How can Big Data help upgrade
Brain Care?
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
br...
Chaired by: Alvaro Fernandez,
CEO of Sharp Brains, YGL Class of 2012
Daniel Sternberg,
Data Scientist at Lumosity
Joan Sev...
Daniel Sternberg,
Data Scientist at Lumosity
How can Big Data help upgrade brain care?
LUMOS LABS, INC.
Daniel Sternberg, PhD
Data Scientist, Lumosity
A Large Scale Approach to Studying
and Enhancing Cognitive...
LUMOS LABS, INC.
Why we need scale
Studying the relationship between health and lifestyle factors and cognitive
performanc...
LUMOS LABS, INC.
The Lumosity Dataset
Measures of performance and enhancement
Over 45 million users
Over 1 billion gamepla...
LUMOS LABS, INC.
Health and lifestyle
Real world cognition
Social behavior
Personality
Profile
Surveys
IP address
Location...
LUMOS LABS, INC.
Preliminary examples of insights
1. Relationships between lifestyle factors and cognitive
performance
2. ...
LUMOS LABS, INC.
Effects of lifestyle factors on cognitive
performance
• Questions cover a variety of
lifestyle and health...
LUMOS LABS, INC.
Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013
B
C
FIGURE 2 | (A) Exercises used in the anal...
LUMOS LABS, INC.
Preliminary examples of insights
1. Relationships between lifestyle factors and cognitive
performance
2. ...
LUMOS LABS, INC.
Aging and learning
20,000+ users who played each
game at least 25 times
Looked at how baseline
performanc...
LUMOS LABS, INC.
Performance at baseline
Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013
LUMOS LABS, INC.
Change in performance with training
Sternberg, Ballard, Hardy, Katz, Doraiswamy & Scanlon, 2013
LUMOS LABS, INC.
Other lifestyle-cognition relationships
that we’re exploring
- Persistence of training gains over time as...
LUMOS LABS, INC.
External Collaborations
We encourage researchers with
interesting research questions
and well-formed anal...
LUMOS LABS, INC.
Data Partnerships
We are also interested in exploring partnerships with other organizations to
gain new i...
Q&A
10 minutes
(Included in session
recording)
Joan Severson,
President of Digital Artefacts
How can Big Data help upgrade brain care?
Evolution
Digital Artefacts
Evolution
Digital Artefacts
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain brain
brain
brain
Digital Artefacts
Revolution
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain
brain brain
brain
brain
Digital A...
Digital Artefacts
Attention
Working Memory
Executive Function
Visuomotor
Coordination
Spatial Processing
Speed of Processing
Verbal Recall
F...
•
•
•
•
•
•
Digital Artefacts
Servers
Raw Data
Archive
Data Processing
Tablets Smart Phones
Clinics Labs
FitBit
Driving
Health Kiosk
Apps
Digital Artefa...
No Formal Marketing to Date
Digital Artefacts
•
•
•
Digital Artefacts
Digital Artefacts
Digital Artefacts
Digital Artefacts
Digital Artefacts
Digital Artefacts
Q&A
10 minutes
(Included in session
recording)
Robert Bilder,
Chief of Medical Psychology
Neuropsychology at UCLA
Semel Institute for Neuroscience
How can Big Data help ...
Big Data Get Personal
Robert M Bilder, PhD
Michael E. Tennenbaum Family Professor of Creativity Research, and
Chief of Med...
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...
Current Sources: Clinical
Data
• NIH & Academic Research Aggregators
• dbGaP and other NCBI resources
• BD2K (“Big Data to...
NIH BD2K
summary of major challenges
• Locating data and software tools
• Gaining access to data and software tools
• Stan...
Multilevel Models from
Biology to Psychology:
Mission Impossible?
Bilder RM, Howe AG, Sabb FW
Journal of Abnormal Psycholo...
Managing assertions about brain-behavior relations
using a neural circuit description framework
Bilder, Howe & Sabb, 2013 ...
Non-clinical data
• Personal data generation and monitoring
• Active & passive monitoring (non-clinical
apps, GPS, communi...
Implications for brain health
• Lots of data about your traits
(genes, phenotypes, habits, … )
• Lots of data about your p...
Lifemap Technology
R. Bilder – TEDx SanDiego, 2010
The only thing missing
is an appropriate
conceptualization of the
known universe.
Before I share my data …
• How will I benefit personally?
• Health? Wealth? Happiness? Prestige? Fun?
Achievement? Stimula...
Healthy.ucla.edu
Promoting wellness of mind, brain
and spirit, fostering creativity, and
enhancing social connectedness
throughout the UCLA...
brain-mind-wellness
UCLA Summer Institute – 2013
personal brain
management
U-Reviews
• Academic reviews of well-being apps
• University-based, student-faculty partnership
• Health/Psych professors ...
Many thanks!
rbilder@mednet.ucla.edu
http://www.semel.ucla.edu/creativity
http://healthy.ucla.edu
Q&A
10 minutes
(Included in session
recording)
Sponsors
Partners
Thank You for
Joining Us!
To Learn More…
Summit
Recordings
Book Market
Report
sharpbrains.
com/book/
sharpbrains.
com/summit/
sharpbrains.com
/marke...
How can Big Data help upgrade brain care?
How can Big Data help upgrade brain care?
How can Big Data help upgrade brain care?
How can Big Data help upgrade brain care?
How can Big Data help upgrade brain care?
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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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  • 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.
  • Transcript of "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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