The brain is the only organ that does not heal itself once injured, but it does adapt and relearn quickly once injured. Whether the brain is cognitively optimized or is dysfunctional, the same brain networks and brain systems are at play to optimize or regulate and repair. That is why studying brain function of optimized brains from astronaut candidates, or individuals within TBI and depression populations can help both ends of the cognitive spectrum to achieve repair for dysfunctional populations or maintain optimal performance. Opportunities to increase coping capabilities neurophysiologically that impact psychological resilience are appealing both clinically and when applied to space travel. The subject of this paper is reporting the results of one such method that is currently being employed in an ongoing UND/NASA Inflatable Lunar Mars Analog Habitat (ILMAH) simulator study. Our Habitat study’s primary goals are many fold: 1) to develop a predictive profile, based on real-time measurable neurophysiological metrics that model cognitive health and resulting task/behavioral health performance; 2) demonstrate the viability of developing a wearable dry sensor device that produces a profile that can be used in extreme environments such as long duration space missions; 3) demonstrate the viability to provide crew countermeasures that mediate negative reduced resilient stress effects on an on-going and as needed basis. Our study method employs the NeuroCoach® Training System that focuses on developing targeted resilient flexible adaptability neural circuit responses through the application of brain training exercises to support psychological resilience. The training program assumption is that if key neural circuits and network systems that support resilient, adaptive behaviors are coupled with proper problem-solving skills, resilient adaptive behaviors emerge. The NeuroCoach® program is based on modern Restorative Cognitive Rehabilitation Training Methods (rCRT). The program provides in-the-moment neural network performance metrics to monitor and adjust the training difficulty level using a Brain Computer Interface. Experimental and clinical results demonstrate the program success at increasing and maintaining optimal cognitive and brain performance quantitatively (by the numbers) and qualitatively (social reintegration). We have found that by studying astronaut crew needs to remain at optimized performance during long duration space travel as well as our studies with various clinical populations with acquired brain dysfunctions presents us with a unique opportunity to compare. They are opposite ends of the spectrum, but both are instructive in what a damaged brain can potentially achieve vs what an optimized brain might suffer during deep space travel.
| Published in | American Journal of Aerospace Engineering (Volume 8, Issue 1) |
| DOI | 10.11648/j.ajae.20210801.13 |
| Page(s) | 14-26 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
Psychological Resilience, Cognitive Retraining, Spaceflight Astronaut Monitoring
| [1] | Vessel E., Russo S., (2015) Effects of Reduced Sensory Stimulation and Assessment of Countermeasures for Sensory Stimulation Augmentation A Report for NASA Behavioral Health and Performance Research: Sensory Stimulation Augmentation Tools for Long Duration Spaceflight. |
| [2] | Roberts, D. R., Albrecht, M. H., Collins, H. R., Asemani, D., Chatterjee, A. R., Spampinato, M. V.,… Antonucci, M. U. (2017). Effects of Spaceflight on Astronaut Brain Structure as Indicated on MRI. New England Journal of Medicine, 377 (18), 1746–1753. https://doi.org/10.1056/NEJMoa1705129. |
| [3] | Marušič, U., Meeusen, R., Pišot, R., & Kavcic, V. (2014). The brain in micro- and hypergravity: The effects of changing gravity on the brain electrocortical activity. European Journal of Sport Science, 14 (8), 813–822. https://doi.org/10.1080/17461391.2014.908959. |
| [4] | Koppelmans, V., Bloomberg, J. J., Mulavara, A. P., & Seidler, R. D. (2016). Brain structural plasticity with spaceflight. Npj Microgravity, 2 (1). https://doi.org/10.1038/s41526-016-0001- 9. |
| [5] | Demertzi, A., Van Ombergen, A., Tomilovskaya, E., Jeurissen, B., Pechenkova, E., Di Perri, C.,... Wuyts, F. L. (2016). Cortical reorganization in an astronaut’s brain after long-duration spaceflight. Brain Structure and Function. |
| [6] | Cassady, K., Koppelmans, V., Reuter-Lorenz, P., De Dios, Y., Gadd, N., Wood, S.,... Seidler, R. (2016). Effects of a spaceflight analog environment on brain connectivity and behavior. NeuroImage, 141, 18–30. https://doi.org/10.1016/j.neuroimage.2016.07.029. |
