GraphVar: A user-friendly toolbox for comprehensive graph analyses of functional brain connectivity.

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2 GraphVar: A user-friendly toolbox for comprehensive graph analyses of functional brain connectivity. features: I. Pipeline construction of graph networks II. Calculation of network topological measures III. Generation of subject specific random networks IV. Perform statistical tests with a GLM framework V. Calculations on raw connectivity matrices (Network based statistics) VI. Parametric and non-parametric testing VII. Dynamic graph analyses (sliding windows) VIII. Interactive and visual results exploration Kruschwitz, J.D 1,2*, List, D. 1,2*, Waller, L. 2, Rubinov, M. 3, Walter, H 1. *equal contribution 1 Charité Universitätsmedizin, Berlin, Germany 2 Technische Universität Dresden, Dresden, Germany 3 University of Cambridge, Cambridge, United Kingdom

3 The Analysis Setup GUI

4 Setup panel

5 Workflows Statistical analyses (GLM) with clinical data

6 Calculate and export graph metrics Statistics on raw matrix Statistics with graph metrics Workflows

7 Setup panel (mouse over help)

8 Parallel Computing (with toolbox)

9 Underlying codes from the Brain Connectivity Toolbox (Rubinov and Sporns, 2010) Setup panel

10 General settings

11 Subject s Association Matrices Load (existing) Connectivity Matrices

12 Time course Time course Time course Time course Individual nodal time courses Node 1 Node 2 Node 3 Node n Generate Connectivity Matrices

13 Label nodes

14 Variable Selection

15 Network construction

16 Network nodes

17 Redefine Network (Subnetwork analyses)

18 Network thresholds

19 Subject specific null model networks

20 Network topological measures

21 Export network topological measures

22 Statistical analyses (GLM)

23 Non-parametric testing

24 Statistical analyses of graph measures

25 Statistics on the raw conn matrices

26 Network-Based-Statistics

27 Generate the Sliding Window matrices with the connectivity measure you desire: - Pearson corr - Partial corr - Covariance - Mutual inf - etc.. Create Connectivity matrix -> sliding windows

28 If SW-matrices are loaded (also manually via Select Subjects Corr Matrix ) the dynamic selection windows will appear in the GUI Select dynamic summary measure (graph metrics/raw matrix)

29 Dynamic summary measures*: - Variance - Standard deviation - Periodicity - PointProcess: rate - PointProcess: intervall - Brain-Network Variability * definition in Dynamic GraphVar Tutorial Select dynamic summary measure (graph metrics/raw matrix)

30 GraphVar will perform all the operations with respect to the dynamic summary measure: e.g. compute the variance of the clustering coefficient across the sliding windows and export or do group statistics on this measure Select dynamic summary measure (graph metrics/raw matrix)

31 Ready to go!

32 Status bar

33 The Results Viewer GUI

34 Results selection box

35 General functions panel

36 Correction for multiple comparisons

37 d: Difference between Group Means F(df1,df2): F-value d(b): Difference between Standardized Regression Weights b: Standardized Regression Weight t(df): t-value p: p-value m: Mean significant not-significant Interpreting GraphVar output

38 Association to dependent variable Thresholds One dimensional graph metrics

39 Non paramteric testing with random networks One dimensional graph metrics

40 Association to dependent variable Thresholds Network nodes Two dimensional graph metrics

41 Association to dependent variable Thresholds Network nodes Network nodes Three dimensional graph metrics

42 Association to dependent variable Network Thresholds level k Associations to Rich club coefficients

43 Export Results

44 Plot single subject association matrices

45 Plot mean connectivity matrix across subjects

46 Show graph component with Network Inspector Identify Graph Components

47 Network Inspector GUI

48 Identify Graph Components

49 Mouse over

50 Show association to dependent variable Show association strenght

51 Open directly in BrainNetViewer (Xia et al., 2013; PlosONE)

52 GraphVar -Team