1. FUNCTIONAL NETWORK RECONFIGURATION RELATED TO INCREASING COGNITIVE EFFORT
KAROLINA FINC
NEUROCOGNITIVE LABORATORY
CENTRE FOR MODERN INTERDISCIPLINARY TECHNOLOGIES
NICOLAUS COPERNICUS UNIVERSITY

2. What is a network?
Nodes
Edges
Russo et al. (2014)
Russo et al. (2014)

3. What is a network? Brain regions Connections Russo et al. (2014)

4. Human brain connectome and MRI/fMRI Human connectome and fMRI
Structural connectivity
Bullmore & Sporns (2009)

5. Human connectome and MRI/fMRI
Structural connectivity
Functional connectivity
Bullmore & Sporns (2009)

6. Human connectome and MRI/fMRI Human connectome and fMRI
Node definition
Structural connectivity
Functional connectivity
Bullmore & Sporns (2009)

7. Human connectome and MRI/fMRI Human connectome and fMRI
Node definition
Structural connectivity
Functional connectivity
Signal extraction
Correlation
calculation
Bullmore & Sporns (2009)

8. Human connectome and MRI/fMRI Human connectome and fMRI
Node definition
Structural connectivity
Functional connectivity
Signal extraction
Correlation
calculation
Correlation matrix
Bullmore & Sporns (2009)

9. Human connectome and MRI/fMRI Human connectome and fMRI
Node definition
Structural connectivity
Functional connectivity
Signal extraction
Correlation
calculation
Binary matrix
Correlation matrix
Bullmore & Sporns (2009)

10. Human connectome and MRI/fMRI Human connectome and fMRI
Node definition
Structural connectivity
Functional connectivity
Signal extraction
Correlation
calculation
Whole-brain graph
Binary matrix
Correlation matrix
Bullmore & Sporns (2009)

11. Human connectome and MRI/fMRI Human connectome and fMRI
Node definition
Structural connectivity
Functional connectivity
Signal extraction
Correlation
calculation
Graph theory approach
Whole-brain graph
Modularity
Binary matrix
Degree
d=2
d=3
Correlation matrix
Path & efficiency
Clustering
Bullmore & Sporns (2009)

12. Whole-brain functional network properties and cognitive performance
Whole-brain functional network properties and behavior
Resting-state fcMRI
Characteristic path length
Intellectual performance
Modularity
Working memory performance
Modularity
Working memory capacity
Characteristic path length
van den Heuvel et al. (2009) | Stevens et al. (2012) | Gamboa et al. (2013)

13. Can whole-brain network properties change during active task performance?

14. Method Method Subjects & fMRI paradigm
35 participants (17 females; Mage = 22.6 ± 3.1; 19-31).
Letter n-back task
Low cognitive effort
High cognitive effort
1-back
Instruction
2-back A A
2-back
target B B
1-back
target
30 s block
10 blocks x 3 sessions
5:30 min per session B A A D

15. Method Method Subjects & fMRI paradigm
35 participants (17 females; Mage = 22.6 ± 3.1; 19-31).
Letter n-back task
Low cognitive effort
High cognitive effort
1-back
Instruction
2-back A A
2-back
target B B
1-back
target
30 s block
10 blocks x 3 sessions
5:30 min per session B A A D

16. Method Method Subjects & fMRI paradigm
35 participants (17 females; Mage = 22.6 ± 3.1; 19-31).
Letter n-back task
Low cognitive effort
High cognitive effort
1-back
Instruction
2-back A A
2-back
target B B
1-back
target
30 s block
10 blocks x 3 sessions
5:30 min per session B A A D

17. Changes of network properties during n-back task Method
MEG
Kitzbichler et al. (2011)
fMRI
Network global and local efficiency measures are stable across n-back task conditions (0-, 1-, 2-, 3-back) (Ginestet & Simmons, 2011)
No significant change of whole-brain modularity from 1-back to 2-back (Stanley et al. , 2014)
Decrease of whole-brain modularity from 0-back to 3-back (Vatansever et al., 2015)

18. Method Data processing Method Method
Anatomical parcellation
(90 nodes)
Functional parcellation
(264 nodes)
Node definition
Tzourio-Mazoyer et al. (2002) | Power et al. (2011)

19. Method Data processing Method Method
Anatomical parcellation
(90 nodes)
Functional parcellation
(264 nodes)
Node definition
Weighted correlation matrices
Fisher’s z-scores
Tzourio-Mazoyer et al. (2002) | Power et al. (2011)

20. Method Data processing Method Method
Anatomical parcellation
(90 nodes)
Functional parcellation
(264 nodes)
Node definition
Weighted correlation matrices
Fisher’s z-scores
Binary correlation matrices
Thresholding
( )
Tzourio-Mazoyer et al. (2002) | Power et al. (2011)

21. Method Method Data processing Method
Anatomical parcellation
(90 nodes)
Functional parcellation
(264 nodes)
Node definition
Weighted correlation matrices
Fisher’s z-scores
Binary correlation matrices
Thresholding
( )
Network properties calculation
global efficiency ⚫ local efficiency ⚫ modularity
Tzourio-Mazoyer et al. (2002) | Power et al. (2011)

22. Results Whole-brain network efficiency change
Global efficiency
Local efficiency

23. Results Whole-brain network modularity change

24. Is the whole-brain network reorganization relevant to explaining human behavior?

25. Results Results Behavioral performance change
Increase of penalized RT
(p < )
Decrease of behavioral performance
1-back
2-back
Ginestet & Simmons (2011)

26. Results
Results
Correlation between network modularity change and behavioral change

27. Conclusions Low cognitive effort High cognitive effort
Segregated network
Integrated network
global efficiency
local efficiency
modularity
Locally specialized processing
Distributed processing

28. Conclusions Low cognitive effort High cognitive effort
Segregated network
Integrated network
global efficiency
local efficiency
modularity
Locally specialized processing
Distributed processing
performance

29. ≠ Conclusions Low cognitive effort High cognitive effort
Segregated network
Integrated network
global efficiency
local efficiency
modularity
Locally specialized processing
Distributed processing
performance ≠

30. Further investigation Modularity change at the subnetwork level

31. Thank you!
Kamil Bonna Dr. Monika Lewandowska Dr. Tomasz Wolak Jan Nikadon Dr. Joanna Dreszer Prof. Włodzisław Duch Dr. Simone Kühn
Dr. Jerzy Łukaszewicz Dr. Jaromir Patyk Alex Lubiński Maja Dobija
Neurocognitive Laboratory at CMIT(ICNT)