Connectivity Metrics - Revision history

192.168.1.1: /* adjacency_plot_und() */

2016-06-28T19:21:54Z

adjacency_plot_und()

← Older revision		Revision as of 15:21, 28 June 2016
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	===Visualization===		===Visualization===
	====adjacency_plot_und()====		====adjacency_plot_und()====
	This function requires two parameters: an adjacency matrix '''''M''''' and a set of spatial coordinates '''''xyz''''' for each node in the adjacency matrix. The function renders a 3d figure depicting each non-zero connection. For large networks, the resulting figure may be uninterpretable, especially if there is a nonzero value for all edges in '''''M'''''; it might be helpful to prune the adjacency matrix before plotting it (e.g., set the main diagonal self-connections and any value below the top x %-ile to zero). To obtain the set of spatial coordinates, '''''xyz''''', see the topic on [[Annotation Coordinates]]. The example below describes the steps involved in spatially depicting the connections in the first of a set of 12 adjacency matrices stored in a 3D array, '''''RR'''''.		This function requires two parameters: an adjacency matrix '''''M''''' and a set of spatial coordinates '''''xyz''''' for each node in the adjacency matrix. The function renders a 3d figure depicting each non-zero connection. For large networks, the resulting figure may be uninterpretable, especially if there is a nonzero value for all edges in '''''M'''''; it might be helpful to prune the adjacency matrix before plotting it (e.g., set the main diagonal self-connections and any value below the top x %-ile to zero). To obtain the set of spatial coordinates, '''''xyz''''', see the topic on [[Annotation Coordinates]]. The example below describes the steps involved in spatially depicting the connections in the first of a set of 12 adjacency matrices stored in a 3D array of squared correlation values, '''''RR'''''.
	lsegs=allregions(1:111); %allregions is a pre-existing cell array of segment names for the 219 node network		lsegs=allregions(1:111); %allregions is a pre-existing cell array of segment names for the 219 node network
	rsegs=allregions(112:end); %the first 111 nodes are lh, the remainder are rh		rsegs=allregions(112:end); %the first 111 nodes are lh, the remainder are rh

192.168.1.1: /* adjacency_plot_und() */

2016-06-28T19:21:25Z

adjacency_plot_und()

← Older revision		Revision as of 15:21, 28 June 2016
Line 34:		Line 34:
	===Visualization===		===Visualization===
	====adjacency_plot_und()====		====adjacency_plot_und()====
	This function requires two parameters: an adjacency matrix '''''M''''' and a set of spatial coordinates '''''xyz''''' for each node in the adjacency matrix. The function renders a 3d figure depicting each non-zero connection. For large networks, the resulting figure may be uninterpretable, especially if there is a nonzero value for all edges in '''''M'''''; it might be helpful to prune the adjacency matrix before plotting it (e.g., set the main diagonal self-connections and any value below the top x %-ile to zero). To obtain the set of spatial coordinates, see the topic on [[Annotation Coordinates]]. The example below describes the steps involved in spatially depicting the connections in the first of a set of 12 adjacency matrices stored in a 3D array, '''''RR'''''.		This function requires two parameters: an adjacency matrix '''''M''''' and a set of spatial coordinates '''''xyz''''' for each node in the adjacency matrix. The function renders a 3d figure depicting each non-zero connection. For large networks, the resulting figure may be uninterpretable, especially if there is a nonzero value for all edges in '''''M'''''; it might be helpful to prune the adjacency matrix before plotting it (e.g., set the main diagonal self-connections and any value below the top x %-ile to zero). To obtain the set of spatial coordinates, '''''xyz''''', see the topic on [[Annotation Coordinates]]. The example below describes the steps involved in spatially depicting the connections in the first of a set of 12 adjacency matrices stored in a 3D array, '''''RR'''''.
	lsegs=allregions(1:111); %allregions is a pre-existing cell array of segment names for the 219 node network		lsegs=allregions(1:111); %allregions is a pre-existing cell array of segment names for the 219 node network
	rsegs=allregions(112:end); %the first 111 nodes are lh, the remainder are rh		rsegs=allregions(112:end); %the first 111 nodes are lh, the remainder are rh

192.168.1.1: /* adjacency_plot_und() */

2016-06-28T19:20:39Z

adjacency_plot_und()

192.168.1.1: /* adjacency_plot_und() */

2016-06-28T19:19:43Z

adjacency_plot_und()

