![Circadian Genetics
• Sleep Longer
[Sleep Duration Polygenic Score]
Hours of Asleep
Change in
Sleep Schedule
• Wake up earlier
[Chronotype Polygenic Score]
Genetic Predisposition to...](https://image.slidesharecdn.com/01-190711213147/75/Rethinking-and-Retooling-Brain-Health-and-Mental-Health-59-2048.jpg)
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Rethinking and Retooling Brain Health and Mental Health
The document discusses the challenges in mental health care, emphasizing the need for improved diagnostic precision and engagement, as many individuals fail to receive adequate support. It proposes a shift towards digital health solutions and continuous monitoring of cognitive and emotional states through technology, enhancing the ability to identify and address mental health issues proactively. The importance of early detection of brain diseases and the role of digital biomarkers are highlighted as essential for effective intervention and treatment.
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Rethinking and Retooling Brain Health and Mental Health
- 5. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Rethinking and Retooling Brain Health and Mental Health Tom Insel, MD Co-founder and President, Mindstrong Health May 7, 2019
- 6. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. What is the Problem We Need to Solve?
- 7. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. What is the Problem We Need to Solve? No change in morbidity or mortality 25 100 75 50 125 Peak (1965 - 1995) Current (2010 - 2018) Suicide Stroke Heart Disease AIDS Childhood Leukemia US Burden of Disease Collaborators, JAMA, 2013. https://www.cdc.gov/vitalsigns/suicide/index.html
- 8. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Why have we failed to bend the curve? 60% not receiving careLack of Engagement Quality Fragmented, episodic, delayed Imprecise Dx Lack of biological validity Lack of Measurement We don’t manage what we don’t measure
- 9. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. DSM approach: Clinical consensus Categorical Non-etiologic Non-therapeutic “Built for billing” 265 categories – symptom based Imprecise Dx
- 10. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. DSM approach: Clinical consensus Categorical Non-etiologic Non-therapeutic “Built for billing” R-DoC approach: Data driven (social, cognitive, neural, genomic) Dimensional Biological-cognitive foundation Validated with clinical response “Built for research” 265 categories – symptom based 5 domains: (neg affect, pos affect, cognition, social, arousal) Imprecise Dx
- 11. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. DIAGNOSTIC PRECISION IN MENTAL HEALTH Depression Anxiety PTSD Bipolar Historical Approach Grouping patients by symptoms; resulting heterogeneity New Temporal Insights Understanding patients based on dynamic patterns in cognitive, neural and clinical constructs Default Mode Attention Negative Affec t Cognitive Control Salienc e Rewa rd Processing Mindstrong Approach Digital stratification into homogenous response subgroups within MDD within PTSD within Bipolar Assay target engagement with continuous digital measures Executive Function Attention Working Memory Impulsivity Information Proc essing Verbal Fluenc y Anxiety Anhedonia Mood Guilt Suic idality Insomnia Lethargy Psyc homotor COGNITIVE NEURAL CLINICAL Grisanzio et al, JAMA Psychiatry, 2017
- 12. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. SOURCES: NSDUH (2013); Kessler, Chiu, Demler, & Walters (2005); Wang, Lane, Olfson, Pincus, Wells, Kessler (2005); Merikangas , He, Burstein, Swendsen, Avenevoli, Case, Georgiades, Heaton, Swanson, Olfson (2011), SSA Publication 13-11827 (2014) ~44 million people in the U.S. with any disorder; ~10 million “serious” Lack of Engagement
- 13. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. SOURCES: NSDUH (2013); Kessler, Chiu, Demler, & Walters (2005); Wang, Lane, Olfson, Pincus, Wells, Kessler (2005); Merikangas , He, Burstein, Swendsen, Avenevoli, Case, Georgiades, Heaton, Swanson, Olfson (2011), SSA Publication 13-11827 (2014) ~44 million people in the U.S. with any disorder; ~10 million “serious” Receive Services Lack of Engagement
- 14. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Underserved Receive Minimally Acceptable Care SOURCES: NSDUH (2013); Kessler, Chiu, Demler, & Walters (2005); Wang, Lane, Olfson, Pincus, Wells, Kessler (2005); Merikangas , He, Burstein, Swendsen, Avenevoli, Case, Georgiades, Heaton, Swanson, Olfson (2011), SSA Publication 13-11827 (2014) ~44 million people in the U.S. with any disorder; ~10 million “serious” Receive Services Lack of Engagement
