Working memory capacity and the functional connectome - insights from resting-state fMRI and voxelwise centrality mapping
The functional connectome represents a comprehensive network map of functional connectivity throughout the human brain. To date, the relationship between the organization of functional connectivity and cognitive performance measures is still poorly understood. In the present study we use resting-state functional magnetic resonance imaging (fMRI) data to explore the link between the functional connectome and working memory capacity in an individual differences design. Working memory capacity, which refers to the maximum amount of context information that an individual can retain in the absence of external stimulation, was assessed outside the MRI scanner and estimated based on behavioral data from a change detection task. Resting-state time series were analyzed by means of voxelwise degree and eigenvector centrality mapping, which are data-driven network analytic approaches for the characterization of functional connectivity. We found working memory capacity to be inversely correlated with both centrality in the right intraparietal sulcus. Exploratory analyses revealed that this relationship was putatively driven by an increase in negative connectivity strength of the structure. This resting-state connectivity finding fits previous task based activation studies that have shown that this area responds to manipulations of working memory load.
KeywordsWorking memory Resting-state fMRI Connectome Intraparietal sulcus Working memory capacity, cognitive ability
Compliance with ethical standards
This work was supported by two grants from the German Research Foundation (DFG) awarded to Christian Montag (MO-2363/2–1 and MO-2363/3–1).
Conflict of interest
All authors declare no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
- Baddeley, A. D. (1986). Working memory. Oxford: Oxford University Press.Google Scholar
- Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical transactions of the Royal Society of London. Series B, Biological Sciences, 360(1457), 1001–1013. doi: 10.1098/rstb.2005.1634.CrossRefGoogle Scholar
- Cohen, J. R., D’Esposito, M. (2016). The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition. Journal of Neuroscience, 36(48), 12083–12094.Google Scholar
- De Vico Fallani, F., Richiardi, J., Chavez, M., & Achard, S. (2014). Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1653). doi: 10.1098/rstb.2013.0521.
- Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673–9678.CrossRefPubMedPubMedCentralGoogle Scholar
- Geib, B. R., Stanley, M. L., Wing, E. A., Laurienti, P. J., & Cabeza, R. (2015). Hippocampal Contributions to the Large-Scale Episodic Memory Network Predict Vivid Visual Memories. Cerebral Cortex, 2015:1–14.Google Scholar
- Lorenc, E. S., Lee, T. G., Chen, A. J.-W., & D’Esposito, M. (2015). The effect of disruption of prefrontal cortical function with Transcranial magnetic stimulation on visual working memory. Frontiers in Systems Neuroscience, 169. doi: 10.3389/fnsys.2015.00169.
- Markett, S., Montag, C., Heeren, B., Saryiska, R., Lachmann, B., Weber, B., & Reuter, M. (2015). Voxelwise eigenvector centrality mapping of the human functional connectome reveals an influence of the catechol-O-methyltransferase val158met polymorphism on the default mode and somatomotor network. Brain Structure and Function. doi: 10.1007/s00429-015-1069-9.PubMedGoogle Scholar
- Niazy, R. K., Xie, J., Miller, K., Beckmann, C. F., & Smith, S. M. (2011). Chapter 17- spectral characteristics of resting state networks. In Y. D. V. D. W. Eus J.W. Van Someren Pieter R. Roelfsema, Huibert D. Mansvelder and Fernando H. Lopes Da Silva (Ed.), Progress in brain research (193, 259–276). Elsevier.Google Scholar
- Pashler, H. (1988). Familiarity and visual change detection. Perception & Psychophysics, 44(4), 369–378.Google Scholar
- Repovš, G., & Barch, D. M. (2012). Working memory related brain network connectivity in individuals with schizophrenia and their siblings. Frontiers in Human Neuroscience, 6(137). doi: 10.3389/fnhum.2012.00137.
- Riley, M. R., & Constantinidis, C. (2016). Role of prefrontal persistent activity in working memory. Frontiers in Systems Neuroscience, 181. doi: 10.3389/fnsys.2015.00181.
- Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., et al. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. Journal of Neuroscience, 27(9), 2349–2356. doi: 10.1523/JNEUROSCI.5587-06.2007.CrossRefPubMedPubMedCentralGoogle Scholar
- Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E. J., & Glasser, M. F. (2015). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience, 18 (11), 1565–1567.Google Scholar
- Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., Uğurbil, K., & Consortium, for the W.-M. H. (2013). The WU-Minn human connectome project: an overview. NeuroImage, 80(C), 62–79. doi: 10.1016/j.neuroimage.2013.05.041.
- Wink, A. M., de Munck, J. C., van der Werf, Y. D., van den Heuvel, O. A., & Barkhof, F. (2012). Fast eigenvector centrality mapping of voxel-wise connectivity in functional magnetic resonance imaging: implementation, validation, and interpretation. Brain Connectivity, 2(5), 265–274.CrossRefPubMedGoogle Scholar
- Yu, Q., Wu, L., Bridwell, D. A., Erhardt, E. B., Du, Y., He, H., et al. (2016). Building an EEG-fMRI multi-modal brain graph: a concurrent EEG-fMRI study. Frontiers in Human Neuroscience, 10. doi: 10.3389/fnhum.2016.00476.