← Older revision		Revision as of 15:19, 28 June 2016
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	>> %having already determined the segment names of interest, I can pass them as parameters:		>> %having already determined the segment names of interest, I can pass them as parameters:
	>> [xyz, hemi, segnames]=getSegCoords('lsegnames', lsegs, 'rsegnames', rsegs);		>> [xyz, hemi, segnames]=getSegCoords('lsegnames', lsegs, 'rsegnames', rsegs);
	>> %% Step 1: ~~simplify our~~ adjacency matrix		>> %% Step 1: determine adjacency matrix threshold
	>> %% ~~by eliminating sub-threshold connections~~		>> %% so we can simplify plot (e.g., keep only top 10%)
	>> %% We use a temporary array, ''ss'' to determine the threshold		>> %% We use a temporary array, ''ss'' to determine the threshold
	>> ss=RR(:,:,1); %Grab the adjacency matrix (squared correlations)		>> ss=RR(:,:,1); %Grab the adjacency matrix (squared correlations)
	>> ss(ss==1)=0; %Eliminate connections on the main diagonal from consideration		>> ss(ss==1)=0; %Eliminate connections on the main diagonal from consideration
	>> ss=sort(ss(:), 'descend'); %%sort all remaining connection weights		>> ss=sort(ss(:), 'descend'); %%sort all remaining connection weights
			>> %% Step 2: Use another temporary array, ''M'', to which we can
			>> %% apply our threshold
	>> M=RR(:,:,1);		>> M=RR(:,:,1);
	>> M(M<ss(4796))=0; %ss had 47961 values. The 4796th value would be the 10th percentile		>> M(M<ss(4796))=0; %ss had 47961 values. The 4796th value would be the 10th percentile
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	>> %M(M<tenth_percentile)=0;		>> %M(M<tenth_percentile)=0;
	>> M(M==1)=0; %eliminate self-connections along the diagonal		>> M(M==1)=0; %eliminate self-connections along the diagonal
			>> %% Step 3: Plot connections in our temporary array ''M''
	>> [x,y,z]=adjacency_plot_und(M,xyz);		>> [x,y,z]=adjacency_plot_und(M,xyz);
	>> plot3(x,y,z);		>> plot3(x,y,z);

192.168.1.1: /* adjacency_plot_und() */

2016-06-28T19:16:55Z

adjacency_plot_und()

← Older revision		Revision as of 15:16, 28 June 2016
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	===Visualization===		===Visualization===
	====adjacency_plot_und()====		====adjacency_plot_und()====
	This function requires two parameters: an adjacency matrix '''''M''''' and a set of spatial coordinates '''''xyz''''' for each node in the adjacency matrix. The function renders a 3d figure depicting each non-zero connection. For large networks, the resulting figure may be uninterpretable, especially if there is a nonzero value for all edges in '''''M'''''; it might be helpful to prune the adjacency matrix before plotting it (e.g., set the main diagonal self-connections and any value below the top x %-ile to zero). To obtain the set of spatial coordinates, see the topic on [[Annotation Coordinates]].		This function requires two parameters: an adjacency matrix '''''M''''' and a set of spatial coordinates '''''xyz''''' for each node in the adjacency matrix. The function renders a 3d figure depicting each non-zero connection. For large networks, the resulting figure may be uninterpretable, especially if there is a nonzero value for all edges in '''''M'''''; it might be helpful to prune the adjacency matrix before plotting it (e.g., set the main diagonal self-connections and any value below the top x %-ile to zero). To obtain the set of spatial coordinates, see the topic on [[Annotation Coordinates]]. The example below describes the steps involved in spatially depicting the connections in the first of a set of 12 adjacency matrices stored in a 3D array, '''''RR'''''.
	>> lsegs=allregions(1:111); %allregions is a pre-existing cell array of segment names for the 219 node network		>> lsegs=allregions(1:111); %allregions is a pre-existing cell array of segment names for the 219 node network
	>> rsegs=allregions(112:end); %the first 111 nodes are lh, the remainder are rh		>> rsegs=allregions(112:end); %the first 111 nodes are lh, the remainder are rh
	>> %having already determined the segment names of interest, I can pass them as parameters:		>> %having already determined the segment names of interest, I can pass them as parameters:
	>> [xyz, hemi, segnames]=getSegCoords('lsegnames', lsegs, 'rsegnames', rsegs);		>> [xyz, hemi, segnames]=getSegCoords('lsegnames', lsegs, 'rsegnames', rsegs);
	>> ss=RR(:,:,1); %~~RR contains 12 219×219~~ adjacency ~~matrices. Here, we will plot the first one.~~		>> %% Step 1: simplify our adjacency matrix
	>> ss(ss==1)=0;		>> %% by eliminating sub-threshold connections
	>> ss=sort(ss(:), 'descend'); %%~~ss has 47961 values; when sorted, the 4796th value would be the top 10%~~		>> %% We use a temporary array, ''ss'' to determine the threshold
			>> ss=RR(:,:,1); %Grab the adjacency matrix (squared correlations)
			>> ss(ss==1)=0; %Eliminate connections on the main diagonal from consideration
			>> ss=sort(ss(:), 'descend'); %%sort all remaining connection weights
	>> M=RR(:,:,1);		>> M=RR(:,:,1);
	>> M(M<ss(4796))=0;		>> M(M<ss(4796))=0; %ss had 47961 values. The 4796th value would be the 10th percentile
	>> M(M==1)=0;		>> %the above step could have also been done less elegantly by:
	>> [x,y,z]=adjacency_plot_und(M,xyz);		>> %tenth_percentile=ss(4796);
			>> %M(M<tenth_percentile)=0;
			>> M(M==1)=0; %eliminate self-connections along the diagonal
			>> [x,y,z]=adjacency_plot_und(M,xyz);
	>> plot3(x,y,z);		>> plot3(x,y,z);

