Assesing intra-, inter-scanner, and inter-site reproducibility of diffusion MRI-based tractometry
Poster No:
1282
Submission Type:
Abstract Submission
Authors:
Daiki Taguma1,2, Isao Yokoi1, Takuji Kinjo3, Shuhei Tsuchida3, Toshikazu Miyata1,4,5, Garikoitz Lerma-Usabiaga6,7, Hiromasa Takemura1,2,4,8
Institutions:
1National Institute for Physiological Sciences, Okazaki, Japan, 2The Graduate Institute of Advanced Studies, SOKENDAI, Hayama, Japan, 3Tamagawa University Brain Science Institute, Machida, Japan, 4Department of Quantitative and Imaging Biology, Headquarters for Co-Creation Strategy, National Institutes of Natural Sciences, Tokyo, Japan, 5Princeton Neuroscience Institute, Princeton University, Princeton, United States, 6Basque Center on Cognition, Brain and Language, Donostia-San Sebastián, Spain, 7IKERBASQUE, Basque Foundation for Science, Bilbao, Spain, 8Core for Spin Life Sciences, Okazaki Collaborative Platform, National Institutes of Natural Sciences, Okazaki, Japan
First Author:
Daiki Taguma
National Institute for Physiological Sciences|The Graduate Institute of Advanced Studies, SOKENDAI
Okazaki, Japan|Hayama, Japan
National Institute for Physiological Sciences|The Graduate Institute of Advanced Studies, SOKENDAI
Okazaki, Japan|Hayama, Japan
Co-Author(s):
Toshikazu Miyata
National Institute for Physiological Sciences|Department of Quantitative and Imaging Biology, Headquarters for Co-Creation Strategy, National Institutes of Natural Sciences|Princeton Neuroscience Institute, Princeton University
Okazaki, Japan|Tokyo, Japan|Princeton, United States
National Institute for Physiological Sciences|Department of Quantitative and Imaging Biology, Headquarters for Co-Creation Strategy, National Institutes of Natural Sciences|Princeton Neuroscience Institute, Princeton University
Okazaki, Japan|Tokyo, Japan|Princeton, United States
Garikoitz Lerma-Usabiaga
Basque Center on Cognition, Brain and Language|IKERBASQUE, Basque Foundation for Science
Donostia-San Sebastián, Spain|Bilbao, Spain
Basque Center on Cognition, Brain and Language|IKERBASQUE, Basque Foundation for Science
Donostia-San Sebastián, Spain|Bilbao, Spain
Hiromasa Takemura
National Institute for Physiological Sciences|The Graduate Institute of Advanced Studies, SOKENDAI|Department of Quantitative and Imaging Biology, Headquarters for Co-Creation Strategy, National Institutes of Natural Sciences|Core for Spin Life Sciences, Okazaki Collaborative Platform, National Institutes of Natural Sciences
Okazaki, Japan|Hayama, Japan|Tokyo, Japan|Okazaki, Japan
National Institute for Physiological Sciences|The Graduate Institute of Advanced Studies, SOKENDAI|Department of Quantitative and Imaging Biology, Headquarters for Co-Creation Strategy, National Institutes of Natural Sciences|Core for Spin Life Sciences, Okazaki Collaborative Platform, National Institutes of Natural Sciences
Okazaki, Japan|Hayama, Japan|Tokyo, Japan|Okazaki, Japan
Introduction:
Diffusion MRI (dMRI)-based tractometry enables a simple statistical assessment of human white matter microstructure while maintaining anatomically meaningful information (Jones et al., 2005). Evaluating reproducibility and generalization across different scanning sites, MRI manufacturers, acquisition sequences, or data analysis methods is essential to understand how dMRI-based tractometry can help establish generalizable scientific conclusions. While previous works (Kruper et al., 2021; Lerma-Usabiaga et al., 2023) reported high scan-rescan reliability for tractometry using tensor-based metrics (such as fractional anisotropy, FA) in data acquired using the same scanner, comparisons between data acquired by using different scanners in different sites are still valuable. It is also essential to assess the tractometry generalizability with relatively complex diffusion models such as NODDI (Zhang et al., 2012). Here, we performed a multi-site, traveling subjects approach (Palacios et al., 2017; Tong et al., 2019; Kurokawa et al., 2021) to evaluate the generalizability of tractometry within/across scanners, sites, sequences, and models. In addition, we tested how much normalization would affect tractometry reproducibility.
