Rethinking and Retooling Brain Health and Mental Health

3. ©2014 Mindstrong. All Rights 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

4. ©2014 Mindstrong. All Rights 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

5. ©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential. DSM approach: Clinical consensus Categorical Non-etiologic Non-therapeutic “Built for billing” 265 categories – symptom based Imprecise Dx

6. ©2014 Mindstrong. All Rights 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

7. ©2014 Mindstrong. All Rights 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

8. ©2014 Mindstrong. All Rights 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

9. ©2014 Mindstrong. All Rights 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

10. ©2014 Mindstrong. All Rights 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

11. ©2014 Mindstrong. All Rights 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

13. ©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential. Childhood Adolescence Early Adulthood Adulthood Psychosocial Functioning Psychotic Symptoms Psychosis onset Prodromal period Delay Quality of Care

14. ©2014 Mindstrong. All Rights 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

17. ©2014 Mindstrong. All Rights 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

18. ©2014 Mindstrong. All Rights 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

19. ©2014 Mindstrong. All Rights 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

20. ©2014 Mindstrong. All Rights 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.)

21. ©2014 Mindstrong. All Rights 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

22. ©2014 Mindstrong. All Rights 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

23. ©2014 Mindstrong. All Rights 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.

25. ©2014 Mindstrong. All Rights 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

26. ©2014 Mindstrong. All Rights 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

27. ©2014 Mindstrong. All Rights 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

30. ©2014 Mindstrong. All Rights 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?

31. ©2014 Mindstrong. All Rights 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?

32. ©2014 Mindstrong. All Rights 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?

33. ©2014 Mindstrong. All Rights 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

34. ©2014 Mindstrong. All Rights 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

35. ©2014 Mindstrong. All Rights 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?

38. ©2014 Mindstrong. All Rights 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?

39. ©2014 Mindstrong. All Rights 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

41. How to detect problems 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

42. Physician Training as a Model to Identify Predictors and Preventative Interventions for Depression under Stress Srijan Sen MD PhD University of Michigan srijan@umich.edu

43. ) 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

44. 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% Before Internship 3 Months 6 Months 9 Months 12 Months PHQDepressionRate Intern Assessment Time

45. Telomere Length Change with Internship Biological Psychiatry – in press

46. Methylation Changes With Stress Individual Factors 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

47. ```

48. Genomic Risk Score and Stress p=5x10-12

49. Sample Size 2014-2016: 37 2017: 546 2018: 2051

50. Sleep Mood b=0.12; p<0.001 Mood Sleep b=0.05; p=0.04

51. Change in Sleep and Wake Time with Internship Stress

52. • More day-to-day variation 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

53. Circadian Genetics • Sleep Longer [Sleep Duration Polygenic Score] Hours of Asleep Change in Sleep Schedule • Wake up earlier [Chronotype Polygenic Score] Genetic Predisposition to...

54. • Current state moderates effect of messages slope = -.074 (on square root scale) p-value = .007 slope = -.039 (on square root scale) p-value = .01 slope = -.051; p-value = .001

55. Promise of Digital Tools 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

56. Acknowledgements • Participating interns and 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

57. ConfidentialMay 7, 2019 Detecting Brain Diseases Early

58. • 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

59. • Extremely high quality 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

60. •Can be deployed widely • Easy on the person • Runs in background • No additional hardware • Low cost • Easy to implement Early Detection: Requirements

61. •Can be done with 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

62. Huntington’s Provides a Promising 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

63. Huntington’s Provides a Promising 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

64. Challenge #2: Develop Disease 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

65. •Assemble AD APOE4 cohort or very large random sample •Identify genetic risk factors for other diseases •Continue development of fingerprints Next Steps

66. Confidential May 7, 2019 Detecting Brain Diseases Early Jan Samzelius, CEO Jan.Samzelius@neurametrix.com +1415-420-6636

67. 2019 SharpBrains Virtual Summit: The Future of Brain Health How to detect problems early Tony Chang 07 May 2019

68. 76 2019 SharpBrains Virtual 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.

69. 77 2019 SharpBrains Virtual 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

70. 78 2019 SharpBrains Virtual 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

71. 79 2019 SharpBrains Virtual 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

72. 80 2019 SharpBrains Virtual 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

73. 81 2019 SharpBrains Virtual 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

74. 82 2019 SharpBrains Virtual 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

75. 83 2019 SharpBrains Virtual 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

76. 84 2019 SharpBrains Virtual 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.

77. 85 2019 SharpBrains Virtual 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%

78. 86 2019 SharpBrains Virtual 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

79. 87 2019 SharpBrains Virtual 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

80. 2019 SharpBrains Virtual Summit: The Future of Brain Health

81. Access recorded talks, Q&A, and more at: SharpBrains.com