Brain Connectivity Network Dynamics Are Correlated with Cognitive Performance in Multiple Sclerosis
Sue-Jin Lin1,2, Aiping Liu3, Alex MacKay4,5, Brenda Kosaka6, Samantha Beveridge7, Irene Vavasour5, Anthony Traboulsee8, and Martin J McKeown1,2,8

1Graduate Program in Neuroscience, University of British Columbia, Vancouver, BC, Canada, 2Pacific Parkinson’s Research Centre, University of British Columbia Hospital, Vancouver, BC, Canada, 3Department of Electrical and Computer Engineering Program, University of British Columbia, Vancouver, BC, Canada, 4Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada, 5Department of Radiology, University of British Columbia Hospital, Vancouver, BC, Canada, 6Department of Psychiatry, University of British Columbia Hospital, Vancouver, BC, Canada, 7Graduate Program in Counselling Psychology, University of British Columbia, Vancouver, BC, Canada, 8Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

Synopsis

Brain connectivity networks are usually estimated with the assumption that neural networks do not change over time. However, functional connectivity is inherently non-stationary, changing across time from seconds to minutes. In healthy subjects, dynamic reconfiguration of functional connectivity assessed by fMRI has been estimated and has been linked to cognitive tests, indicating that flexibility of connectivity normally contributes to cognitive performance. In this study, we applied a novel time-varying analysis to study network dynamics in healthy controls and subjects with Multiple Sclerosis (MS).

Purpose

To investigate whether there are significant differences in network dynamics between Multiple Sclerosis (MS) subjects and healthy controls, and whether network dynamics correlate with behavioural data

Introduction

Brain connectivity networks are usually estimated with the assumption that neural networks do not change over time (i.e. only include average time courses for connectivity analysis). However, functional connectivity is inherently non-stationary, changing across time from seconds to minutes. Lately dynamic reconfiguration of functional connectivity assessed by fMRI has been estimated1,2,3 in healthy subjects and has been linked to cognitive tests such as working memory4, indicating that flexibility of connectivity normally contributes to cognitive performance. There are some studies investigating network dynamics in disease groups5, but little is known about the relations between dynamic features and cognitive performance. In this study, we applied a novel time-varying analysis to study network dynamics in healthy controls and subjects with Multiple Sclerosis (MS) as well as the correlations with cognition.

Methods

In this study, resting state fMRI scans of fifteen healthy subjects (28.93±5.0 y/o) and eighteen relapsing-remitting MS subjects (32.00±4.93 y/o) were acquired in a Philips Achieva 3.0 Tesla MRI scanner with echo-planar imaging sequence and the following parameters: 3×3×3 mm3 resolution, 36 slices, 2000 ms TR, 30 ms TE, 90 degree flip angel, and 240 volumes/dynamics. 3D T1 weighted images were acquired with 1×1×1 mm3 resolution, 60 slices, 28 ms TR, 4 ms TE and 27 degree flip angle. MS subjects underwent a comprehensive neuropsychological battery which evaluated executive skills including mental flexibility, concept formation, attentional switching, information generation, and working memory as well as processing speed abilities including attentional and visual scanning. Together with clinical data, there were 31 variables for every MS subject. Image preprocessing steps were performed in each subject’s native space with functions from SPM (UCL, London) and FSL (FMRIB, Oxford). Thirty-six cognition-associated regions-of-interest (ROIs) were extracted using the open source Freesurfer program (MGH, Boston). The ROIs acted as masks to determine the appropriate voxels making up the ROI timecourses. A sliding window approach was used (Matlab) with window length 60 time points. In total there were 181 windows for each subject. Five network features were acquired based on learned dynamic connectivity: network variation (NV), network power (NP), flexibility of homologous connections (FOHC), flexibility of cross-hemispheric connections (non-homologues regions, FOCC), and flexibility of intrahemispheric connections (within hemisphere, FOWC). Correlation analysis was performed between network features and behavioural variables in MS subjects only.

Results

MS subjects demonstrated significantly lower network power (p=0.02) compared to healthy subjects, which is the summed correlation values across windows, and higher flexibility of homologous connections (p=0.007, which is the difference of homologous regions between two windows) [figure 1&2]. MS subjects also demonstrated higher network variation (summed differences between two windows), higher flexibility of intrahemispheric connectivity (summed differences of left/right regions between two windows) and cross-hemisphere connections (summed differences except homologues between two windows). Nevertheless, these results did not reach statistical significance (p=0.06, 0.064, and 0.068, respectively).

In addition, correlation analysis revealed that Verbal Fluency Test performance was negatively correlated with NV (p=0.03), FOWC (p=0.03), FOCC (p=0.033), and FOHC (p=0.012) [figure 3].

Discussion

Our results imply that MS subjects demonstrate weak dynamic connectivity globally and potentially larger fluctuations across time, which may reflect network instability. The increased network flexibility in interhemispheric connectivity (based on homologous regions) in MS subjects may be a compensatory mechanism to deal with possible damage in transcallosal information transfer.

There was a significant negative correlation between dynamic features and the Verbal Fluency Test which requires executive skills to spontaneously generate verbal information according to rules. Presumably generating many words requires not only participation of distributed brain regions, but also network flexibility.

Conclusion

With time-varying approaches, we revealed that network dynamics (i.e. temporal changes of functional connectivity) are altered in MS subjects and several network features are correlated with aspects of cognitive performance. This suggests that connectivity in MS subjects becomes “overly flexible” to compensate for decreased network power. In addition, connectivity flexibility may play a role in cognitive flexibility in MS. Taken together, learning network dynamics may provide another dimension to probe cognitive dysfunction in MS.

Acknowledgements

We acknowledge the MS Society of Canada for funding this study.

References

1, Hutchison RM, Womelsdorf T, Allen EA, et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage. 2013; 80: 360-78.

2, Jones DT, Vemuri P, Murphy MC, et al. Non-stationarity in the "resting brain's" modular architecture. PLoS One. 2012; 7(6):e39731.

3, Allen EA, Damaraju E, Plis SM, et al. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex. 2014; 24(3): 663-76.

4, Braun U, Schäfer A, Walter H, et al. Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc Natl Acad Sci U S A. 2015; 112(37):11678-83.

5, Damaraju E, Allen EA, Belger A, et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin. 2014; 24(5):298-308.

Figures

Figure 1. MS showed lower network power with p = 0.02(uncorrected)

Figure 2. MS showed higher flexibility of interhemispheric connections with p = 0.007 (uncorrected)

Figure 3. Verbal Fluency Test (FAS) performance is negatively correlated with 4 features of network dynamics (NV: network variation, FOCC: flexibility of cross-hemispheric connections, FOWC: flexibility of intrahemispheric connections, FOHC: flexibility of homologous connections).



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
3741