Big Data Unlocks Brain Care Insights
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Current standards of brain and mental care often rely on trials of insufficient scale, which not only limits our ability to diagnose, prevent, treat and personalize care but often leads to incorrect conclusions and undesirable results. What tools and data are becoming available via large-scale web-based and mobile applications, and how can researchers, innovators and practitioners connect with these initiatives? - Chair: Alvaro Fernandez, CEO of SharpBrains, YGL Class of 2012 - Daniel Sternberg, Data Scientist at Lumosity - Joan Severson, President of Digital Artefacts - Robert Bilder, Chief of Medical Psychology-Neuropsychology at UCLA Semel Institute for Neuroscience
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Big Data Unlocks Brain Care Insights
- 1. How can Big Data help upgrade Brain Care? brain brain brain brain brain brain brain brain brain brain brain brain brain brain brain brain brain
- 2. Chaired by: Alvaro Fernandez, CEO of Sharp Brains, YGL Class of 2012 Daniel Sternberg, Data Scientist at Lumosity Joan Severson, President of Digital Artefacts Robert Bilder, Chief of Medical Psychology Neuropsychology at UCLA Semel Institute for Neuroscience How can Big Data help upgrade brain care?
- 3. Daniel Sternberg, Data Scientist at Lumosity How can Big Data help upgrade brain care?
- 4. LUMOS LABS, INC. Daniel Sternberg, PhD Data Scientist, Lumosity A Large Scale Approach to Studying and Enhancing Cognitive Performance
- 5. LUMOS LABS, INC. Why we need scale Studying the relationship between health and lifestyle factors and cognitive performance • Individual effects tend to be small, especially for within-individual variables • Unequal sampling from different demographic groups • Large scale can allow you to estimate the actual functional relationship between continuous variables (like age) and cognitive performance Measuring enhancement • Effect sizes are generally small to medium • Small RCTs can provide basic validation for a particular cognitive training intervention, but a larger scale can allow continuous iteration and testing of smaller changes to the approach in order to maximize efficacy
- 6. LUMOS LABS, INC. The Lumosity Dataset Measures of performance and enhancement Over 45 million users Over 1 billion gameplays Cognitive training games 40+ exercises Brain Performance Test An online, repeatable assessment battery
- 7. LUMOS LABS, INC. Health and lifestyle Real world cognition Social behavior Personality Profile Surveys IP address Location estimate Location-related covariates Date of Birth Gender Education Level The Lumosity Dataset Demographics
- 8. LUMOS LABS, INC. Preliminary examples of insights 1. Relationships between lifestyle factors and cognitive performance 2. Decline in cognitive performance with age and the impact of training Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013
- 9. LUMOS LABS, INC. Effects of lifestyle factors on cognitive performance • Questions cover a variety of lifestyle and health variables that have been shown to affect cognition • Survey data for 750,000 users who took the survey between May 2011 and January 2012 • Looked at how survey responses related to performance the first time a user played one of the following games Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013 Spatial memory span Memory Matrix Measure = threshold memory span N = 161,717 males = 65,095 (40.3%), females = 96,662 (59.7%) mean age = 37.97 yrs. (sd=15.7) Arithmetic Raindrops Measure = Number correct before 3 errors N = 127,048 males = 53,169 (41.8%), females = 73,879 (58.2%) mean age = 37.34 yrs. (sd=15.6) 1-back matching task Speed Match Measure = Number correct N = 162,462 males = 65,285 (40.2%), females = 97,177 (59.8%) mean age = 37.98 yrs. (sd=15.7)