- 15. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Underserved Receive Minimally Acceptable Care SOURCES: NSDUH (2013); Kessler, Chiu, Demler, & Walters (2005); Wang, Lane, Olfson, Pincus, Wells, Kessler (2005); Merikangas , He, Burstein, Swendsen, Avenevoli, Case, Georgiades, Heaton, Swanson, Olfson (2011), SSA Publication 13-11827 (2014) ~44 million people in the U.S. with any disorder; ~10 million “serious” No Benefit Some Benefit Full Benefit Receive Services The 40-40-30 Rule Lack of Engagement
- 16. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Fragmented and Episodic Psychological Care Medical Care Social Supports Family Support Quality of Care
- 17. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Childhood Adolescence Early Adulthood Adulthood Psychosocial Functioning Psychotic Symptoms Psychosis onset Prodromal period Delay Quality of Care
- 18. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Childhood Adolescence Early Adulthood Adulthood Psychosocial Functioning Psychotic Symptoms Psychosis onset Prodromal period Duration of Untreated Psychosis = 74 weeks Addington et al, Psychiatric Services, 2015 Delay Quality of Care Treatment onset
- 19. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Why have we failed to bend the curve? 60% not receiving careLack of Engagement Quality Fragmented, episodic, delayed Imprecise Dx Lack of biological validity Lack of Measurement We don’t manage what we don’t measure
- 20. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. WHAT WE DO TODAY • Subjective • Episodic • Clinic-based • High burden MEASURING MOOD, COGNITION, AND BEHAVIOR
- 21. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. WHAT WE DO TODAY • Subjective • Episodic • Clinic-based • High burden MEASURING MOOD, COGNITION, AND BEHAVIOR WHAT WE NEED • Objective • Continuous • Ecological • Passive
- 22. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. 22 Smartphones A medical tool for global health – improving diagnosis and connecting care Over 4 billion globally and 7 billion by 2024 Over 70 daily checks Over 2600 daily “touches” More ubiquitous than clean water, indoor plumbing, and stable electricity0 1000 2000 3000 4000 5000 6000 7000 8000 2017 2018 Forecast 2024 Smartphone Penetration (in millions) Middle East & Africa India South East Asia & Oceania North East Asia Central & Eastern Europe Western Europe Latin America North America https://www.ericsson.com/assets/local/mobility-report/documents/2018/ericsson-mobility-report-november-2018.pdf
- 23. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. 23 SENSORS Activity Location Sociality VOICE/SPEECH Prosody Sentiment Coherence HCI - KEYBOARD Reaction Time Attention Memory Executive Function DIGITAL PHENOTYPING A New Kind of Biomarker
- 24. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. 24 SENSORS Activity Location Sociality VOICE/SPEECH Prosody Sentiment Coherence HCI - KEYBOARD Reaction Time Attention Memory Executive Function DIGITAL PHENOTYPING A New Kind of Biomarker Digital phenotype can also include “digital exhaust” (social media posts, search terms, AI personal assistants etc.)
- 25. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. 25 SENSORS Activity Location Sociality VOICE/SPEECH Prosody Sentiment Coherence HCI - KEYBOARD Reaction Time Attention Memory Executive Function DIGITAL PHENOTYPING A New Kind of Biomarker Digital phenotype can also include “digital exhaust” (social media posts, search terms, AI personal assistants etc.) Feature Extraction Pattern Recognition Machine Learning Digital Phenotype = Cognition, Mood, Behavior
- 26. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Cognitive performance Digital biomarker Dagum, Digital Medicine, 2018 Volunteers (n = 27) compared on neurocognitive tests and digital biomarkers. Correlations across multiple cognitive trait measures = .7 - .8 (roughly test–retest variance) Digital Biomarkers and Cognitive Traits
- 27. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. pa ge detecting deterioration to prevent crisis I'm doing a lot better. I was experiencing a lot of auditory hallucinations. They made it difficult to sleep which made things progressively worse. I checked myself into the hospital. They adjusted my medications, gave group therapy, and monitored me. I believe I slept for 12 hours each night 3 days in a row. What a relief! The hallucinations finally subsided.
