1. Seed-based functional connectivity and its applications
I3T Workshop
Seed-based functional connectivity and its applications
Xiangyu Long
Postdoctoral fellow
Developmental Neuroimaging Lab February 15th, 2017

2. Acknowledgement
China:
Dr. Yufeng Zang;
Germany:
Dr. Arno Villringer and Dr. Daniel Margulies;
Canada:
Dr. Catherine Lebel;

3. Last but not least: Internet
Before we start…
Mac OS or Linux system
Matlab:
SPM
Nifti Tools
BrainNet Viewer
REST toolbox
GRETNA
AFNI
Last but not least: Internet

4. Outline
Background
The applications
How to perform
Parcellation analysis
Graph-theory based analysis

5. Background
Why use seed-based functional connectivity (seed-FC) analysis?
Straight forward;
Easy to learn and understand;
Easy to implement;
Fast;
Easy to interpret;

6. Background
Margulies et al.,2010

7. Background Resting-state fMRI (rs-fMRI)
No need to perform any tasks during scanning, but are instructed to remain awake;
Eyes closed: might fall asleep, less extra stimulation;
Fixation on screen: more extra stimulation, less head motion;
Infant: fall asleep;
‎Van Den Heuvel et al ., 2010

8. Background
Low frequency fluctuations (LFFs, Hz) of blood-oxygen level dependent (BOLD) signals is related to spontaneous neuronal activity.
The cardiac cycle
The respiratory cycle
low-frequency fluctuations
‎Beckmann et al;., 2005

9. Background
Pearson correlation coefficient, p<0.05 TR
BOLD

10. Background
Functional networks
‎Buckner et al;., 2013

11. FC can be positive and negative.
Background
FC can be positive and negative.
‎Fox et al;., 2005

12. FC could be indirect connection.
Background
FC could be indirect connection.
r(A,B) = 0.5 A B C
Solution: Partial correlation

13. FC could be directional: effective connectivity.
Background
FC could be directional: effective connectivity.
Dynamic Causal modelling
Granger causality A B

14. FC doesn’t mean structural connectivity.
Background
FC doesn’t mean structural connectivity.
White matter fibers
‎Honey et al;., 2008

15. FC in the frequency domain: The Coherence Coefficient
Background
FC in the frequency domain: The Coherence Coefficient
Sun et al., 2003

16. FC analysis could be static and dynamic.
Background
FC analysis could be static and dynamic.
Static FC: r=0.3
Dynamic FC
Sliding windows
Hindriks et al., 2015

17. Background Types of FC analysis Voxel-wise ROI-wise
‎Vogel et al;., 2010

18. Types of the region-of-interest (ROI)
Background
Types of the region-of-interest (ROI)
Sphere
Anatomical regions
Activations
Whole brain
coordinates
Atlas or manual
Comparisons etc.

19. Signals from white matter and cerebral spinal fluid; Global signal;
Background
Noise signals:
Head motion;
Signals from white matter and cerebral spinal fluid;
Global signal;
Power et al., 2014

20. Background Fisher’s z transform:

21. Background
The Brodmann areas:
‎Brodmann ., 1909

22. Background
The Automated Anatomical Labeling template: widely used for the graph theory analysis
‎‎Tzourio-Mazoyer., 2002

23. The Human Brainnetome Atlas:
Background
The Human Brainnetome Atlas:
‎Fan et al;., 2016

24. How do we implement the functional connectivity anaysis in neuroscience study?

25. From task activation to functional network
The applications
From task activation to functional network
‎Biswal et al;., 1995

26. Functional network development/plasticity
The applications
Functional network development/plasticity
‎Sherman et al;., 2014

27. Functional network changes between populations
The applications
Functional network changes between populations
‎Castellanos et al;., 2008

28. Functional network and behavior/age/gender etc.
The applications
Functional network and behavior/age/gender etc.
‎Mennes et al;., 2012

