Download Greg_Lab_Meeting_5-29-2012

Propranolol
• Sympatholytic
• Lipophilic – crosses BBB
• Treatment for hypertension, anxiety, tremor, PTSD?
• James Black won the Nobel Prize for Medicine in 1968
Pitman study
• Chronic PTSD patients recounted trauma experiences followed by
propranolol (n=9) or placebo (n=10) in double blind administration.
• Patients who had received propranolol one week later showed lower
physiological reactivity to the trauma reminders.
Other studies
• VanStegeren et al. 2005 – Beta blockade (propranalol) reduced
amygdala response to emotional images with a corresponding
decline in memory of these images.
• Hurlemann et al. 2010 – Propranolol reduced activation in BLA to
fearful, neutral, and happy faces.
• Onur et al. 2009 – Roboxetine (beta agonist) increased BLA
activation specific to fearful faces.
Clinical uses
• Performance anxiety (stage fright, exams, etc.)
• Can be given for panic D/O, agoraphobia
• Some patients complain it helps the physical symptoms but not
psychological
Hypotheses
• CWAS results will show
– Lower connectivity of the amygala
– Lower connectivity of regions that interact with amygdala e.g.
insula, striatum, IFG etc.
Our study
• Propranolol (40 mg) or placebo
• Double blinded
• Administered 1 hour before scanning to fasting subject
• Resting state of ~6 minutes prior to task
• Analysis contains 15 drug; 16 placebo
MDMR
• If a voxels connectivity pattern distinguishes two groups,
then the connectivity of that voxel will be very similar to
the subjects in the same group but different than the
subjects in the other group
Receptor activity
• Propranolol binds non-selectively to both β1 and β2
receptors.
• High levels of β1 are found in the anterior cingulate,
hippocampus and other regions in the rat brain (Rainbow
et al 1983).
• High levels of β2 are found in the various thalamic nuclei
and other regions in the rat brain (Rainbow et al 1983).
• beta-receptors in humans are highest in all subfields of
the hippocampus, followed by cerebellum, and then
thalamic nuclei, basal ganglia, midbrain, and cerebral
cortex (Reznikoff et al 1986)
Amygdala
• BLA contains higher density of beta
receptors in rat amygdala but in human all
nuclei have the same levels (Renzikoff
1986).
References
• Brunet A, Orr SP, Tremblay J, Robertson K, Nader K, Pitman RK
(2007). "Effect of post-retrieval propranolol on psychophysiologic
responding during subsequent script-driven traumatic imagery in
post-traumatic stress disorder". Journal of Psychiatric Research 42
(6): 503–6.
http://wiki.biac.duke.edu/biac:analysis:resting_pipeline
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Usage:
resting_pipeline.py --func /path/to/run4.bxh --steps all --outpath /here/ -p func
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Program to run through Nan-kuei Chen's resting state analysis pipeline:
steps:
0 - convert data to nii in LAS orientation ( we suggest LAS if you are skipping this step )
1 - slice time correction
2 - motion correction, then regress out motion parameter
3 - skull stripping
4 - normalize data
5 - regress out WM/CSF
6 - bandpass filter
7 - produce correlation matrix from label file
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Options:
-h, --help
show this help message and exit
-f /path/to/BXH, --func=/path/to/BXH
bxh ( or nifti ) file for functional run
--throwaway=4
number of timepoints to dis-regard from beginning of
run
-p func, --prefix=func
prefix for all resulting images, defaults to name of
input
-s 0,1,2,3, --steps=0,1,2,3
comma seperated string of steps. 'all' will run
everything, default is all
-o PATH, --outpath=PATH
location to store output files
--sliceorder=string
sliceorder if slicetime correction ( odd=interleaved
(1,3,5,2,4,6), up=ascending, down=descending,
even=interleaved (2,4,6,1,3,5) ). Default is to read
this from input image, if available.
--tr=MSEC
TR of functional data in MSEC
--ref=FILE
pointer to FLIRT reference image if not using standard
brain
--flirtmat=FILE
a pre-defined flirt matrix to apply to your functional
data. (ie: func2standard.mat)
--refwm=FILE
pointer to WM mask of reference image if not using
standard brain
--refcsf=FILE
pointer to CSF mask of reference image if not using
standard brain
--refacpoint=45,63,36
AC point of reference image if not using standard MNI
brain
--betfval=0.5
f value to use while skull stripping. default is 0.5
--lpfreq=0.08
frequency cutoff for lowpass filtering in HZ. default
is .08hz
--corrlabel=FILE
pointer to 3D label containing ROIs for the
correlation search. default is the 116 region AAL
label file
--corrtext=FILE
pointer to text file containing names/indices for ROIs
for the correlation search. default is the 116 region
AAL label txt file
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--cleanup
delete files from intermediate steps?
Correlation Matrices
• BIAC’s resting state pipeline
to process every subject's
resting state data into a nodebased correlation matrix
representing connectivity
between different regions of
the brain (AAL 116 regions)
• Randomise to regress with
covariates and get group
mean correlation matrices for
placebo and drug groups.
In MATLAB
• Load group p-value statistics map for each group
into MATLAB and binarize >0.95.
• Create structure with 116 x 116 binarized
connectivity map and list of 116 regions.
• Connectivity matrix can be input into any Sporns
script in the BCT (also weighted instead of
binary matrices).
Terminology
• N x N binary graph where N = 116 regions
(AAL Brain Atlas)
• G : binary graph
• Undirected edges between connected nodes
• T : threshold for correlation (Fisher’s r-to-z)
• eij = 1 for z(i, j) > T; otherwise eij = 0
• 1 = graph edge; 0 = no edge
• Gi is a subgraph of all nodes that are direct neighbors of the ith node
• Degree of each node Ki is defined as the number of nodes in the
subgraph Gi
Graph theoretical measures
• Degree of Connectivity, Kp of a
graph is the average of the
degrees of all the nodes in the
graph. Measure of sparsity.
• Kcost is the cost of the network,
which is the total number of
edges of a graph divided by the
maximum number of possible
edges. How expensive it is to
build the network.
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Kp 
N
K
i
iG
1
Kcost 
Ki