| [7] | Pyne, S. J. (2007). The extraterrestrial Earth: Antarctica as analogue for space exploration. Space Policy, 23 (3), 147–149. https://doi.org/10.1016/j.spacepol.2007.06.00. |
| [8] | Garrett-Bakelman, F. E. (2019). The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight. HUMAN PHYSIOLOGY, 24. |
| [9] | J. R. Ball, Safe Passage: Astronaut Care for Exploration Missions, Institute of Medicine, National Academy of Sciences, Washington, DC, 2001. |
| [10] | N. Kanas, D. Manzey, Space Psychology and Psychiatry, second ed., Microcosm Press, El Segundo, CA, and Springer, Dordrecht, The Netherlands, 2008. |
| [11] | D. A. Shayler, Disasters and Accidents in Manned Spaceflight, Springer/Praxis, Chichester, UK, 2000. |
| [12] | P. Suedfeld, Canadian space psychology: the future may be almost here, Canadian Psychology 44 (2) (2003) 85–92. |
| [13] | P. Suedfeld, Invulnerability, coping, salutogenesis, integration: four phases of space psychology, Aviation, Space and Environmental Medicine 76 (2005) B61–B66. |
| [14] | Sweet, T, Panda, N., Hein, A, Das, S, Hurley, S, Olschowka, J., Williams, J., & O’Banion, (2014) M, Central Nervous System Effects of Whole-Body Proton Irradiation, Radiation Research 182, 18–34. |
| [15] | Denisova, N., Shukitt-Hale, Rabin & Joseph (2002) Brain Signaling and Behavioral Responses Induced by Exposure to Fe-Particle Radiation Radiation Research 158 725-734. |
| [16] | Heuer, H.; Manzey, D.; Lorenz, B.; Sangals, J. Impairments of manual tracking performance during spaceflight are associated with specific effects of microgravity on visuomotor transformations. Ergonomics 2003, 46, 920–934. |
| [17] | Johannes, B.; Salnitski, V. P.; Polyakov, V. V.; Kirsch, K. A. Changes in the autonomic reactivity pattern to psychological load under long-term microgravity—twelve men during 6-month spaceflights. Aviakosmicheskaia i Ekologicheskaia Meditsina 2003, 37, 6–16. |
| [18] | Manzey, D.; Lorenz, B. Poljakov, V. Mental performance in extreme environments: Results from a performance monitoring study during a 438-day spaceflight. Ergonomics 1998, 41, 537–559. |
| [19] | Manzey, D.; Lorenz, B.; Heuers, H.; Sangals, J. Impairments of manual tracking performance during spaceflight: More converging evidence from a 20-day space mission. Ergonomics 2000, 43, 589–609. |
| [20] | Whitmore, M.; McQuilkin, M. L.; Woolford, B. J. Habitability and performance issues for long duration space flights. Hum. Perferm. Extrem. Environ. 1998, 3, 64–74. |
| [21] | Sha, Z., Wager, T. D., Mechelli, A., & He, Y. (2019). Common Dysfunction of Large-Scale Neurocognitive Networks Across Psychiatric Disorders. Biological Psychiatry, 85 (5), 379–388. https://doi.org/10.1016/j.biopsych.2018.11.011 |
| [22] | Redish, A. D., & Gordon, J. A. (Eds.). (2016). Computational psychiatry: new perspectives on mental illness. Cambridge, Massachusetts: The MIT Press. |
| [23] | da Silva, A. G., Malloy-Diniz, L. F., Garcia, M. S., Figueiredo, C. G. S., Figueiredo, R. N., Diaz, A. P., & Palha, A. P. (2018). Cognition As a Therapeutic Target in the Suicidal Patient Approach. Frontiers in Psychiatry, 9. https://doi.org/10.3389/fpsyt.2018.00031 |
| [24] | Kesler, S., Hadi Hosseini, S. M., Heckler, C., Janelsins, M., Palesh, O., Mustian, K., & Morrow, G. (2013). Cognitive Training for Improving Executive Function in Chemotherapy-Treated Breast Cancer Survivors. Clinical Breast Cancer, 13 (4), 299–306. https://doi.org/10.1016/j.clbc.2013.02.004 |
| [25] | Copersino, M. L. (2017). Cognitive mechanisms and therapeutic targets of addiction. Current Opinion in Behavioral Sciences, 13, 91–98. https://doi.org/10.1016/j.cobeha.2016.11.005. |
| [26] | Vinogradov, S., Fisher, M., & de Villers-Sidani, E. (2012). Cognitive Training for Impaired Neural Systems in Neuropsychiatric Illness. Neuropsychopharmacology, 37 (1), 43–76. https://doi.org/10.1038/npp.2011.251. |
| [27] | Motter, J. N., Pimontel, M. A., Rindskopf, D., Devanand, D. P., Doraiswamy, P. M., & Sneed, J. R. (2016). Computerized cognitive training and functional recovery in major depressive disorder: A meta-analysis. Journal of Affective Disorders, 189, 184–191. https://doi.org/10.1016/j.jad.2015.09.022. |
| [28] | Santarnecchi, E., Bossini, L., Vatti, G., Fagiolini, A., La Porta, P., Di Lorenzo, G.,… Rossi, A. (2019). Psychological and Brain Connectivity Changes Following Trauma-Focused CBT and EMDR Treatment in Single-Episode PTSD Patients. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.00129. |
