Abstract

Research Article

Comparison of resting-state functional and effective connectivity between default mode network and memory encoding related areas

Supat Saetia*, Fernando Rosas, Yousuke Ogata, Natsue Yoshimura and Yasuharu Koike

Published: 24 April, 2020 | Volume 4 - Issue 1 | Pages: 029-037

Currently brain connectivity modelling, constructed from data acquired by non-invasive technique such as functional magnetic resonance imaging (fMRI), is a well-received approach to illustrate brain function. However, not all connectivity models contains equal amount of information. There are two types of connectivity model that could be constructed from fMRI data, functional and effective connectivity. Effective connectivity includes information about the direction of the connection, while functional connectivity does not. This makes interpretation of effective connectivity more meaningful than functional connectivity. The objective of this study is to show the improvement in interpretability of effective connectivity model in comparison to functional connectivity model. In this study, we show how the difference in the information contained within these two model impacts the interpretation of the resulting connectivity model by analyzing resting-state fMRI data on episodic memory-related cognitive function using CONN Toolbox bivariate correlation measurement for functional connectivity analysis and Tigramite causal discovery framework for effective connectivity analysis on an episodic memory related resting-state fMRI dataset. The comparison between functional and effective connectivity results show that effective connectivity contains more information than the functional connectivity, and the difference in the information contained within these two types of model could significantly impact the intepretation of true brain function. In conclusion, we show that for the connectivity between specific pair of brain regions, effective connectivity analysis reveals more informative characteristic of the connectivity in comparison to functional connectivity where the depicted connectivity lack any additional characteristic information such as the direction of the connection or whether it is a unidirectional or bidirectional. These additional information improve interpretability of brain connectivity study. Thus, we would like to emphasis the important of brain function study using effective connectivity modelling to obtain valid interpretation of true brain function as currently a large body of research in this field focuses only on functional connectivity model.

Read Full Article HTML DOI: 10.29328/journal.jnnd.1001031 Cite this Article Read Full Article PDF

References

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  75. Nielsen FA, Balslev D, Hansen LK. Mining the posterior cingulate: Segregation between memory and pain components. NeuroImage. 2005; 27: 520-532. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15946864
  76. Sestieri C, Corbetta M, Romani GL, Shulman GL. Episodic Memory Retrieval, Parietal Cortex, and the Default Mode Network: Functional and Topographic Analyses. J Neurosci. 2011; 31: 4407-4420. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21430142
  77. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, et al. A default mode of brain function. Proceedings of the National Academy of Sciences. 2001; 98: 676-682.
  78. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network. Annals of the New York Academy of Sciences. 1124; 1-38.
  79. Gusnard DA, Akbudak E, Shulman GL, Raichle ME. Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function. Proc Natl Acad Sci U S A. 2001; 98: 4259-4264. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11259662
  80. Addis DR, Wong AT, Schacter DL. Remembering the past and imagining the future: Common and distinct neural substrates during event construction and elaboration. Neuropsychologia. 2007; 45: 1363-1377. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17126370
  81. Leech R, Kamourieh S, Beckmann CF, Sharp DJ. Fractionating the Default Mode Network: Distinct Contributions of the Ventral and Dorsal Posterior Cingulate Cortex to Cognitive Control. J Neurosci. 2011; 31: 3217-3224. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21368033
  82. Mason MF, Norton MI, Van Horn JD, Wegner DM, Grafton ST, et al. Wandering Minds: The Default Network and Stimulus-Independent Thought. Science. 2007; 315: 393-395. PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17234951

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