Transcript Genes, Brain Function and Behavior in Autism Spectrum Disorders Susan Bookheimer, Ph.D. Joaquin Fuster Professor of Cognitive Neuroscience, UCLA Director, Center for Cognitive Neuroscience Director,

Genes, Brain Function and Behavior in
Autism Spectrum Disorders
Susan Bookheimer, Ph.D.
Joaquin Fuster Professor of Cognitive Neuroscience, UCLA
Director, Center for Cognitive Neuroscience
Director, Intellectual and Developmental Disabilities Research Center
Director, UCLA Autism Center of Excellence
Co-Director, UCLA Center for Autism Research and Treatment
Disclosures
• none
Training Objectives
• Learn how risk genes affect the brain in development,
and learn about new research linking genes to brain
and behavior
• Understand the concepts of structural and functional
connectivity in the brain and how these constructs are
measured using brain imaging
• Learn how interventions can affect brain
development
• Learn about the concepts of translational research,
how to combine different levels of scientific inquiry
into a comprehensive approach to understanding
developmental disorders, using autism as an example
Overall message
• Autism is a disorder of developmental disconnection
• Autism spectrum disorders arise from differences in
the trajectory of brain development that affects the
formation of brain pathways essential for developing
social motivation, joint attention, and social
communication
• Differences in brain development are governed by
numerous genes, but experience can significantly
modify their expression
• Understanding the brain in autism empowers us to
intervene at a fundamental level
Outline
• A little about finding genes for autism
• Translational Research- linking genes, gene
expression, models, behavior—treatment
• Brain systems affected in autism using fMRI
• Brain connectivity
• Biomarkers and early interventions
Finding Risk Genes for Autism
Genetic risk
Autism
• Common variant approaches: 80+%
• Huge gigantic studies- 1000-10,000 people, compare those with and
without ASD; or family association
• Over 50 and maybe several hundred risk genes
• Rare Variants: - 5-20%
• Find genetic syndromes- accidents or mistakes in the genome that
causes disorders with high incidence of autism
• Eg Fragile X, Timothy Syndrome; 22q duplication
Autism
risk
O
O
Translational Research
• Take either genetic approach, develop animal
model
• See what the genes actually do
• See if you can replicate some aspect of ASD
behavior in an animal model
• Find ways of measuring the effect of genesbiomarkers
• Develop new understanding of the cause of
behavioral problems
• Develop new ideas for intervention; test them in
animals
• Develop preventions
That is why we care about genes
Candidate
Genes for
Autism
Based on Abrahams and Geschwind
Nature Reviews Genetics 2008
•Risk genes share pathways
•Involved in cell adhesion,
signaling, synaptogenesis,
neural migration, dendritic
growth
•Autism risk genes share
the common feature of
being involved in making
neural connections in
early development
Structural Connectivity
What does an autism risk gene do? Using mini-cams, watch changes in
dendritic growth before and after PTEN gene Knock out.
Trachtenburg Lab, UCLA
PTEN is a gene that regulates neural growth
- prevents overgrowth in specific layers of the brain
-KO neurons exhibit large-scale apical dendrite growth
8 weeks postnatal
12 weeks postnatal
8wk versus 12wk difference
100mm
Orange: newly grown dendrites
Gray: original structure
Dendritic growth is restricted to the apical dendrites:
Local, short connections are greatly enhanced
Long range connections are relatively unchanged (disorganized?)
Terminal branch lengths
Pten KO
Pten KO
Apical
100mm
Control
100mm
Pten KO
Basal
Thus far…..
• Many autism risk genes- hundreds?
• Share common properties- signal neurons to
find connections
• In animal models, see the function of altering
the genes and the effect on the brain
• Changes brain connectivity- increased local,
same or less long range connectivity
• What about human brain?
Imaging the Human Brain
• Structural MRI- measure the anatomy- size of
structures (grey matter), and the connections
between structures (white matter)
• Functional MRI- measure the brain in action;
what is going on in the brain when individuals
with autism try to do those things that are
difficult
Structural Brain Imaging in Autism
•
•
•
•
No obvious, diagnostic differences in brain structure
Brain overgrowth early in life, slowed growth trajectory
Some differences in size of individual structures
Unclear relation to autism behaviors
From Courchesne, Mol. Psych, 2002
Few extremes;
otherwise
differences “on
average”
From Courchesne, Mol. Psych,
2002
Regional differences in size and
trajectory
• Structures such as the amygdala (emotion
center) may differ in autism
From Schumann et al, J Neuroscience, 2004
Abnormal brain size is specific to radiate white
matter (Hebert et al)
Abnormal brain volume increase is
post-natal
White matter development in highrisk ASD: Wolff et al, 2012 Am J Psychiatry
How do differences in brain size relate
to behavior in autism?
