Panta Sandeep R, Anderson Nathaniel E, Maurer J Michael, Harenski Keith A, Nyalakanti Prashanth K, Calhoun Vince D, Kiehl Kent A
The Mind Research Network, Albuquerque, NM, USA.
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
Neuroimage Rep. 2021 Oct 16;1(4):100057. doi: 10.1016/j.ynirp.2021.100057. eCollection 2021 Dec.
When aggregating neuroimaging data across many subjects, an important consideration is establishing some group-level uniformity prior to further statistical analysis. Spatial normalization and motion correction are two important preprocessing steps that help achieve this goal. Researchers have also often excluded left-handed subjects due to presumptions about variable asymmetries relating to both brain structure and function, which may interfere with achieving a desired level of group homogeneity. It is well-known, however, that hand-preference is not a binary attribute and is not a perfect representation of structural asymmetry or hemispheric specialization. In an effort to demonstrate a more objective, data-driven approach for quantifying asymmetries across handedness, we tested the reliability of single-subject classification of handedness using data obtained from structural MRI in extant samples. We utilized data from deformation fields created during the spatial normalization process within regions of interest (ROIs), including the motor and somatosensory cortex, and Broca's and Wernicke's areas. Using these deformation fields as features in machine learning classifiers, we achieved classification accuracies greater than 75% across two independent datasets (i.e., a sample of incarcerated adult offenders and a sample of community adults from the Netherlands). These results demonstrate reliability of morphological features attributable to handedness as represented in neuroimaging data and further suggest that application of data-driven techniques may be a principled approach for addressing asymmetries in group analysis.
在汇总多个受试者的神经影像数据时，一个重要的考虑因素是在进一步的统计分析之前建立某种组水平的一致性。空间归一化和运动校正是有助于实现这一目标的两个重要预处理步骤。由于对与脑结构和功能相关的可变不对称性的推测，研究人员也经常排除左利手受试者，因为这些不对称性可能会干扰达到所需的组同质性水平。然而，众所周知，用手偏好不是一个二元属性，也不是结构不对称或半球特化的完美表征。为了展示一种更客观、数据驱动的方法来量化不同用手习惯的不对称性，我们使用现存样本中从结构磁共振成像（MRI）获得的数据，测试了单受试者用手习惯分类的可靠性。我们利用了在感兴趣区域（ROI）（包括运动和体感皮层以及布洛卡区和韦尼克区）的空间归一化过程中创建的变形场数据。将这些变形场用作机器学习分类器中的特征，我们在两个独立数据集（即被监禁成年罪犯样本和来自荷兰的社区成年人样本）上实现了大于75%的分类准确率。这些结果证明了神经影像数据中所呈现的、归因于用手习惯的形态特征的可靠性，并进一步表明应用数据驱动技术可能是在组分析中解决不对称性问题的一种有原则的方法。
