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How can Big Data help upgrade brain care?

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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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How can Big Data help upgrade brain care?

  1. 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. 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. 3. Daniel Sternberg, Data Scientist at Lumosity How can Big Data help upgrade brain care?
  4. 4. LUMOS LABS, INC. Daniel Sternberg, PhD Data Scientist, Lumosity A Large Scale Approach to Studying and Enhancing Cognitive Performance
  5. 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. 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. 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. 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. 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. 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. 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. 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. 13. LUMOS LABS, INC. Performance at baseline Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013
  14. 14. LUMOS LABS, INC. Change in performance with training Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013
  15. 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. 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. 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. 18. Q&A 10 minutes (Included in session recording)
  19. 19. Joan Severson, President of Digital Artefacts How can Big Data help upgrade brain care?
  20. 20. Evolution Digital Artefacts
  21. 21. Evolution Digital Artefacts
  22. 22. brain brain brain brain brain brain brain brain brain brain brain brain brain brain brain brain brain Digital Artefacts
  23. 23. Revolution brain brain brain brain brain brain brain brain brain brain brain brain brain brain brain brain brain Digital Artefacts
  24. 24. Digital Artefacts
  25. 25. Attention Working Memory Executive Function Visuomotor Coordination Spatial Processing Speed of Processing Verbal Recall Fine Motor Fatigue Digital Artefacts
  26. 26. • • • • • • Digital Artefacts
  27. 27. Servers Raw Data Archive Data Processing Tablets Smart Phones Clinics Labs FitBit Driving Health Kiosk Apps Digital Artefacts
  28. 28. No Formal Marketing to Date Digital Artefacts
  29. 29. • • • Digital Artefacts
  30. 30. Digital Artefacts
  31. 31. Digital Artefacts
  32. 32. Digital Artefacts
  33. 33. Digital Artefacts
  34. 34. Digital Artefacts
  35. 35. Q&A 10 minutes (Included in session recording)
  36. 36. Robert Bilder, Chief of Medical Psychology Neuropsychology at UCLA Semel Institute for Neuroscience How can Big Data help upgrade brain care?
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. 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
  41. 41. 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)
  42. 42. Managing assertions about brain-behavior relations using a neural circuit description framework Bilder, Howe & Sabb, 2013 - JAP
  43. 43. 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)
  44. 44. 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
  45. 45. Lifemap Technology R. Bilder – TEDx SanDiego, 2010
  46. 46. The only thing missing is an appropriate conceptualization of the known universe.
  47. 47. 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
  48. 48. Healthy.ucla.edu
  49. 49. Promoting wellness of mind, brain and spirit, fostering creativity, and enhancing social connectedness throughout the UCLA community.
  50. 50. brain-mind-wellness UCLA Summer Institute – 2013 personal brain management
  51. 51. 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
  52. 52. Many thanks! rbilder@mednet.ucla.edu http://www.semel.ucla.edu/creativity http://healthy.ucla.edu
  53. 53. Q&A 10 minutes (Included in session recording)
  54. 54. Sponsors Partners Thank You for Joining Us!
  55. 55. To Learn More… Summit Recordings Book Market Report sharpbrains. com/book/ sharpbrains. com/summit/ sharpbrains.com /market-report/

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