Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.
Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.
J Neuroimaging. 2021 Mar;31(2):261-271. doi: 10.1111/jon.12814. Epub 2020 Dec 3.
Source-based morphometry(SBM) has been used in multicenter studies pooling magnetic resonance imaging data across different scanners to advance the reproducibility of neuroscience research. In the present study, we developed an analysis strategy for Scanner-Specific Detection (SS-Detect) of SBPs in multiscanner studies, and evaluated its performance relative to a conventional strategy.
In the first experiment, the SimTB toolbox was used to generate simulated datasets mimicking 20 different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical gray matter volume (GMV) datasets from two different scanners. Moreover, we applied two strategies to compare SBPs between schizophrenia patients' and healthy controls' GMV from two scanners.
The outputs of the conventional strategy were limited to whole-sample-level results across all scanners; the outputs of SS-Detect included whole-sample-level and scanner-specific results. In the first simulation experiment, SS-Detect successfully estimated all simulated SBPs, including the common and scanner-specific SBPs, whereas the conventional strategy detected only some of the whole-sample SBPs. The second simulation experiment showed that both strategies could detect the simulated SBP. Quantitative evaluations of both experiments demonstrated greater accuracy of the SS-Detect in estimating spatial SBPs and subject-specific loading parameters. In the third experiment, SS-Detect detected more significant between-group SBPs, and these SBPs corresponded with the results from voxel-based morphometry analysis, suggesting that SS-Detect has higher sensitivity in detecting between-group differences.
SS-Detect outperformed the conventional strategy and can be considered advantageous when SBM is applied to a multiscanner study.
基于源的形态计量学（SBM）已被用于汇集来自不同扫描仪的磁共振成像数据的多中心研究，以提高神经科学研究的可重复性。在本研究中，我们开发了一种用于多扫描仪研究中扫描仪特异性检测（SS-Detect）的分析策略，并评估了其相对于传统策略的性能。
在第一个实验中，使用 SimTB 工具箱生成模拟数据集，模拟具有常见和扫描仪特异性 SBP 的 20 种不同扫描仪。在第二个实验中，我们从两个不同扫描仪的经验灰质体积（GMV）数据集生成了一个模拟 SBP。此外，我们应用两种策略来比较来自两个扫描仪的精神分裂症患者和健康对照组 GMV 之间的 SBP。
传统策略的输出仅限于所有扫描仪的全样本水平结果；SS-Detect 的输出包括全样本水平和扫描仪特异性结果。在第一个模拟实验中，SS-Detect 成功估计了所有模拟 SBP，包括常见和扫描仪特异性 SBP，而传统策略仅检测到了一些全样本 SBP。第二个模拟实验表明，两种策略都可以检测到模拟 SBP。对两个实验的定量评估表明，SS-Detect 在估计空间 SBP 和受试者特异性加载参数方面具有更高的准确性。在第三个实验中，SS-Detect 检测到更多的组间 SBP，这些 SBP 与基于体素的形态计量学分析的结果相对应，表明 SS-Detect 在检测组间差异方面具有更高的敏感性。
SS-Detect 优于传统策略，在 SBM 应用于多扫描仪研究时可以被认为是有利的。
