Cortical Connectivity Gradient Fingerprinting across Varying Time Scales
Poster No:
1261
Submission Type:
Abstract Submission
Authors:
Tianyong Xu1, Feiyan Chen1
Institutions:
1School of Physics, Zhejiang University, Hangzhou, China
First Author:
Co-Author:
Introduction:
Functional connectivity fingerprinting has been widely explored in neuroscience, demonstrating significant stability across various time scales, from days to months (Finn et al., 2015; Gordon et al., 2017; Horien et al., 2019). Connectivity gradients derived from functional connectivity provide a robust framework for simplifying complex, high-dimensional brain connectivity data into meaningful low-dimensional representations, revealing the brain's spatial organization (Margulies et al., 2016; Syndor et al., 2021). However, a crucial question remains: can these gradients accurately distinguish individuals by balancing the stable, universal aspects of functional connectivity with unique individual characteristics? Additionally, the consistency of these gradients across different temporal scales is still uncertain, emphasizing the need for systematic exploration of their reliability and applications in personalized neuroscience. This study seeks to address this question by analyzing three extensive datasets spanning days, weeks, months, and even years.
Methods:
In this study, we explored the stability and individual specificity of connectivity gradients across varying time scales using three resting-state fMRI datasets. The first dataset, the publicly available Midnight Scan Club (MSC), involved ten participants scanned across ten sessions over ten days (Gordon et al., 2017). The second dataset employed a dense-sampling protocol, featuring up to 36 sessions over four months (unpublished data). The third dataset consisted of two scans per participant collected over a longitudinal period exceeding five years (Xu et al., 2024). For each participant, we computed connectivity gradients and aligned them to a group-averaged gradient template for consistency in comparative analyses (Vos de Wael et al., 2020). We assessed individual identification capacity by measuring the similarity of connectivity gradients between session scans both within and across individuals using Pearson correlation. Identification accuracy was determined by how often the highest similarity of a session scan was correctly linked to the same individual rather than to another participant.
Results:
Our research uncovers three pivotal insights. First, individuals can be accurately identified using the first three connectivity gradient components over varying timeframes, achieving over 0.97 accuracy in Dataset 1 (days) and 0.99 in Dataset 2 (weeks and months) (Fig. 1A, 1C). Longitudinal scans in Dataset 3 show higher similarity within subjects compared to between subjects (p < 0.001, Fig. 1D), highlighting the robustness of connectivity gradients in reflecting individual neural signatures. Second, identification accuracy is affected by factors such as functional connectivity sparsity, time series volume, and the number of regions of interest (ROIs) (Fig. 1B), emphasizing critical methodological considerations. Finally, distinct brain networks contribute to the high intra- and inter-subject similarity in gradient patterns: the default mode network (DMN) and sensory-motor network (SMN) in Gradient 1, the visual network (VIS) in Gradient 2, and the fronto-parietal network (FPN) in Gradient 3 (Fig. 2A). Within-subject variability is influenced by the SMN (Gradient 1) and VIS (Gradient 2), while inter-subject variability stems from higher-order networks like the DMN and FPN (Fig. 2B). These findings highlight the importance of association networks in individual variability and the role of primary networks in ensuring stability over time.
Conclusions:
The present study revealed that connectivity gradients hold great promise for balancing the stable, universal architecture of brain connectivity with the unique variations that define individual identity, all while preserving stability across different time scales. This presents offer novel insights of how the brain maintains its core functional structure while embracing the dynamic variability that reflects individuality.
Higher Cognitive Functions:
Higher Cognitive Functions Other 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Other - Individual fingerprinting; Connectivity gradient; Functional connectivity
1|2Indicates the priority used for review
Abstract Information
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Gordon, E. M., Laumann, T. O., Gilmore, A. W., Newbold, D. J., Greene, D. J., Berg, J. J., Ortega, M., Hoyt-Drazen, C., Gratton, C., Sun, H., Hampton, J. M., Coalson, R. S., Nguyen, A. L., McDermott, K. B., Shimony, J. S., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Nelson, S. M., & Dosenbach, N. U. F. (2017). Precision Functional Mapping of Individual Human Brains. Neuron, 95(4), 791–807.e7.
Horien, C., Shen, X., Scheinost, D., & Constable, R. T. (2019). The individual functional connectome is unique and stable over months to years. NeuroImage, 189, 676–687.
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., Petrides, M., Jefferies, E., & Smallwood, J. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences of the United States of America, 113(44), 12574–12579.
Sydnor, V. J., Larsen, B., Bassett, D. S., Alexander-Bloch, A., Fair, D. A., Liston, C., Mackey, A. P., Milham, M. P., Pines, A., Roalf, D. R., Seidlitz, J., Xu, T., Raznahan, A., & Satterthwaite, T. D. (2021). Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron, 109(18), 2820–2846.
Vos de Wael, R., Benkarim, O., Paquola, C., Lariviere, S., Royer, J., Tavakol, S., Xu, T., Hong, S. J., Langs, G., Valk, S., Misic, B., Milham, M., Margulies, D., Smallwood, J., & Bernhardt, B. C. (2020). BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Communications biology, 3(1), 103.
Xu, T., Wu, Y., Zhang, Y., Zuo, X. N., Chen, F., & Zhou, C. (2024). Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development. Neuroscience bulletin, 40(1), 50–64.