| [29] | Santarnecchi, E., Brem, A.-K., Levenbaum, E., Thompson, T., Kadosh, R. C., & Pascual-Leone, A. (2015). Enhancing cognition using transcranial electrical stimulation. Current Opinion in Behavioral Sciences, 4, 171–178. https://doi.org/10.1016/j.cobeha.2015.06.003. |
| [30] | Brem, A.-K., Almquist, J. N.-F., Mansfield, K., Plessow, F., Sella, F., Santarnecchi, E.,… Myers, E. (2018). Modulating fluid intelligence performance through combined cognitive training and brain stimulation. Neuropsychologia, 118, 107–114. https://doi.org/10.1016/j.neuropsychologia.2018.04.008. |
| [31] | Novakovic-Agopian, T., Chen, A. J.-W., Rome, S., Abrams, G., Castelli, H., Rossi, A.,… D’Esposito, M. (2011). Rehabilitation of Executive Functioning With Training in Attention Regulation Applied to Individually Defined Goals: A Pilot Study Bridging Theory, Assessment, and Treatment. Journal of Head Trauma Rehabilitation, 26 (5), 325–338. https://doi.org/10.1097/HTR.0b013e3181f1ead2. |
| [32] | Kong, F., Ma, X., You, X., & Xiang, Y. (2018). The resilient brain: psychological resilience mediates the effect of amplitude of low-frequency fluctuations in orbitofrontal cortex on subjective well-being in young healthy adults. Social Cognitive and Affective Neuroscience, 13 (7), 755–763. https://doi.org/10.1093/scan/nsy045. |
| [33] | Kong, F., Wang, X., Hu, S., & Liu, J. (2015). Neural correlates of psychological resilience and their relation to life satisfaction in a sample of healthy young adults. NeuroImage, 123, 165–172. https://doi.org/10.1016/j.neuroimage.2015.08.020. |
| [34] | Paban, V., Modolo, J., Mheich, A., & Hassan, M. (2018). Psychological resilience correlates with EEG source-space brain network flexibility. Network Neuroscience, (Just Accepted), 1–25. |
| [35] | Van der Werff, S. J., van den Berg, S. M., Pannekoek, J. N., Elzinga, B. M., and Van Der Wee, N. J. (2013). Neuroimaging resilience to stress: a review. Frontiers in behavioral neuroscience 7, 39. |
| [36] | Vidaurre, D., Hunt, L. T., Quinn, A. J., Hunt, B. A., Brookes, M. J., Nobre, A. C., and Woolrich, M. W. (2018). Spontaneous cortical activity transiently organizes into frequency specific phase-coupling networks. Nature communications 9, 2987. |
| [37] | Waugh, C. E., and Koster, E. H. (2015). A resilience framework for promoting stable remission from depression. Clinical Psychology Review 41, 49-60. |
| [38] | Kalisch, R, Muller, M., & Tuscher, O. (2015). A conceptual framework for neurobiological study of resilience. Behavioral and Brain Sciences, 38, E92. Doi: 10.1017/S0140525X1400082X. |
| [39] | Sari, Berna A., Koster, Ernst H. W., Pourtois, Gilles, Derakshan, Nazanin, Training working memory to improve attentional control in anxiety: A proof-of-principle study using behavioral and electrophysiological measures. Biological Psychology http://dx.doi.org/10.1016/j.biopsycho.2015.09.008. |
| [40] | Posner, M. I., Rothbart, M. K., & Tang, Y.-Y. (2015). Enhancing attention through training. Current Opinion in Behavioral Sciences, 4, 1–5. https://doi.org/10.1016/j.cobeha.2014.12.008. |
| [41] | Wang, H., Hua, C., Wang, Q., Fu, Q., & Fetlework, T. (2019). Training state and performance evaluation of working memory based on task-related EEG. Biomedical Signal Processing and Control, 51, 296–308. https://doi.org/10.1016/j.bspc.2019.03.002. |
| [42] | Larsen, S. E., Lotfi, S., Bennett, K. P., Larson, C. L., Dean-Bernhoft, C., & Lee, H.-J. (2019). A pilot randomized trial of a dual n-back emotional working memory training program for veterans with elevated PTSD symptoms. Psychiatry Research, 275, 261–268. https://doi.org/10.1016/j.psychres.2019.02.015. |
| [43] | Leszkowicz, E. (n. d.). Effects of cognitive training in aging in MRI/fMRI studies. 11. |
| [44] | Han, K., Chapman, S. B., & Krawczyk, D. C. (2018). Neuroplasticity of cognitive control networks following cognitive training for chronic traumatic brain injury. NeuroImage: Clinical, 18, 262–278. https://doi.org/10.1016/j.nicl.2018.01.030. |
| [45] | Zatorre, R. J., Fields, R. D., & Johansen-Berg, H. (2012). Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nature Neuroscience, 15 (4), 528–536. https://doi.org/10.1038/nn.3045. |
| [46] | Hosseini, S. M. H., Pritchard-Berman, M., Sosa, N., Ceja, A., & Kesler, S. R. (2016). Task-based neurofeedback training: A novel approach toward training executive functions. NeuroImage, 134, 153–159. https://doi.org/10.1016/j.neuroimage.2016.03.035. |
| [47] | Belleville, S., Clement, F., Mellah, S., Gilbert, B., Fontaine, F., & Gauthier, S. (2011). Training-related brain plasticity in subjects at risk of developing Alzheimer’s disease. Brain, 134 (6), 1623–1634. https://doi.org/10.1093/brain/awr037. |
