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The network approach to characterizing psychopathology departs from traditional latent categorical and dimensional approaches. Causal interplay among symptoms contributed to dynamic psychopathology system. Therefore, analyzing the symptom clusters is critical for understanding mental disorders. Furthermore, despite extensive research studying the topological features of symptom networks, the control relationships between symptoms remain largely unclear. Here, we present a novel systematizing concept, module control, to analyze the control principle of the symptom network at a module level. We introduce Module Control Network (MCN) to identify key modules that regulate the network's behavior. By applying our approach to a multivariate psychological dataset, we discover that non-emotional modules, such as sleep-related and stress-related modules, are the primary controlling modules in the symptom network. Our findings indicate that module control can expose central symptom cluster governing psychopathology network, offering novel insights into the underlying mechanisms of mental disorders and individualized approach to psychological interventions.

Mood disorders, including depression and anxiety, often manifest through facial expressions. While previous research has explored the connection between facial features and emotions, machine learning algorithms for estimating mood disorder severity have been hindered by small datasets and limited real-world application. To address this gap, we analyzed facial videos of 11,427 participants, a dataset two orders of magnitude larger than previous studies. This comprehensive collection includes standardized facial expression videos from reading tasks, along with a detailed psychological scale that measures depression, anxiety, and stress. By examining the relationships among these emotional states and employing clustering analysis, we identified distinct subgroups embodying different emotional profiles. We then trained tree-based classifiers and deep learning models to estimate emotional states from facial features. Results indicate that models previously effective on small datasets experienced decreased performance when applied to our large dataset, highlighting the importance of data scale and mitigating overfitting in practical settings. Notably, our study identified subtle shifts in pupil dynamics and gaze orientation as potential markers of mood disorders, providing valuable information on the interaction between facial expressions and mental health. This research marks the first large-scale and comprehensive investigation of facial expressions in the context of mental health, laying the groundwork for future data-driven advancements in this field.

Objectives: The study aims to investigate the relationship between insomnia and response time. Additionally, it aims to develop a machine learning model to predict the presence of insomnia in participants using response time data. Methods: A mobile application was designed to administer scale tests and collect response time data from 2729 participants. The relationship between symptom severity and response time was explored, and a machine learning model was developed to predict the presence of insomnia. Results: The result revealed a statistically significant difference (p<.001) in the total response time between participants with or without insomnia symptoms. A correlation was observed between the severity of specific insomnia aspects and response times at the individual questions level. The machine learning model demonstrated a high predictive accuracy of 0.743 in predicting insomnia symptoms based on response time data. Conclusions: These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures, demonstrating the effectiveness of using response time as a diagnostic tool in the assessment of insomnia.

Depression, a common mental disorder, significantly influences individuals and imposes considerable societal impacts. The complexity and heterogeneity of the disorder necessitate prompt and effective detection, which nonetheless, poses a difficult challenge. This situation highlights an urgent requirement for improved detection methods. Exploiting auditory data through advanced machine learning paradigms presents promising research directions. Yet, existing techniques mainly rely on single-dimensional feature models, potentially neglecting the abundance of information hidden in various speech characteristics. To rectify this, we present the novel Attention-Based Acoustic Feature Fusion Network (ABAFnet) for depression detection. ABAFnet combines four different acoustic features into a comprehensive deep learning model, thereby effectively integrating and blending multi-tiered features. We present a novel weight adjustment module for late fusion that boosts performance by efficaciously synthesizing these features. The effectiveness of our approach is confirmed via extensive validation on two clinical speech databases, CNRAC and CS-NRAC, thereby outperforming previous methods in depression detection and subtype classification. Further in-depth analysis confirms the key role of each feature and highlights the importance of MFCCrelated features in speech-based depression detection.

Real-world network systems are inherently dynamic, with network topologies undergoing continuous changes over time. External control signals can be applied to a designated set of nodes within a network, known as the Minimum Driver Set (MDS), to steer the network from any state to a desired one. However, the efficacy of the incumbent MDS may diminish as the network topologies evolve. Previous research has often overlooked this challenge, assuming foreknowledge of future changes in network topologies. In reality, the evolution of network topologies is typically unpredictable, rendering the control of dynamic networks exceptionally challenging. Here, we introduce adaptive control - a novel approach to dynamically construct a series of MDSs to accommodate variations in network topology without prior knowledge. We present an efficient algorithm for adaptive control that minimizes adjustments to MDSs and overall control costs throughout the control period. Extensive experimental evaluation on synthetic and real dynamic networks demonstrated our algorithm's superior performance over several state-of-the-art methods. Adaptive control is general and broadly applicable to various applications in diverse fields.

Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning approach, netMoST, to subtyping psychiatric disorders. NetMoST identifies polygenic risk SNP-allele modules from genome-wide genotyping data as polygenic haplotype biomarkers (PHBs) for disease subtyping. We applied netMoST to subtype a cohort of schizophrenia subjects into three distinct biotypes with differentiable genetic, neuroimaging and functional characteristics. The PHBs of the first biotype (36.9% of all patients) were related to neurodevelopment and cognition, the PHBs of the second biotype (28.4%) were enriched for neuroimmune functions, and the PHBs of the third biotype (34.7%) were associated with the transport of calcium ions and neurotransmitters. Neuroimaging patterns provided additional support to the new biotypes, with unique regional homogeneity (ReHo) patterns observed in the brains of each biotype compared with healthy controls. Our findings demonstrated netMoST's capability for uncovering novel biotypes of complex diseases such as schizophrenia. The results also showed the power of exploring polygenic allelic patterns that transcend the conventional GWAS approaches., Comment: 21 pages,4 figures