• Control for IQ, many differences go away
• Differences are generally small, hard to
repeat
• No clear relationship to autism symptoms,
severity
• Need to look at brain function as well as
structure
• Relate brain activity to behavior
Functional MRI
•
•
•
•
•
•
Maps the brain “at work”
Measures where blood flow increases
Non-invasive (no needles, drugs, etc)
No radiation involved
Safe for children and repeated scanning
Only works to show increased activity during
a task, in comparison with a second, control
task.
Functional MRI
3-Tesla MRI: huge magnet, measures movement of water molecules
Fast imaging: scan the whole brain every 2-3 seconds
Repeat for 5-10 minutes- hundreds of scans of the entire brain
Perform a task-experiment while scanning
Blood flow increases to areas of the brain
that are working harder to perform the task;
blood has iron; iron is magnetic
Compare images during the task to a rest
or control condition to locate brain activity
fMRI activation studies of ASDrelevant skills
• Emotional understanding and Responsiveness
– Response to facial affect
– Imitation and observation of affect
– Reward processing
• Communication
– Language learning
– Understanding communicative intent
• Sensory Responsiveness
Functional Imaging:
Studies of Face Perception in
Autism
FMRI of Face Processing in Autism
• Faces are special in social communication:
– Convey emotion/mental state of others
– Form a bond through mutual gaze
– Gaze directs attention- allows joint attention, referencing,
promotes imitation
• Perception of faces- do they see the same thing as
typical children?
• Response to facial emotion: perception, regulation
of emotion, or connections between emotion and
control areas
• Gaze perception and response to gaze direction
• Imitation and observation of others: “Mirror”
neuron systems
Faces vs. Objects: Focus point
Hadjikhani et al 2004 NeuroImage
Face familiarity in autism
Pierce et al 2004 Brain
Face familiarity in autism
Pierce et al 2004 Brain
Facial Emotions: Experimental Paradigm
“Match”
“Label”
Match Expressions: Perception only;
implicit emotional processing
Label Expressions: Explicit cognitive
analysis
Control
Reduced Activity in the Amygdala
(Primary Fear Center) during Match
TD
ASD
Implicit vs. Explicit Appraisal of Emotion:
Amygdala activity is not regulated by
context in ASD
0.4
Z score
0.3
Match
0.2
Label
0.1
0
TD
ASD
Imitation
• Imitation is impaired in autism and
predicts language development
• Mirror neurons: fire during both the
performance and observation of a
meaningful motor behavior
• Understanding the intention of others
actions by internalizing them (as if you
are doing it yourself)
Imitating and Observing
Emotions in Autism
Mirella Dapretto Nat Neurosci. 2005
Angry
Fearful
Happy
Sad
Neutral
faces
2 Conditions
---“Just look at the expression on each face”
---“Imitate the expression you see on each face”
I.
II.
Watch faces
Imitate expressions
+
Activity During Imitation
Motor areas
TD Imitate
Visual areas
Mirror
neuron
area
R
L
Motor areas
ASD Imitate
Visual areas
R
L
R
L
Imitate:
TD > ASD
Imitation: TD > Autism
Anterior Insula
z: -12
z: 8
Connection
from mirror
centers to
emotion
centers
L
R
Amygdala: fear center
Negative faces
L
R
Ventral
striatum:
Reward
Centers
(happy faces)
MNS Activity and Autism Severity
ADOS: Negative Correlation with Symptom Severity
The less activity in mirror area,
worse the autism
1.50
ADOS; r(8) = -.70, p < .02
ADI; r(8) = -.85, p < .002
R
R
ADI: Negative Correlation with Symptom Severity
Raw Parameter Estimates
1.00
Linear (ADOS; r(8) = -.70, p
< .02)
Linear (ADOS; r(8) = -.70, p
< .02)
Linear (ADI; r(8) = -.85, p <
.002)
0.50
0.00
-0.50
-1.00
-1.50
-2.00
0
5
10
15
20
ADOS and ADI Scores (Social Subscales)
R
L
25
30
Gaze Direction and Affect
Processing
Mari Davies, Ph.D.
• Observing a persons gaze enables
us to engage in joint attention
• Monitoring gaze is critical for
learning well as social engagement
• Interpreting emotional expressions
relies on monitoring gaze direction:
meaning differs if gaze is directed
to you or directed elsewhere
Directed vs. averted gaze, different
emotions
Anger: has a different meaning
if gaze is directed towards you!
