Pattern dissimilarity of functional connectome between hemispheres：a multivariate brain asymmetry
Poster No:
1422
Submission Type:
Abstract Submission
Authors:
Qiuhui Bi1, Chenxi Zhao2, Gaolang Gong3
Institutions:
1Beijing Normal University, Beijing, Beijing, 2Beijing Normal University, Beijing, China, 3Beijing Normal University, Beijing, N/A
First Author:
Co-Author(s):
Introduction:
The functional lateralization or asymmetry between the left and right hemispheres has been extensively documented (1,2). Recent studies revealed hemispheric asymmetries of resting-state functional connectivity (FC). However, previous studies predominantly relied on univariate analyses, without capturing the differences in multivariate FC patterns between the hemispheres (3). In this study, we focused on the multivariate FC pattern of each hemisphere and developed a novel imaging measure, pattern dissimilarity of hemispheric functional connectivity (PDHC), to investigate the multivariate asymmetry of the FC patterns of the two hemispheres.
Methods:
Datasets: The research study included four three independent fMRI datasets: the HCP-S1200 dataset, the dHCP dataset, an in-house Turner Syndrome (TS) dataset.
Functional connectome of each hemisphere: As is demonstrated in Fig. 1A, two widely-used connectivity-based atlases, i.e. AICHA and BNA, were employed for brain parcellation. FC was calculated separately for each hemisphere as the Pearson correlation between the regional BOLD time-courses. Fisher's r-to-z transformation was applied to improve the normality.
Pattern dissimilarity of hemispheric connectome (PDHC): For the AICHA and BNA atlases, the functional connectome of each hemisphere consists of 19,755 and 7,503 connections respectively, which could be regarded as the multivariate feature. Therefore, the distance between the left and right multivariate feature, referred to as the pattern dissimilarity of hemispheric functional connectome (PDHC) can be calculated as follows:
PDHC=1-Corr(LH, RH)
where Corr()denotes Pearson's correlation method; LH and RH denote the left and right hemispheric functional connectomes, respectively.
Functional connectome of each hemisphere: As is demonstrated in Fig. 1A, two widely-used connectivity-based atlases, i.e. AICHA and BNA, were employed for brain parcellation. FC was calculated separately for each hemisphere as the Pearson correlation between the regional BOLD time-courses. Fisher's r-to-z transformation was applied to improve the normality.
Pattern dissimilarity of hemispheric connectome (PDHC): For the AICHA and BNA atlases, the functional connectome of each hemisphere consists of 19,755 and 7,503 connections respectively, which could be regarded as the multivariate feature. Therefore, the distance between the left and right multivariate feature, referred to as the pattern dissimilarity of hemispheric functional connectome (PDHC) can be calculated as follows:
PDHC=1-Corr(LH, RH)
where Corr()denotes Pearson's correlation method; LH and RH denote the left and right hemispheric functional connectomes, respectively.
Results:
We first assessed the stability across different can sessions (REST1 vs. REST2) and brain parcellations (AICHA vs. BNA). As illustrated in Fig. 1B, C, the PHDC value demonstrated remarkable reproducibility between short-term test-retest scan sessions (ICC: 0.63–0.67) and across different brain parcellations (ICC: 0.82–0.85). Then, to investigate whether PDHC is highly individual-specific, we compared PDHC value with inter-subject pattern dissimilarity between ipsilateral and contralateral brain. The results in Fig 1D demonstrated that the FC patterns between the left and right hemispheres of a single individual are more closely coupled than those between the ipsilateral and contralateral hemispheres of different individuals.
We then used twin data in the HCP as well as the TS dataset to investigate the genetic influence on PDHC. The results shown in Fig. 2A demonstrated that the PDHC is a moderately to highly heritable (Falconer's H2: 0.3–0.62, SOLAR's H2 (4): 0.21–0.31, P<10-3). Furthermore, the comparison between healthy controls and patients with Turner syndrome revealed that PDHC values were significantly lower in the X-chromosome loss group (T: 2.9–3.1, P<6×10-3) (Fig. 2B).
Finally, we evaluated the development of PDHC during the initial stages of life using dHCP dataset. As shown in Figure 2C, longitudinal analysis showed significant deceased PDHC values with PMA in newborn infants for both atlases (R: -0.40–-0.37, P<10-16). Further, a LOOCV framework was employed to investigate whether the PDHC could serve as a neurofunctional indicator of the infants PMA. The results in Figure 2D showed a significant correlation between PDHC-predicted PMA and observed PMA (R: 0.53–0.57; β close to 1), suggesting that accurate prediction of the PMA is possible using PDHC.
We then used twin data in the HCP as well as the TS dataset to investigate the genetic influence on PDHC. The results shown in Fig. 2A demonstrated that the PDHC is a moderately to highly heritable (Falconer's H2: 0.3–0.62, SOLAR's H2 (4): 0.21–0.31, P<10-3). Furthermore, the comparison between healthy controls and patients with Turner syndrome revealed that PDHC values were significantly lower in the X-chromosome loss group (T: 2.9–3.1, P<6×10-3) (Fig. 2B).
Finally, we evaluated the development of PDHC during the initial stages of life using dHCP dataset. As shown in Figure 2C, longitudinal analysis showed significant deceased PDHC values with PMA in newborn infants for both atlases (R: -0.40–-0.37, P<10-16). Further, a LOOCV framework was employed to investigate whether the PDHC could serve as a neurofunctional indicator of the infants PMA. The results in Figure 2D showed a significant correlation between PDHC-predicted PMA and observed PMA (R: 0.53–0.57; β close to 1), suggesting that accurate prediction of the PMA is possible using PDHC.
·Figure 1
·Figure 2
Conclusions:
The present study proposes a novel metric for evaluating the multivariate asymmetry of the FC patterns, namely PDHC, which is robust to imaging acquisition and brain network resolution, and capable of capturing the individualized characteristics of the hemispheric FC asymmetries. Furthermore, the genetic basis and early development of this particular functional asymmetry were thoroughly explored.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Multivariate Approaches 2
Keywords:
FUNCTIONAL MRI
Other - functional connectome; brain asymmetry; pattern dissimilarity of hemispheric functional connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
Güntürkün, O., & Ocklenburg, S. (2017). Ontogenesis of Lateralization. Neuron, 94(2), Article 2. https://doi.org/10.1016/j.neuron.2017.02.045
Haynes, J.-D. (2015). A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. Neuron, 87(2), 257–270. https://doi.org/10.1016/j.neuron.2015.05.025
Ocklenburg, S., & Güntürkün, O. (2018). The lateralized brain: The neuroscience and evolution of hemispheric asymmetries. Elsevier/Academic Press.