- 10. LUMOS LABS, INC. Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013 B C FIGURE 2 | (A) Exercises used in the analysis of the health and lifestyle survey. (B) The effect of reported sleep on game performance. (C) The effect of reported alcohol intake on game performance (controlling for age, gender, and level of education). quadratic effects of alcohol for all three tasks. Low to moder- as alcohol intake increased from there. The presence of nega- Effects of lifestyle factors on cognitive performance
- 11. LUMOS LABS, INC. Preliminary examples of insights 1. Relationships between lifestyle factors and cognitive performance 2. Decline in cognitive performance with age and the impact of training Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013
- 12. LUMOS LABS, INC. Aging and learning 20,000+ users who played each game at least 25 times Looked at how baseline performance and the learning trajectory in the tasks differed by age Normalized game scores based on a separate dataset of approximately 1,000,000 users per game in order to compare games to each other Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013 Spatial memory span Memory Matrix Measure = threshold memory span N = 23,109 males = 11,156 (49.1%), females = 11,562 (50.9%) mean age = 44.63 yrs. (sd=15.5) range = 18-74 Working memory 2-back Memory Match Measure = n correct in 45 seconds N = 22,718 males = 11,294 (48.7%), females = 11,855 (51.3%) mean age = 44.59 yrs (sd=15.4) range = 18-74 Arithmetic Raindrops Measure = n correct before 3 errors N = 41,338 males = 19,444 (47.0%), females = 21,894 (53.0%) mean age = 41.21 yrs. (sd=15.3) range = 18-74 V Word Bubbles Measure = n words correct in 3 minutes N = 107,478 males = 34,339 (31.9%), females = 73,139 (68.1%) mean age = 38.82 yrs. (sd=14.7) range = 18-74
- 13. LUMOS LABS, INC. Performance at baseline Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013
- 14. LUMOS LABS, INC. Change in performance with training Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013
- 15. LUMOS LABS, INC. Other lifestyle-cognition relationships that we’re exploring - Persistence of training gains over time as a function of age Ballard, Sternberg, Hardy and Scanlon (2012) SfN poster - Time of day and circadian rhythms Sternberg, Hardy and Scanlon (2013) EScoNS poster - Cognitive profiles related to job categories - Structure of individual learning curves - Geographic and education-related variables
- 16. LUMOS LABS, INC. External Collaborations We encourage researchers with interesting research questions and well-formed analysis plans to apply to access portions of our dataset. You can learn more by visiting http://hcp.lumosity.com and clicking on Get Involved. All data shared with researchers is de-identified in adherence with our Privacy Policy and Terms of Service. A few of our ongoing data collaborations: • • Effects of cognitive training on emotion regulation • Identifying meaningful cognitive decline in aging populations • Applying decision-making models to a multi-alternative forced choice task, and relationships to personality • Interactions of age and gender in the effects of sleep on cognitive performance
- 17. LUMOS LABS, INC. Data Partnerships We are also interested in exploring partnerships with other organizations to gain new insights that are only made possible by combining efforts. A few possible data “mashups”: - Genetic factors related to cognitive profiles - Real-time health and activity monitoring - Cognitive training and academic performance - Cognitive training and job performance
- 18. Q&A 10 minutes (Included in session recording)
- 19. Joan Severson, President of Digital Artefacts How can Big Data help upgrade brain care?
- 26. Attention Working Memory Executive Function Visuomotor Coordination Spatial Processing Speed of Processing Verbal Recall Fine Motor Fatigue Digital Artefacts
- 28. Servers Raw Data Archive Data Processing Tablets Smart Phones Clinics Labs FitBit Driving Health Kiosk Apps Digital Artefacts
- 29. No Formal Marketing to Date Digital Artefacts
- 36. Q&A 10 minutes (Included in session recording)
- 37. Robert Bilder, Chief of Medical Psychology Neuropsychology at UCLA Semel Institute for Neuroscience How can Big Data help upgrade brain care?