Smiling at you- a natural
reward.
Typically Developing Children: Directed
vs Averted Gaze (negative emotions)
Direct
Averted
Visual brain regions only
Autism
Direct
Averted
Visual cortex
Implicit language learning in ASD
• Language impairment is a core deficit in ASD
• Early language development is implicit, based
on probabilistic segmentation of word
boundaries
• Learning an artificial grammar depends on
basal ganglia activity
Word Segmentation
Scott-VanZeeland et al 2010
Unstressed
Language
Stressed
Language
Random
Syllables
Transitional
Probabilities
Only
Transitional
Probabilities
+ Prosodic Cues
No Transitional
Probabilities
No Prosodic Cues
nimoluvorifaliduramanuto…
ro
go
bi ku da
bu
pa tu ti pi
do la
pabikugolatudaropitibudo…
pabiku
daropi
golatu
tibudo
po ba
vu
gi
no
fu ko
ga
fi
mu ka
vi
kagipovuganomubakafufibako…
li
lu to
fa
du ma
ri
ra mo nu
vo
ni
novuka
pofimu
vikoga
bafugi
lidura
vorifa
manuto
nimolu
Implicit Language Learning
Unstressed
Language
Stressed
Language
144 s
30 s
144 s
30 s
144 s
TIME (seconds)
Random
Syllables
30 s
Implicit Language Learning:
Language (left inferior frontal) and striatal
(implicit learning) regions
Lang - rest
TD - ASD
Exponential increase model as an index of learning
Social Rewards
• Social rewards may be primary reinforcers during early
development
• Neonates orient preferentially to smiling faces
(Legerstee et al., 1998)
• Reduced orientation to faces is seen early in
autism (Dawson, et al., 1998; Osterling et al., 1994)
• Reward processing deficits could reduce social
motivation
• Romantic & maternal love (Bartels & Zeki, 2004), attractive (Aharon et al., 2001) and
happy (Phillips et al., 1998) faces and eye contact (Kempe et al., 2001), have shown
reward-like responses in adults using fMRI
Pupil Dilation to emotional faces in
autism
L. Sepeta
• Pupil Dilation a measure of pleasure or
reward
• People are attracted to large pupils
• Belladona drug taken to increase
attractiveness
• Tracked Eye movement and pupil
diameter in young children with ASD
• Controlled for eye gaze direction
Increased Pupillary Dilation to Happy Faces
in TD, not ASD
Probabilistic Learning and Reward
in ASD using fMRI
Scott-VanZeeland et al, 2010
• 18 boys with Autism Spectrum Disorders (ASD),
18 age and IQ matched typically developing
(TD) boys
• Stimulus-Response task with unknown
association probabilities
– Based on Knowlton Probabilistic Classification
Task
– Stimuli predict a given outcome (1 or 2); 67%
deterministic, 33% random
• Children earned up to $5 for the monetary task
Monetary Reward Condition
Response
Monetary
Feedback
“1”
or
“2”
fMRI scans (3 Tesla) on 18 highfunctioning ASD, 18 control
children
Or
3.75 - 6.25 s
Separately model hemodynamic response to rewards and to
learning
Social Reward Condition
Response
“1”
or
“2”
Stimulus-response association is only probabilistic
Seems random to subject
Post test of explicit knowledge is at chance
Social
Feedback
Or
Response to Rewards: Ventral
Striatum (primary reward center)
Every occurrence of a reward (any type) vs. no-reward
Effect of rewards on implicit learning
Total Performance
100.00
90.00
80.00
Percent Correct
70.00
60.00
ASD Money Total
TD Money Total
50.00
ASD Social Total
TD Social Total
40.00
30.00
20.00
10.00
0.00
1
2
Time
3
Dorsal Striatum, AC deficits in ASD
during implicit learning
R
L
Typical children > ASD children
Variation in reward center activity in typical children
predicts social reciprocity
R
Understanding Communicative Intent: Neural
Basis of Irony Comprehension
Ting Wang, Ph.D. Archives Gen Psych 2008
• Highly verbal ASD children still
have social communication
deficits
• Focus on intent of
communication (vs. actual words)
•Auditory and visual
Understanding Communicative
Intent
or
(Wang et al., Arch Gen Psych, 2007)
Neutral instructions
21’
90’
rest
Attend Face
21’
rest
0”
90’
Attend Voice
21’
90’
rest
21’
rest
time
7’51”
No Irony
90’
21’
rest
