Rethinking and Retooling Brain Health and Mental Health

SharpBrains
SharpBrainsMarket/ Innovation Analyst on Digital Brain Health. Young Global Leader @ World Economic Forum
Rethinking and Retooling Brain Health and Mental Health
Rethinking and Retooling Brain Health and Mental Health
Rethinking and Retooling Brain Health and Mental Health
Rethinking and Retooling Brain Health and Mental Health
©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential.
Rethinking and Retooling
Brain Health and Mental Health
Tom Insel, MD
Co-founder and President, Mindstrong Health
May 7, 2019
©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential.
What is the Problem We Need to Solve?
©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
©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
©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
©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
©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
©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
©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
©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
©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
©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential.
Fragmented
and
Episodic
Psychological
Care
Medical
Care
Social
Supports
Family
Support
Quality of Care
©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
©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
©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
©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential.
WHAT WE DO TODAY
• Subjective
• Episodic
• Clinic-based
• High burden
MEASURING MOOD, COGNITION, AND BEHAVIOR
©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
©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
©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
©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.)
©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
©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
©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.
©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential.
tracking brain health in a 48 year old woman
under care for bipolar disorder with psychosis
©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
©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
©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
©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential.
©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
©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?
©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?
©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?
©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
©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
©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?
©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential.
The Digital Mental Health Challenge
©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential.
The Digital Mental Health Challenge
©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?
©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
©2017 Mindstrong Inc. All Rights Reserved. Proprietary and Confidential.
Transforming Brain Health
tom@mindstronghealth.com
Thank You!
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
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
Rethinking and Retooling Brain Health and Mental Health
)
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
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
Rethinking and Retooling Brain Health and Mental Health
Telomere Length Change with
Internship
Biological Psychiatry – in press
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
```
Genomic Risk Score and Stress
p=5x10-12
Sample Size
2014-2016: 37
2017: 546
2018: 2051
Sleep Mood b=0.12; p<0.001
Mood Sleep b=0.05; p=0.04
Change in Sleep and Wake
Time with Internship Stress
• 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
Circadian Genetics
• Sleep Longer
[Sleep Duration Polygenic Score]
Hours of Asleep
Change in
Sleep Schedule
• Wake up earlier
[Chronotype Polygenic Score]
Genetic Predisposition to...
Rethinking and Retooling Brain Health and Mental Health
Rethinking and Retooling Brain Health and Mental Health
• 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
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
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
ConfidentialMay 7, 2019
Detecting Brain Diseases Early
• 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
• 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
•Can be deployed widely
• Easy on the person
• Runs in background
• No additional hardware
• Low cost
• Easy to implement
Early Detection: Requirements
•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
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
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
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
•Assemble AD APOE4 cohort or very large
random sample
•Identify genetic risk factors for other
diseases
•Continue development of fingerprints
Next Steps
Confidential
May 7, 2019
Detecting Brain Diseases Early
Jan Samzelius, CEO
Jan.Samzelius@neurametrix.com
+1415-420-6636
2019 SharpBrains Virtual Summit: The Future of Brain Health
How to detect problems early
Tony Chang
07 May 2019
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.
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
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
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
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
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
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
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
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.
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%
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
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
2019 SharpBrains Virtual Summit: The Future of Brain Health
Rethinking and Retooling Brain Health and Mental Health
Access recorded talks, Q&A, and more at:
SharpBrains.com
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Rethinking and Retooling Brain Health and Mental Health

  • 5. ©2014 Mindstrong. All Rights 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. All Rights Reserved. Proprietary and Confidential. What is the Problem We Need to Solve?
  • 7. ©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
  • 8. ©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
  • 9. ©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
  • 10. ©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
  • 11. ©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
  • 12. ©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
  • 13. ©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
  • 14. ©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
  • 15. ©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
  • 16. ©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential. Fragmented and Episodic Psychological Care Medical Care Social Supports Family Support Quality of Care
  • 17. ©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
  • 18. ©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
  • 19. ©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
  • 20. ©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential. WHAT WE DO TODAY • Subjective • Episodic • Clinic-based • High burden MEASURING MOOD, COGNITION, AND BEHAVIOR
  • 21. ©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
  • 22. ©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
  • 23. ©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
  • 24. ©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.)
  • 25. ©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
  • 26. ©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
  • 27. ©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.
  • 28. ©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential. tracking brain health in a 48 year old woman under care for bipolar disorder with psychosis
  • 29. ©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
  • 30. ©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
  • 31. ©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
  • 32. ©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential.
  • 33. ©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
  • 34. ©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?
  • 35. ©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?
  • 36. ©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?
  • 37. ©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
  • 38. ©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
  • 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?
  • 40. ©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential. The Digital Mental Health Challenge
  • 41. ©2014 Mindstrong. All Rights Reserved. Proprietary and Confidential. The Digital Mental Health Challenge
  • 42. ©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?
  • 43. ©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
  • 44. ©2017 Mindstrong Inc. All Rights Reserved. Proprietary and Confidential. Transforming Brain Health tom@mindstronghealth.com Thank You!
  • 45. 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
  • 46. 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
  • 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 3 Months 6 Months 9 Months 12 Months PHQDepressionRate Intern Assessment Time
  • 51. Telomere Length Change with Internship Biological Psychiatry – in press
  • 52. 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
  • 53. ```
  • 54. Genomic Risk Score and Stress p=5x10-12
  • 56. Sleep Mood b=0.12; p<0.001 Mood Sleep b=0.05; p=0.04
  • 57. Change in Sleep and Wake Time with Internship Stress
  • 58. • 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
  • 59. Circadian Genetics • Sleep Longer [Sleep Duration Polygenic Score] Hours of Asleep Change in Sleep Schedule • Wake up earlier [Chronotype Polygenic Score] Genetic Predisposition to...
  • 62. • 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
  • 63. 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
  • 64. 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
  • 65. ConfidentialMay 7, 2019 Detecting Brain 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 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
  • 68. •Can be deployed widely • Easy on the person • Runs in background • No additional hardware • Low cost • Easy to implement Early Detection: Requirements
  • 69. •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
  • 70. 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
  • 71. 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
  • 72. 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
  • 73. •Assemble AD APOE4 cohort or very large random sample •Identify genetic risk factors for other diseases •Continue development of fingerprints Next Steps
  • 74. Confidential May 7, 2019 Detecting Brain Diseases Early Jan Samzelius, CEO Jan.Samzelius@neurametrix.com +1415-420-6636
  • 75. 2019 SharpBrains Virtual Summit: The Future of Brain Health How to detect problems early Tony Chang 07 May 2019
  • 76. 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.
  • 77. 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
  • 78. 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
  • 79. 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
  • 80. 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
  • 81. 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
  • 82. 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
  • 83. 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
  • 84. 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.
  • 85. 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%
  • 86. 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
  • 87. 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
  • 88. 2019 SharpBrains Virtual Summit: The Future of Brain Health
  • 90. Access recorded talks, Q&A, and more at: SharpBrains.com