Neurocognitive and Translational
Interventions
Greg Siegle & Kristen Ellard
Christen Deveney
Natasha Hansen
Tracy Dennis
Rudi De Raedt
Jan Mohlman
Many many demos!
Neurocognitive Therapies:
Aligning goals and methods
Greg J. Siegle, Ph.D.
University of Pittsburgh
School of Medicine
Papers and software listed at: tinyurl.com/cogtraining
Neurocognitive therapies
• Inherit from neuroscience
• Usually centered around repetitive exercises
• Usually target specific mechanisms
Papers and software listed at: tinyurl.com/cogtraining
Why neurocognitive therapies?
•
•
•
•
I. Mechanistic targets
II. Precision medicine
III. Improved outcomes
IV. Easier Dissemination
Papers and software listed at: tinyurl.com/cogtraining
I. Mechanistic targets
Running example: Cognitive Control Training
Adaptive “Paced Auditory
Serial Attention Task
(PASAT)”
Computer-based
version of Wells (2000)
attention training task
5
3
9
2
7
16
9
13
4
3x/week for 2 weeks
I. Mechanistic targets
Change in depressive symptoms
and rumination
Pre-Post Cognitive Control Training
fMRI (n=6)
(with the new cohort of IOP controls who got better meds)
30
25
20
60
55
50
45
Pre Post
IOP only
(n=20;
5 increased)
Post waitperiod
CCT
(n=9; 1
increased)
Pre Post
IOP + cognitive
control training
(n=15; 0 increased)
% change
RSQ
rumination
BDI
35
Pre Post
IOP only
(n=20; 6
increased)
32.5
RSQ
58
56
30.0
54
27.5
52
25.0
50
22.5
48
0
1 2 3 4 5
day
6 7
Siegle et al (2007) Cognitive Therapy and Research
0.05
0
5
10 15
seconds
Performance on the adaptive PASAT
also increased continuously (n=13)
2500
2400
2300
2200
2100
5
0
-5
-10
-15
-20
-25
2000
0 1 2 3 4 5 6 7
day
-0.05
Siegle et al (2007) Cognitive Therapy and Research
Mean MD(ISI)
60
0.1
2 4 6 8 10 12
seconds
Symptoms and rumination
decreased continuously (n=19)
BDI
post
pre
0.15
0.3
0.2
0.1
0
Pre
Post
IOP + cognitive
control training
(n=15; 2
increased)
Siegle et al (2007) Cognitive Therapy and Research (plus a few more subjects)
35.0
Digit sorting, 5 digits
Personal relevance rating,
negative words
65
% change in MD(ISI)
40
1
2
3
4
Day
5
6
Siegle et al (2007) Cognitive Therapy and Research
1
2
3
4
Day
5
6
Goal I: Mechanistic targets
• Rationale: Better
target purported
disease
mechanisms
• Model : RDoC
• Research
Methods:
Mechanistic
assessment –
EEG, fMRI,
neuroendocrine,
TMS…
I. Mechanistic targets
Example – using fMRI to see what
different neurocog interventions target
Price et al (2013), Cognitive Therapy & Research
I. Mechanistic targets
Where we’re heading
Mechanistic Targets
Brain systems
associated with
emotion, cognition,
self-related processing,
attention, conflict
processing….
You know – the stuff
you always hear about
Symptoms
Low mood
Anhedonia
Worry
Rumination
Cravings
Sleep
Eating
Think about the study…. 15 target exercises x 15 symptom sets… 225 cells…
I. Mechanistic targets
Problems for good canonical studies
with even a few mechanisms and
interventions
• Too large & troublesome for conventional methods
– 3 interventions, 3 mechanisms  N=30 per cell:
N=270….
• Still have trouble dealing with
– people with multiple mechanisms (we are not our disease)
– brain systems that do lots of things (why drugs are nonspecific)
• Predictors are not the mechanisms to target
I. Mechanistic targets
Predictors are not disorder mechanisms
e.g., don’t “fix” low subgenual
cingulate activity
Treatment
Planning
Predicting Response to
Cognitive Therapy
Cohort 1
Anxious youths!
20
10
5
R2 = .65
R2 = .91
w/out the
outlier
0
it you?
What’sIsthe
emotion?