- 38. Big Data Get Personal Robert M Bilder, PhD Michael E. Tennenbaum Family Professor of Creativity Research, and Chief of Medical Psychology – Neuropsychology, UCLA Jane & Terry Semel Institute for Neuroscience & Human Behavior, Stewart & Lynda Resnick Neuropsychiatric Hospital, Departments of Psychiatry & Biobehavioral Sciences and Psychology David Geffen School of Medicine at UCLA, and College of Letters & Science at UCLA September 19th, 2013
- 39. BIG DATA 1.00E+10 1.00E+11 1.00E+12 1.00E+13 1.00E+14 1.00E+15 1.00E+16 1.00E+17 1.00E+18 1.00E+19 1.00E+20 1.00E+21 1.00E+22 1.00E+23 1.00E+24 1.00E+25 ALL WORDS EVER SPOKEN 500 Total N of LHC collisions per day PETA EXA TERA ZETTA YOTTA 600
- 41. Current Sources: Clinical Data • NIH & Academic Research Aggregators • dbGaP and other NCBI resources • BD2K (“Big Data to Knowledge”) RFA ($24M/yr) • Neurosynth, Enigma, ADNI, FITBIR • Clinically-oriented NPO’s • TranSmart (tune in tomorrow to hear: Pete Chiarelli, CEO of One Mind for Research) • Patients Like Me; MJ Fox Foundation; others • Affordable Care Act – EMR mandate • Cross-overs: personal genomics, health aggregators
- 42. NIH BD2K summary of major challenges • Locating data and software tools • Gaining access to data and software tools • Standardizing data and metadata • Extending policies and practices for data and software sharing • Organizing, managing, and processing biomedical Big Data • Developing new methods for analyzing biomedical Big Data • Training researchers for analyzing and for designing tools for analyzing biomedical Big Data effectively
- 43. Multilevel Models from Biology to Psychology: Mission Impossible? Bilder RM, Howe AG, Sabb FW Journal of Abnormal Psychology, 2013 Aug;122(3):917-27. It might be argued that the task of the psychologist, the task of understanding behavior and reducing the vagaries of human thought to a mechanical process of cause and effect, is a more difficult one than that of any other scientist. (D. O. Hebb, 1949, p. xi)
- 44. Managing assertions about brain-behavior relations using a neural circuit description framework Bilder, Howe & Sabb, 2013 - JAP
- 45. Non-clinical data • Personal data generation and monitoring • Active & passive monitoring (non-clinical apps, GPS, communication [including Web usage and wifi network usage, cameras, microphones, social net analysis) • Brain-training performance and neurofeedback data • Google, ISP’s & telecoms, automotive • Macro-monitoring: Energy and resource utilization • Power grid, water use • Aerial reconnaissance and other emission sensing (gases, light, heat, other RF)
- 46. Implications for brain health • Lots of data about your traits (genes, phenotypes, habits, … ) • Lots of data about your past states (experiences, exposures, performance, …) • Lots of predictions about your future (health, wealth, wisdom, …) • Opportunity: promote behavior change through action planning in line with values and goals
- 47. Lifemap Technology R. Bilder – TEDx SanDiego, 2010
- 48. The only thing missing is an appropriate conceptualization of the known universe.
- 50. Before I share my data … • How will I benefit personally? • Health? Wealth? Happiness? Prestige? Fun? Achievement? Stimulation? Understanding? Security? • What are the risks? • Wasting my time • Wasting my money • Compromising my privacy • Exposing myself to marketers • Exposing myself to scams or piracy • Exposing weaknesses to insurers or governments
- 52. Healthy.ucla.edu
- 53. Promoting wellness of mind, brain and spirit, fostering creativity, and enhancing social connectedness throughout the UCLA community.
- 54. brain-mind-wellness UCLA Summer Institute – 2013 personal brain management
- 56. U-Reviews • Academic reviews of well-being apps • University-based, student-faculty partnership • Health/Psych professors provide oversight and didactic input on science & design issues (reliability, validity, etc.) • SRP program – students join teams dedicated to specific app domains • Develop narrative summary and review criteria, including “snake oil factor” • Examples: • Brain-Training • Sleep • Heart Rate Variability • Meditation
- 57. Many thanks! rbilder@mednet.ucla.edu http://www.semel.ucla.edu/creativity http://healthy.ucla.edu
- 58. Q&A 10 minutes (Included in session recording)
- 60. To Learn More… Summit Recordings Book Market Report sharpbrains. com/book/ sharpbrains. com/summit/ sharpbrains.com /market-report/
Notes de l'éditeur
- It was not long ago that computers and internet search were controlled by experts
- It was not long ago that cognitive testing was controlled by experts
- The BrainBaseline platform and consumer based mobile technologies makes once specialized tools available to everyone
- The BrainBaseline platform and consumer based mobile technologies makes once specialized tools available to everyone
- The data from these tests has been combined with user-generated demographic data to develop the worlds largest normative dataset of self-administered cognitive tests.
