Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, Canada.
Department of Software and IT Engineering, École de Technologie supérieure (ETS), Montreal, H3C 1K3, Canada.
Neuroimage. 2020 Feb 1;206:116226. doi: 10.1016/j.neuroimage.2019.116226. Epub 2019 Oct 5.
Accurate prediction of individuals' brain age is critical to establish a baseline for normal brain development. This study proposes to model brain development with a novel non-negative projective dictionary learning (NPDL) approach, which learns a discriminative representation of multi-modal neuroimaging data for predicting brain age. Our approach encodes the variability of subjects in different age groups using separate dictionaries, projecting features into a low-dimensional manifold such that information is preserved only for the corresponding age group. The proposed framework improves upon previous discriminative dictionary learning methods by incorporating orthogonality and non-negativity constraints, which remove representation redundancy and perform implicit feature selection. We study brain development on multi-modal brain imaging data from the PING dataset (N = 841, age = 3-21 years). The proposed analysis uses our NDPL framework to predict the age of subjects based on cortical measures from T1-weighted MRI and connectome from diffusion weighted imaging (DWI). We also investigate the association between age prediction and cognition, and study the influence of gender on prediction accuracy. Experimental results demonstrate the usefulness of NDPL for modeling brain development.
准确预测个体的大脑年龄对于建立正常大脑发育的基线至关重要。本研究提出了一种新的非负投影字典学习（NPDL）方法来对大脑发育进行建模，该方法使用一种新的非负投影字典学习（NPDL）方法，通过对多模态神经影像学数据进行有判别力的表示来预测大脑年龄。我们的方法使用单独的字典来对不同年龄组的受试者的变异性进行编码，将特征投影到低维流形中，从而仅保留对应年龄组的信息。与之前的判别字典学习方法相比，我们的方法通过引入正交性和非负性约束来提高性能，这消除了表示冗余并执行了隐式特征选择。我们研究了 PING 数据集（N=841，年龄=3-21 岁）的多模态脑成像数据中的大脑发育。拟议的分析使用我们的 NPDL 框架，根据 T1 加权 MRI 的皮质测量值和扩散加权成像（DWI）的连接体来预测受试者的年龄。我们还研究了年龄预测与认知之间的关系，并研究了性别对预测准确性的影响。实验结果表明，NPDL 对于建模大脑发育非常有用。