10
-5
-10
-20 -10
0
10 20
30
(% change 6-10s following
negative word)
CBT, N=40
R2=.31
p<.0005
-5
-15
-1.5 -1
Fu et al (2008)
Biological Psychiatry
Siegle et al (2006)
American Journal of Psychiatry
-0.5 0
0.5
sgACC Z
1
1.5
Cohort 2 & 3 combined
(N=46)
Cohort 2 (N=17)
20
20
10
Residual BDI
Residual BDI
0
-10
Sustained BA25 Activity
Siegle et al (2006)
American Journal of
Psychiatry
UGLY
UGLY
5
p<.005
p<.005
PARS
residual
Residual BDI
15
0
-10
10
0
-10
0
0.1
0.2
-20
-0.2
% change
 High sgAcc is
a “treatment
inhibitor”
% Change
Archives Gen Psychiatry
-0.1
0
% change
0.1
Hamilton et al (2011)
Human Brain Mapping
0.2
Lozano et al (2008)
Biological Psychiatry
Remitter - Low Pretreatment (N=17)
Non-remitter - Low Pretreatment (N=6)
0.1
0.1
% Change
-20
Siegle et al (2012) -0.4 -0.3 -0.2 -0.1
0.05
0
-0.05
-0.1
0.05
0
-0.05
2
4
6
8
Seconds
10
12
-0.1
Siegle et al (2012) Archives Gen Psychiatry
2
4
6
8
Seconds
10
12
I. Mechanistic targets
Today’s Radically Different Approach
(RDA) ™
• Detect many-to-one mapping of intervention
features to neural change
Mechanistic Targets
Brain systems
associated with
emotion, cognition,
self-related processing,
attention, conflict
processing….
You know – the stuff
you always hear about
Critical features that
MAKE PEOPLE BETTER
Attentional flexibility
Sustained executive control
Arousal
Symptoms
Attentional Bias
Low mood
Anhedonia
Worry
Rumination
Cravings
Sleep
Eating
I. Mechanistic targets
Domains of recovery –
e.g., psychotherapies
II. Precision Medicine
Goal II: Precision medicine:
Disease mechanisms
• Goal: Target each person’s
mechanism
• Methods: This could mean
interventions in which
ASSESSMENT OF
MECHANISM is paired with
outcome
Increasing mood
(N=29 control, 73 depressed)
Siegle et al (in prep)
Chib et al (2013) Transl Psy
II. Precision Medicine
Alternate goal II: Preserved strengths
CBT
Targeting
mechanisms &
Treatment planning
10
5
R2 = .65
R2 = .91
w/out the
outlier
0
10
-10
0
10 20
30
(% change 6-10s following
negative word)
CBT, N=40
R2=.31
p<.0005
-5
-15
-1.5 -1
Fu et al (2008)
Biological Psychiatry
Siegle et al (2006)
American Journal of Psychiatry
-0.5 0
0.5
sgACC Z
1
1.5
0
Cohort 2 & 3 combined
(N=46)
Cohort 2 (N=17)
20
20
10
Residual BDI
Residual BDI
pupil
mm
0
-10
Sustained BA25 Activity
Siegle et al (2006)
American Journal of
Psychiatry
UGLY
UGLY
5
p<.005
p<.005
-5
-20 -10
0
-10
0
% change
0.1
0.2
1
2
-0.2
-0.1
0
% change
0.1
0.2
.35
.30
.25
.20
.15
.10
Responder (residual RSQ <0)
10/10 correctly predicted
.4
.3
.2
.1
.05
.0
Never-depressed Depressed
(44)
(21)
Jones, Siegle et al (2010)
Cognitive, Affective, &
Behavioral Neuroscience
Siegle, Price et al (in press,
Clinical Psychological
Science)
Nonresponder (residual RSQ >0)
7/8 correctly predicted
.5
.0
.1
.2
.3
Power at Trial Frequency
seconds
0
-20
p=.01
3
10
-10
-20
Siegle et al (2012) -0.4 -0.3 -0.2 -0.1
Archives Gen Psychiatry
Control (21)
Depressed (42)
it you?
What’sIsthe
emotion?
PARS
residual
Residual BDI
15
Power off Trial Frequency
Anxious youths!
20
.6
Treatment as Usual
Cognitive Control Training
10
Residual RSQ rumination
Cohort 1
Depressed patients have
decreased stimulus-related
pupillary responses
Integrating Assessment &
NeuroRehabilitation!
