Neuroinformatic approaches to neuropsychology represent computational neuropsychology efforts to develop, validate, and implement computer hardware and software for capturing, modeling, and simulating data in digital format (Bilder, 2011; Jagaroo, 2009; Parsons, 2016).
Collaborative Knowledgebases: Computational neuropsychology within the neuroinformatic framework develops platforms wherein assessment data can be automatically fed into databases. After which pattern seeking algorithms are employed to dredge these databases and find logical relationships among data sets. Furthermore, this approach allows neuropsychologists to decide on the ways in which centralized computer platforms can be structured for collaborative knowledgebases. [Cognitive Atlas; PubGraph; PubAtlas; OpenfMRI; Phenowiki; Neuropsychnorms]
Modeling and Simulation (Cognitive Ontologies): Modeling and simulation tools can be developed to refine both neuropsychological models of normal cortical function and models of neuropsychological dysregulation. Moreover, neuroinformatic techniques can enhance integration of neuropsychological data with converging results from other neuroscience subdisciplines of neuroscience. The complexity and the computational sophistication of neuroinformatic-based neuropsychology models and simulations offer new dimensions to neuropsychology.
Neuroinformatics Groups: For more information on neuroinformatics in neuropsychology, visit the Society for Neuroinformatics in Neuropsychology (site under construction); Consortium for Neuropsychiatric Phenomics; International Neuroinformatics Coordinating Facility.
CNS Lab and Neuroinformatics: The CNS lab uses neuroinformatics (artificial neural networks (ANNs), machine learning, big data, meta-analyses, and novel data analytics) for research into automatic and controlled processing as well as the question of whether cognition is a general construct (e.g. processing speed) or a specific set of differentiated resources (e.g. verbal and spatial working memory) that dedifferentiate across the lifespan resulting in a common factor (see Parsons et al., 2005 for example). Moreover, the CNS Lab uses a series of confirmatory and exploratory models to determine whether observed individual differences are impacted by the covariance present among all cognitive tests.
Findings from previous work revealed that individual differences tended to impact neuropsychological assessment outcomes when common variance is controlled. This suggests that this methodology may enhance the ability to detect domain specific effects (see Parsons et al., 2005). Dr. Parsons has also compared the data analytic applicability of a backpropagated ANN with regression (see Parsons et al., 2004). Findings revealed that the backpropagated ANN outperformed the regression model. This work has been extended to machine learning (Iyer et al., 2009; MacMahan, Parberry, Parsons, 2015a. 2015b, 2015c; Wu (Parsons) et al., 2010a, 2010b, 2010c, 2010d; Wu & Parsons, 2011a, 2011b, 2013).
Artificial Neural Network Architectures and Analytics:
·McMahan, T., Parberry, I., & Parsons, T.D. (2015). Evaluating Player Task Engagement and Arousal using Electroencephalography. Proceedings of the 6th International Conference on Applied Human Factors and Ergonomics, Las Vegas, USA July 26-30, 2015.(PDF)
·McMahan, T., Parberry, I., & Parsons, T.D. (2015). Evaluating Electroencephalography Engagement Indices during Video Game Play. Proceedings of the Foundations of Digital Games Conference, June 22 - June 25, 2015. (PDF)
·McMahan, T., Parberry, I., & Parsons, T.D. (2015). Modality Specific Assessment of Video Game Player's Experience Using the Emotiv Entertainment Computing, 7, 1-6.(PDF)
·Taylor, J., Parberry, I., & Parsons, T.D. (2015). Comparing Player Attention on Procedurally Generated vs. Hand Crafted Sokoban Levels with an Auditory Stroop Test. Proceedings of the Foundations of Digital Games Conference, June 22 - June 25, 2015.