- 28. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. tracking brain health in a 48 year old woman under care for bipolar disorder with psychosis
- 29. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. WHAT WE DO TODAY • Subjective • Episodic • Clinic-based • High Burden MEASURING MOOD, COGNITION, AND BEHAVIOR WHAT WE NEED Objective Continuous Ecological Passive
- 30. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Mobile Interventions CBT, DBT, IPT; Coaching; Peer Support; Crisis Intervention The Digital Health Landscape Not an App But An Operating System
- 31. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Mobile Interventions CBT, DBT, IPT; Coaching; Peer Support; Crisis Intervention The Digital Health Landscape Learning Engine Not an App But An Operating System
- 32. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential.
- 33. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Why have we failed to bend the curve? 60% not receiving careLack of Engagement Quality Fragmented, episodic, delayed Imprecise Dx Lack of biological validity Lack of Measurement We don’t manage what we don’t measure
- 34. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Lack of Engagement Lack of Quality Lack of Measurement Imprecise Dx Objective, continuous, ubiquitous measures The Digital Future for Brain Health?
- 35. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Lack of Engagement Lack of Quality Lack of Measurement Imprecise Dx Objective, continuous, ubiquitous measures Anonymous, person-centered online care The Digital Future for Brain Health?
- 36. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Lack of Engagement Lack of Quality Lack of Measurement Imprecise Dx Objective, continuous, ubiquitous measures Anonymous, person-centered online care Coordinated, connected care with quality metrics The Digital Future for Brain Health?
- 37. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Lack of Engagement Lack of Quality Lack of Measurement Imprecise Dx Objective, continuous, ubiquitous measures Anonymous, person-centered online care Coordinated, connected care with quality metrics The Digital Future for Brain Health? Digital smoke alarms for early detection of recovery and relapse
- 38. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. Digital Tools to Reduce Suicide Predictive signals HCI data Speech/text signals Online classifiers Crisis intervention Upskilling tools for volunteers On demand support Social networks Postvention Care management Peer support AI nurse High Tech + High Touch
- 39. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. The Digital Mental Health Challenge Where Are We? Improve real world outcomes Adopted by patients and providers Save time and money Value? Does it work?
- 40. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. The Digital Mental Health Challenge
- 41. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. The Digital Mental Health Challenge
- 42. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. The Digital Mental Health Challenge Where Are We? Improve real world outcomes Adopted by patients and providers Save time and money Value? Does it work? Privacy – Surveillance? Agency – To me or By me? Data – Who? When? Where? Trust? Acceptance?
- 43. ©2014 Mindstrong. AllRights Reserved. Proprietary and Confidential. The Digital Mental Health Challenge Where Are We? Improve real world outcomes Adopted by patients and providers Save time and money Value? Does it work? Privacy – Surveillance? Agency – To me or By me? Data – Who? When? Where? Trust? Acceptance? Empowering Patients + Families with Information and Connection
- 44. ©2017 Mindstrong Inc.All Rights Reserved. Proprietary and Confidential. Transforming Brain Health [email protected] Thank You!
- 45. How to detectproblems early: Examples in Alzheimer’s Disease, Parkinson’s, anxiety and PTSD Chaired by: Dr. Deanna Belsky, Associate at Dolby Family Ventures Jan Samzelius, Co-Founder and Chief Scientist of NeuraMetrix Dr. Tony Chang, Associate at M Ventures Dr. Srijan Sen, Professor of Depression and Neuroscience at University of Michigan
- 46. Physician Training asa Model to Identify Predictors and Preventative Interventions for Depression under Stress Srijan Sen MD PhD University of Michigan [email protected]
- 48. ) Sample - 12 Cohorts -18,340 subjects (61% participation) - 2051 in 2018 cohort to date - 80+ Sites - Across Specialties - Internal Medicine, Transitional, Surgery, Pediatrics, OB-GYN, Psychiatry, Emergency Medicine, Family Medicine
- 49. 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% Before Internship 3Months 6 Months 9 Months 12 Months PHQDepressionRate Intern Assessment Time
- 51. Telomere Length Changewith Internship Biological Psychiatry – in press
- 52. Methylation Changes With Stress IndividualFactors Longer Duty Hours Medical Errors Stressful Life Events Program Factors Low Quality Faculty Feedback Higher Doximity Research Ranking Lower Ethnic Diversity Polygenic Risk Score High Neuroticism Low Subjective Well-Being Childhood Stress History of Depression Female Gender Minority Ethnicity Sleep Quality US Medical School
- 53.