Effects of Attentional
Modulation
ASD
TD
Significant
Group x Condition
Interaction
Just Pay
Attention
3.5
3.0
2.5
Attend to Face
or Tone of Voice
t
2.0
1.5
1.0
0.5
0
(Wang et al., Arch Gen Psychiatry, 2008)
MPFC Activity & Symptom
Severity
3.5
3
t
Significant negative correlation
between social responsiveness
(Constantino et al., 2003) &
MPFC activity across irony
conditions
Parameter Estimate
2.5
2
1.5
1
0.5
0
-0.5
-1
-1.5
40
60
80
100
120
140
Social Responsiveness Scale
(Wang et al., Arch Gen Psychiatry, 2007)
Sensory Over-Responsiveness
• While not a core deficit in autism, experienced
by up to 75%
• Associated with anxiety
• One of the most difficult features to deal with
• Multiple forms: sensitivity to tough (clothing);
textures (food); loud sounds; visual noise
Sensory responsiveness and
habituation in ASD (Green et al, 2013)
TD and ASD group differences in brain activation in
response to simultaneous tactile and auditory stimuli
Z
5
LL
TD
2.3
Z
5
LL
ASD
2.3
Z
5
L
L
ASD>TD
1.7
1.7
Z=-14
Z=-2
Z=6
Z=66
ASD brain over-reactivity to sensory stimuli related to severity of sensory overresponsivity (SOR) symptoms)
Amygdala
response
0.8
0.6
0.4
0.2
0
-0.2 0
-0.4
1
SOR symptoms
2
-1
1.5
Somatosensory
cortex response
1
-1
Somatosensory Cortex
Auditory
Cortex
Auditory cortex
response
Amygdala
1
0.5
0
0
1
SOR symptoms
2
-1
2
1.5
1
0.5
0
0
1
SOR symptoms
2
Primary Auditory Habituation
Right Amygdala Habituation
0.15
0.1
ASD
0.05
TD
0
Early
Late
-0.05
0.65
0.6
0.55
0.5
0.45
0.4
0.35
0.3
0.25
0.2
ASD
TD
1
0.6
0.1
0.5
0.05
ASD
0
-0.15
4
0.7
0.15
-0.1
3
Primary Somatosensory
Habituation
Left Amygdala Habituation
-0.05
2
Early
Late
TD
ASD
0.4
TD
0.3
0.2
1
2
3
4
Implications for Interventions and
Outcome
• Understanding the cause of the symptom
presentation is critical to designing
appropriate treatments
• Appears to be a few general system
impairments that affect multiple behavioral
outcomes
• Eg standard desensitization techniques
should not work in low habituation rate
anxiety profiles
Commonalities in functional anatomy
• Brain regions showing reduced activity in ASD
consistently involve networks connecting parts
of the frontal lobe with subcortical (more
primitive) brain regions. These regions are
involved in appropriately selecting out what is
important, receiving positive and negative
emotional signals, processing them and learning
from them
• How does this relate to the gene functions we
identified earlier?
Autism as a Connectivity Disorder:
Linking Genes and Brain
• Examine 3 top autism risk genes: CNTNAP2; MET;
OXTR
• Functional Connectivity: Correlations between activity
in regions observed across time
Connectivity during artificial language
learning (Moore et al)
TD
ASD
Local negative
Long-Range Positive
Weak long-range
negative
Expression of CNTNAP2 gene compared
to language learning differences between
TD and ASD children
Fetal expression of CNTNAP2 in frontal
cortex and basal ganglia
Courtesy Brett Abrahams
Functional Connectivity Analysis
1) Remove task and noise
from data using general
linear model
2) Extract seed timeseries
3) Enter time series into correlation
analysis within subjects
4) Average within groups and
compare across groups to get
final maps
Expression of autism risk
polymorphism: Cntnp2
Fetal expression
Brain activation correlates of risk allele in autism: 32
children ASD + TD
Courtesy Brett Abrahams
Scott-VanZeeland et al 2010
CNTNAP2 Risk carriers have stronger local and weaker
long-range connectivity than non-risk
Maximum correlation coefficient within
regions for genetic groups
NR > Risk
Risk > NR
• Non-risk allele carriers show stronger connectivity with posterior regions
including visual cortices and precuneus
• Risk allele carriers show stronger local connectivity with frontal cortices,
particularly in the right hemisphere
• An abundance of local frontal connectivity and reduced long-range connectivity
has been hypothesized to play a role in autism pathogenesis (Courchesne & Pierce
