Norm atlas of lifespan for age and cognitive ability prediction based on dual-paradigm fMRI
With extensive attention to human brain health worldwide,researchers are devoted to discovering the development and aging mechanisms of individual brains to assess the degree of brain aging and cognitive ability.The rapid development of functional Magnetic Resonance Imaging(fMRI)has provided robust technical support for neuroscience research.Different measures(functional connectivity and functional connectivity density,etc.)from resting-state fMRI have been widely used in related works.However,these studies do not provide sufficient coverage of the entire lifespan brain change owing to the utilization of a single diagram.Therefore,we propose a norm atlas of lifespan for age and cognitive ability prediction based on dual-paradigm fMRI,which combines unsupervised and supervised learning.Specifically,functional connectivity derived from resting-state and natural stimulation diagrams are integrated simultaneously,aiming at responding to the synchronous and asynchronous states of brain neural activity of both paradigms.The whole workflow is divided into the following steps.Firstly,the functional connectivity data of both paradigms are fed into a variational auto-encoder(VAE)as the input of the encoder and the output of the decoder,respectively.Then,the asynchronous and synchronous states of brain neural activity in the resting-state and natural stimulation paradigm can be revived by the latent variables(norm).Secondly,the functional connectivity patterns for each subject are calculated using the lifespan norm as a feature representation for the downstream prediction tasks.Thirdly,all subjects are split into training and testing sets using five-fold cross-validation,and the corresponding norm features of each subject are fed into the prediction module based on the local receptive field block.To achieve the learning and fusion of features,the prediction module combines the mechanisms of multi-scale learning and residual connection.Finally,the prediction of age and cognitive ability scores(e.g.,fluid intelligence,reaction time scores)is performed.The proposed method has been validated on the Cambridge Center for Neuroscience of Aging dataset(sample size:424,age range:18 to 88 years).The mean absolute errors for age,fluid intelligence,and reaction time scores are 5.97,3.72,and 0.108,respectively,which are superior to other related studies.Moreover,the trend of fluctuations in prediction errors across lifespan is consistent with cognitive behavior scores.To the best of our knowledge,it is the first time for the discovery of such a trend revealed by a data-driven approach.The results indicate that the combination of norm atlas and local receptive field module can make predictions for age and cognitive ability at high accuracy,emphasizing feature modeling's effectivity of proposed norm atlas.The complex parameter optimization for VAE and prediction module are not performed,instead,empirical settings are adopted as much as possible.This strategy not only highlights the generalization of the proposed method,but also explains the norms from a biomedical aspect,and explores the"black-box"effect of deep learning.The proposed method is benefit to reveal the patterns of brain neurodevelopment throughout lifespan and has the potential for clinical applications.
万方数据
维普