29. Investigating neuroanatomy
The applications
Investigating neuroanatomy
‎Margulies et al;., 2009

30. Graph theory based network analysis:
The applications
Graph theory based network analysis:
‎Bullmore et al;., 2009

31. Background
Today’ workshop: Basic FC analysis
Static, undirectional FC;
Voxel-wise, ROI-wise;
Preliminary applications: parcellation and graph;

32. How to conduct a resting-state fMRI functional connectivity study?

33. Set up the T1 and EPI sequence; Prepare a moderate visual stimuli:
How to perform
Overview:
Invite participants;
Set up the T1 and EPI sequence;
Prepare a moderate visual stimuli:
A cross on screen An inscape movie etc.;
Vanderwal et al., 2015

34. How to perform
Scan;
Transfer the data to your local hard drive;
Pre-processing the data;
Select the seeds;
Generate FC maps/matrix;
Statistical analysis;
Discuss the results;

35. Pre-processing the data:
How to perform
Pre-processing the data:
T1 image segmentation: grey matter, whiter matter and cerebral spinal fluid;
Slice timing;
Head motion correction;
Sptial normalization to a standard space (MNI);
Nuisance signals removal: head motion, average signals within WM and CSF, scrubbing, ICA-fix, etc.,
‎Power et al;., 2014

36. How to perform
Band-pass filtering (0.01 to 0.1 Hz);
Linear trend removal;
Spatial smoothing (~ 6mm FWHM);

37. How to perform
AFNI: (Linux/Mac OS)

38. How to perform AFNI commands:
3dcalc -a subj01.nii –b subj02.nii -expr ‘a+b’ -prefix Corr_example_Z.nii
Command
input
options
Output, can be any name

39. How to perform Band-pass filtering:
3dFourier -lowpass 0.1 -highpass retrend –prefix rest.nii.gz rest_filt.nii.gz
Linear trend removal:
3dTstat -mean -prefix ${rest}_filt_mean.nii.gz rest_filt.nii.gz
3dDetrend -polort 2 -prefix rest_dt.nii.gz rest_filt.nii.gz
3dcalc -a rest_filt_mean.nii.gz -b rest_dt.nii.gz -expr 'a+b' -prefix rest_pp.nii.gz
Covariates regression:
3dmaskave –mask csf_mask.nii.gz -quiet rest_pp.nii.gz > csf.1D
3dmaskave –mask wm_mask.nii.gz -quiet rest_pp.nii.gz > wm.1D
3dmaskave –mask global_mask.nii.gz -quiet rest_pp.nii.gz > wm.1D
1dcat csf.1D wm.1D global.1D headmotion.1D > covs.1D
3dDetrend -prefix rest_proc.nii.gz -vector covs.1D -polort 1 rest_pp.nii.gz

40. Q&A
So far, any questions?

41. How to perform
Let’s create functional connectivity maps!

42. How to perform
Quick & (not so) Dirty:
Online FC tool: Neurosynth

43. How to perform
AFNI: InstaCorr

44. How to perform
AFNI: InstaCorr

45. Select the coordinates:
How to perform
Traditional way:
Select the coordinates:
‎Greicius et al;., 2003

46. How to perform

47. How to perform

48. How to perform

49. How to perform
Click on the AFNI interface

50. How to perform
Click middle button or
Option+click in Mac OS

51. How to perform
Underlay: T1.nii
Overlay: PCC_ROI.nii

52. How to perform Get the averaged time series within the ROI:
3dmaskave -quiet -mask PCC_ROI.nii rest_example.nii.gz >>seed.1D
Generate the FC map:
3dfim+ -input rest_example.nii.gz -ideal_file seed.1D -mask BrainMask.nii -out Correlation -bucket Corr_example.nii
Fisher’s z transform:
3dcalc -a Corr_example.nii -expr 'log((1+a)/(1-a))/2' -prefix Corr_example_Z.nii

