Event Abstract

Investigating the correlation between crystallized IQ and network metrics in cerebellum using resting-state fMRI

  • 1 Technical University of Crete, School of Electrical and Computer Engineering, Greece
  • 2 University of Crete, School of Medicine, Greece
  • 3 Max Planck Institute for Human Cognitive and Brain Sciences, Germany

The network of cerebellum was analyzed in order to investigate its overall organization in individuals with high and low crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were collected from 150 subjects in resting-state from the Human Connectome Project database [15, 16] and further separated into two categorical groups based on their IQ scores, resulting to 76 low-IQ and 74 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas [2, 3] (lobules I-IV, V, VI, Crus I, Crus II, VIIb, VIIIa, VIIIb, IX, X) as shown in Fig. 1 (cerebellum’s flat surface representation [3] is also provided where color coding has been applied based on each lobule’s volumetric size). These lobules serve different functions e.g. cognition [4, 5, 10, 11], emotion [4, 10, 12]. Lobule Vermis Crus I was found to contain less than 0.005% of the total cerebellar volume and thus was removed from further analysis. Thereafter, correlation matrices were constructed by computing the zero-lag temporal Pearson correlation coefficients between the average BOLD time-series for each pair of ROIs. By computing conventional graph metrics, small-world network properties [6, 7, 8, 18] were discovered using the weighted clustering coefficient (averaged over all nodes to define its global version) and characteristic path length (Supplementary Table 1) for estimating the trade-off between segregation and integration. In addition, the connectivity metric was computed for extracting the average cost per network (Supplementary Table 1). In this study, the concept of Minimum Spanning Tree (MST) was adapted and further implemented in order to avoid methodological biases and enable graph comparisons as well as retain only the strongest connections per network (average weighted and undirected graphs per IQ group are illustrated in Fig. 2 with their corresponding MSTs, where the size of each node in the MST linearly depends on its betweenness centrality value) [9, 13, 14]. Subsequently, six global (degree correlation, diameter, leaf fraction, kappa, radius, tree hierarchy) and three local (betweenness centrality, degree, eccentricity) MST metrics were calculated in order to retrieve useful information concerning the functional and structural characteristics of each MST (Supplementary Table 1) [14]. Moreover, the local metrics of degree (DEG) and betweenness centrality (BC) were used to detect hubs, i.e. nodes with high importance, for both IQ groups, as presented in Fig. 3 (where each node’s size linearly depends on the corresponding BC (A), DEG (B) values, according to the bar plots which represent the percentage of subjects with the highest BC (C), DEG (D) values per ROI). Additionally, the correlations between the median response time (MRT) and the corresponding lobule metrics per IQ group and gender were calculated. Studies in cerebrum have shown that efficiency of networks at rest is higher in more intelligent individuals [1, 15]. Cerebellar lobules have specific (some of them are reciprocal, some not) connections to cerebral sites (mainly frontal and parietal areas) so as to serve cognitive functions in cognitive function [4, 5, 10, 11]. Given the connection of the cerebellum to areas which at rest show high efficiency in high IQ, we are interested to examine whether a similar hypothesis stands for cerebellum. Our findings (Supplementary Table 2) show that: (i) Small-world network organization characterizes cerebellar networks of both men and women at rest state and for low and high-IQ subjects without significant differences. (ii) Maximum correlation between MRT and DEG (r = 0.48, p = 0.001) and BC (r = 0.41, p = 0.005) showed a positive correlation as well as a significant dominance of the Left X lobule in low IQ individuals in the women population. (iii) Higher values in DEG and BC were identified in lobules Left VI, Left Crus I, Right VI, Right Crus I. (iv) There were several differences in local network parameters: lobules Left VI, Left Crus I and Right VI had high local values (DEG & BC) and are hubs, i.e. nodes with high importance, with higher values in individuals with high IQ. (v) More importantly, there are interesting differences between men (low-IQ: 30, high-IQ: 36) and women (low-IQ: 46, high-IQ: 38) as follows. (a) In women there are differences in network parameters among high and low IQ individuals indicative of more robust network organization in cases of higher IQ. (b) Among low-IQ individuals, men network parameters showed more effective organization. Attempting to explain the findings for the lobules with higher correlation with the median response time, we refer to published studies. Left VI lobule is related to motor control, lobules Left Crus I, Right Crus I, Right VI and Right X are related to cognitive and limbic areas of the cerebral hemispheres [4, 5, 10, 11]. It is worth noticing that there are overlapping regions. Nevertheless, the aforementioned differences between men and women, as well as high and low IQ individuals, show more effective organization in high IQ females in relation to low IQ females and more effective organization in males. In summary, to our knowledge this is the first network study of the cerebellum that attempts to assess local and widespread brain-connectivity characteristics in relation to low and high-IQ men and women. Several studies have discovered differences between low and high-IQ subjects in cerebral organization and network metrics, such as differences in several brain regions or connections, small-world network organizations, as well as differences in network parameters and neural efficiency [1, 15]. Nonetheless, future studies are essential in order to explain cerebrum and cerebellar local and widespread findings.

Figure 1
Figure 2
Figure 3

Acknowledgements

Data collection and sharing for this project was provided by the MGH-USC Human Connectome Project (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). The authors declare no competing financial or other interests.

