Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data
Open accessAbstract
Tractography based on diffusion tensor imaging (DTI) data has been used as a tool by a large number of recent studies to investigate structural connectome. Despite its great success in offering unique 3D neuroanatomy information, DTI is an indirect observation with limited resolution and accuracy and its reliability is still unclear. Thus, it is essential to answer this fundamental question: how reliable is DTI tractography in constructing large-scale connectome? To answer this question, we employed neuron tracing data of 1772 experiments on the mouse brain released by the Allen Mouse Brain Connectivity Atlas (AMCA) as the ground-truth to assess the performance of DTI tractography in inferring white matter fiber pathways and inter-regional connections. For the first time in the neuroimaging field, the performance of whole brain DTI tractography in constructing a large-scale connectome has been evaluated by comparison with tracing data. Our results suggested that only with the optimized tractography parameters and the appropriate scale of brain parcellation scheme, can DTI produce relatively reliable fiber pathways and a large-scale connectome. Meanwhile, a considerable amount of errors were also identified in optimized DTI tractography results, which we believe could be potentially alleviated by efforts in developing better DTI tractography approaches. In this scenario, our framework could serve as a reliable and quantitative test bed to identify errors in tractography results which will facilitate the development of such novel tractography algorithms and the selection of optimal parameters.
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Opting against SIFT2 provides a more direct comparison between our results and similar studies that used Allen tracer data without reference to spatial embedding.
“This also provides a more direct comparison between our results and those of similar studies that have used the Allen tracer data to benchmark tractography performance without reference to the role of spatial embedding (Aydogan et al., 2018; Calabrese et al., 2015; Chen et al., 2015).”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Chen et al., 2015
The Introduction and Methods §2.1 identify the Allen Mouse Brain Connectivity Atlas tracer data as the benchmark for evaluating DTI tractography. But Results §3.1 examines how streamline length affects accuracy, and §3.3 compares performance across anatomical locations. The description of the study as proceeding without reference to spatial factors is therefore too broad; the paper does not address the citing authors’ decision against SIFT2. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe overall results confirm previously reported trade-offs between sensitivity and specificity of tract reconstructions across tractography methods.
“As such, our overall results confirm previously reported trade-offs between sensitivity and specificity of tract reconstructions across tractography methods (Bastiani et al. 2012; Thomas et al. 2014; Chen et al. 2015; Donahue et al. 2016).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Chen et al. 2015
The Results (§3.1) explicitly report a sensitivity–specificity trade-off when tuning fiber-number thresholds and DTI tractography parameters, and say it agrees with previous findings. Chen et al. do not establish that trade-off across different tractography methods; their analysis concerns parameter choices within DTI tractography. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe FA threshold had significantly more effect on connectome reconstruction sensitivity and specificity than the step size and angle threshold.
“The FA threshold had significantly more effect on connectome reconstruction sensitivity and specificity than the step size and angle threshold, in line with previous research (Azadbakht et al. 2015; Chen et al. 2015).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Chen et al. 2015
Results §3.1 says the FA threshold had more impact on the reconstructed connectome than the angular threshold, and describes a sensitivity–specificity tradeoff when parameters were varied. Methods §2.2 evaluates FA and angular thresholds, but not step size; the cited passages also do not establish that the difference was statistically significant. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAn increasing FA threshold negatively influenced sensitivity and the Jaccard index.
“In fact, an increasing FA threshold negatively influenced sensitivity and the Jaccard index, suggesting that an FA threshold should be omitted for this type of datasets and analyses (Azadbakht et al. 2015; Chen et al. 2015).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Chen et al. 2015
Results §3.1 reports that smaller FA thresholds gave higher AUCs and that more restrictive tractography thresholds missed more connections, consistent with reduced sensitivity. The paper does not report a Jaccard index. Its Discussion §4 identifies an FA threshold of 0.1 as best for its mouse-brain data, rather than recommending that the threshold be omitted. (LLM, conf=0.87)
Experimental — accuracy not yet measured
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- Thomas C Ye FQ Irfanoglu MO Modi P Saleem KS Leopold DA Pierpaoli C 2014 Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited Proc Natl Acad Sci 111 16574 16579 10.1073/pnas.1405672111 25368179 PMC4246325 · DOI · Paper page
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“6.1 (Wang, Benner, Sorensen, & Wedden, 2007) using an FA threshold of 0.1 and a curvature threshold of 40◦, based on the study by Chen et al. (2015).”
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- Authors
- Chen, H., Liu, T., Zhao, Y., Zhang, T., Li, Y., Li, M., Zhang, H., Kuang, H., Guo, L., Tsien, J. Z., & Liu, T.
- Affiliations
- University of Georgia (US)
- North China University of Science and Technology (CN)
- Augusta University (US)
- Northwestern Polytechnical University (CN)
- Corresponding author
- Tsien, Joe Z.
- Liu, Tianming
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 115
- Pages
- 202-213
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4461481
- OpenAlex
- W2014292780
- Cited by
- 46
- References
- 53
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 1 · 2023: 3 · 2022: 5 · 2021: 8 · 2020: 5 · 2019: 3 · 2018: 7 · 2017: 5 · 2016: 2 · 2015: 2
- FWCI
- 4.32
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (R01AG042599)
- National Institute on Drug Abuse (R01DA033393)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
- Keywords
- diffusion tensor imaging, tractography, neural tracing, structural connectome, brain parcellation, Allen Mouse Brain Connectivity Atlas, whole-brain connectome
- MeSH terms
- Algorithms, Animals, Atlases as Topic, Brain, Brain Mapping, Image Processing, Computer-Assisted, Functional Laterality, Nerve Fibers, Neural Pathways, Neurons, Reproducibility of Results, Imaging, Three-Dimensional, Mice, Diffusion Tensor Imaging, Connectome
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