53. A region from a atlas as the ROI: e.g., AAL atlas,
How to perform
A region from a atlas as the ROI: e.g., AAL atlas,
3dcalc -a AAL_61x73x61.nii -expr 'within(a,10.5,11.5)' -prefix aal11.nii
3dresample -prefix AAL_epi.nii -inset AAL_T1.nii -master func.nii

54. How to perform
After generate the FC maps:
Interact with other measurements;
Within group statistic;
Between group statistic;

55. How to perform Interact with other measurements:
3dfim+ -input all_FC.nii.gz -ideal_file age.1D -mask BrainMask.nii -out Correlation -bucket FC_Age.nii
……..
Age: ……

56. How to perform
Within and between group statistics: e.g., t-tests

57. How to perform Check the results: ROI analysis
1) Label the clusters within the statistic maps:
3dclust -quite -savemask rois.nii ttest+orig.
2) Extract the ROI:
3dcalc -a rois.nii -expr 'within(a,2.9,3.1)' -prefix cluster3.nii
3) Calculate the FC value within the ROI:
3dmaskave -quite -mask cluster3.nii subject01fc.nii >> avgvalue.txt

58. How to perform
Control
FC value
Patient

59. Q&A
So far, any questions?

60. Parcellation analysis
Functional connectivity-based pacellation:
Assume the neighbor voxels that have the similar functional connectivity patterns belong to the same function area;
Create the study specific ROIs instead of using templates;
Further FC analysis, subregions comparion, etc.

61. Parcellation analysis
Nebel et al., 2012.

62. Parcellation analysis
Paradigm:
… …
Functional connectivity maps
k = 3
Similarity matrix

63. Parcellation analysis
Choose a ROI, e.g., AAL regions;
Make sure the template is in the same space with functional images;
Create voxel seeds within the ROI: (by NIFTI tools)
a = load_nii(’ROI.nii');
b = find(a.img>0);
c = a;
for i = 1 : length(b)
d = zeros(size(a.img));
d(b(i)) = 1;
c.img = d;
number = ['000',num2str(i)];
number = number(end-3:end);
save_nii(c,['voxel_',number,'.nii']);
end
Calculate the FC map of each voxel; 59

64. Parcellation analysis
Calculate the similarity of each pair of the FC maps;
Spatial correlation;
eta2;
Run classification on the similarity matrix;
K-means: kmeans;
Hierarchy clustering: linkage and dendrogram;
‎Cohen et al;., 2009

65. Parcellation analysis
Number of clusters:
Overlap between two datasets: Dice’s coefficient X Y

66. Graph theory-based analysis: Whole brain FC analysis;
How to perform
Graph theory-based analysis:
Whole brain FC analysis;
Gnerally use regions instead of voxels;
Small-world property;
Brain regions
FC values

67. Templates: AAL_90_3mm.nii or AAL_61x73x61_116.nii;
How to perform
What we need:
Templates: AAL_90_3mm.nii or AAL_61x73x61_116.nii;
ROIs: numbered within the nifti file;
Data: rest_example.nii.gz
‎Bullmore et al;., 2009

68. How to perform Create the connectivity matrix:
3dNetCorr -inset rest_example.nii.gz -in_rois AAL_90_3mm.nii -fish_z -mask BrainMask.nii -prefix aal_fc_matrix
Then switch to Matlab:
conmat = ach_readnetcc(‘aal_fc_matrix_000.netcc’);
save(‘conmat’,’conmat’);
save('subj01.edge', 'conmat','-ASCII','-DOUBLE','-TABS');

69. How to perform
View the connections: run BrainNet in Matlab;

70. How to perform

71. How to perform
Surface:

72. How to perform
Node:

73. How to perform
“Load File” interface:

74. How to perform

75. How to perform

76. How to perform

77. How to perform
Open Gretna in Matlab;

78. How to perform Select conmat.mat; Network Type: weighted;
Network Member: positive;
Threshold Type: similarity;
Select all network metrics;
Select Output directory;
Press RUN.

79. How to perform

80. Thank you for your time!