·Wu, D., Lance, B., & Parsons, T.D. (2013). Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection. PLOS ONE, 1-18. (PDF)
·Wu, D., & Parsons, T.D. (2012). Customized Cognitive State Recognition Using Minimal User-Specific Data. Proceedings of the Military Health Systems Research Symposium, Fort Lauderdale, FL, August 2012.(PDF)
·Wu, D., & Parsons, T.D. (2011). Active Learning for Arousal Classification. Lecture Notes in Computer Science, 6975, 132-141. (PDF)
·Wu, D., & Parsons, T.D. (2011). Inductive Transfer Learning for Handling Individual Differences in Affective Computing. Lecture Notes in Computer Science, 6975, 142-151 (PDF)
·Wu, D., Parsons, T.D., Mower, E., & Narayanan, S. (2010). Speech Emotion Estimation in 3D Space. Proceedings of the IEEE International Conference on Multimedia & Expo, Singapore, July 19-23, 2010. (PDF)
·Wu, D., Parsons, T.D., & Narayanan, S.S. (2010). Acoustic Feature Analysis in Speech Emotion Primitives Estimation. Proceedings of InterSpeech, Makuhari, Japan, September 26-30, 2010. (PDF)
·Wu, D., Courtney, C., Lance, B., Narayanan, S.S., Dawson, M., Oie, K., & Parsons, T.D. (2010). Optimal Arousal Identification and Classification for Affective Computing: Virtual Reality Stroop Task. IEEE Transactions on Affective Computing, 1, 109-118.(PDF)
·Parsons, T.D., Rizzo, A.A., & Buckwalter, J.G. (2004). Backpropagation and Regression: Comparative Utility for Neuropsychologists. Journal of Clinical and Experimental Neuropsychology, 26, 1, 95-104. (PDF)
·Parsons, T.D., & Buckwalter, J.G. (2002). Aging and Cognitive Decline in Women using Estrogen Replacement Therapy: Comparison of Backpropagation and Regression Models. Archives of Clinical Neuropsychology, 17, 8, 728-729.
·Parsons, T.D., Rizzo, A.A., & Buckwalter, J.G. (2002). Comparison of Backpropagation and Regression for Estimation of Processing speed. Journal of the International Neuropsychological Society, 8, 2, 153-154.
·ENNA--eHarmony Neural Network Architecture: Method and Apparatus for eHarmony's Matching System. (U.S. Patent Submitted). Patent with Buckwalter, J.G., Forgatch, G., Carter, S., & Warren, N.C.
Meta-analyses, Epidemiology, & Power:
·Parsons, T.D., & Rizzo, A.A. (2008). Affective Outcomes of Virtual Reality Exposure Therapy for Anxiety and Specific Phobias: A Meta-Analysis. Journal of Behavior Therapy and Experimental Psychiatry, 39, 250-261. (PDF)
·Robertson, K.R., Smurzynski, M., Parsons, T.D., Wu, K., Bosch, R., Wu, J., McArthur, J.C., Collier, A.C., Evans, S.R., Ellis, R.J. (2007). The prevalence and incidence of neurocognitive impairment in the HAART era. AIDS, 21, 1915-1921.
·Iudicello, J.E., Woods, S.P., Parsons, T.D., Moran, L.M., Carey, C.L., Grant, I., & The HNRC Group. (2007). Fluency in HIV-1 Disease: A Meta-Analytic Review. Journal of the International Neuropsychological Society, 13, 183-189. (PDF)
·Parsons, T.D., Rogers, S.A., Braaten, A.J., Woods, S.P., and Tröster, A.I. (2006). Cognitive Sequelae of Subthalamic Nucleus Deep Brain Stimulation in Parkinson's Disease: A Meta-Analysis. Lancet Neurology, 5, 578-88.(PDF)
·Woods, S.P., Rippeth, J.D., Carey, C.L., Conover, E., Parsons, T.D., & Tröster, A.I. (2006). Statistical power of studies examining the cognitive effects of subthalamic nucleus deep brain stimulation in Parkinson's disease. The Clinical Neuropsychologist, 20, 27-38. (PDF)
·Kilian, S., Parsons, T.D., Blesi, G., Grills, Fonteh, A., Pogoda, J., Cowan, R. & Harrington, M. (2002). Neurocognitive performance and molecular phenotype of migraineurs and controls. Headache.
·Harrington, M., Blesi, G., Kilian, S., Parsons, T.D., Grills, C., Partma, C., Chequer, R., Pogoda, J., Fonteh, A., & Cowan, R. (2002). Classification and case definitions of headache and non-headache cohorts in a molecular composition study. Headache.
·Grills, C., Blesi, G., Kilian, S., Parsons, T.D., Fonteh, A., Chequer, R., Pogoda, J., Cowan, R., & Harrington, M. (2002). Psychological and Molecular Phenotypes of Interictal Migraineurs. Headache.
·Harrington, M., Swedberg, S., Biringer, R., Stochaj, W., Kilian, S., Parsons, T.D., Grills, C., Pogoda, J., Fonteh, A., & Cowan, R. (2002). Proteome analysis of cerebrospinal fluid from headache and non-headache cohorts. Headache.
·Harrington, M., Dalleska, N., Barcellos, T., Navarro, E., McLaughlin, J., Kilian, S., Parsons, T.D., Grills, C., Fonteh, A., Pogoda, J., & Cowan, R. (2002). Elemental analyses of cerebrospinal fluid, blood plasma and urine from headache and non-headache cohorts, Headache.