Individual modeling of neurophysiological brain connectivity

Poster No:


Submission Type:

Abstract Submission 


Shanna Kulik1, Linda Douw1, Edwin van Dellen2, Martijn Steenwijk1, Jeroen Geurts1, Cornelis Stam1, Arjan Hillebrand1, Menno Schoonheim1, Prejaas Tewarie3


1Amsterdam University Medical Center, Amsterdam, NETHERLANDS, 2University Medical Center Utrecht, Utrecht, NETHERLANDS, 3Amsterdam UMC, Amsterdam, Noord-Holland

First Author:

Shanna Kulik  
Amsterdam University Medical Center


Linda Douw  
Amsterdam University Medical Center
Edwin van Dellen  
University Medical Center Utrecht
Martijn Steenwijk  
Amsterdam University Medical Center
Jeroen Geurts  
Amsterdam University Medical Center
Cornelis Stam  
Amsterdam University Medical Center
Arjan Hillebrand  
Amsterdam University Medical Center
Menno Schoonheim  
Amsterdam University Medical Center
Prejaas Tewarie  
Amsterdam UMC
Amsterdam, Noord-Holland


Brain functional connectivity displays specific patterns between interconnected brain regions. These patterns have been shown to be specific to each individual and show clinically relevant changes in neurological and psychiatric disorders. How FC patterns emerge at the individual level in relation to the anatomical connections of the brain is currently unknown. Therefore, to gain knowledge on the structure-function relation, comparing simulation-based FC with empirically measured data could be utilized. In this way, brain (dys)functioning might be better understood and it could eventually lead to individualized predictions of disease trajectories. Therefore, this study aimed to individually simulate functional connectivity (FC) of forty healthy controls utilizing the Jansen and Rit neural mass model.


The Jansen and Rit model simulates physiological brain activity resembling magnetoencephalography data. Neural masses were coupled according to the structural connections of empirical data to form a network of neural masses reflecting large-scale brain activity. By utilizing the individual structural connectivity (SC), FC was simulated and correlated to individual empirical FC. To increase the match between simulated and empirical FC, model parameters were individually tuned. FC was calculated with different phase- (phase lag index (PLI) and phase locking value (PLV)) and amplitude- (amplitude envelope correlation (AEC)) based metrics to analyze which of these metrics best predicted individual empirical FC. To analyze the impact of the individual SC as input to the model, individual simulations were compared to simulations with the average SC. The specificity of the individual SC was tested by correlating the simulated FC with empirical FC of the other participants.


The amplitude-based metric showed the highest match between individually simulated and empirical FC (median correlation 0.192) and was significantly higher than the phase-based metrics (median correlation PLI: 0.099, PLV: 0.144). By using the average SC as input to the model, correlations between the resulting FC with individual empirical FC were significantly lower compared to the correlations with the individual SC as input to the model. Correlations between simulated FC and participants' own empirical FC did not give a better match when comparing them to correlations between simulated FC and the empirical FC of another participant.


Based on these results, this work underlines and important first step towards individual prediction of FC and could in the future be used to predict disease progression.

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural) 2
Diffusion MRI Modeling and Analysis
EEG/MEG Modeling and Analysis 1


Computational Neuroscience

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.

Resting state

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.


Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Diffusion MRI
Computational modeling

For human MRI, what field strength scanner do you use?


Which processing packages did you use for your study?

Other, Please list  -   MRTrix 3.0

Provide references using author date format

Bastos AM, Schoffelen JM. 2015. A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls. Front Syst Neurosci 9: 175
Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, et al. 2015. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci 18: 1664-71
Hallett M, de Haan W, Deco G, Dengler R, Di Iorio R, et al. 2020. Human brain connectivity: Clinical applications for clinical neurophysiology. Clin Neurophysiol 131: 1621-51
Jansen BH, Rit VG. 1995. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol Cybern 73: 357-66
Brookes MJ, Woolrich M, Luckhoo H, Price D, Hale JR, et al. 2011. Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc Natl Acad Sci U S A 108: 16783-8
Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK. 2012. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat Neurosci 15: 884-90
Lachaux JP, Rodriguez E, Martinerie J, Varela FJ. 1999. Measuring phase synchrony in brain signals. Hum Brain Mapp 8: 194-208
Stam CJ, Nolte G, Daffertshofer A. 2007. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28: 1178-93