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The UAMS/PRI Brain Imaging Research Center (BIRC)
PRI Advisory Board meeting, February 7, 2017Topics• Why the significant financial investment?• What goals drove its development?• What is it and why should I be excited about it?• Who is it?• What does it value as its greater
accomplishments?• Where is it going?• How can I help it?
The “why” of your Brain Imaging Research Center
• Psychiatric disorders are brain disorders• Most psychiatric disorders are developmental disorders
reflecting altered brain development• Susceptible and resilient responses to risk factors
reflect brain responses to these risk factors• The course of illness reflects the course of brain
functional and structural organization • Treatment response is determined by (1) brain states
prior to treatment and (2) brain changes during treatment
Human functional brain imaging is the biotechnology of psychiatry, seeking the “how” behind these associations
The challenge of defining human brain-behavior relationships
The adult human brain is an amazingly complex information processing system• Its estimated 86 billion neurons each make
an average of 100,000 connections resulting in 8.6 quadrillion possible functional interactions
• Complex brain functions represent the product of time-varying combinations of its functional connectivity
• The BIRC focus is on exploration of spatial-temporal properties of brain organization
The “what” in your Brain Imaging Research Center
A center of excellence in brain imaging technology development and its application to
clinical problem solvingAnd…
A center of excellence in education, training and career development towards a next generation of
more impacting imaging scientists
The “where” of your Brain Imaging Research Center
The promise of functional brain imaging
Behavior
Experience
Behavior
Experience
Behavior
Experience
Behavior
Experience
The single most impressive/important thing that we have learned from functional brain imaging
TIM
E
“Birth gives you a brain. Life turns it into a mind”
Modified from Jeffrey Eugenides
The “what” in your Brain Imaging Research Center
Areas of technology, research and training emphasis:• Outcomes related to early life exposure to
trauma and adversity• Human brain development• Women’s health• Human brain variation: cognition• Drug addiction
Wildeman et al. JAMA Pediatrics168(8):706-713, 2014
Age-Specific Risk for First Confirmed Maltreatment in 2011
Proportion of Children Having Ever Experienced Confirmed Maltreatment in 2011
Estimated cumulative risk of confirmed maltreatment of US children
Synthetic cohort life tables for 2003-2011 CPS data of confirmed cases
The “Who” in your Brain Imaging Research Center
Practical examples of the focus, approach, outcomes and impact of the BIRC: Drug
addiction
Addiction is a chronically relapsing, trauma-related, developmental disorder and major public health problem associated with partially effective
treatments
Stages of the Addiction Process and Corresponding Strategies to Halt
AddictionD
RU
G U
SE P
RO
BLE
MS
TIME
Impulsive Use
ADDICTION
Com
pulsi
ve U
se
Treatment
Intervention
None
INITIATION
Prevention
MAINTENANCEDE
VELO
PMEN
T
Risk/Resiliency Factors
trauma
4
12
5
3Addiction as an acquired brain state
Altered decision making in at-risk adolescents
Predicting treatment response
3
1
2
Variation in risk for relapse4
The resilient brain5
Addiction as an acquired brain disorder
Neurobiological advances from the brain disease model of addiction
Volkow ND, Koob GF, McClellan ATN Eng J Med 374(4):363371, 2016
Brain signature of addiction
Performance accuracyTraining sample (LOOCV)
specificity 90%sensitivity 85%
Test sample (independent)specificity 90%sensitivity 75%
Individual addiction classification scores predict trait impulsiveness and cocaine use
Cocaine dependent subjectsBIS-11 (motor impulsiveness): r = 0.43, p < 0.01cocaine use (years): r = 0.52, p < 0.005
Healthy comparison subjectsBIS-11 (motor impulsiveness): r =0.48, p < 0.05
Inferences• Cocaine addiction related to disorganization of the brain
mechanisms of behavioral control• Represents acquired brain state due to prolonged, intermittent
cocaine abuse
Stages of the Addiction Process and Corresponding Strategies to Halt
AddictionD
RU
G U
SE P
RO
BLE
MS
TIME
Impulsive Use
ADDICTION
Com
pulsi
ve U
se
Treatment
Intervention
None
INITIATION
Prevention
MAINTENANCEDE
VELO
PMEN
T
Risk/Resiliency Factors
trauma
4
12
5
3Addiction as an acquired brain state
Altered decision making in at-risk adolescents
Predicting treatment response
3
1
2
Variation in risk for relapse4
The resilient brain5
Impulsive choice behavior in adolescence as a modifiable risk factor
for addition
the heightened impatience of adolescence predicts adolescent drug use and is
predicted by variation in the faculty for future-oriented thought
Figure 1. Valuation Network: Larger Later (LL) Choice Trials vs. Control Trials
-1.2-1
-0.8-0.6-0.4-0.2
00.20.40.60.8
-12 -10 -8 -6 -4 -2 0
LL vs. Control trials
lnK
R = 0.45p = 0.013
amygdala
hippocampus
ventromedial prefrontal cortex
insula
1a. Network Map: Regions of Coactivation
1b. Correlation between lnkand Network Activation
Greater network activation related to preference for the present predicts more impulsive choices
Controlling for age, sex and drug use frequency: r = 0.56, p = 0.004
$350 today? or
$1,000 in 6 months?
