Beyond attention – new insights into the neural basis of intelligence and cognitive abilities through machine learning-based predictive modeling approaches

智力对教育、职业，甚至对健康和长寿都有预测性的相关性。虽然智力在人脑的结构和功能中具有生物学基础的假设已经相对确立，但对这一基础的全面理解仍然缺乏，但这构成了正在进行的研究的重要目标。将基于机器学习的预测建模方法引入神经科学，以及网络分析的进展和大型神经成像数据集的发布，为从新的角度解决这一目标开辟了新的机会。在前一个项目公布的结果的基础上，该项目揭示了大脑网络重构作为一种有前景的智能生物标记物，这里提议的后续项目将把重点扩大到a)个人智力分数的预测而不是解释事后的差异(预测建模而不是相关分析)，b)智能预测网络指纹的识别(大脑区域之间的通信模式)，以及c)如何在人脑中实现智能预测神经特征的问题。为了实现这些目标，我们将首先应用已建立的基于连接组的预测建模(CPM)方法，并在我们的研究组开发的协方差最大化特征向量预测(CMEP)方法中，对来自三个大样本(来自人类连接组项目的N=806；来自阿姆斯特丹开放MRI集合的N=138和N=184)的功能磁共振成像(FMRI)数据进行测试，以检验以下假设：1.可以通过大脑网络的重新配置显著预测个体的智力得分。2.不同的脑网络对智力预测的贡献存在显著差异。最后，在计划项目的最后部分，将实现神经网络，目的是验证以下假设：3.结合神经功能的不同特征，可以显著提高对智力的预测。对这些研究问题的回答将增进我们对人类智力的神经生物学基础的理解，并从神经生物学的角度提供关于智力和认知亚能力之间假设关系的心理学理论-这些见解也有助于我们对认知障碍疾病的理解。最后，开发的方法学将用于未来的神经科学研究。

Intelligence has predictive relevance for education, occupation, and even for health and longevity. Although the assumption that intelligence has a biological basis within the structure and function of the human brain is relatively established, a comprehensive understanding of this basis is still lacking but constitutes an important aim of ongoing research. The introduction of machine learning-based predictive modeling approaches to neuroscience together with advances in network analyses and the release of large neuroimaging data sets opens new opportunities to address this aim from a new perspective. Building on the published results of the previous project, which revealed brain network reconfiguration as a promising biomarker of intelligence, the here proposed follow-up project will broaden the focus to a) the prediction of individual intelligence scores instead of explaining variance post-hoc (predictive modeling instead of correlation analyses), b) the identification of intelligence-predictive network fingerprints (communication patterns between brain regions), and c) the question of how intelligence-predictive neural characteristics are implemented within the human brain. To achieve these goals, we will first apply the established connectome-based predictive modeling (CPM) approach and the, in our research group developed, covariance maximizing eigenvector-based prediction (CMEP) methodology to functional magnetic resonance imaging (fMRI) data from three large samples (N = 806 from the Human Connectome Project; N = 138 and N = 184 from the Amsterdam Open MRI Collection) to test the following hypotheses: 1. Individual intelligence scores can be significantly predicted from brain network reconfiguration. 2. The contribution of different brain networks to the prediction of intelligence differs significantly. Finally, in the last part of the planned project, Neural Networks will be implemented with the aim of testing the following hypothesis: 3. The prediction of intelligence can be significantly improved by combining different features of neural functioning. Answers to these research questions will enhance our understanding about the neurobiological basis of human intelligence and inform psychological theories about postulated relationships between intelligence and cognitive sub-abilities from a neurobiological perspective - insights which also contribute to our understanding about diseases with cognitive impairments. Finally, the developed methodology will be made available for future neuroscientific research.