Accurately extracting brain tissue is a critical and primary step in brain neuroimaging research. Due to the differences in brain size and structure between humans and nonhuman primates, the performance of the existing tools for brain tissue extraction, working on macaque brain MRI, is constrained. A new transfer learning training strategy was utilized to address the limitations, such as insufficient training data and unsatisfactory model generalization ability, when deep neural networks processing the limited samples of macaque magnetic resonance imaging(MRI). First, the project combines two human brain MRI data modes to pre-train the neural network, in order to achieve faster training and more accurate brain extraction. Then, a residual network structure in the U-Net model was added, in order to propose a ResTLU-Net model that aims to improve the generalization ability of multiple research sites data. The results demonstrated that the ResTLU-Net, combined with the proposed transfer learning strategy, achieved comparable accuracy for the macaque brain MRI extraction tasks on different macaque brain MRI volumes that were produced by various medical centers. The mean Dice of the ResTLU-Net was 95.81% (no need for denoise and recorrect), and the method required only approximately 30–60 s for one extraction task on an NVIDIA 1660S GPU.

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Cross-species connectome atlas (CCA) that can provide connectionally homogeneous and homologous brain nodes is essential and customized for cross-species neuroscience. However, existing CCAs were flawed in design and coarse-grained in results. In this study, a normative mapping framework of CCA was proposed and applied on human and macaque striatum. Specifically, all striatal voxels in the 2 species were mixed together and classified based on their represented and characterized feature of within-striatum resting-state functional connectivity, which was shared between the species. Six pairs of striatal parcels in these species were delineated in both hemispheres. Furthermore, this striatal parcellation was demonstrated by the best-matched whole-brain functional and structural connectivity between interspecies corresponding subregions. Besides, detailed interspecies differences in whole-brain multimodal connectivities and involved brain functions of these subregions were described to flesh out this CCA of striatum. In particular, this flexible and scalable mapping framework enables reliable construction of CCA of the whole brain, which would enable reliable findings in future cross-species research and advance our understandings into how the human brain works.

To date, reliable biomarkers remain unclear that could link functional connectivity to patients’ symptoms for detecting and predicting the process from normal aging to Alzheimer’s disease (AD) in elderly people with specific genotypes. To address this, individual-specific functional connectivity is constructed for elderly participants with/without APOE ε4 allele. Then, we utilize recursive feature selection-based machine learning to reveal individual brain-behavior relationships and to predict the symptom transition in different genotypes. Our findings reveal that compared with conventional atlas-based functional connectivity, individual-specific functional connectivity exhibits higher classification and prediction performance from normal aging to AD in both APOE ε4 groups, while no significant performance is detected when the data of two genotyping groups are combined. Furthermore, individual-specific between-network connectivity constitutes a major contributor to assessing cognitive symptoms. This study highlights the essential role of individual variation in cortical functional anatomy and the integration of brain and behavior in predicting individualized symptoms.