“On task” power (2400ms ISI)
Predicting Response to
Cognitive Therapy
Residual RSQ rumination
Treatment
Planning
CCT
5
0
-5
-10
-15
-20
-1
-0.5
0
Power On-Off Trial Frequency
0.5
10
5
0
-5
-10
-15
-20
-2
-1.5
-1
-0.5
0
Power On-Off Trial Frequency
.4
II. Precision Medicine
Cost-Optimized Sequential Assessment Decision
Algorithms (COSADAs)
Demographics & Self Report
$0-15 per patient
Behavioral & Clinical Assessment
$50-75 per patient
Psychophysiology
~$100 per patient
$500-$1000 per patient
fMRI
II. Precision Medicine
COSADA for Cognitive Therapy
Predicted Remitter
7/8
21/22
8/10
44 remitters
22
14
24 nonremitters
5
3
18/19
2/2
38/41 remitters
3
2
1/1
94% correct
87% overall
21/22 nonremitters
Predicted Non-Remitter
$15*68=$1020
$150*31=$4650
=$10,670 vs
$34,000 for fMRI or
$45,220 for
$500*10=$5000 everything
III. Improving Outcomes
Goal III: Improved Outcomes
• Rationale: Who doesn’t want to get more
people better?
• Method: What’s the outcome?
– Symptom change
– Task performance change
– Mechanism change
III. Improving Outcomes
Symptom change
Validation:
RCT of Cognitive Control Training
with a real control condition!
Targeting subtypes
26
Beck Depression Inventory II scores
25
*
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
Cognitive Control Training
Pre-Training
Peripheral Vision Task
Sustained / longer term!
With generalization!
And the gains were preserved
over the next year!
% with <5 IOP visits post-treatment year
Proximal/Placebo
controlled
90
85
80
75
p<.05
70
65
60
55
50
CCT
Matched non-CCT
IOP patients
Post-Training
Calkins et al (2014) Behavioral and Cognitive Psychotherapy
Siegle, Price et al (in press, Clinical Psychological Science)
With
Rebecca
Price,
Ph.D.
III. Improving Outcomes
Behavior Change
• It’s easy to change
task behavior on a
single task
• It’s hard to change
other tasks
• AND we’d want to
know what
NORMATIVE
behavior is on other
tasks…. Like in
neuropsych testing…
•
•
•
•
N=11,430 volunteers on the web
– 6 weeks of adaptive training 10-minutes 3x/day
3 groups
– Reasoning/problem solving training (N=4678)
– Non-reasoning training (N=4,014)
– Obscure questions (N=2,738)
All used the tasks ~20 times and improved on the tasks
they trained on – some minimally
None improved on other tasks above the other groups
Owens et al, Nature (2009)
III. Improving Outcomes
Mechanistic change
• Do we care if symptoms change if the
mechanisms don’t also change?
Change in Symptoms
Observed
Change in
Observed Intervention could be
physiological
working by predicted
or
mechanism.
neuroimaging
data during
the
intervention
Not
Mechanism isn’t
Observed targeted, but that
doesn’t matter –
mechanism isn’t key to
symptom change.
Siegle et al (2007) Cognitive Therapy and Research
Not Observed
Intervention targeting the
mechanism but mechanism
isn’t (immediately) key to
symptom change for this
person
Intervention not working,
possibly because it’s not
targeting the mechanism. Must
revise the intervention OR the
assessment
IV. Easier Dissemination
Goal IV: Easier Dissemination
Data from n=24 elderly caregivers
who used it at least 4 times:
Mean # of uses = 39
3500
Median Interstimulus interval
per day
3000
Median
Interstimulus
Interval
2500
2000
1500
0
10
20
day
Callan et al (submitted)
30
40
IV. Easier Dissemination
Goal IV: Easier Dissemination
http://selfesteemgames.mcgill.ca/games/sematrix.htm
IV. Easier Dissemination
Beyond behavior…
mobile consumer-accessible EEG. Bargain: $499!
ANT
App store!
Neurosky
Muse
IV. Easier Dissemination
Mobile EEG can measure stuff kinda
like conventional lab EEG.