172.101.108.133: /* [lambda,efficiency,ecc,radius,diameter] = charpath(D,diagonal_dist,infinite_dist) */

2016-06-23T02:30:40Z

[lambda,efficiency,ecc,radius,diameter] = charpath(D,diagonal_dist,infinite_dist)

← Older revision		Revision as of 22:30, 22 June 2016
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	====[lambda,efficiency,ecc,radius,diameter] = charpath(D,diagonal_dist,infinite_dist)~~====~~		====charpath====
			<code>[lambda,efficiency,ecc,radius,diameter] = charpath(D,diagonal_dist,infinite_dist)</code>
	*The network characteristic path length (''lambda'') is the average shortest path length between all pairs of nodes in the network, and is primarily influenced by long paths		*The network characteristic path length (''lambda'') is the average shortest path length between all pairs of nodes in the network, and is primarily influenced by long paths
	*The global efficiency is the average inverse shortest path length in the network and may be meaningfully computed on disconnected networks and is primarily influenced by short paths		*The global efficiency is the average inverse shortest path length in the network and may be meaningfully computed on disconnected networks and is primarily influenced by short paths

192.168.1.1: /* Brain Connectivity Toolbox (BCT) */

2016-06-22T18:29:14Z

Brain Connectivity Toolbox (BCT)

← Older revision		Revision as of 14:29, 22 June 2016
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	== Brain Connectivity Toolbox (BCT) ==		== Brain Connectivity Toolbox (BCT) ==
	The BCT is a set of MATLAB functions that provide a number of potentially useful network metrics.		The BCT is a set of MATLAB functions that provide a number of potentially useful network metrics. Most of the information in this section is taken directly from Rubinov & Sporns (2010).

	===Measures of Integration ===		===Measures of Integration ===
	Functional integration in the brain is the ability to rapidly combine		Functional integration in the brain is the ability to rapidly combine

192.168.1.1: /* [lambda,efficiency,ecc,radius,diameter] = charpath(D,diagonal_dist,infinite_dist) */

2016-06-22T18:28:03Z

[lambda,efficiency,ecc,radius,diameter] = charpath(D,diagonal_dist,infinite_dist)

← Older revision		Revision as of 14:28, 22 June 2016
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	====[lambda,efficiency,ecc,radius,diameter] = charpath(D,diagonal_dist,infinite_dist)====		====[lambda,efficiency,ecc,radius,diameter] = charpath(D,diagonal_dist,infinite_dist)====
	*The network characteristic path length is the average shortest path length between all pairs of nodes in the network, and is primarily influenced by long paths		*The network characteristic path length (''lambda'') is the average shortest path length between all pairs of nodes in the network, and is primarily influenced by long paths
	*The global efficiency is the average inverse shortest path length in the network and may be meaningfully computed on disconnected networks and is primarily influenced by short paths		*The global efficiency is the average inverse shortest path length in the network and may be meaningfully computed on disconnected networks and is primarily influenced by short paths
	*The nodal eccentricity is the maximal path length between a node and any other node in the network.		*The nodal eccentricity is the maximal path length between a node and any other node in the network.

192.168.1.1: /* Measures of Integration */

2016-06-22T18:27:40Z

Measures of Integration

@@ Line 4: / Line 4: @@
 The BCT is a set of MATLAB functions that provide a number of potentially useful network metrics.
 ===Measures of Integration ===
 ====[lambda,efficiency,ecc,radius,diameter] = charpath(D,diagonal_dist,infinite_dist)====
-The network characteristic path length is the average shortest path length between all pairs of nodes in the network. The global efficiency is the average inverse shortest path length in the network. The nodal eccentricity is the maximal path length between a node and any other node in the network. The radius is the minimal eccentricity, and the diameter is the maximal eccentricity.
+*The network characteristic path length is the average shortest path length between all pairs of nodes in the network, and is primarily influenced by long paths
 The distance matrix '''''D''''' is obtained by first calling distance_bin (binarized) or distance_wei (weighted). The input to either of these functions should be a connection-length matrix '''''M''''', where larger values in '''''M''''' correspond to greater distances. Assuming that the elements of '''''M''''' represent correlations, squared correlations, or neural network connections, the matrix should first be inverted (because higher values in M would usually be interpreted as closer connections).

