organization and maintenance of human behavior.

Space Radiation: The Number One Risk to Astronaut Health beyond Low Earth Orbit

Projecting a vision for space radiobiological research necessitates understanding the nature of the space radiation environment and how radiation risks influence mission planning, timelines and operational decisions. Exposure to space radiation increases the risks of astronauts developing cancer, experiencing central nervous system (CNS) decrements, exhibiting degenerative tissue effects or developing acute radiation syndrome. One or more of these deleterious health effects could develop during future multi-year space exploration missions beyond low Earth orbit (LEO). Shielding is an effective countermeasure against solar particle events (SPEs), but is ineffective in protecting crew members from the biological impacts of fast moving, highly-charged galactic cosmic radiation (GCR) nuclei. Astronauts traveling on a protracted voyage to Mars may be exposed to SPE radiation events, overlaid on a more predictable flux of GCR. Therefore, ground-based research studies employing model organisms seeking to accurately mimic the biological effects of the space radiation environment must concatenate exposures to both proton and heavy ion sources. New techniques in genomics, proteomics, metabolomics and other “omics” areas should also be intelligently employed and correlated with phenotypic observations. This approach will more precisely elucidate the effects of space radiation on human physiology and aid in developing personalized radiological countermeasures for astronauts.

Psychological and behavioral changes during confinement in a 520-day simulated interplanetary mission to mars.

Behavioral health risks are among the most serious and difficult to mitigate risks of confinement in space craft during long-duration space exploration missions. We report on behavioral and psychological reactions of a multinational crew of 6 healthy males confined in a 550 m3 chamber for 520 days during the first Earth-based, high-fidelity simulated mission to Mars. Rest-activity of crewmembers was objectively measured throughout the mission with wrist-worn actigraphs. Once weekly throughout the mission crewmembers completed the Beck Depression Inventory-II (BDI-II), Profile of Moods State short form (POMS), conflict questionnaire, the Psychomotor Vigilance Test (PVT-B), and series of visual analogue scales on stress and fatigue. We observed substantial inter-individual differences in the behavioral responses of crewmembers to the prolonged mission confinement and isolation. The crewmember with the highest average POMS total mood disturbance score throughout the mission also reported symptoms of depression in 93% of mission weeks, which reached mild-to-moderate levels in >10% of mission weeks. Conflicts with mission control were reported five times more often than conflicts among crewmembers. Two crewmembers who had the highest ratings of stress and physical exhaustion accounted for 85% of the perceived conflicts. One of them developed a persistent sleep onset insomnia with ratings of poor sleep quality, which resulted in chronic partial sleep deprivation, elevated ratings of daytime tiredness, and frequent deficits in behavioral alertness. Sleep-wake timing was altered in two other crewmembers, beginning in the first few months of the mission and persisting throughout. Two crewmembers showed neither behavioral disturbances nor reports of psychological distress during the 17-month period of mission confinement. These results highlight the importance of identifying behavioral, psychological, and biological markers of characteristics that predispose prospective crewmembers to both effective and ineffective behavioral reactions during the confinement of prolonged spaceflight, to inform crew selection, training, and individualized countermeasures.

A network of networks processing model for image regularization

We introduce a network of networks (NoN) model to solve image regularization problems. The method is motivated by the fact that natural image formation involves both local processing and globally coordinated parallel processing. Both forms are readily implemented using an NoN architecture. The modeling is very powerful in that it achieves high-quality adaptive processing, and it reduces the computational difference between inhomogeneous and homogeneous conditions. This method is able to provide fast, quality imaging in early vision, and its replicating structure and sparse connectivity readily lend themselves to hardware implementations.

Functional brain imaging of a complex navigation task following one night of total sleep deprivation: a preliminary study

Several neuroimaging studies have demonstrated compensatory cerebral responses consequent to sleep deprivation (SD), but all have focused on simple tasks with limited behavioral response options. We assessed the cerebral effects associated with SD during the performance of a complex, open‐ended, dual‐joystick, 3D navigation task (simulated orbital docking) in a cross‐over protocol, with counterbalanced orders of normal sleep (NS) and a single night of total SD (∼27 h). Behavioral performance on multiple measures was comparable in the two sleep conditions. Functional magnetic resonance imaging revealed multiple compensatory SD > NS cerebral responses, including the posterior superior temporal sulcus [Brodmann area (BA) 39/22/37], prefrontal cortex (BA 9), lateral temporal cortex (BA 22/21), and right substantia nigra. Right posterior cingulate cortex (BA 31) exhibited NS > SD activity. Our findings extend the compensatory cerebral response hypothesis to complex, open‐ended tasks.