FRN, N=19
Going to sleep, N=1
Data from
Greg Hajcak
Proudfit
F3
Delta (1-4Hz) EEG, P8
With
Nicole
Prause
0
5
10
15
20
minutes
25
Eyes open vs. Eyes closed, N=38
d Theta
d Alpha
Theta
0.5
0.5
0
0
-0.5
-0.5
d Beta
d Gamma
0.2
0.1
0.1
0
0
Beta
0.5
0.5
0
0
-0.5
-0.5
Cohen’s d
Alpha
0.2
Gamma
0.2
0.2
0.1
0.1
0
0
Statistical significance (p)
With
Wendy
D’Andrea
30
IV. Easier Dissemination
Approach: Individual Profiles
• Standard “calibration” task
5 minutes
Physiology
Rest Eyes open
Rest Eyes closed
See NORMED emotional pictures
Working memory
Count Backwards from 213 by 7's
Ruminate on something negative
Movement
Draw a stick figure
• Derive profiles for dimensions of interest via
machine learning Categorize new states via
similarity to profiles
IV. Easier Dissemination
Calibrated arousal, in N=1
Theta
4
2313, r=.86
3.5
Alpha
0.2
0.2
0
0
-0.2
-0.2
3
10/56
variables
preserved
2.5
2
1.5
0
0.2
0.4
0.6
0.8
minutes
1
1.2
Beta
Gamma
0.2
0.2
0
0
-0.2
-0.2
1.4
12
DrawStickFigure
Ruminate
10
CountBackwards
EroticHighArousalPictures
8
PositiveLowArousalPictures
NegativeLowArousalPictures
6
PositiveHighArousalPictures
NeutralLowArousalPictures
4
NegativeHighArousalPictures
rest
2
EyesClosed
0
EyesOpen
0
50
100
150
200
250
300
350
0.5
1
1.5
Summary
• Neurocognitive Therapies are here.
• They are promising
• Many ways to get involved
– Clarify GOALS
– Clarify OUTCOMES
– Choose methods consistent with goals and
outcomes
• Applied/basic scientist collaborations will be
essential
Papers and software listed at: tinyurl.com/cogtraining
Collaborators
• Adult
Depression Team
Edward Friedman, M.D.
Michael Thase, M.D.
Mandy Collier, B.S.
Ashley McFarland, M.S.
Agnes Haggerty, B.S.
Susan Berman, M.A.
Luanne Smith Shutt, R.N.
Lisa Stupar, M.A.
Crystal Spotts, R.N.
Mauri Cesare, B.S.
Emilie Muelly, B.S.
Roma Konecky, B.S.
Lisa Farace, B.S.
Kelly Magee, B.S.
The Clinician Accessible Neuroimaging
(CAN) group:
Edward Friedman, M.D.
Sandar Kornblith, Ph.D.
Michael Greenberg, Ph.D.
Kathryn Roekline, Ph.D.
Beth Pacoe, Ph.D.
Karen Woodall, Ph.D.
Larry Elbaum, Ph.D.
Mikey Diamond, Ph.D.
Susan Berman, M.A.
Benjamin Paul, B.S.
Michelle Feingold, M.S.W.
Wendy D’Andrea, Ph.D.
Nicole Prause, Ph.D.
Kyung Hwa Lee Ph.D.
Rebecca Price, Ph.D.
Naho Ichikawa Ph.D.
Monica Barback, Ph.D.
Neilly Buckalew, M.D.
Greg Hajcak, Ph.D.
Thomas Kraynak, Amanda Collier,
Steve Freed, Jonathan DePierro,
Ashley Doukas
William Bird
Vinod Sharma, Ph.D.
Wes Thompson, Ph.D.
Emily Ottonowski
• Other Collaborators
Kate Fissell, M.S.
V. Andrew Stenger, Ph.D.
Costin Tanase, Ph.D.
Kwan Jin Jung, Ph.D.
Stuart Steinhauer, Ph.D.
Cameron Carter, M.D.
Wesley Thompson, Ph.D.
Program in Cognitive Affective Neuroscience (PICAN)
Mood Disorders Treatment and Research Program (MDTRP)
Cognitive Clinical Neuroscience lab, Biometrics Research Lab,
TREND Group
Stuart Steinhauer, Ph.D.
Cameron Carter, M.D.
BenjaminPaul, B.S.
MichelleHorner, D.O.
Darcy Mandell, B.S.
DongliZhou, M.S.
Naho Ichikawa, M.S.
Kyung Hwa Lee, M.S.
Neil Jones, Ph.D.
Christine Larson, Ph.D.
WivekaRamel, Ph.D.
PhilippeGoldin, Ph.D.