N ( N  1) iG
Graph theoretical measures
• Ei_corr is a measure of the
strength of the functional
connectivity ith node and
the nodes in the subgraph
Gi is:
1
Ei _ corr   | z (i, j ) | eij
Ki iG
• The strength of the
functional connectivity of
the graph is:
1
Ecorr 
N
• The clustering coefficient of
a node is the ratio of the
number of existing
connections to the number
of all possible connections
in the subgraph Gi
E
i _ corr
iG
1
Ci 
Ki ( Ki  1) / 2
Graph theoretical measures
• The clustering coefficient of a network
is the average of clustering coefficients
of all nodes. It is measure of local
density or cliquishness.
• The mean shortest path length of a
node is Li.
• In which min {Li,j} is the shortest path
length between the ith node and the
jth node; the path length is the
number of edges included in the path
connecting two nodes.
• The mean shortest path length of a
network Lp is the average of the mean
path lengths between the nodes. It is a
measure of the extent of average
connectivity or overall routing
efficiency of the network.
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Cp 
N
C
i
iG
1
Li 
min{ Li , j}

N  1 i  jG
1
Lp   Li
N iG
Efficiency
• Efficiency of a
network is efficient Eglobal 
transfer of information
at a low cost
1
1

N ( N  1) i  jG Li , j
Small-world networks
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Compared to random networks,
small-world networks have similar
path lengths but higher clustering
coefficients.
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Small worldness combines these
two into a scalar quantitative
measure (>1 for small world
networks).
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In sum, nodes in small world
networks have few neighbors but
every node can be reached in a few
hops (e.g. electric grid, social
networks, genetic networks).
L ≈ log (N)


C preal
C
rand
p
Lreal
p
rand
p
L
1
1



random network measures
• Approximation for
clustering coefficient of a
random network
• Approximation for
shortest path length of a
random network
C
rand
p
rand
p
L
K

N
ln( N )

ln( K )
Closeness Centrality
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Closeness is the inverse of farness, which is the sum distances to all other
nodes.
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Closeness Centrality describes the connectedness of a node in undirected
networks.
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A node that is connected by a lot of short paths to other nodes, can be
interpreted as relatively autonomous as opposed to all nodes that are less
connected by short paths
closeness (ni ) 
g 1

g
d
(
n
,
n
)
i
j
j 1
• g is total nodes in network (116 in this case),
is the shortest distance between node i and j.
*Someone is important if they are close to all other people.
Betweenness Centrality
• Betweenness centrality is a measure of a node’s centrality
in a network equal to the number of shortest paths from
all vertices to all others that pass through that node.
• Betweenness centrality is a more useful measure of the
load placed on the given node in the network.
 st (v)
g (v)  
s  v t  st
• Normalized (N-1)(N-2) for
directed, (N-1)(N-2)/2 for
undirected.
*between a lot of other people. Communication flow, if you are in
communication paths, you can control communication flow and are important.
Connectivity measures
• Correlation (time domain)
• Partial covariance – attenuating contribution of other
sources of variance (e.g. Liu 2008; Brain)
• Wavelet-based connectivity –fMRI timeseries have a long
memory (slow decay positive autocorrelation) (e.g.
Archard 2006 JofN).
• Coherence (frequency domain).
Model for group contrasts and covariates
for permutation testing
correlation matrix – 116 x 116
placebo
drug
Graph visualization of resting connectivity
placebo
drug
resting connectivity contrasts
Placebo > Drug
Drug > Placebo
Graph visualization of connectivity contrasts
Placebo > Drug
Drug > Placebo