| [48] | Karbach, J., & Schubert, T. (2013). Training-induced cognitive and neural plasticity. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00048. |
| [49] | Gonzalez-Escamilla, G., Muthuraman, M., Chirumamilla, V. C., Vogt, J., & Groppa, S. (2018). Brain Networks Reorganization During Maturation and Healthy Aging-Emphases for Resilience. Frontiers in Psychiatry, 9. https://doi.org/10.3389/fpsyt.2018.00601. |
| [50] | Iadipaolo, A. S., Marusak, H. A., Paulisin, S. M., Sala-Hamrick, K., Crespo, L. M., Elrahal, F.,… Rabinak, C. A. (2018). Distinct neural correlates of trait resilience within core neurocognitive networks in at-risk children and adolescents. NeuroImage: Clinical, 20, 24–34. https://doi.org/10.1016/j.nicl.2018.06.026. |
| [51] | Kong, Feng, Xiaosi Ma, Xuqun You, and Yanhui Xiang. “The Resilient Brain: Psychological Resilience Mediates the Effect of Amplitude of Low-Frequency Fluctuations in Orbitofrontal Cortex on Subjective Well-Being in Young Healthy Adults.” Social Cognitive and Affective Neuroscience 13, no. 7 (September 4, 2018): 755–63. |
| [52] | Ibrahim, G. M., Cassel, D., Morgan, B. R., Smith, M. L., Otsubo, H., Ochi, A.,… Doesburg, S. (2014). Resilience of developing brain networks to interictal epileptiform discharges is associated with cognitive outcome. Brain, 137 (10), 2690–2702. https://doi.org/10.1093/brain/awu214. |
| [53] | Joyce, K. E., Hayasaka, S., & Laurienti, P. J. (2013). The Human Functional Brain Network Demonstrates Structural and Dynamical Resilience to Targeted Attack. PLoS Computational Biology, 9 (1), e1002885. https://doi.org/10.1371/journal.pcbi.1002885. |
| [54] | De León, P., Harris, G. L., & Wargetz, A. M. (2013, July). Design construction and implementation of an inflatable lunar habitat base with pressurized rover and suit ports. In 43rd International Conference on Environmental Systems (pp. 14-18). |
| [55] | Mayer, T., Blachowicz, A., Probst, A. J., Vaishampayan, P., Checinska, A., Swarmer, T.,... & Venkateswaran, K. (2016). Microbial succession in an inflated lunar/Mars analog habitat during a 30-day human occupation. Microbiome, 4 (1), 22. |
| [56] | Sheffield, J. M., Kandala, S., Tamminga, C. A., Pearlson, G. D., Keshavan, M. S., Sweeney, J. A.,… Barch, D. M. (2017). Transdiagnostic Associations Between Functional Brain Network Integrity and Cognition. JAMA Psychiatry, 74 (6), 605. https://doi.org/10.1001/jamapsychiatry.2017.0669. |
| [57] | Donar, J., Blumberger, D. M., & Daskalakis, Z. J. (2016). The Neural Crossroads of Psychiatric Illness: An Emerging Target for Brain Stimulation. Trends in Cognitive Sc Prichep, L. S., & John, E. R. (1992). QEEG profiles of psychiatric disorders. Brain Topography, 4 (4), 249–257. Siences, 20 (2), 107–120. https://doi.org/10.1016/j.tics.2015.10.007. |
| [58] | Prichep, L. S., & John, E. R. (1992). QEEG profiles of psychiatric disorders. Brain Topography, 4 (4), 249–257. |
| [59] | Cole, M., Repovs, G., & Anticevic, A. (2014). The frontoparietal control system: A central role in mental health. The Neuroscientist, 1–13. |
| [60] | Gratton, G. (2018). Brain reflections: A circuit-based framework for understanding information processing and cognitive control. Psychophysiology, 55 (3), e13038. https://doi.org/10.1111/psyp.13038. |
| [61] | Cai, W., Chen, T., Ryali, S., Kochalka, J., Li, C.-S. R., & Menon, V. (2016). Causal Interactions Within a Frontal-Cingulate-Parietal Network During Cognitive Control: Convergent Evidence from a Multisite–Multitask Investigation. Cerebral Cortex, 26 (5), 2140–2153. https://doi.org/10.1093/cercor/bhv046. |
| [62] | Sternberg, S. (1965). High Speed Scanning in Human Memory. J. Comp. Physiol. Psychol, 59, 439. |
| [63] | Siemann, J., Herrmann, M., & Galashan, D. (2018). The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis. Scientific Reports, 8 (1). https://doi.org/10.1038/s41598-018-20049-1. |
| [64] | Braboszcz, C., & Delorme, A. (2011). Lost in thoughts: Neural markers of low alertness during mind wandering. NeuroImage, 54 (4), 3040–3047. https://doi.org/10.1016/j.neuroimage.2010.10.008. |
| [65] | WJII referencevan der Werff, S. J., van den Berg, S. M., Pannekoek, J. N., Elzinga, B. M., and Van Der Wee, N.J. (2013). Neuroimaging resilience to stress: a review. Frontiers in behavioral neuroscience 7, 39. |
| [66] | Southwick, S. M., & Charney, D. S. (2012). The science of resilience: Implications for the prevention and treatment of depression. Science, 338, 79–82. |