- Ecologically valid measures of cognitive functionAbility to measure effective of therapeutic and behavior based interventions through high frequency measures meeting construct and test-retest reliability HIV-CNS clinical data analysis indicates BrainBaseline testing instruments exceed traditional clinician administered testingTesting instruments are designed to take advantage of Multiple modalities of interaction via tablet and mobile phone computing interfaces – capacitive touchscreen, camera, multi-touch, audio, voice recording,
- These are a handful of collaborations that would be of relevance to Novartis
- These are a handful of collaborations that would be of relevance to Novartis
- Datasets so large and complex that they become awkward to work with using on-hand database management tools.degree of complexity within the data set amount of value that can be derived from innovative vs. non-innovative analysis techniques use of longitudinal information supplements the analysis Mike2.0DATA IN THE WORLD – ESTIMATES OF 600 EXABYTES CIRCA 2010-2012http://www.quora.com/How-much-data-is-in-the-world-and-what-is-the-rate-of-new-data-being-addedhttp://phys.org/news/2011-02-world-scientists-total-technological-capacity.html AS OF 2011, 295 EXABYTES
- So what I am proposing is that we emphasize the crowd-sourcing of big data about human phenotypes.We are just completing work on a massive PHENOMICS study examining cognitive, behavioral, and imaging in a few thousand individuals, and great though that has been it is very expensive and time-consuming. And we still may not have enough data to answer core genetic questions.But if we can crowd-source data acquisition for these phenotypes – we can acquire data on millions of people and dramatically accelerate the revision cycle of knowledge.What we need are systems that people actually want to use and that balance utility to the user with utility to the researcher.This is a model I described in a TEDx talk in 2010, and which aims to provide users with the capacity to gather and organize information about their favorite subjects – themselves!; and then use it to make predictions about their own futures and choose actions that are compatible with their stated goals.The implementation of an engine like this is already being targeted by multiple software firms, for example Huff Po has contracted a local firm to create a GPS for the Sould to help people stay on track.I propose that we use the fabulous tranSmart infrastructure described yesterday by Dr. Athey, to reach out and gather the phenotypes of the healthy, for the good of all.
- Thanks very much Dan and One Mind team for giving me an opportunity to share a few words. I’m bob bilder and I love logos, acronyms, and slogans.This slide is a candidate bumper sticker for a program we call BruinBrains. The idea of this program is to use knowledge about the brain to help our students, faculty, and staff use our brains better.Why do I bring this up today?I do so because I think this work is riding the next wave of human evolution – where we use our brains to change our brains - and I believe this can have the single greatest impact on brain and mental health.And I know the one thing you are looking for as you have your first cup of coffee this morning is… more statistics and probability theory!
- So we are trying to advance this vision here on our campus, where the rubber meets the road. This slide illustrates a new offering this summer – our summer institute in Brain-Mind-Wellness that brings together Mindfulness practice and theory, integrative east west medicine, and the one I teach is “personal brain management.” We are already gathering web-based cognitive data on people who participate in a Bruin exercise programs, and are developing further platforms to integrate and kick the tires on systems that engage healthy people and encourage their sharing of knowledge about themselves for the common good. We believe this is an important alternative to the for profit organizations that are emerging to help aggregate data from individuals on a massive scale (for example Lumosity for cognitive data, or Facebook for everything else). What we really need are more trusted alternatives, where individual can share these valuable data about themselves for the good of all. I believe 1 Mind 4 Research can do the same – and develop itself as a trusted resource - on a national scale, and I hope we can help.Thanks for listening!
- So I hope you’ll write.And also visit us at the Tennenbaum Center for the Biology of Creativity and Like us on Facebook.Thank you.