Service neuro-diagnostique et neuro-interventionnel DISIM, University Hospitals of Geneva, Geneva, Switzerland.
J Alzheimers Dis. 2010;22(1):315-27. doi: 10.3233/JAD-2010-100840.
Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.
尽管横断面弥散张量成像（DTI）研究显示轻度认知障碍（MCI）存在显著的白质变化，但该技术在预测进一步认知下降方面的效用仍存在争议。35 名健康对照者（HC）和 67 名 MCI 患者具有 DTI 基线数据，在一年时进行神经心理学评估。其中，有 40 名稳定（sMCI；9 名单域遗忘，7 名单域额，24 名多域）和 27 名进展（pMCI；7 名单域遗忘，4 名单域额，16 名多域）。使用基于束的空间统计学测量各向异性分数（FA）和纵向、径向和平均扩散率。统计学包括组间比较和支持向量机（SVM）对 MCI 病例的个体分类。FA 在包括胼胝体腹侧部分、右侧颞叶和额叶通路在内的分布式网络中，HC 明显高于 MCI。在多重比较校正后，sMCI 与 pMCI 之间或 MCI 亚型之间无显著组间差异。然而，SVM 分析允许达到高达 91.4%（HC 与 MCI）和 98.4%（sMCI 与 pMCI）的个体分类准确率。当分别考虑 MCI 亚组时，多域 MCI 组中稳定与进展性认知下降的最小 SVM 分类准确率为 97.5%。DTI 数据的 SVM 分析可对稳定与进展性 MCI 进行高度准确的个体分类，与 MCI 亚型无关，表明该方法可能成为早期个体检测进展为痴呆的 MCI 患者的一种易于应用的工具。
