Intrinsic effective connectivity as the basis of functional localization in the cortex
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
1438
Submission Type:
Abstract Submission
Authors:
Younghyun Oh1,2, Takuya Ito3, Seok-Jun Hong1,2,4,5,6
Institutions:
1IBS Center for Neuroscience Imaging Research, Sungkyunkwan University, Suwon, South Korea, 2Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea, 3IBM Research, Yorktown, NY, United States, 4Department of Intelligence Precision Health Care, Sungkyunkwan University, Suwon, South Korea, 5Department of Intelligence Precision Health Care, Sungkyunkwan University, Suwon, Korea, South Korea, 6Center for the Developing Brain, Child Mind Institute, New York, NY, United States
First Author:
Younghyun Oh
IBS Center for Neuroscience Imaging Research, Sungkyunkwan University|Department of Biomedical Engineering, Sungkyunkwan University
Suwon, South Korea|Suwon, South Korea
IBS Center for Neuroscience Imaging Research, Sungkyunkwan University|Department of Biomedical Engineering, Sungkyunkwan University
Suwon, South Korea|Suwon, South Korea
Co-Author(s):
Seok-Jun Hong
IBS Center for Neuroscience Imaging Research, Sungkyunkwan University|Department of Biomedical Engineering, Sungkyunkwan University|Department of Intelligence Precision Health Care, Sungkyunkwan University|Department of Intelligence Precision Health Care, Sungkyunkwan University|Center for the Developing Brain, Child Mind Institute
Suwon, South Korea|Suwon, South Korea|Suwon, South Korea|Suwon, Korea, South Korea|New York, NY, United States
IBS Center for Neuroscience Imaging Research, Sungkyunkwan University|Department of Biomedical Engineering, Sungkyunkwan University|Department of Intelligence Precision Health Care, Sungkyunkwan University|Department of Intelligence Precision Health Care, Sungkyunkwan University|Center for the Developing Brain, Child Mind Institute
Suwon, South Korea|Suwon, South Korea|Suwon, South Korea|Suwon, Korea, South Korea|New York, NY, United States
Introduction:
How do inter-areal connections shape brain function? One theoretical framework posits that intrinsic (i.e., stimulus-independent) inter-areal connectivity patterns, or a region's connectivity receptive field (CRF), is as a major determinant of its functional localization (Passingham et al., 2002). In our previous study (Oh et al., 2024), we introduced the integrated effective connectivity (iEC) – a biologically grounded measure that reliably estimates directed intrinsic connectivity using empirical resting-state fMRI data. Here, we found that CRFs derived from task-free iEC (i) predicts the task tuning curves of diverse cognitive tasks in an independent task dataset, (ii) identifies functionally localized clusters of brain regions, and (iii) systematically vary along the cytoarchitectural hierarchy.
Methods:
We used two publicly available datasets for our study: MDTB dataset for measuring diverse task activation patterns (King et al., 2019) and HCP S1200 dataset for inferring the intrinsic brain connectivity (Van Essen et al., 2012). Both resting and task fMRI data were parcellated using Glasser atlas (Glasser et al., 2016). Details on data preprocessing, task regression, and iEC methodology can be found in the original manuscripts (Ito & Murray, 2024; Oh et al., 2024). In brief, we estimated the averaged task activation of each parcel for each task (25 tasks in total). We then computed the cross-task correlation between all pairs of brain regions. This reflected the similarity of the cognitive tuning curves between brain regions, which is commonly referred to as the signal correlation (SC) in electrophysiological studies (Fig 1B). iEC was estimated at the group-level (n=220) from the same parcellation scheme in both resting-state and task fMRI. The CRF (fingerprint) matrix was generated by calculating the pairwise cosine similarity of the outgoing connections between all pairs of brain regions (Fig 1C).
·Figure1
Results:
We first evaluated how well each connectivity metric could predict cognitive tuning curves (i.e., the SC matrix), a key indicator of functional localization (Fig 2A). Of all the metrics tested, the iEC CRF performed best (R² = 0.47; Fig 2B), suggesting that the intrinsic iEC conveys unique task signatures across the brain regions. Notably, the FC CRF performed worse than raw FC, suggesting that FC may not be a valid measure of connectivity. Another key indicator of functional localization is the degree of functional modularity. When we projected the iEC CRF matrix onto a 2D manifold using multidimensional scaling, the iEC CRF could delineate known functional modules (e.g., visual and sensorimotor cortices) (Fig 2C). Furthermore, when we quantified network segregation using the 7 functional network partitions, the iEC CRF matrix displayed the strongest segregation, followed by SC and then FC CRF (Fig. 2D). Finally, we investigated whether statistical properties of a region's CRFs (e.g., wideness vs. sharpness of a receptive field) could determine its place within hierarchical organization. Thus, we calculated the sharpness (kurtosis) of each region's CRF (Fig 2E) and found that visual cortex regions exhibited the highest kurtosis, while heteromodal areas had the lowest (Fig 2F). Examining kurtosis in relation to cortical type data revealed a systematic decrease along the cytoarchitectural hierarchy (Fig 2G), suggesting a mechanistic basis for the emergence of broader cognitive tunings in higher-order cortical areas.
·Figure2
Conclusions:
In this study, we demonstrated that intrinsic iEC CRF serves as a general, task-invariant measure of functional localization. Moreover, the iEC CRF reveals potential mechanisms underlying hierarchical variation in functional receptive fields. Overall, these findings underscore the critical role that intrinsic inter-areal connectivity patterns play in shaping the functional properties of individual brain regions.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Keywords:
Other - Network Neuroscience
1|2Indicates the priority used for review
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Provide references using APA citation style.
Ito, T., & Murray, J. D. (2024). The impact of functional correlations on task information coding. Network Neuroscience (Cambridge, Mass.), 1–24.
King, M., Hernandez-Castillo, C. R., Poldrack, R. A., Ivry, R. B., & Diedrichsen, J. (2019). Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nature Neuroscience, 22(8), 1371–1378.
Oh, Y., Ann, Y., Lee, J.-J., Ito, T., Froudist-Walsh, S., Paquola, C., Milham, M., Spreng, R. N., Margulies, D., Bernhardt, B., Woo, C.-W., & Hong, S.-J. (2024). In vivo cartography of state-dependent signal flow hierarchy in the human cerebral cortex. In Research Square. https://doi.org/10.21203/rs.3.rs-5219295/v1
Passingham, R. E., Stephan, K. E., & Kötter, R. (2002). The anatomical basis of functional localization in the cortex. Nature Reviews. Neuroscience, 3(8), 606–616.
Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: a data acquisition perspective. NeuroImage, 62(4), 2222–2231.