2005; Marcel Just and colleagues)
OXTR and Functional Connectivity in the
Salience Network in ASD
Leanna Hernandez et al, in prep

Network of brain regions including the anterior insula and medial
frontal cortex important for marking the most relevant information in
the environment (Seeley et al. 2007)

Hub region: Right anterior insula (AI)
(Uddin et al. 2009)


AI: Consistently hypoactivated in individuals with ASD during
social cognitive tasks (Di Martino et al. 2009)
Seed-based Salience Network Connectivity:

Seed the AI; relate activity in amygdala, hypothalamus, dorsal
anterior cingulate cortex, superior temporal pole (Seeley et al. 2007)
Right Anterior Insula - Whole Brain Connectivity
GG “Non Risk”
12
L
AA “Risk” + AG “Intermediate”
z
2.3 Corrected
L
GG “Non Risk” > AA “Risk” + AG “Intermediate”
z
L
Right Anterior Insula - mPFC Connectivity
R AI - mPFC Connectivity
OXTR rs53576 Genotype Groups
L
0 .2 5
*
0 .2 0
0 .1 5
**
GG “Non Risk”
AG “Intermediate”
AA “Risk”
0 .1 0
0 .0 5
0 .0 0
-0 .0 5
* p < 0.05
** p < 0.01
-0 .1 0
R AI - mPFC Connectivity
Non Risk vs. Intermediate + Risk
*
*
GG “Non Risk”
TD
ASD
AG+AA “Intermediate + Risk”
MET Gene Effects- Functional Connectivity
• Reduced connectivity in DFM in MET risk carriers
• Long range (posterior MPC to anterior medial PFC)
• Gene by group interaction
What is the functional significance of
“aberrant connectivity”?
Graph Theory / Network Approach
What are the properties of a wellconnected brain network?
Small world / biological networks:
• Good clustering (high local efficiency)
• short cuts (low path length/high global
efficiency)
•
Random
Small world
Local Efficiency
Global Efficiency
Modularity
Intrinsic Connectivity Networks:
Typical Development
• Increasing integration within
intrinsic connectivity networks
(Fair et al 2007, 2008; Kelly et al 2009)
Typical Development of Intrinsic
Connectivity Networks
• Increasing segregation
between intrinsic connectivity
networks
(Fair et al. 2007; Stevens et al. 2009; Dosenbach
et al. 2010)
Fair et al. 2007
Methods: Graph Analyses
•
Resting state fcMRI data
•
Find peak coordinates for all regions and create
spherical ROIs around peaks (31 regions)
•
Extract activity from each region and compute a
correlation matrix for in every possible pair of
ROIs for TD and ASD groups
Visualize connectivity matrices/networks for
each group as well as group differences
Subdivide into known, primary networks
•
•
Clustering / Local Efficiency
Clustering Coefficient (CC)
Clustering coefficient relative to random networks
TD
Lower Local
Efficiency in ASD
Functional
Networks
Clustering Coefficient
ASD
*
p < 0.05
Network Sparsity
Modularity
Modularity
Clustering coefficient relative to random networks
TD
Coefficient
Clustering
(Q)
Modularity
Lower modularity
in ASD (more
difficult to identify
communities)
ASD
*
Network Sparsity
p < 0.05
TD
Task Negative
Network
Task Positive Network
ASD
Abnormal
functional circuitry
(macroscopic)
Abnormal brain
development- neural
migration, synapse
formation, circuit
formation (microscopic)
Genetic
risk
Environmental
factors
Specific
behavioral
anomalies
Atypically biased
experience
Autism Spectrum
Disorders
Reinforcement/enh
ancement of
abnormal circuitry
Future Directions
Conduct longitudinal studies in high-risk infants
Examine trajectories of brain and behavioral development
Intervene in high-risk toddlers to change trajectory
Sensorimotor Network
Network
Auditory Network
Visual
Salience Network
8-week-old
rs-fcMRI
English > Rest
Japanese > Rest
English > Japanese
6-week-old
passive fMRI
L
L
L
Today’s research is tomorrow’s treatment
www.autism.ucla.edu
Collaborators:
Mirella Dapretto, Ph.D.
Jeff Rudie, Ph.D.
Ashley Scott-VanZeeland, Ph.D.
Leanna Hernandez
Daniel Geschwind, M.D., Ph.D.
Pat Levitt, Ph.D.
Funding Sources
NICHD
Autism Center of Excellence
2P50 HD055784
1 R01 HD073983-01
The kids and families