| [67] | Burns, R. A., Anstey, K. J., & Windsor, T. D. (2011). Subjective Well-Being Mediates the Effects of Resilience and Mastery on Depression and Anxiety in a Large Community Sample of Young and Middle-Aged Adults. Australian & New Zealand Journal of Psychiatry, 45 (3), 240–248. https://doi.org/10.3109/00048674.2010.529604. |
| [68] | Curtis, W. J., & Cicchetti, D. (2007). Emotion and resilience: A multilevel investigation of hemispheric electroencephalogram asymmetry and emotion regulation in maltreated and nonmaltreated children. Development and Psychopathology, 19 (03), 811. https://doi.org/10.1017/S0954579407000405. |
| [69] | Zhang, Y., Xie, B., Chen, H., Li, M., Guo, X., & Chen, H. (2016). Disrupted resting-state insular subregions functional connectivity in post-traumatic stress disorder: Medicine, 95 (27), e4083. https://doi.org/10.1097/MD.0000000000004083. |
| [70] | Thompson, R. W., Arnkoff, D. B., & Glass, C. R. (2011). Conceptualizing Mindfulness and Acceptance as Components of Psychological Resilience to Trauma. Trauma, Violence, & Abuse, 12 (4), 220–235. https://doi.org/10.1177/1524838011416375. |
| [71] | Craske, M. G., Kircanski, K., Zelikowsky, M., Mystkowski, J., Chowdhury, N., & Baker, A. (2008). Optimizing inhibitory learning during exposure therapy. Behaviour Research and Therapy, 46 (1), 5–27. https://doi.org/10.1016/j.brat.2007.10.003. |
| [72] | Karatsoreos, I. N., & McEwen, B. S. (2011). Psychobiological allostasis: resistance, resilience and vulnerability. Trends in Cognitive Sciences, 15 (12), 576–584. https://doi.org/10.1016/j.tics.2011.10.005. |
| [73] | Chiong, W., Wilson, S. M., D’Esposito, M., Kayser, A. S., Grossman, S. N., Poorzand, P.,… Rankin, K. P. (2013). The salience network causally influences default mode network activity during moral reasoning. Brain, 136 (6), 1929–1941. https://doi.org/10.1093/brain/awt066. |
| [74] | Raichle, M. E. (2015). The Brain’s Default Mode Network. Annual Review of Neuroscience, 38 (1), 433–447. https://doi.org/10.1146/annurev-neuro-071013-014030. |
| [75] | Cattell, R. B., & Cattell, R. B. (1987). Intelligence: its structure, growth, and action. Amsterdam; New York: New York: North-Holland; Sole distributors for the U.S.A. and Canada, Elsevier Science Pub. Co. |
| [76] | Deary, I. (2008). Why do intelligent people live longer? Nature, 456 (7219), 175–176. doi: 10.1038/456175a. |
| [77] | Li, Yonghui, Yong Liu, Jun Li, Wen Qin, Kuncheng Li, Chunshui Yu, and Tianzi Jiang. “Brain Anatomical Network and Intelligence.” Edited by Olaf Sporns. PLoS Computational Biology 5, no. 5 (May 29, 2009): e1000395. https://doi.org/10.1371/journal.pcbi.1000395. |
| [78] | Santarnecchi, Emiliano, Alexandra Emmendorfer, and Alvaro Pascual-Leone. “Dissecting the Parieto-Frontal Correlates of Fluid Intelligence: A Comprehensive ALE Meta-Analysis Study.” Intelligence 63 (July 2017): 9–28. https://doi.org/10.1016/j.intell.2017.04.008. |
| [79] | Marsman, Anouk, René C. W. Mandl, Dennis W. J. Klomp, Wiepke Cahn, René S. Kahn, Peter R. Luijten, and Hilleke E. Hulshoff Pol. “Intelligence and Brain Efficiency: Investigating the Association between Working Memory Performance, Glutamate, and GABA.” Frontiers in Psychiatry 8 (September 15, 2017). https://doi.org/10.3389/fpsyt.2017.00154. |
| [80] | Bathelt, J., G. Scerif, A. C. Nobre, and D. E. Astle. “Whole-Brain White Matter Organization, Intelligence, and Educational Attainment.” Trends in Neuroscience and Education 15 (June 2019): 38–47. https://doi.org/10.1016/j.tine.2019.02.004. |
| [81] | Santarnecchi, Emiliano, Alexandra Emmendorfer, Sayedhedayatollah Tadayon, Simone Rossi, Alessandro Rossi, and Alvaro Pascual-Leone. “Network Connectivity Correlates of Variability in Fluid Intelligence Performance.” Intelligence 65 (November 2017): 35–47. https://doi.org/10.1016/j.intell.2017.10.002. |
| [82] | Cattell, Raymond B., and Raymond B. Cattell. Intelligence: Its Structure, Growth, and Action. Advances in Psychology 35. Amsterdam; New York: New York: North-Holland; Sole distributors for the U.S.A. and Canada, Elsevier Science Pub. Co, 1987. |
| [83] | Hilger, Kirsten, Matthias Ekman, Christian J. Fiebach, and Ulrike Basten. “Efficient Hubs in the Intelligent Brain: Nodal Efficiency of Hub Regions in the Salience Network Is Associated with General Intelligence.” Intelligence 60 (January 2017): 10–25. https://doi.org/10.1016/j.intell.2016.11.001. |
| [84] | Jung, Rex E., and Richard J. Haier. “The Parieto-Frontal Integration Theory (P-FIT) of Intelligence: Converging Neuroimaging Evidence.” Behavioral and Brain Sciences 30, no. 2 (April 2007): 135–54. https://doi.org/10.1017/S0140525X07001185. |
