# 公開ソフトウェア / Software

** Energy Landscape Analysis Toolkit (ELAT) **[Download]

The energy landscape analysis is a powerful method for analyzing multivariate time-series data (e.g. fMRI data).

We provide MATLAB code for the entire analysis described in our paper [Ezaki et al. (2017)].

__Requirements__

MATLAB (ver. later than R2016b)

MATLAB Statistics and Machine Learning Toolbox

__Limitations__

The energy landscape analysis is not applicable to the data if the number of variables, *N*, is larger than 15.

Note that estimation of the maximum entropy distribution is possible even for *N* > 200.

The length of data is crucial. We do not encourage to use this method to very short data (*T*max<1000).

__What can we do after this analysis?__

ELAT provides:

1. Categorization of the possible 2^N states into relevant groups based on the maximum entropy distribution.

2. Disconnectivity graph which characterizes the energy landscape.

1. can be used to label states for further analysis (e.g., elaborate a new dynamical index).

2. may characterize the difference between two groups (subject groups etc.) of data.

See references listed in [Ezaki et al. (2017)] for examples of the use of the energy landscape analysis.

__Download ELAT __

2018/02/05 ELAT ver1.2 is available! [Download].

Update:

1. Added the correspondences between the labeling numbers of local minima (i.e., 1,2,3,..) and state numbers shown in the console.

Old versions

2018/01/16 ELAT ver 1.1

Updates:

1. Added accuracy of fitting, *r*.

2. Definitions of ROI and state number were modified.

2017/7/10 ELAT ver 1.0

__Contact information__

E-mail: ezaki0705@gmail.com

__How to use__

__Preparations__

1. Prepare multivariate time-series data (a). (Number of variables must be smaller than 15).

Each line (row) of the data file corresponds to time-series of a single variable.

2. ELAT provides a simple binarization method. If this is not suitable for your data, prepare binarized data.

3. If necessary, prepare a list of variable names (b).

We provide test data files (a) "testdata.dat" and (b) "roiname.dat".

__Analysis__

4. Download ELAT and unzip it to a relevant directory.

5. Run "StartProgram.m".

6. Select data files (a) and (b).

If the data (a) is already binarized, select "Binarized data";

otherwise, select "Continuous data" and set a threshold value ("0" was used in [Ezaki et al. (2017)]).

7. Select output folder where results will be saved.

If you need the list of basins for further analyses, select "Save Basin List".

8. Execute.

__Alternatively, you can directly run this program by executing main.m.__

In this case, please set an appropriate path to the analyzed data file in **main.m**.

__Data structure / Definition of the state number __

**In this case, the state number is 28 + 1 = 29. The state number ranges from 1 to 2^N (not from 0 to 2^N–1).**

**Note: In ELAT ver. 1.0, the definition of variable names is reversed. Please use the latest version.**