Visual Neuroscience Laboratory, IBILI, Faculty of Medicine, University of Coimbra, Portugal.
Hum Brain Mapp. 2014 Jan;35(1):89-106. doi: 10.1002/hbm.22161. Epub 2012 Sep 11.
Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1.
神经纤维瘤病 1 型（NF1）是一种常见的遗传疾病，与认知功能障碍有关。然而，NF1 认知缺陷的病理生理学尚不清楚。NF1 大脑中已经报道了异常的脑结构，包括总脑体积增加、白质（WM）和灰质（GM）异常。这些先前的研究采用了单变量模型驱动的方法，防止了对大脑解剖结构的细微和空间分布差异的检测。多元模式分析允许从多个空间位置结合信息，从而产生超越单个体素的辨别力。在这里，我们首次使用多元数据驱动的分类方法研究了 NF1 大脑中的细微异常。我们使用支持向量机（SVM）对 39 名 NF1 患者和 60 名非患者的结构 T1 加权 MRI 扫描的全脑 GM 和 WM 段进行分类，这些患者分为儿童/青少年和成年组。我们还采用体素形态计量学（VBM）作为单变量金标准来研究大脑结构差异。SVM 分类器正确分类了 94%的病例（敏感性 92%；特异性 96%），揭示了存在可区分 NF1 个体与对照组的大脑结构异常。相应地，VBM 分析显示与 SVM 权重图一致的结构差异，代表了用于组间区分的最相关脑区。这些脑区包括海马体、基底节、丘脑和视觉皮层。因此，这种多元数据驱动的分析确定了在没有可见病理学的情况下大脑结构的细微异常。我们的结果进一步深入了解了 NF1 认知表型的已知特征的神经解剖学相关性。
