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Robert M. Bilder, PhD, ABPP-CN
The Future of Neuropsychology:
What is the End Game?
Thomas D. Parsons, PhD
Neuropsychological Assessment and Technology:
What’s New and Why We Should Use It
Liz Angoff, PhD
Practical Applications of AI in Neuropsychology
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Robert M. Bilder, PhD, ABPP-CN
The Future of Neuropsychology: What is the End Game?
Abstract: Clinical neuropsychology is poised for revolutionary changes in how information is acquired from individuals about their brains and how we use that information. The traditional methods of testing people using clinical “laboratory” procedures is inefficient, suboptimal in both ecological and biological validity, and relies on outdated methods for data collection and analysis. New methods to acquire data using ubiquitous sensing and data aggregation will displace the older methods, and new clinical methods will be developed to maximize the benefits of the new data. Old normative tables from cross-sectional standardization studies will be replaced by dynamically updated studies of diverse individuals, both healthy and with clinical conditions, and expected scores will be based on mathematical models that consider social and structural determinants of neuropsychological function in appropriate clinical context. Clinical decision support systems fueled by evidence-based algorithms operating with all the information available in electronic health records will replace the currently conventional clinician-centered approach to differential diagnosis and treatment recommendations. Clinicians will need to identify what uniquely human aspects of clinical neuropsychology continue to be done better by humans than by machines (e.g., patient centered, clinician-partnered care). Given the pace of change the time to consider the future is now.
Robert Bilder is the Tennenbaum Family Distinguished Professor of Psychiatry and Psychology, Chief of Psychology at UCLA Health, and Director of the Center for the Biology of Creativity at UCLA’s Semel Institute for Neuroscience and Human Behavior. He is a board-certified clinical neuropsychologist and directs training programs in Clinical Neuropsychology at UCLA. His research focuses on brain and behavior, with aims to eliminate artificial boundaries between mental health and illness, and between every day and exceptional creativity. His current NIH grants examine reward mechanisms and have established a National Neuropsychology Network to aggregate data on a large scale to help develop the next generation of neuropsychological assessment methods, data sharing and data harmonization. He has a long-standing interest in promoting innovation and technology, served as a member of the expert panel at the Minnesota Update Conference for neuropsychology and heads the Disruptive Technology Initiative for the American Academy of Clinical Neuropsychology. His team recently completed the “Big C” project to examine brain function in exceptional creativity and now directs a National Endowment for the Arts Research Lab to measure impact of the arts on well-being, and he co-leads the Mind Well pod of the UCLA Semel Healthy Campus Initiative Center to enhance the psychological well-being of students, staff and faculty across the campus.
Learning Objectives:
- 1. Describe at least two limitations of current models of neuropsychological assessment.
- 2. Describe at least two innovations that are likely to be included in future models of neuropsychological assessment.
- 3. Discuss the roles of clinical neuropsychologists in future models of care.
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DEI: Current models of neuropsychological (NP) assessment suffer from both the methods lacking sensitivity to diversity and the interpretation based on stratified group norms that are outdated and often poorly tailored to understand individual patients. This presentation will highlight how future models can include more comprehensive and appropriate regressors to help estimate expected scores based on social and other contextual determinants of NP function. The presentation will also address the risks of algorithmic discrimination, and continued problems with the digital divide that may impact equitable care.
Financial disclosures: Dr. Bilder is supported in part by a grant from the NIMH (R01MH118514), the National Neuropsychology Network, that focused on data aggregation.
References:
Bilder, R. M., & Reise, S. P. (2019). Neuropsychological tests of the future: How do we get there from here? The Clinical Neuropsychologist, 33(2), 220-245.
Bilder, R. M., Widaman, K. F., Bauer, R. M., Drane, D., Loring, D. W., Umfleet, L. G., ... & Shih, S. (2023). Construct identification in the neuropsychological battery: What are we measuring? Neuropsychology, 37(4), 351.
Reise, S. P., Wong, E., Block, J., Widaman, K. F., Gullett, J. M., Bauer, R. M., ... & Bilder, R. M. (2023). Computerized adaptive test strategies for the matrix reasoning subtest of the Wechsler Adult Intelligence Scale, (WAIS-IV). Journal of the International Neuropsychological Society, 1-10.
Loring, D. W., Bauer, R. M., Cavanagh, L., Drane, D. L., Enriquez, K. D., Reise, S. P., ... & NNN Study Group. (2022). Rationale and design of the National Neuropsychology Network. Journal of the International Neuropsychological Society, 28(1), 1-11.