| [85] | Barbey, A. K., R. Colom, J. Solomon, F. Krueger, C. Forbes, and J. Grafman. “An Integrative Architecture for General Intelligence and Executive Function Revealed by Lesion Mapping.” Brain 135, no. 4 (April 1, 2012): 1154–64. https://doi.org/10.1093/brain/aws021. |
| [86] | Glascher, J., D. Rudrauf, R. Colom, L. K. Paul, D. Tranel, H. Damasio, and R. Adolphs. “Distributed Neural System for General Intelligence Revealed by Lesion Mapping.” Proceedings of the National Academy of Sciences 107, no. 10 (March 9, 2010): 4705–9. https://doi.org/10.1073/pnas.0910397107. |
| [87] | Dehaene, Stanislas, Jean-Pierre Changeux, and Lionel Naccache. “The Global Neuronal Workspace Model of Conscious Access: From Neuronal Architectures to Clinical Applications.” In Characterizing Consciousness: From Cognition to the Clinic?, edited by Stanislas Dehaene and Yves Christen, 55–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. https://doi.org/10.1007/978-3-642-18015-6_4. |
| [88] | Baars, Bernard J., Stan Franklin, and Thomas Zoega Ramsoy. “Global Workspace Dynamics: Cortical ‘Binding and Propagation’ Enables Conscious Contents.” Frontiers in Psychology 4 (2013). https://doi.org/10.3389/fpsyg.2013.00200. |
| [89] | Burns, Richard A., Kaarin J. Anstey, and Timothy D. Windsor. “Subjective Well-Being Mediates the Effects of Resilience and Mastery on Depression and Anxiety in a Large Community Sample of Young and Middle-Aged Adults.” Australian & New Zealand Journal of Psychiatry 45, no. 3 (March 2011): 240–48. https://doi.org/10.3109/00048674.2010.529604. |
| [90] | Ren, Ping, Benjamin Chapman, Zhengwu Zhang, Giovanni Schifitto, and Feng Lin. “Functional and Structural Connectivity of the Amygdala Underpins Locus of Control in Mild Cognitive Impairment.” NeuroImage: Clinical 20 (2018): 297–304. https://doi.org/10.1016/j.nicl.2018.07.021. |
| [91] | Wang, Shui-Hua, Khan Muhammad, Yiding Lv, Yuxiu Sui, Liangxiu Han, and Yu-Dong Zhang. “Identification of Alcoholism Based on Wavelet Renyi Entropy and Three-Segment Encoded Jaya Algorithm.” Complexity 2018 (2018): 1–13. https://doi.org/10.1155/2018/3198184. |
| [92] | Luo, Lizhu, Kunhua Wu, Yi Lu, Shan Gao, Xiangchao Kong, Fengmei Lu, Fengchun Wu, Huawang Wu, and Jiaojian Wang. “Increased Functional Connectivity Between Medulla and Inferior Parietal Cortex in Medication-Free Major Depressive Disorder.” Frontiers in Neuroscience 12 (December 10, 2018). https://doi.org/10.3389/fnins.2018.00926. |
| [93] | Spray, Amy, Anton L. Beer, Richard P. Bentall, Vanessa Sluming, and Georg Meyer. “Microstructure of the Superior Temporal Gyrus and Hallucination Proneness - a Multi-Compartment Diffusion Imaging Study.” NeuroImage: Clinical 20 (2018): 1–6. https://doi.org/10.1016/j.nicl.2018.06.027. |
| [94] | Troche, Stefan J., Felicitas L. Wagner, Annik E. Voelke, Claudia M. Roebers, and Thomas H. Rammsayer. “Individual Differences in Working Memory Capacity Explain the Relationship between General Discrimination Ability and Psychometric Intelligence.” Intelligence 44 (May 2014): 40–50. https://doi.org/10.1016/j.intell.2014.02.009. |
| [95] | Buzzell, George A., John E. Richards, Lauren K. White, Tyson V. Barker, Daniel S. Pine, and Nathan A. Fox. “Development of the Error-Monitoring System from Ages 9–35: Unique Insight Provided by MRI-Constrained Source Localization of EEG.” NeuroImage 157 (August 2017): 13–26. https://doi.org/10.1016/j.neuroimage.2017.05.045. |
| [96] | Badre, David, and Derek Evan Nee. “Frontal Cortex and the Hierarchical Control of Behavior.” Trends in Cognitive Sciences 22, no. 2 (February 2018): 170–88. https://doi.org/10.1016/j.tics.2017.11.005. |
| [97] | Jiang, Jiefeng, Jeffrey Beck, Katherine Heller, and Tobias Egner. “An Insula-Frontostriatal Network Mediates Flexible Cognitive Control by Adaptively Predicting Changing Control Demands.” Nature Communications 6, no. 1 (December 2015). https://doi.org/10.1038/ncomms9165. |
| [98] | Tops, Mattie, and Maarten A. S. Boksem. “A Potential Role of the Inferior Frontal Gyrus and Anterior Insula in Cognitive Control, Brain Rhythms, and Event-Related Potentials.” Frontiers in Psychology 2 (2011). https://doi.org/10.3389/fpsyg.2011.00330. |
| [99] | Pourtois, Gilles, and Wim Ntebaert Wim Notebaert, eds. Cognitive and Affective Control. Frontiers Research Topics. Frontiers Media SA, 2013. https://doi.org/10.3389/978-2-88919-092-8. |