Methods:
5 healthy volunteers (mean age = 34.0; 5 males) participated in MRI experiments using two Siemens 3T Verio Scanners at the NIPS and a 3T PrismaFit Scanner at Tamagawa University (Figure 1). 2 of 5 volunteers also participated in an MRI experiment using a Siemens 3T PrismaFit Scanner at the BCBL. We acquired two dMRI data acquisition protocols in each experiment, one based on the UK Biobank protocol (Miller et al., 2016) and HARP (Koike et al., 2021). Across scanners, dMRI acquisition protocol parameters were identical except for echo time, where the minimally achievable echo time was selected. We also acquired T1-weighted images for registration, tissue segmentation, and identifying regions-of-interests (ROIs).
dMRI data were processed using the RTP2 pipeline (Lerma-Usabiaga et al., 2023), including preprocessing, identifying ROIs, and identifying 20 major white matter tracts and their metrics. We then calculated tract profiles using tensor-based metric (mean diffusivity, MD) and NODDI-based metric (orientation dispersion index, ODI) and averaged these metrics across all nodes. We assessed reproducibility across datasets by calculating the intraclass correlation coefficient (ICC) across datasets by considering data for each tract as independent samples. Finally, we evaluated the reproducibility of each tract metric after normalizing with the mean across all tracts.
dMRI data were processed using the RTP2 pipeline (Lerma-Usabiaga et al., 2023), including preprocessing, identifying ROIs, and identifying 20 major white matter tracts and their metrics. We then calculated tract profiles using tensor-based metric (mean diffusivity, MD) and NODDI-based metric (orientation dispersion index, ODI) and averaged these metrics across all nodes. We assessed reproducibility across datasets by calculating the intraclass correlation coefficient (ICC) across datasets by considering data for each tract as independent samples. Finally, we evaluated the reproducibility of each tract metric after normalizing with the mean across all tracts.
Results:
Consistent with the previous reports (Kruper et al., 2021; Lerma-Usabiaga et al., 2023), tractometry showed high reproducibility (ICC > 0.98) for datasets acquired with UK Biobank protocol using the same scanner for both tensor-based and NODDI metrics (Figure 2, top panels). However, reproducibility decreased when we compared datasets acquired from the same subjects with different scanners in the same institute (Figure 2, second panel). The reproducibility became worse between datasets acquired in different institutes and machine types (Figure 2, third panels), demonstrating a presence of systematic bias in tractometry for such comparison. However, we found that normalization improved the tractometry reproducibility across datasets from different sites by reducing this systematic error (Figure 2, bottom panels). We observed a consistent trend in the data acquired with the HARP protocol.
Conclusions:
By analyzing this novel dataset, we found that while tractometry is highly reproducible if the same scanner is used, the reproducibility decreases depending on the degree of differences in scanners. The next step is to build a predictive modeling approach allowing the prediction of tractometry data on specific subjects when scanned using different scanners with different protocols.
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Keywords:
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Reproducibility
1|2Indicates the priority used for review
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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Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
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Provide references using APA citation style.
Koike, S. et al. (2021). Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan. NeuroImage: Clinical, 30, 102600.
Kruper, J. et al. (2021). Evaluating the reliability of human brain white matter tractometry. Aperture Neuro, 1, Article 10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669.
Kurokawa, R. et al. (2021). Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition. NeuroImage, 245, 118675.
Lerma-Usabiaga, G. et al. (2023). Reproducible Tract Profiles 2 (RTP2) suite, from diffusion MRI acquisition to clinical practice and research. Scientific Reports, 13, Article 6010.
Miller, K. L. et al. (2016). Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience, 19, 1523–1536.
Palacios, E. M. et al. (2017). Toward precision and reproducibility of diffusion tensor imaging: A multicenter diffusion phantom and traveling volunteer study. American Journal of Neuroradiology, 38(3), 537–545.
Tong, Q. et al. (2019). Reproducibility of multi-shell diffusion tractography on traveling subjects: A multicenter study prospective. Magnetic Resonance Imaging, 59, 1–9.
Zhang, H. et al. (2012). NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 61(4), 1000–1016.