Towards automated enhancement, segmentation and classification of digital brain images using networks of networks

Abstract An adaptive image processing algorithm (IEEE Trans. Neural Networks 8 (1997) 169), based on biological principles, is extended and applied to enhance, segment and classify digital brain images acquired using magnetic resonance imaging (MRI). The algorithm is based on the network of networks (NoN) neural computing theory (World Cong. Neural Networks 1 (1995) 561). The initial algorithm, developed by Guan and Sutton (GS), uses vector connections among model neurons to delineate regions of pixels that have dynamic boundaries, corresponding to critical features of images. The boundaries subdivide large regions into smaller regions, where the smaller regions operate as gradient descent networks to enhance local aspects of blurred images. In this report, the authors develop and test two new algorithms that build upon the success of the GS algorithm. The first algorithm, termed the segmentation-variance (SV) algorithm, maps pixels into discrete groups using a segmentation function of the local variance. Boundaries formed at the transition zones among groups are subsequently tuned to automatically delineate brain regions of interest. This is demonstrated using MRI scans of the human brain. In a second algorithm, pixel groupings formed using the SV algorithm are consolidated into larger groups to generate histograms. Dynamic maps and histograms capture features of object relationships and are used to classify structures. The resultant algorithm is called the dynamic segmentation and classification (DSC) algorithm. It is tested on MRI scans of four geometric patterns under various noise conditions. The SV and DSC algorithms illustrate proof of principal concepts in the evolution towards automated segmentation and classification of digital brain images.

If we compute faster, do we understand better?

Practitioners of cognitive science, “theoretical” neuroscience, and psychology have made less use of high-performance computing for testing theories than have those in many other areas of science. Why is this? In high-performance scientific computation, potentially billions of operations must lead to a trustable conclusion. Technical problems with the stability of algorithms aside, this requirement also places extremely rigorous constraints on the accuracy of the underlying theory. For example, electromagnetic interactions seem to hold accurately from atomic to galactic scales. Large-scale computations using elementary principles are possible and useful. Many have commented that the behavioral and neural sciences are largely pretheoretical. One consequence is that we cannot trust our few theories to scale well for a very good reason: They don’t. We have some quite good computational theories for single neurons and some large-scale aspects of behavior seem to be surprisingly lawful. However, we have little idea about how to go from the behavior of a single neuron to the behavior of the 1011 neurons involved when the brain actually does something. Neural networks have offered one potential way to leap this enormous gap in scale, since many elementary units cooperate in a neural network computation. As currently formulated, however, neural networks seem to lack essential mechanisms that are required for flexible control of the computation, and they also neglect structure at intermediate scales of organization. We will present some speculations related to controllability and scaling in neural networks.

Computational and Neurobiological Features of a Network of Networks

We describe a “network of networks” model of cortical computation. The model is motivated by anatomical and dynamic features of modularity in the neocortex and it makes a number of predictions concerning intermediate levels of brain organization. There are also implications for pathophysiology. New experimental techniques may be particularly well suited for testing the model and its unusual computational properties.

Learning Motor Sequences with and without Knowledge of Governing Rules

Objective. To investigate the behavioral and neural effects of rule-based knowledge on motor sequence learning. Methods. The authors developed a novel 2-dimensional variant of the serial reaction time (SRT) task to test the effect of prior, verbalizable rule knowledge on motor learning behavior. To examine neurophysiological effects, they also performed functional magnetic resonance imaging on a small cohort of subjects while performing the same task. Results. Behavioral data demonstrated that instruction on sequence-governing rules enhanced behavioral performance in both learning magnitudes and rates. The neuroimaging data revealed substantially different, but partially overlapping, learning-related activation patterns with and without prior rule instruction. Direct comparison of these 2 conditions revealed significantly different involvement of bilateral superior and anterior prefrontal cortex (Brodmann areas 8 and 10, respectively), right superior temporal cortex (BA 38/21), and left cerebellum. Conclusions. These behavioral findings demonstrate an advantage of teaching governing rules prior to 2D-SRT task performance. While these neuroimaging findings remain to be replicated in a larger cohort of subjects, results suggest that substantially different—though partially overlapping—brain regions subserve learning in these 2 rehabilitation-relevant conditions. Thus, appropriate choice of pretraining may benefit, for example, rehabilitation populations, at least in motor skill acquisition that requires sequencing.