Institute for the Developing Mind, Children's Hospital Los Angeles, Los Angeles CA, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 90027, USA.
Department of Psychiatry, Columbia University, New York, NY 10032, USA; New York State Psychiatric Institute, New York, NY 10032, USA.
Neuroimage. 2015 May 1;111:215-27. doi: 10.1016/j.neuroimage.2015.02.022. Epub 2015 Feb 17.
We hypothesize that coordinated functional activity within discrete neural circuits induces morphological organization and plasticity within those circuits. Identifying regions of morphological covariation that are independent of morphological covariation in other regions therefore may therefore allow us to identify discrete neural systems within the brain. Comparing the magnitude of these variations in individuals who have psychiatric disorders with the magnitude of variations in healthy controls may allow us to identify aberrant neural pathways in psychiatric illnesses. We measured surface morphological features by applying nonlinear, high-dimensional warping algorithms to manually defined brain regions. We transferred those measures onto the surface of a unit sphere via conformal mapping and then used spherical wavelets and their scaling coefficients to simplify the data structure representing these surface morphological features of each brain region. We used principal component analysis (PCA) to calculate covariation in these morphological measures, as represented by their scaling coefficients, across several brain regions. We then assessed whether brain subregions that covaried in morphology, as identified by large eigenvalues in the PCA, identified specific neural pathways of the brain. To do so, we spatially registered the subnuclei for each eigenvector into the coordinate space of a Diffusion Tensor Imaging dataset; we used these subnuclei as seed regions to track and compare fiber pathways with known fiber pathways identified in neuroanatomical atlases. We applied these procedures to anatomical MRI data in a cohort of 82 healthy participants (42 children, 18 males, age 10.5 ± 2.43 years; 40 adults, 22 males, age 32.42 ± 10.7 years) and 107 participants with Tourette's Syndrome (TS) (71 children, 59 males, age 11.19 ± 2.2 years; 36 adults, 21 males, age 37.34 ± 10.9 years). We evaluated the construct validity of the identified covariation in morphology using DTI data from a different set of 20 healthy adults (10 males, mean age 29.7 ± 7.7 years). The PCA identified portions of structures that covaried across the brain, the eigenvalues measuring the magnitude of the covariation in morphology along the respective eigenvectors. Our results showed that the eigenvectors, and the DTI fibers tracked from their associated brain regions, corresponded with known neural pathways in the brain. In addition, the eigenvectors that captured morphological covariation across regions, and the principal components along those eigenvectors, identified neural pathways with aberrant morphological features associated with TS. These findings suggest that covariations in brain morphology can identify aberrant neural pathways in specific neuropsychiatric disorders.
我们假设离散神经回路中协调的功能活动会引起这些回路中的形态组织和可塑性。因此，确定与其他区域的形态变化无关的形态变化区域，可能有助于我们在大脑中识别离散的神经系统。将具有精神疾病的个体与健康对照组的这些变化幅度进行比较，可能有助于我们识别精神疾病中的异常神经通路。我们通过将非线性、高维变形算法应用于手动定义的脑区来测量表面形态特征。我们通过共形映射将这些测量值转换到单位球体的表面上，然后使用球形小波及其标度系数来简化表示每个脑区表面形态特征的数据结构。我们使用主成分分析（PCA）来计算这些形态测量值的协变，其表示形式为其标度系数，跨越几个脑区。然后，我们评估通过 PCA 计算出的大特征值所识别的形态变化的脑区亚区是否可以识别大脑的特定神经通路。为此，我们将每个特征向量的亚核在弥散张量成像（DTI）数据集的坐标空间中进行空间配准；我们使用这些亚核作为种子区域来跟踪和比较与神经解剖图谱中确定的已知纤维通路的纤维通路。我们将这些程序应用于 82 名健康参与者（42 名儿童，18 名男性，年龄 10.5±2.43 岁；40 名成人，22 名男性，年龄 32.42±10.7 岁）和 107 名妥瑞氏症（TS）患者（71 名儿童，59 名男性，年龄 11.19±2.2 岁；36 名成人，21 名男性，年龄 37.34±10.9 岁）的解剖磁共振成像（MRI）数据。我们使用来自另一组 20 名健康成年人（10 名男性，平均年龄 29.7±7.7 岁）的 DTI 数据来评估形态学变化的结构效度。PCA 确定了大脑中跨结构变化的部分，特征值衡量了沿各自特征向量的形态变化幅度。我们的结果表明，特征向量以及与其相关脑区跟踪的 DTI 纤维与大脑中的已知神经通路相对应。此外，捕获跨区域形态变化的特征向量以及沿这些特征向量的主成分，确定了与 TS 相关的具有异常形态特征的神经通路。这些发现表明，大脑形态变化的协变可以识别特定神经精神疾病中的异常神经通路。
