• Sympatholytic
• Lipophilic – crosses BBB
• Treatment for hypertension, anxiety, tremor, PTSD?
• James Black won the Nobel Prize for Medicine in 1968

• 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.

• 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.

• 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

• CWAS results will show
– Lower connectivity of the amygala
– Lower connectivity of regions that interact with amygdala e.g. insula, striatum, IFG etc.

• 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

• 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

• Propranolol binds non-selectively to both β
1 receptors. and β
2
• High levels of β
1 are found in the anterior cingulate, hippocampus and other regions in the rat brain (Rainbow et al 1983).
• High levels of β
2 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)

• 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.

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• http://wiki.biac.duke.edu/biac:analysis:resting_pipeline
Usage: resting_pipeline.py --func /path/to/run4.bxh --steps all --outpath /here/ -p func
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
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
--cleanup delete files from intermediate steps?

• 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.

• 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).

• 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)
• e ij
= 1 for z(i, j) > T; otherwise e ij
= 0
• 1 = graph edge; 0 = no edge
• G i is a subgraph of all nodes that are direct neighbors of the ith node
• Degree of each node K i subgraph G i is defined as the number of nodes in the

• Degree of Connectivity, K p graph is the average of the of a degrees of all the nodes in the graph. Measure of sparsity.
• K cost 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.
K p

K cost

1
N
 i

G
K i
N ( N
1

1 )
 i

G
K i

• E i_corr is a measure of the strength of the functional connectivity ith node and the nodes in the subgraph
G i is:
• The strength of the functional connectivity of the graph is:
• 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 G i
E i _ corr

1
K i
 i

G
| z ( i , j ) |
 e ij
E corr

1
N
 i

G
E i _ corr
C i

K i
( K i
1

1 ) / 2

• 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 L i
.
• In which min {L i,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 L p 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.
C p

L i

N
1

1 i

 j

G min{ L i , j
}
L p

1
N
 i

G
C i
1
N
 i

G
L i

• Efficiency of a network is efficient transfer of information
E global
 at a low cost
N ( N
1

1 ) i

 j

G
1
L i , j

• Compared to random networks, small-world networks have similar path lengths but higher clustering coefficients.
• Small worldness combines these two into a scalar quantitative measure (>1 for small world networks).
• 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 real p
C rand p

1
 
L real p
L rand p

1
 



• Approximation for clustering coefficient of a random network
• Approximation for shortest path length of a random network
C p rand 
K
N
L rand p
 ln( N ) ln( K )

• Closeness is the inverse of farness, which is the sum distances to all other nodes.
• Closeness Centrality describes the connectedness of a node in undirected networks.
• 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 ( n i
)

 g j

1 g d

1
( n i
, n j
)
• 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 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. g ( v )
 s


 v t

( v ) st
 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.

• 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).

placebo drug

Graph visualization of resting connectivity placebo drug

Placebo > Drug Drug > Placebo

Placebo > Drug Drug > Placebo