Thomas D. Parsons, PhD
Neuropsychological Assessment and Technology:
What’s New and Why We Should Use It
Abstract: While science and industry have embraced technology, neuropsychology has been slow to follow suit. Potential reasons for this reserve involve substantive questions pertaining to assessment; the economics of clinical practice in a changing healthcare environment; and a reticence to venture into new assessment models. While discussions continue within the field of neuropsychology regarding how to respond to technology, new capabilities continue to emerge that offer potential for enhancing the ways in which science and industry function as well as the fundamental nature of our society. Neuropsychologists in the digital age have increasing access to emerging technologies. This talk emphasizes developing scientific and technological potentials (e.g., novel simulations, sensors, data analytics) for augmented characterization of neurocognitive, behavioral, affective, and social processes. Particular emphasis is placed upon clinical extended reality (Clinical XR).
Thomas D. Parsons, PhD is the Grace Center Professor of Innovation in Clinical Education, Simulation Science, and Immersive Technology (Tenured) at Arizona State University. He directs the Computational Neuropsychology and Simulation (CNS) laboratory. Professor Parsons is also a clinical neuropsychologist and Veteran. Dr. Parsons serves on several emerging neurocognitive technology task forces: National Football League; Alzheimer's Association; and the Federal Aviation Administration. Work in the CNS lab integrates computational social science, clinical neuropsychology, psychophysiology, virtual humans, and simulation technologies for novel assessment, modeling and training (edtech) of neurocognitive, affective, and social processes. Dr. Parsons is a leading scientist in this area and he has been PI of 18 funded projects during his career and an investigator on an additional 13 funded projects (more than $20 million in funding). He is currently PI for a $5.2 million dollar grant “Virtual Insomnia Patients (VIPs) to Automate and Accelerate Dissemination of Cognitive Behavioral Therapy for Insomnia (CBT-I; see pic below).” In addition to his social networking efforts (patents for eHarmony.com's matching system, U.S. Patent Nos. 2004/6735568; 2014/0180942 A1), he has invented and validated virtual reality-based assessments and training platforms. In addition to his five books, he has more than 200 publications in peer-reviewed journals and book chapters. His contributions to neuropsychology were recognized when he received the 2013 National Academy of Neuropsychology Early Career Achievement award. In 2014, he was awarded fellow status in the National Academy of Neuropsychology. In 2023, Dr. Parsons received the Cybertherapy Lifetime Achievement Award in Paris.
Learning Objectives:
- 1. Describe the history of computerized testing and how these developments shaped the present status of automated assessment.
- 2. Identify specific changes to practice models that can increase access to neuropsychological care.
- 3. Demonstrate an understanding for the ways that current and developing technologies can enhance traditional methods of neurocognitive assessment.
- 4. Describe ways in which virtual environments and scenario-based assessment can enhance the evaluation of patient capabilities and provide data with increased ecological relevance.
- 5. Discuss incorporating computational neuropsychology and data analytics to address diagnostic and assessment issues.
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DEI: This talk will include a discussion of the ways in which algorithms have the potential to hide, speed up, and deepen discrimination while appearing neutral and even benevolent. The range of discriminatory designs that encode inequity by explicitly amplifying racial hierarchies will be included.
Financial disclosures: None
References:
Parsons, T. D., & Duffield, T. (2019). National Institutes of Health initiatives for advancing scientific developments in clinical neuropsychology. The Clinical Neuropsychologist, 33(2), 246-270.
Parsons, T.D. (2019). Ethical Challenges in Digital Psychology and Cyberpsychology. Cambridge: Cambridge University Press.
Parsons, T.D. (2017). Cyberpsychology and the Brain: The Interaction of Neuroscience and Affective Computing. Cambridge: Cambridge University Press.
Kane, R. L., & Parsons, T.D. (2017). The Role of Technology in Clinical Neuropsychology. Oxford: Oxford University Press.
Parsons, T.D. (2016). Clinical Neuropsychology and Technology: What’s New and How We Can Use It. New York: Springer Press.
Liz Angoff, PhD
Practical Applications of AI in Neuropsychology
Abstract: AI offers the potential to transform our practice as psychologists. In this talk, we move beyond increasing efficiency, and address how we can use AI to increase accessibility and truly empower the patients and families we work with. Assessment is a complex process, full of technical terms, psychological jargon, and deficit language. This creates immense barriers for those we serve to fully understand their neuropsychological profile. AI offers a promising solution by helping us transform the language we use, to create positive and simple narratives, provide summaries that make it easier to understand the main points of our work, real-world examples, and compelling metaphors tailored to individual profiles. This session will help participants become familiar and comfortable with AI tools and strategies, to improve our ability to communicate testing results.
Liz Angoff, Ph.D. is a Licensed Educational Psychologist with a Diplomate in School Neuropsychology, providing assessment and consultation services to children and their families in the Bay Area, CA. She is the author of the Brain Building Books, tools for engaging children in understanding their learning and developmental differences as part of the assessment process. More information about Dr. Liz and her work is available at www.ExplainingBrains.com
Learning Objectives:
- 1. Describe how AI works.
- 2. Use AI to create reports that are easier to read and more accessible to families.
- 3. Use AI to develop metaphors and real-world examples of different psychological processes to help patients and families understand the testing results.
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