Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA.
School of Psychological Science, University of Western Australia, Perth, Australia.
Brain. 2021 May 7;144(4):1089-1102. doi: 10.1093/brain/awab007.
The brain signature concept aims to characterize brain regions most strongly associated with an outcome of interest. Brain signatures derive their power from data-driven searches that select features based solely on performance metrics of prediction or classification. This approach has important potential to delineate biologically relevant brain substrates for prediction or classification of future trajectories. Recent work has used exploratory voxel-wise or atlas-based searches, with some using machine learning techniques to define salient features. These have shown undoubted usefulness, but two issues remain. The preponderance of recent work has been aimed at categorical rather than continuous outcomes, and it is rare for non-atlas reliant voxel-based signatures to be reported that would be useful for modelling and hypothesis testing. We describe a cross-validated signature region model for structural brain components associated with baseline and longitudinal episodic memory across cognitively heterogeneous populations including normal, mild impairment and dementia. We used three non-overlapping cohorts of older participants: from the UC Davis Aging and Diversity cohort (n = 255; mean age 75.3 ± 7.1 years; 128 cognitively normal, 97 mild cognitive impairment, 30 demented and seven unclassified); from Alzheimer's Disease Neuroimaging Initiative (ADNI) 1 (n = 379; mean age 75.1 ± 7.2; 82 cognitively normal, 176 mild cognitive impairment, 121 Alzheimer's dementia); and from ADNI2/GO (n = 680; mean age 72.5 ± 7.1; 220 cognitively normal, 381 mild cognitive impairment and 79 Alzheimer's dementia). We used voxel-wise regression analysis, correcting for multiple comparisons, to generate an array of regional masks corresponding to different association strength levels of cortical grey matter with baseline memory and brain atrophy with memory change. Cognitive measures were episodic memory using Spanish and English Neuropsychological Assessment Scales instruments for UC Davis and ADNI-Mem for ADNI 1 and ADNI2/GO. Performance metric was the adjusted R2 coefficient of determination of each model explaining outcomes in two cohorts other than where it was computed. We compared within-cohort performances of signature models against each other and against other recent signature models of episodic memory. Findings were: (i) two independently generated signature region of interest models performed similarly in a third separate cohort; (ii) a signature region of interest generated in one imaging cohort replicated its performance level when explaining cognitive outcomes in each of other, separate cohorts; and (iii) this approach better explained baseline and longitudinal memory than other recent theory-driven and data-driven models. This suggests our approach can generate signatures that may be easily and robustly applied for modelling and hypothesis testing in mixed cognition cohorts.
大脑特征概念旨在描述与感兴趣的结果最密切相关的脑区。大脑特征的力量来自于数据驱动的搜索，该搜索仅根据预测或分类的性能指标选择特征。这种方法具有重要的潜力，可以描绘出用于预测或分类未来轨迹的生物学相关的大脑基质。最近的研究使用了探索性的体素或图谱搜索，其中一些使用机器学习技术来定义显著特征。这些已经显示出无疑的用处，但仍存在两个问题。最近的研究主要集中在分类结果上，而不是连续结果上，很少有基于非图谱的体素特征报告，这些特征对于建模和假设检验很有用。我们描述了一种经过交叉验证的结构脑成分特征区域模型，该模型与认知异质人群（包括正常、轻度认知障碍和痴呆）的基线和纵向情景记忆相关。我们使用了三个不重叠的老年参与者队列：来自加利福尼亚大学戴维斯老龄化和多样性队列（n=255；平均年龄 75.3±7.1 岁；128 名认知正常，97 名轻度认知障碍，30 名痴呆，7 名未分类）；来自阿尔茨海默病神经影像学倡议（ADNI）1（n=379；平均年龄 75.1±7.2；82 名认知正常，176 名轻度认知障碍，121 名阿尔茨海默病痴呆）；以及来自 ADNI2/GO（n=680；平均年龄 72.5±7.1；220 名认知正常，381 名轻度认知障碍，79 名阿尔茨海默病痴呆）。我们使用体素回归分析，校正了多重比较，生成了一组与皮质灰质与基线记忆的不同关联强度水平以及与记忆变化相关的脑萎缩相对应的区域掩模。认知测量使用西班牙语和英语神经心理学评估量表来测量加州大学戴维斯分校和 ADNI-Mem 的情景记忆，以及 ADNI2/GO 的情景记忆。性能指标是在除了计算模型的队列之外的两个队列中解释结果的每个模型的调整后的 R2 决定系数。我们比较了特征模型在同一队列内的表现，并与其他最近的情景记忆特征模型进行了比较。结果表明：（i）两个独立生成的特征区域模型在第三个独立队列中的表现相似；（ii）在一个成像队列中生成的特征区域模型在解释其他独立队列中的认知结果时，其表现水平与在该队列中的表现水平相似；（iii）这种方法比其他最近的基于理论和数据驱动的模型更好地解释了基线和纵向记忆。这表明我们的方法可以生成特征，这些特征可以很容易和稳健地应用于混合认知队列的建模和假设检验。
