Cognitive Neuroscience Division, Department of Neurology, and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, and G.H. Sergievsky Center, Columbia University Irving Medical Center, New York, NY, United States of America.
Department of Radiology, Weill Cornell Medicine, Brain Health Imaging Institute, New York, NY, United States of America.
PLoS One. 2021 Apr 8;16(4):e0249947. doi: 10.1371/journal.pone.0249947. eCollection 2021.
Functional connectivity, both in resting state and task performance, has steadily increased its share of neuroimaging research effort in the last 1.5 decades. In the current study, we investigated the predictive utility regarding behavioral performance and task information for 240 participants, aged 20-77, for both voxel activation and functional connectivity in 12 cognitive tasks, belonging to 4 cognitive reference domains (Episodic Memory, Fluid Reasoning, Perceptual Speed, and Vocabulary). We also added a model only comprising brain-structure information not specifically acquired during performance of a cognitive task. We used a simple brain-behavioral prediction technique based on Principal Component Analysis (PCA) and regression and studied the utility of both modalities in quasi out-of-sample predictions, using split-sample simulations (= 5-fold Monte Carlo cross validation) with 1,000 iterations for which a regression model predicting a cognitive outcome was estimated in a training sample, with a subsequent assessment of prediction success in a non-overlapping test sample. The sample assignments were identical for functional connectivity, voxel activation, and brain structure, enabling apples-to-apples comparisons of predictive utility. All 3 models that were investigated included the demographic covariates age, gender, and years of education. A minimal reference model using simple linear regression with just these 3 covariates was included for comparison as well and was evaluated with the same resampling scheme as described above. Results of the comparison between voxel activation and functional connectivity were mixed and showed some dependency on cognitive outcome; however, mean differences in predictive utility between voxel activation and functional connectivity were rather small in terms of within-modality variability or predictive success. More notably, only in the case of Fluid Reasoning did concurrent functional neuroimaging provided compelling about cognitive performance beyond structural brain imaging or the minimal reference model.
在过去的 15 年中,功能连接无论是在静息状态还是任务表现中,都在神经影像学研究中占据了越来越大的份额。在当前的研究中,我们针对 240 名年龄在 20 至 77 岁的参与者,调查了 12 项认知任务的体素激活和功能连接的行为表现和任务信息的预测能力,这些任务属于 4 个认知参考领域(情节记忆、流体推理、知觉速度和词汇)。我们还添加了一个仅包含在执行认知任务期间未专门获取的大脑结构信息的模型。我们使用了一种基于主成分分析(PCA)和回归的简单脑-行为预测技术,并使用分样模拟(= 5 折蒙特卡罗交叉验证)进行了准外样本预测研究,在 1000 次迭代中,在训练样本中估计了一个预测认知结果的回归模型,然后在非重叠测试样本中评估预测成功。对于功能连接、体素激活和大脑结构,样本分配是相同的,这使得可以对预测效果进行苹果对苹果的比较。所研究的所有 3 个模型都包括人口统计学协变量年龄、性别和受教育年限。我们还包含了一个仅使用这 3 个协变量的简单线性回归的最小参考模型,并使用与上述相同的重采样方案进行了评估。体素激活和功能连接之间的比较结果好坏参半,并且显示出与认知结果有些依赖关系;但是,体素激活和功能连接之间的预测效果差异在模态内变异性或预测成功率方面相对较小。更值得注意的是,只有在流体推理的情况下,功能神经影像学的同时应用才能提供比结构脑成像或最小参考模型更有说服力的认知表现信息。