References

[1] Basten, U., Hilger, K., and Fiebach, C. J. (2015). Where smart brains are different: a quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence 51, 10–27. doi:10.1016/j.intell.2015.04.009
[2] Diedrichsen, J., Balsters, J. H., Flavell, J., Cussans, E., and Ramnani, N. (2009). A probabilistic MR atlas of the human cerebellum. Neuroimage 46, 39–46. doi:10.1016/j.neuroimage.2009.01.045
[3] Diedrichsen, J., and Zotow, E. (2015). Surface-based display of volume-averaged cerebellar imaging data. PLoS One 10:e0133402. doi:10.1371/journal.pone.0133402
[4] E, K. H., Chen, S. H. A., Ho, M. H. R., and Desmond, J. E. (2014). A meta-analysis of cerebellar contributions to higher cognition from PET and fMRI studies. Hum. Brain Mapp. 35, 593–615. doi:10.1002/hbm.22194
[5] Koziol, L. F., Budding, D., Andreasen, N., D’Arrigo, S., Bulgheroni, S., Imamizu, H., et al. (2014). Consensus paper: the cerebellum’s role in movement and cognition. Cerebellum 13, 151–177. doi:10.1007/s12311-013-0511-x
[6] Reijneveld, J. C., Ponten, S. C., Berendse, H. W., and Stam, C. J. (2007). The application of graph theoretical analysis to complex networks in the brain. Clin. Neurophysiol. 118, 2317–2331. doi:10.1016/j.clinph.2007.08.010
[7] Rubinov, M., and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069. doi:10.1016/j.neuroimage.2009.10.003
[8] Stam, C. J., de Haan, W., Daffertshofer, A., Jones, B. F., Manshanden, I., Van Cappellen van Walsum, A. M., et al. (2009). Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain 132, 213–224. doi:10.1093/brain/awn262
[9] Stam, C. J., Tewarie, P., Van Dellen, E., van Straaten, E. C. W., Hillebrand, A., and Van Mieghem, P. (2014). The trees and the forest: characterization of complex brain networks with minimum spanning trees. Int. J. Psychophysiol. 92, 129–138. doi:10.1016/j.ijpsycho.2014.04.001
[10] Stoodley, C. J., and Schmahmann, J. D. (2009). Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex 46, 831–844. doi:10.1016/j.cortex.2009.11.008
[11] Stoodley, C. J., Valera, E. M., and Schmahmann, J. D. (2012). Functional topography of the cerebellum for motor and cognitive tasks: an fMRI study. Neuroimage 59, 1560-1570. doi:10.1016/j.neuroimage.2011.08.065
[12] Styliadis, C., Ioannides, A. A., Bamidis, P. D., Papadelis, C. (2015). Distinct cerebellar lobules process arousal, valence and their interaction in parallel following a temporal hierarchy. Neuroimage 110, 149–161. doi: 10.1016/j.neuroimage.2015.02.006
[13] Tewarie, P., Hillebrand, A., Schoonheim, M. M., van Dijk, B. W., Geurts, J. J. G., Barkhof, F., et al. (2014). Functional brain network analysis using minimum spanning trees in Multiple Sclerosis: an MEG source-space study. Neuroimage 88, 308–318. doi:10.1016/j.neuroimage.2013.10.022
[14] Tewarie, P., van Dellen, E., Hillebrand, A., and Stam, C. J. (2015). The minimum spanning tree: an unbiased method for brain network analysis. Neuroimage 104, 177–188. doi:10.1016/j.neuroimage.2014.10.015
[15] van den Heuvel, M. P., Stam, C. J., Kahn, R. S., and Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. J. Neurosci. 29, 7619–7624. doi:10.1523/JNEUROSCI.1443-09.2009
[16] Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., et al. (2012). The Human Connectome Project: A data acquisition perspective. Neuroimage 62, 2222–2231. doi:10.1016/j.neuroimage.2012.02.018
[17] Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., and Ugurbil, K. (2013). The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79. doi:10.1016/j.neuroimage.2013.05.041
[18] Wang, J., Zuo, X., and He, Y. (2010). Graph-based network analysis of resting-state functional MRI. Front Syst Neurosci 4, 16. doi:10.3389/fnsys.2010.00016

Keywords: Cerebellum, fMRI, small-world network, minimum spanning tree, crystallized IQ, median respone time

Conference: SAN2016 Meeting, Corfu, Greece, 6 Oct - 9 Oct, 2016.

Presentation Type: Oral Presentation in SAN 2016 Conference

Topic: Oral Presentations

Citation: Pezoulas V, Zervakis M, Micheloyannis S and Klados MA (2016). Investigating the correlation between crystallized IQ and network metrics in cerebellum using resting-state fMRI. Conference Abstract: SAN2016 Meeting. doi: 10.3389/conf.fnhum.2016.220.00013

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 29 Jul 2016; Published Online: 30 Jul 2016.

* Correspondence: Mr. Vasileios Pezoulas, Technical University of Crete, School of Electrical and Computer Engineering, Chania, Greece, bpezoulas@gmail.com

© 2007 - 2019 Frontiers Media S.A. All Rights Reserved