On the Reliability, Validity, and Utility of Single-Subject Brain Structural Similarity Networks
Poster No:
1179
Submission Type:
Abstract Submission
Authors:
Minchul Kim1, Yae Ji Kim2, Marvin Chun3, Kwangsun Yoo4
Institutions:
1Kangbuk Samsung Hospital, Seoul, Seoul, 2Sungkyunkwan University, Seoul, Seoul, 3Department of Psychology, Yale University, , CT, USA, New Haven, CT, 4Sungkyunkwan University, ., Seoul
First Author:
Co-Author(s):
Introduction:
The T1 brain MRI-based volumetric similarity network (SSN) offers an advantage in clinical settings due to its ease of acquisition, however, its reliability and validity remain unclear (Clarkson et al., 2011). This study aimed to assess the reproducibility and utility of the SSN as a foundation for future research and clinical applications.
Methods:
We analyzed three MRI datasets with repeated scans (Dataset 1: n=86 subjects [Yoo et al., 2022], Dataset 2: n=50, Dataset 3: n=81). SSNs were generated using five inter-regional similarity metrics (manhattan, correlate, z transformed correlate, cosine, and histogram intersection) between brain parcellations from the Shen atlas (Shen et al., 2013). We 1) examined whether the SSN reflects the cytoarchitecture of the brain, 2) assessed test-retest reliability using connectome fingerprints (D1, D2, & D3) (Finn et al., 2015), and 3) tested their behavioral relevance to predict attention performance (gradual onset continuous performance task, multiple object tracking, visual short-term memory, and general attention) using connectome-based predictive modeling (D1) (Shen et al., 2017).
Results:
The SSN showed a significant correlation (r = 0.22, p<0.01) with the cytoarchitectonic covariance network, suggesting its biological relevance. It also yielded a high test-retest reliability across the datasets, as evidenced by a high identification accuracy (90%, 98%, and 80% in D1, D2, and D3, respectively). The accuracy was comparable to the original voxel-based results (92%, 98%, and 98%), ROI-based accuracy (95%, 76%, and 98%), and functional connectome-based accuracy (80% and 86% in D1 and D2, respectively).
However, SSN showed limited ability to predict individuals' attention functions (r = 0.22 at best, using the correlate SSN to predict sustained attention) compared to the functional connectome (r = 0.38).
However, SSN showed limited ability to predict individuals' attention functions (r = 0.22 at best, using the correlate SSN to predict sustained attention) compared to the functional connectome (r = 0.38).
Conclusions:
The SSN correlates with cytoarchitecture and demonstrates high reliability. However, behavior prediction accuracy requires further improvement, particularly compared to fMRI (Ooi et al., 2022).
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Connectivity (eg. functional, effective, structural) 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Perception, Attention and Motor Behavior:
Attention: Visual
Keywords:
STRUCTURAL MRI
Structures
Other - structural similarity network; fingerprinting; predictive modeling
1|2Indicates the priority used for review
·Schematic of analysis and results
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
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.
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015). Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664-1671.
Ooi, L. Q. R., Chen, J., Zhang, S., Kong, R., Tam, A., Li, J., Dhamala, E., Zhou, J. H., Holmes, A. J., & Yeo, B. T. (2022). Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. Neuroimage, 263, 119636.
Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., & Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols, 12(3), 506-518.
Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage, 82, 403-415.
Yoo, K., Rosenberg, M. D., Kwon, Y. H., Lin, Q., Avery, E. W., Sheinost, D., Constable, R. T., & Chun, M. M. (2022). A brain-based general measure of attention. Nature Human Behaviour, 6(6), 782-795.