| [100] | Cole, Michael W., and Walter Schneider. “The Cognitive Control Network: Integrated Cortical Regions with Dissociable Functions.” NeuroImage 37, no. 1 (August 2007): 343–60. https://doi.org/10.1016/j.neuroimage.2007.03.071. |
| [101] | McTeague, Lisa M., Madeleine S. Goodkind, and Amit Etkin. “Transdiagnostic Impairment of Cognitive Control in Mental Illness.” Journal of Psychiatric Research 83 (December 2016): 37–46. https://doi.org/10.1016/j.jpsychires.2016.08.001. |
| [102] | He, Kaiwen, Marco Huertas, Su Z. Hong, XiaoXiu Tie, Johannes W. Hell, Harel Shouval, and Alfredo Kirkwood. “Distinct Eligibility Traces for LTP and LTD in Cortical Synapses.” Neuron 88, no. 3 (November 2015): 528–38. https://doi.org/10.1016/j.neuron.2015.09.037. |
| [103] | Sha, Zhiqiang, Tor D. Wager, Andrea Mechelli, and Yong He. “Common Dysfunction of Large-Scale Neurocognitive Networks Across Psychiatric Disorders.” Biological Psychiatry 85, no. 5 (March 2019): 379–88. https://doi.org/10.1016/j.biopsych.2018.11.011. |
| [104] | Gerstner, Wulfram, Marco Lehmann, Vasiliki Liakoni, Dane Corneil, and Johanni Brea. “Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules.” Frontiers in Neural Circuits 12 (July 31, 2018). |
| [105] | Suvrathan, Aparna. “Beyond STDP — towards Diverse and Functionally Relevant Plasticity Rules.” Current Opinion in Neurobiology 54 (February 2019): 12–19. https://doi.org/10.1016/j.conb.2018.06.011. |
| [106] | Lisman, John, and Nelson Spruston. “Postsynaptic Depolarization Requirements for LTP and LTD: A Critique of Spike Timing-Dependent Plasticity.” Nature Neuroscience 8, no. 7 (2005): 839. |
| [107] | Suvrathan, Aparna, Hannah L. Payne, and Jennifer L. Raymond. “Timing Rules for Synaptic Plasticity Matched to Behavioral Function.” Neuron 92, no. 5 (December 2016): 959–67. https://doi.org/10.1016/j.neuron.2016.10.022. |
APA Style
Curtis Cripe, Rebecca Cooper, Sonnee Weedn. (2021). Improving Psychological Resilience with Cognitive Retraining Methods Using EEG Brain Network Biomarkers: Example from UND/NASA Lunar/Martian Habitat. American Journal of Aerospace Engineering, 8(1), 14-26. https://doi.org/10.11648/j.ajae.20210801.13
ACS Style
Curtis Cripe; Rebecca Cooper; Sonnee Weedn. Improving Psychological Resilience with Cognitive Retraining Methods Using EEG Brain Network Biomarkers: Example from UND/NASA Lunar/Martian Habitat. Am. J. Aerosp. Eng. 2021, 8(1), 14-26. doi: 10.11648/j.ajae.20210801.13
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ajae.20210801.13,
author = {Curtis Cripe and Rebecca Cooper and Sonnee Weedn},
title = {Improving Psychological Resilience with Cognitive Retraining Methods Using EEG Brain Network Biomarkers: Example from UND/NASA Lunar/Martian Habitat},
journal = {American Journal of Aerospace Engineering},
volume = {8},
number = {1},
pages = {14-26},
doi = {10.11648/j.ajae.20210801.13},
url = {https://doi.org/10.11648/j.ajae.20210801.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajae.20210801.13},
abstract = {The brain is the only organ that does not heal itself once injured, but it does adapt and relearn quickly once injured. Whether the brain is cognitively optimized or is dysfunctional, the same brain networks and brain systems are at play to optimize or regulate and repair. That is why studying brain function of optimized brains from astronaut candidates, or individuals within TBI and depression populations can help both ends of the cognitive spectrum to achieve repair for dysfunctional populations or maintain optimal performance. Opportunities to increase coping capabilities neurophysiologically that impact psychological resilience are appealing both clinically and when applied to space travel. The subject of this paper is reporting the results of one such method that is currently being employed in an ongoing UND/NASA Inflatable Lunar Mars Analog Habitat (ILMAH) simulator study. Our Habitat study’s primary goals are many fold: 1) to develop a predictive profile, based on real-time measurable neurophysiological metrics that model cognitive health and resulting task/behavioral health performance; 2) demonstrate the viability of developing a wearable dry sensor device that produces a profile that can be used in extreme environments such as long duration space missions; 3) demonstrate the viability to provide crew countermeasures that mediate negative reduced resilient stress effects on an on-going and as needed basis. Our study method employs the NeuroCoach® Training System that focuses on developing targeted resilient flexible adaptability neural circuit responses through the application of brain training exercises to