192.168.1.1: /* Brain Connectivity Toolbox (BCT) */

2016-06-22T18:08:42Z

Brain Connectivity Toolbox (BCT)

@@ Line 3: / Line 3: @@
 == Brain Connectivity Toolbox (BCT) ==
 The BCT is a set of MATLAB functions that provide a number of potentially useful network metrics.
 ===Visualization===

← Older revision		Revision as of 15:20, 28 June 2016
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	====adjacency_plot_und()====		====adjacency_plot_und()====
	This function requires two parameters: an adjacency matrix '''''M''''' and a set of spatial coordinates '''''xyz''''' for each node in the adjacency matrix. The function renders a 3d figure depicting each non-zero connection. For large networks, the resulting figure may be uninterpretable, especially if there is a nonzero value for all edges in '''''M'''''; it might be helpful to prune the adjacency matrix before plotting it (e.g., set the main diagonal self-connections and any value below the top x %-ile to zero). To obtain the set of spatial coordinates, see the topic on [[Annotation Coordinates]]. The example below describes the steps involved in spatially depicting the connections in the first of a set of 12 adjacency matrices stored in a 3D array, '''''RR'''''.		This function requires two parameters: an adjacency matrix '''''M''''' and a set of spatial coordinates '''''xyz''''' for each node in the adjacency matrix. The function renders a 3d figure depicting each non-zero connection. For large networks, the resulting figure may be uninterpretable, especially if there is a nonzero value for all edges in '''''M'''''; it might be helpful to prune the adjacency matrix before plotting it (e.g., set the main diagonal self-connections and any value below the top x %-ile to zero). To obtain the set of spatial coordinates, see the topic on [[Annotation Coordinates]]. The example below describes the steps involved in spatially depicting the connections in the first of a set of 12 adjacency matrices stored in a 3D array, '''''RR'''''.
	>> lsegs=allregions(1:111); %allregions is a pre-existing cell array of segment names for the 219 node network		lsegs=allregions(1:111); %allregions is a pre-existing cell array of segment names for the 219 node network
	>> rsegs=allregions(112:end); %the first 111 nodes are lh, the remainder are rh		rsegs=allregions(112:end); %the first 111 nodes are lh, the remainder are rh
	>> %having already determined the segment names of interest, I can pass them as parameters:		%having already determined the segment names of interest, I can pass them as parameters:
	>> [xyz, hemi, segnames]=getSegCoords('lsegnames', lsegs, 'rsegnames', rsegs);		[xyz, hemi, segnames]=getSegCoords('lsegnames', lsegs, 'rsegnames', rsegs);
	>> %% Step 1: determine adjacency matrix threshold		%% Step 1: determine adjacency matrix threshold
	>> %% so we can simplify plot (e.g., keep only top 10%)		%% so we can simplify plot (e.g., keep only top 10%)
	>> %% We use a temporary array, ''ss'' to determine the threshold		%% We use a temporary array, ''ss'' to determine the threshold
	>> ss=RR(:,:,1); %Grab the adjacency matrix (squared correlations)		ss=RR(:,:,1); %Grab the adjacency matrix (squared correlations)
	>> ss(ss==1)=0; %Eliminate connections on the main diagonal from consideration		ss(ss==1)=0; %Eliminate connections on the main diagonal from consideration
	>> ss=sort(ss(:), 'descend'); %%sort all remaining connection weights		ss=sort(ss(:), 'descend'); %%sort all remaining connection weights
	>> %% Step 2: Use another temporary array, ''M'', to which we can		%% Step 2: Use another temporary array, ''M'', to which we can
	>> %% apply our threshold		%% apply our threshold
	>> M=RR(:,:,1);		M=RR(:,:,1);
	>> M(M<ss(4796))=0; %ss had 47961 values. The 4796th value would be the 10th percentile		M(M<ss(4796))=0; %ss had 47961 values. The 4796th value would be the 10th percentile
	>> %the above step could have also been done less elegantly by:		%the above step could have also been done less elegantly by:
	>> %tenth_percentile=ss(4796);		%tenth_percentile=ss(4796);
	>> %M(M<tenth_percentile)=0;		%M(M<tenth_percentile)=0;
	>> M(M==1)=0; %eliminate self-connections along the diagonal		M(M==1)=0; %eliminate self-connections along the diagonal
	>> %% Step 3: Plot connections in our temporary array ''M''		%% Step 3: Plot connections in our temporary array ''M''
	>> [x,y,z]=adjacency_plot_und(M,xyz);		[x,y,z]=adjacency_plot_und(M,xyz);
	>> plot3(x,y,z);		plot3(x,y,z);



	[[Category: Network Analyses]]		[[Category: Network Analyses]]