Poster No:
1723
Submission Type:
Abstract Submission
Authors:
Sonsoles Alonso1, Luke Hearne2, Luca Cocchi3, James Shine4, Diego Vidaurre1
Institutions:
1Aarhus University, Aarhus C, Aarhus, 2QIMR Berghofer Medical Research Institute, Herston, Queensland, 3QIMR Berghofer Medical Research Institute, Brisbane, Queensland, 4University of Sydney, Sydney, New South Wales
First Author:
Co-Author(s):
Luke Hearne
QIMR Berghofer Medical Research Institute
Herston, Queensland
Luca Cocchi
QIMR Berghofer Medical Research Institute
Brisbane, Queensland
Mac Shine
University of Sydney
Sydney, New South Wales
Introduction:
Current methods for time-varying functional connectivity (FC) often model the overall dynamics of the entire network. This inherent approach results in symmetric FC measures where all possible pairwise connections need to be modelled, hampering the modelling of specific connections. To overcome this limitation, our study introduces a novel approach, Targeted Time-Varying FC (TTVFC), which explicitly models the temporal dynamics of specific connections of interest rather than the entire brain network. TTVFC facilitates the exploration of the relationship between two sets of brain time series, X and Y, rigorously testing the statistical significance of their fluctuations throughout ongoing tasks.
Methods:
In this study, we applied this novel methodology to explore the dynamic relationship between thalamic and cortical activity. The analysis involved 7T fMRI data with a repetition time of 0.58 seconds, obtained from 60 participants across three 10-minute task sessions. The data were originally presented in Hearne et al. (2017). Specifically, we utilized preprocessed timeseries extracted from the cortex using the atlas by Gordon et al. (2016) and from the thalamus, employing the Morel atlas (Niemann et al., 2000), as detailed in Shine et al. (2019). The experimental task was deliberately designed to systematically manipulate reasoning complexity while minimizing working memory demands (Birney et al., 2006). Implementing TTVFC involves utilizing a variant of the Hidden Markov Model (HMM; Vidaurre et al., 2017). This variant derives a discrete sequence of sequential states, each characterized by a distinct set of beta coefficients obtained from regressing Y time series on X time series. State time courses, indicating the probability of a given state being active at each time point, were also estimated. To understand the variations in state fluctuations, each representing a specific pattern of thalamocortical connections, we examined their changes in response to cognitive complexity. The statistical significance of these fluctuations over time was determined by employing a clustered-based permutation t-test (Maris and Oostenveld 2007), assessing the differences across levels of complexity.
Results:
Our findings reveal that thalamocortical dynamics are intricately linked to distinct problem-solving patterns. Remarkably, TTVFC, without prior knowledge of task timings, effectively describes fluctuations in the interaction of targeted brain regions in response to different cognitive processes induced by the task. Importantly, the application of a conventional HMM approach, where states represent matrices of connectivity across all elements in X and Y, proved inadequate in capturing distinctions in problem-solving tasks.
Conclusions:
This suggests that emphasizing specific connections, rather than relying on the whole-brain FC approach, more effectively elucidates certain cognitive processes. The study underscores the utility of TTVFC in understanding and modelling targeted brain dynamics, extending its applicability beyond fMRI data to various neuroimaging data modalities.
Higher Cognitive Functions:
Reasoning and Problem Solving
Modeling and Analysis Methods:
Bayesian Modeling
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Methods Development 2
Keywords:
Thalamus
Other - time-varying FC; Hidden Markov Model; targeted connections; asymmetric FC matrix
1|2Indicates the priority used for review
Provide references using author date format
Birney, D. P., Halford, G. S., & Andrews, G. (2006). Measuring the Influence of Complexity on Relational Reasoning: The Development of the Latin Square Task. Educational and Psychological Measurement, 66(1), 146-171.
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016). Generation and evaluation of a cortical area parcellation from resting-state correlations. Cerebral Cortex, 26(1), 288-303.
Hearne, L. J., Cocchi, L., Zalesky, A., & Mattingley, J. B. (2017). Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning. Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 37(35), 8399-8411.
Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of neuroscience methods, 164(1), 177–190.
Niemann, K., Mennicken, V. R., Jeanmonod, D., & Morel, A. (2000). The Morel stereotactic atlas of the human thalamus: atlas-to-MR registration of internally consistent canonical model. NeuroImage, 12(5), 601-616.
Shine, J. M., Hearne, L. J., Breakspear, M., Hwang, K., Müller, E. J., Sporns, O., Poldrack, R. A., Mattingley, J. B., & Cocchi, L. (2019). The Low-Dimensional Neural Architecture of Cognitive Complexity Is Related to Activity in Medial Thalamic Nuclei. Neuron, 104(5), 849-855.E3.
Vidaurre, D., Smith, S. M., & Woolrich, M. W. (2017). Brain network dynamics are hierarchically organized in time. Proceedings of the National Academy of Sciences, 114(48), 12827-12832.