- 54. Genomic Risk Scoreand Stress p=5x10-12
- 55. Sample Size 2014-2016: 37 2017:546 2018: 2051
- 56. Sleep Mood b=0.12;p<0.001 Mood Sleep b=0.05; p=0.04
- 57. Change in Sleepand Wake Time with Internship Stress
- 58. • More day-to-dayvariation in time asleep and wake time associated with higher depressive symptom score during internship Sleep Variation and Depression • More day-to-day variation in time asleep and wake time associated with higher depressive symptom score during internship
- 59. Circadian Genetics • SleepLonger [Sleep Duration Polygenic Score] Hours of Asleep Change in Sleep Schedule • Wake up earlier [Chronotype Polygenic Score] Genetic Predisposition to...
- 62. • Current state moderates effectof messages slope = -.074 (on square root scale) p-value = .007 slope = -.039 (on square root scale) p-value = .01 slope = -.051; p-value = .001
- 63. Promise of DigitalTools in Depression • Prediction – objective, real-time biomarkers – elucidate pathway from biology to phenotype • Prevention – improved precision in customizing for the right person at the right time
- 64. Acknowledgements • Participating internsand program directors • Key Personnel – Yu Fang – Elena Frank – Joan Zhao – Douglas Mata – Yu Fang – David Kalmbach – Arbormoon Software Funding: NIMH, UM Depression Center, Alfred A. Taubman Medical Institute, American Foundation for Suicide Prevention • Key Collaborators – Connie Guille – Zhenke Wu – Ambuj Tewari – Danny Forger – Peter Song – Margit Burmeister – Todd Arnedt – Laura Scott
- 65. ConfidentialMay 7, 2019 DetectingBrain Diseases Early
- 66. • Bill Gates:…Alzheimer’s starts damaging the brain more than a decade before symptoms start showing. That’s probably when we need to start treating people to have the best shot at an effective drug. • 400 trial failures due to lack of early detection Early Detection is Key to Solving Brain Diseases
- 67. • Extremely highquality data • So sensitive, it can pick up very slight changes • So statistically powerful that diagnostic capability can be proven • Basically, 0% false negatives or positives, AUC=1.0 • This means much higher bar than we are used to – r2 of 0.9 or better • Generates lots of variables, so fingerprints for each disease can be generated Early Detection: Requirements
- 68. •Can be deployedwidely • Easy on the person • Runs in background • No additional hardware • Low cost • Easy to implement Early Detection: Requirements
- 69. •Can be donewith significant risk factors • Huntington’s (HD) 100% • Alzheimer’s (AD) APOE4 • 1 – 20% of population – 35-40% risk • 2 – 2% of population – 100% risk Challenge #1: Proving Diagnostic Capability
- 70. Huntington’s Provides aPromising Model 0.1 0.2 0.3 0.4 0.5 0.5 1.5 2.5 3.5 Inconsistency by group Controls Presymptomatic Symptomatic Inconsistency
- 71. Huntington’s Provides aPromising Model 0.1 0.2 0.3 0.4 0.5 0.5 1.5 2.5 3.5 Inconsistency by group Controls Presymptomatic Symptomatic Inconsistency DiseaseActiveHealthy
- 72. Challenge #2: DevelopDisease Fingerprints 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 5 10 15 20 25 30 35 PD patients and controls PD patients Controls
- 73. •Assemble AD APOE4cohort or very large random sample •Identify genetic risk factors for other diseases •Continue development of fingerprints Next Steps
- 74. Confidential May 7, 2019 DetectingBrain Diseases Early Jan Samzelius, CEO [email protected] +1415-420-6636
- 75. 2019 SharpBrains VirtualSummit: The Future of Brain Health How to detect problems early Tony Chang 07 May 2019
- 76. 76 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | Healthcare Next generation of drugs Biologics and small- molecules Investment focus: Oncology, Immunology & Immuno-Oncology First-in-class Life Sciences Next generation life science technologies Next generation tools and services for biotech research & production Tools and laboratory supply for the academic research and industrial testing Performance Materials Next generation materials Innovative display materials, pigments and functional materials, and applications High-tech materials for electronics and applications Investments in new business fields Cross-sector and brand new business verticals for the Merck Group Interest in multiple fields, ranging from deep health- tech solutions, agtech & food tech, to deep tech enabling technologies New Businesses At M Ventures we discover exciting ideas and novel technologies by thinking beyond our own imagination; helping our partners create tomorrow’s greatest ideas, building the future together.
