Hi,

I want to visualize the correlation matrix using the Matlab command "imagesc".

But, the matrix exported from Brainstorm to Matlab is a somewhat different matrix to visualize N x N matrix.

Here I am having

TF: [36856×1×100 double]

36856 indicates ch information

100 indicates frequency information from 1Hz to 100Hz.

The number of MEG channels is 271.

How can I visualize the 271x271 matrix at Frequency f.

conn =

struct with fields:

```
TF: [36856×1×100 double]
TFmask: []
Std: []
DataType: 'data'
Time: 315
TimeBands: []
Freqs: [100×1 double]
RefRowNames: {1×271 cell}
RowNames: {1×271 cell}
Measure: 'other'
Method: 'henv'
DataFile: ''
SurfaceFile: ''
GridLoc: []
GridAtlas: []
Atlas: []
HeadModelFile: ''
HeadModelType: []
nAvg: 30
Leff: 1
ColormapType: []
DisplayUnits: []
Options: [1×1 struct]
```

Hi @sugata_hisato,

The connectivity matrix (`TF`

field) is saved as: ` [Nr x Ntime x Nfreq]`

.

`Nr`

represents the connectivity between two files, thus `Na x Nb`

. However, the relation between `Na x Nb`

and Nr may not be direct as it's optimized to save storage space for some connectivity metrics.

https://neuroimage.usc.edu/brainstorm/Tutorials/Connectivity#On_the_hard_drive

E.g. in correlation, as corr(a,b) == corr(b,a) only one value is saved. In your case 271*271 = 73441 elements; then the upper triangle of the connectivity matrix (36585 elements) plus the diagonal (271 elements) are saved, giving 36856 elements.

You can retrieve the connectivity matrix in the shape: `[Na x Nb x Ntime x Nfreq]`

by using:

```
R = bst_memory('GetConnectMatrix', conn);
```

Best,

Raymundo

Hi, Raymundo,

I could reshape TF: [36856×1×100 double] using bst_memory and visualize the connectivity matrix by Matlab command.

Thanks a lot!

Hi,

I want to get each connectivity scout label from the exported connectivity structure from Brainstorm to Matlab.

Could you let me know how I can get all the combinations of scout labels (6328 scout x scout labels) from the below information?

struct with fields:

```
TF: [6328×1×100 double]
TFmask: []
Std: []
DataType: 'matrix'
Time: 15
TimeBands: []
Freqs: [100×1 double]
RefRowNames: {1×112 cell}
RowNames: {1×112 cell}
Measure: 'other'
Method: 'henv'
DataFile: ''
SurfaceFile: ''
GridLoc: [11602×3 double]
GridAtlas: []
Atlas: [1×1 struct]
HeadModelFile: ''
HeadModelType: []
nAvg: 11
Leff: 1
ColormapType: []
DisplayUnits: []
Options: [1×1 struct]
History: {1×3 cell}
```

See the documentation for the fields of the connectivity files in the Connectivity tutorial:

https://neuroimage.usc.edu/brainstorm/Tutorials/Connectivity?highlight=(RefRowNames)#On_the_hard_drive

After you reshape the TF matrix, you get a R matrix with dimensions [112 x 112 x 1 x nFreqs].

The labels for rows and columns are `RefRowNames`

and `RowNames`

.