-1.5
-1
-0.5
0
0.5
1
1.5
-12 -10 -8 -6 -4 -2 0
SS vs. Control trials
lnK
R = -0.41p = 0.023
Figure 2. Cognitive Control/Executive Function Network: Smaller Sooner (SS) Choice Trials vs. Control Trials
mid-cingulate cortex
dorsomedialprefrontal cortex
dorsolateralprefrontal cortex
precuneus
inferior parietal cortex
2a. Network Map: Regions of Coactivation
2b. Correlation between lnkand Network Activation
Greater network activation related to preference for the future predicts less impulsive choices
Controlling for age, sex and drug use frequency: r = -0.44, p = 0.029
$350 today? or
$1,000 in 6 months?
-1.8
-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0-0.5 0 0.5 1 1.5 2 2.5 3
Valu
ation
Net
wor
k
Cognitive Control Network
r= -0.67, p< 0.0001
Individual differences in choice impulsivity due to relative activation of two neural processing networks? .
Competition between brain states of present and future preference predicts choice impulsivity
All decision making vs control trials
Stages of the Addiction Process and Corresponding Strategies to Halt
AddictionD
RU
G U
SE P
RO
BLE
MS
TIME
Impulsive Use
ADDICTION
Com
pulsi
ve U
se
Treatment
Intervention
None
INITIATION
Prevention
MAINTENANCEDE
VELO
PMEN
T
Risk/Resiliency Factors
trauma
4
12
5
3Addiction as an acquired brain state
Altered decision making in at-risk adolescents
Predicting treatment response
3
1
2
Variation in risk for relapse4
The resilient brain5
What behavioral and brain states predict treatment attempts to reduce adolescent
drug misuse?
Each year of delay in the onset of adolescent drug misuse decreases the probability of adult drug use
disorders by 8-10%
Predicting drug use in at-risk adolescents$350 today?
or $1,000 in 6
months?
Level of recruitment of a pDMN involved in episodic future thinking predicts subsequent decreases in drug use during treatment
The future of the BIRC and the furtherance of its impact
Data Science!
Typical resting state fMRI study has approximately 113 million data elements
Q&A: Joshua Gordon, NIMH Director Psychiatry needs more
mathematics The US National Institute of Mental Health (NIMH) has a new director. On 12 September, psychiatrist Joshua Gordon took the reins at the institute, which has a budget of US$1.5 billion. He previously researched how genes predispose people to psychiatric illnesses by acting on neural circuits, at Columbia University in New York City. His predecessor, Thomas Insel, left the NIMH to join Verily Life Sciences, a start-up owned by Google’s parent company Alphabet, in 2015. Gordon says that his priorities at the NIMH will include “low-hanging clinical fruit, neural circuits and mathematics — lots of mathematics”, and explains to Nature what that means.
Keith Bush PhD
Education:BS Chemical Engineering, Univ PennMS Computer Science, Colorado StatePhD Computer Science, Colorado StatePostDoc in Machine Learning, McGill Univ
Career Goal: Become an international leader in the research and development of computational neuroscience and its clinical applications.
Career Workforce Setting: academia
Career Development Plan: Exploit real-time fMRI-guided neuromodulation in engineering control systems to characterize the neural basis of volition and its relationship to psychiatric disorders, emphasizing mood and emotion disregulation.
Skill Development Focus: Develop expert knowledge of the neurological and psychophysiological bases of cognitive control and emotion processing.
Assistant Professor
Michael Chung PhD
Education:BS Biology, National Central University,
TaiwanMS Statistics, University of ArkansasPhD Mathematics, University of Arkansas
Career Goal: an independent, productive investigator exploring the population-level multivariate association of genetic and neuroimaging variables with risk for and expression of drug use and other behavioral states
Career Workforce Setting: academia
Career Development/Mentoring Plan: Big data science approach integrating human neuroscience, genetics, computer science, and mathematics
Skill Development Focus: Application of deep learning and machine learning to high dimensional brain imaging and genetic data within large data bases
T32 Post-doctoral traineeMentors: Keith Bush and Clint Kilts
Thank you