support psychological resilience. The training program assumption is that if key neural circuits and network systems that support resilient, adaptive behaviors are coupled with proper problem-solving skills, resilient adaptive behaviors emerge. The NeuroCoach® program is based on modern Restorative Cognitive Rehabilitation Training Methods (rCRT). The program provides in-the-moment neural network performance metrics to monitor and adjust the training difficulty level using a Brain Computer Interface. Experimental and clinical results demonstrate the program success at increasing and maintaining optimal cognitive and brain performance quantitatively (by the numbers) and qualitatively (social reintegration). We have found that by studying astronaut crew needs to remain at optimized performance during long duration space travel as well as our studies with various clinical populations with acquired brain dysfunctions presents us with a unique opportunity to compare. They are opposite ends of the spectrum, but both are instructive in what a damaged brain can potentially achieve vs what an optimized brain might suffer during deep space travel.},
year = {2021}
}
TY - JOUR T1 - Improving Psychological Resilience with Cognitive Retraining Methods Using EEG Brain Network Biomarkers: Example from UND/NASA Lunar/Martian Habitat AU - Curtis Cripe AU - Rebecca Cooper AU - Sonnee Weedn Y1 - 2021/06/28 PY - 2021 N1 - https://doi.org/10.11648/j.ajae.20210801.13 DO - 10.11648/j.ajae.20210801.13 T2 - American Journal of Aerospace Engineering JF - American Journal of Aerospace Engineering JO - American Journal of Aerospace Engineering SP - 14 EP - 26 PB - Science Publishing Group SN - 2376-4821 UR - https://doi.org/10.11648/j.ajae.20210801.13 AB - The brain is the only organ that does not heal itself once injured, but it does adapt and relearn quickly once injured. Whether the brain is cognitively optimized or is dysfunctional, the same brain networks and brain systems are at play to optimize or regulate and repair. That is why studying brain function of optimized brains from astronaut candidates, or individuals within TBI and depression populations can help both ends of the cognitive spectrum to achieve repair for dysfunctional populations or maintain optimal performance. Opportunities to increase coping capabilities neurophysiologically that impact psychological resilience are appealing both clinically and when applied to space travel. The subject of this paper is reporting the results of one such method that is currently being employed in an ongoing UND/NASA Inflatable Lunar Mars Analog Habitat (ILMAH) simulator study. Our Habitat study’s primary goals are many fold: 1) to develop a predictive profile, based on real-time measurable neurophysiological metrics that model cognitive health and resulting task/behavioral health performance; 2) demonstrate the viability of developing a wearable dry sensor device that produces a profile that can be used in extreme environments such as long duration space missions; 3) demonstrate the viability to provide crew countermeasures that mediate negative reduced resilient stress effects on an on-going and as needed basis. Our study method employs the NeuroCoach® Training System that focuses on developing targeted resilient flexible adaptability neural circuit responses through the application of brain training exercises to support psychological resilience. The training program assumption is that if key neural circuits and network systems that support resilient, adaptive behaviors are coupled with proper problem-solving skills, resilient adaptive behaviors emerge. The NeuroCoach® program is based on modern Restorative Cognitive Rehabilitation Training Methods (rCRT). The program provides in-the-moment neural network performance metrics to monitor and adjust the training difficulty level using a Brain Computer Interface. Experimental and clinical results demonstrate the program success at increasing and maintaining optimal cognitive and brain performance quantitatively (by the numbers) and qualitatively (social reintegration). We have found that by studying astronaut crew needs to remain at optimized performance during long duration space travel as well as our studies with various clinical populations with acquired brain dysfunctions presents us with a unique opportunity to compare. They are opposite ends of the spectrum, but both are instructive in what a damaged brain can potentially achieve vs what an optimized brain might suffer during deep space travel. VL - 8 IS - 1 ER -
Email: