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Resting‐state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence‐based and correlation‐based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging. [ABSTRACT FROM AUTHOR]

Critical minerals refer to minerals that have an important strategic position in high-tech application and economic development such as national defense, military industry and emerging industry, but their resource security is at risk. In the context of a new round of technological revolution and industrial transformation, critical minerals have become the focus in the world competition. The United States, the European Union, Japan, Canada, Australia and China have released the lists of critical minerals respectively. Their lists have a high degree of coincidence. In terms of the demand and governance situation, the distribution areas of critical minerals are highly concentrated but their demand shows structural differentiation. Moreover, developed countries have great power but China has weak power in the global governance of critical minerals. In terms of improving the global governance capability, China needs to create the evaluation system for sustainable development of critical minerals, strengthen international cooperation, promote the reform of governance and enhance China’s voice in the word. Moreover, China should carry out the strategy of critical minerals reserves.

Featured Application: A general framework for functional integration evaluation of human brain is proposed, which effectively combines whole-brain functional connectivity analysis with effective connectivity analysis based on Dynamic Causal Modeling. Much effort has been devoted towards the identification of brain areas recruited during driving—as one of the most common motor skills of human beings. However, how driving experience impacts on the brain's intrinsic functional architecture has not been fully investigated. Using resting-state fMRI data collected from 20 taxi drivers and 20 nondrivers, this paper asks whether there exists specific brain network integration encoding driving behavior. First, to address this, we proposed a general framework combining whole-brain functional connectivity analysis with effective connectivity analysis based on spectral Dynamic Causal Modeling. The validation results indicated that the application of this framework could effectively discover the brain network that best explained the observed BOLD fluctuations. Second, by segmenting supplementary motor area (SMA) into pre-SMA and SMA proper sub-regions, we used the above framework and discovered a hierarchical architecture with pre-SMA located at the higher level in both driver and control groups. Third, we further evaluated the possibility that driving behavior could be encoded by directed connections among the hierarchy, and found that the effective connectivity from pre-SMA to left superior frontal gyrus could distinguish drivers from nondrivers with a sensitivity of 80%. Our findings provide a new paradigm for analyzing the brain's intrinsic functional integration, and may shed new light on the theory of neuroplasticity that training and experience can remodel the patterns of correlated spontaneous brain activity between specific processing regions. Meanwhile, from a methodological advantage perspective, our proposed framework takes the functional connectivity results as a prior, enabling subsequent spectral DCM to efficiently assess functional integration at a whole-brain scale, which is not available by only using other DCM methods, such as stochastic DCM or the State-of-the-Art multimodal DCM. [ABSTRACT FROM AUTHOR]

Dividing a pre-defined brain region into several heterogenous subregions is crucial for understanding its functional segregation and integration. Due to the high dimensionality of brain functional features, clustering is often postponed until dimensionality reduction in traditional parcellation frameworks occurs. However, under such stepwise parcellation, it is very easy to fall into the dilemma of local optimum since dimensionality reduction could not take into account the requirement of clustering. In this study, we developed a new parcellation framework based on the discriminative embedded clustering (DEC), combining subspace learning and clustering in a common procedure with alternative minimization adopted to approach global optimum. We tested the proposed framework in functional connectivity-based parcellation of the hippocampus. The hippocampus was parcellated into three spatial coherent subregions along the anteroventral–posterodorsal axis; the three subregions exhibited distinct functional connectivity changes in taxi drivers relative to non-driver controls. Moreover, compared with traditional stepwise methods, the proposed DEC-based framework demonstrated higher parcellation consistency across different scans within individuals. The study proposed a new brain parcellation framework with joint dimensionality reduction and clustering; the findings might shed new light on the functional plasticity of hippocampal subregions related to long-term navigation experience. [ABSTRACT FROM AUTHOR]