- 77. 77 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | Pushing the boundary Benefits of earlier brain health detection is well recognized Early symptom At risk group Triggers screening Clinical diagnosis Treatment Functional decline Earlier detection benefits: Medical: earlier access to intervention to improve outcome Financial: better disease outcome leads healthcare cost saving R&D: open up new time windows for treatment investigation Social & emotional: Enable patients to plan ahead while cognitively able to understand choices
- 78. 78 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | Road blocks Status quo hampers early brain health detection Social Biology Tools Social stigma Lack of awareness Subjective measures Accessibility of tools Heterogenous causes Inadequate biomarkers
- 79. 79 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | Untapped data sources The human body generates vast amounts of proxy data Source: Kourtis et al., npj Digital Medicine, 2019
- 80. 80 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | Turning data into action Proxy data can be useful in different context of early detection Know the patient population High degree of control over environment Know the patient population Low degree of control over environment Don’t know the patient population Low degree of control over environment
- 81. 81 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | In pursuit Investments into digital proxies for early detection is on the rise 0 5 10 15 20 25 0 20 40 60 80 100 120 2014 2015 2016 2017 2018 No.ofdeals USDmillion N.A. Europe APAC Israel Deal value ($m) Source: Pitchbook; M Ventures analysis
- 82. 82 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | M Ventures investment – Sonde Health Sonde is developing a voice-based health measurement tech platform Founded: 2015 Location: Boston, MA, US Investments: Closed $16m Series A in Apr 2019 IP: Exclusive license from MIT Lincoln Lab
- 83. 83 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | M Ventures investment – Sonde Health Speech production starts in the brain – voice as a proxy Complexity of Core Speech Network Approximate view of speech production Speech Concept Sentences and words Syllables and phonemes Prosodics Phonetic representation: Position/state of articulators/ folds Timing and coordination of articulators and vocal folds Neural signaling Muscle activation Differentbrainregions Auditoryandtactileself-monitoring Sentence/word s from concept Prosodics Syllables Phonemes Articulator positions Vocal cord source state Articular and cords timing/coordination Neural motor signals Auditory feedback Tactile feedback Parkinson’s mTBI Cognitive Impairment
- 84. 84 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | M Ventures investment – Sonde Health Translate vocal signatures into brain health measurement models Vocal biomarker 1 Distinct Acoustic Feature of voice correlated with a with an element of health, e.g. pitch slope Vocal biomarker 2 Distinct Acoustic Feature of voice correlated with a with an element of health, e.g. Formant Frequencies Vocal biomarker 3 Distinct Acoustic Feature of voice correlated with a with an element of health, e.g. Harmonic to Noise Ratio Health measures are the outcome of Sonde’s machine learning scoring models Vocal biomarkers are distinct acoustic features, correlated with an element of health or disease Health measure • Disease screening • Response to treatment • Etc. etc.
- 85. 85 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | M Ventures investment – Sonde Health Voice-based health measure on self-owned device is feasible Proof of concept in depression • Voice sample collected from more than 4k subjects from U.S. and India • Samples of only 6 seconds of free speech were collected, no baseline • Sample collection was performed remotely, on self-owned smartphones (no site personnel, no lab technologies) by means of app download • The performance of Sonde’s technology was on par with gold standard clinical screening instrument Sonde’s depression screening vocal biomarker achieved • 80% true positive rate (or sensitivity) • below 10% false positive rate (or >90% specificity) reported PHQ-9 sensitivity=77.5% & specificity=86.7%
- 86. 86 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | Voice as a proxy Big tech brands also showing interest to measure health through voice Amazon is building a 'health & wellness’ team within Alexa as it aims to upend health care – CNBC, May 2018
- 87. 87 2019 SharpBrainsVirtual Summit: The Future of Brain Health | May 2019 | Key take away New data opens up new opportunities, but also new challenges Extending early brain disease detection into the real world Seamlessly integrate into everyday life Proxy data that translate into actionable insight Scalable across geographies & conditions
- 88. 2019 SharpBrains VirtualSummit: The Future of Brain Health
- 90. Access recorded talks,Q&A, and more at: SharpBrains.com