Joint hub identification for brain networks by multivariate graph inference

Identifying multilayer network hub by graph representation learning

The recent advances in neuroimaging technology allow us to understand how the human brain is wired in vivo and how functional activity is synchronized across multiple regions. Growing evidence shows that the complexity of the functional connectivity is far beyond the widely used mono-layer network. Indeed, the hierarchical processing information among distinct brain regions and across multiple channels requires using a more advanced multilayer model to understand the synchronization across the brain that underlies functional brain networks. However, the principled approach for characterizing network organization in the context of multilayer topologies is largely unexplored. In this work, we present a novel multi-variate hub identification method that takes both the intra- and inter-layer network topologies into account. Specifically, we put the spotlight on the multilayer graph embeddings that allow us to separate connector hubs (connecting across network modules) with their peripheral nodes. The removal of these hub nodes breaks down the entire multilayer brain network into a set of disconnected communities. We have evaluated our novel multilayer hub identification method in task-based and resting-state functional images. Complimenting ongoing findings using mono-layer brain networks, our multilayer network analysis provides a new understanding of brain network topology that links functional connectivities with brain states and disease progression.

Accelerating crop improvement via integration of transcriptome-based network biology and genome editing

MAIN CONCLUSION

Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.

A Novel Spatio-Temporal Hub Identification in Brain Networks by Learning Dynamic Graph Embedding on Grassmannian Manifolds

Mounting evidence has revealed that functional brain networks are intrinsically dynamic, undergoing changes over time, even in the resting-state environment. Notably, recent studies have highlighted the existence of a small number of critical brain regions within each functional brain network that exhibit a flexible role in adapting the geometric pattern of brain connectivity over time, referred to as "temporal hub" regions. Therefore, the identification of these temporal hubs becomes pivotal for comprehending the mechanisms that underlie the dynamic evolution of brain connectivity. However, existing spatio-temporal hub identification methods rely on static network-based approaches, wherein each temporal hub region is independently inferred from individual time-segmented networks without considering their temporal consistency and consequently fails to align the evolution of hubs with the dynamic changes in brain states. To address this limitation, we propose a novel spatio-temporal hub identification method that fully leverages dynamic graph embedding to distinguish temporal hubs from peripheral nodes, in which dynamic graph embeddings are learned from both spatial and temporal dimensions. Specifically, to preserve the temporal consistency of evolving networks, we model the dynamic graph embedding as a physical model of time, where the network-to-network transition is mathematically expressed as a total variation of dynamic graph embedding with respect to time. Furthermore, a Grassmannian manifold optimization scheme is introduced to enhance graph embedding learning and capture the time-varying topology of brain networks. Experimental results on both synthetic and real fMRI data demonstrate superior temporal consistency in hub identification, surpassing conventional approaches.

MQGA: A quantitative analysis of brain network hubs using multi-graph theoretical indices

Recent advancements in large-scale network studies have shown that connector hubs and provincial hubs are vital for coordinating complex cognitive tasks by facilitating information transfer between and within specialized modules. However, current methods for identifying these hubs often lack standardized measurement criteria, hindering quantitative analysis. This study proposes a novel computational method utilizing multi-graph theoretical index calculations to quantitatively analyze hub attributes in brain networks. Using benchmark network, random simulation network (N = 100), resting fMRI data from the ADHD-200 NYU dataset (HC = 110, ADHD = 146), and the Peking dataset (HC = 120, ADHD = 83), we introduce the Multi-criteria Quantitative Graph Analysis (MQGA) method, which employs betweenness centrality, degree centrality, and participation coefficient to determine the connector (con) hub index and provincial (pro) hub index. The method's accuracy, reliability, and stability were validated through correlation analysis of hub indices and labels, vulnerability tests, and consistency analysis across subjects. Results indicate that as network sparsity increases, the con hub index increases while the pro hub index decreases, with the optimal hub node index at 4 % sparsity. Vulnerability tests revealed that removing con nodes had a greater impact on network integrity than removing pro nodes. Both con and pro exhibited stability in consistency analyses, but con was more stable. The stability of hub scores in disease groups was significantly lower than in the healthy control group. High con values were found in the precuneus, postcentral gyrus, and precentral gyrus, whereas high pro values were identified in the precentral gyrus, postcentral gyrus, superior parietal lobule, precuneus, and superior temporal gyrus. This approach enhances the accuracy and sensitivity of hub node identification, facilitating precise comparisons and producing consistent, replicable results, advancing our understanding of brain network hub nodes, their roles in cognitive processes, and their implications for brain disease research.

Brain functional changes across mood states in bipolar disorder: from a large-scale network perspective

BACKGROUND

Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent.

METHODS

Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis.

RESULTS

A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients.

CONCLUSIONS

Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.

A comprehensive survey of complex brain network representation

Recent years have shown great merits in utilizing neuroimaging data to understand brain structural and functional changes, as well as its relationship to different neurodegenerative diseases and other clinical phenotypes. Brain networks, derived from different neuroimaging modalities, have attracted increasing attention due to their potential to gain system-level insights to characterize brain dynamics and abnormalities in neurological conditions. Traditional methods aim to pre-define multiple topological features of brain networks and relate these features to different clinical measures or demographical variables. With the enormous successes in deep learning techniques, graph learning methods have played significant roles in brain network analysis. In this survey, we first provide a brief overview of neuroimaging-derived brain networks. Then, we focus on presenting a comprehensive overview of both traditional methods and state-of-the-art deep-learning methods for brain network mining. Major models, and objectives of these methods are reviewed within this paper. Finally, we discuss several promising research directions in this field.