Finding cancer driver genes has been a focal theme of cancer research and clinical studies. One of the recent approaches is based on network structural controllability that focuses on finding a control scheme and driver genes that can steer the cell from an arbitrary state to a designated state. While theoretically sound, this approach is impractical for many reasons, e.g., the control scheme is often not unique and half of the nodes may be driver genes for the cell. We developed a novel approach that transcends structural controllability. Instead of considering driver genes for one control scheme, we considered control hub genes that reside in the middle of a control path of every control scheme. Control hubs are the most vulnerable spots for controlling the cell and exogenous stimuli on them may render the cell uncontrollable. We adopted control hubs as cancer-keep genes (CKGs) and applied them to a gene regulatory network of bladder cancer (BLCA). All the genes on the cell cycle and p53 singling pathways in BLCA are CKGs, confirming the importance of these genes and the two pathways in cancer. A smaller set of 35 sensitive CKGs (sCKGs) for BLCA was identified by removing network links. Six sCKGs (RPS6KA3, FGFR3, N-cadherin (CDH2), EP300, caspase-1, and FN1) were subjected to small-interferencing-RNA knockdown in four cell lines to validate their effects on the proliferation or migration of cancer cells. Knocking down RPS6KA3 in a mouse model of BLCA significantly inhibited the growth of tumor xenografts in the mouse model. Combined, our results demonstrated the value of CKGs as therapeutic targets for cancer therapy and the potential of CKGs as an effective means for studying and characterizing cancer etiology., Comment: Contact the corresponding authors for supplementary material

Recently, Winter and Hahn [1] commented on our work on identifying subtypes of major psychiatry disorders (MPDs) based on neurobiological features using machine learning [2]. They questioned the generalizability of our methods and the statistical significance, stability, and overfitting of the results, and proposed a pipeline for disease subtyping. We appreciate their earnest consideration of our work, however, we need to point out their misconceptions of basic machine-learning concepts and delineate some key issues involved.

Network medicine has been pursued for Covid-19 drug repurposing. One such approach adopts structural controllability, a theory for controlling a network (the cell). Motivated to protect the cell from viral infections, we extended this theory to total controllability and introduced a new concept of control hubs. Perturbation to any control hub renders the cell uncontrollable by exogenous stimuli, e.g., viral infections, so control hubs are ideal drug targets. We developed an efficient algorithm for finding all control hubs and applied it to the largest homogenous human protein-protein interaction network. Our new method outperforms several popular gene-selection methods, including that based on structural controllability. The final 65 druggable control hubs are enriched with functions of cell proliferation, regulation of apoptosis, and responses to cellular stress and nutrient levels, revealing critical pathways induced by SARS-CoV-2. These druggable control hubs led to drugs in 4 major categories: antiviral and anti-inflammatory agents, drugs on central nerve systems, and dietary supplements and hormones that boost immunity. Their functions also provided deep insights into the therapeutic mechanisms of the drugs for Covid-19 therapy, making the new approach an explainable drug repurposing method. A remarkable example is Fostamatinib that has been shown to lower mortality, shorten the length of ICU stay, and reduce disease severity of hospitalized Covid-19 patients. The drug targets 10 control hubs, 9 of which are kinases that play key roles in cell differentiation and programmed death. One such kinase is RIPK1 that directly interacts with viral protein nsp12, the RdRp of the virus. The study produced many control hubs that were not targets of existing drugs but were enriched with proteins on membranes and the NF-$\kappa$B pathway, so are excellent candidate targets for new drugs., Comment: Contact the corresponding authors for Supplemental File and Tables

Unveiling the underlying control principles of complex networks is one of the ultimate goals of network science. We introduce a novel concept, control hub, to reveal a cornerstone of the control structure of a network. The control hubs of a network are the nodes that lie in the middle of a control path in every control scheme of the network. We present a theorem based on graph theory for identifying control hubs without computing all control schemes. We develop an algorithm to identify all control hubs in O(N0.5L) time complexity for a network of N nodes and L links., Comment: 5 pages, 2 figures

Controlling a complex network towards a desired state is of great importance in many applications. A network can be controlled by inputting suitable external signals into some selected nodes, which are called driver nodes. Previous works found there exist two control modes in dense networks: distributed and centralized modes. For networks with the distributed mode, most of the nodes can be act as driver nodes; and those with the centralized mode, most of the nodes never be the driver nodes. Here we present an efficient algorithm to change the control type of nodes, from input nodes to redundant nodes, which is done by reversing edges of the network. We conclude four possible cases when reversing an edge and show the control mode can be changed by reversing very few in-edges of driver nodes. We evaluate the performance of our algorithm on both synthetic and real networks. The experimental results show that the control mode of a network can be easily changed by reversing a few elaborately selected edges, and the number of possible driver nodes is dramatically decreased. Our methods provide the ability to design the desired control modes of the network for different control scenarios, which may be used in many application regions.

Controlling a complex network is of great importance in many applications. The network can be controlled by inputting external control signals through some selected nodes, which are called input nodes. Previous works found that the majority of the nodes in dense networks are either the input nodes or not, which leads to the bimodality in controlling the complex networks. Due to the physical or economic constraints of many real control scenarios, altering the control mode of a network may be critical to many applications. Here we develop a graph-based algorithm to alter the control mode of a network. The main idea is to change the control connectivity of nodes by removing carefully selected edges. We rigorously prove the correctness of our algorithm and evaluate its performance on both synthetic and real networks. The experimental results show that the control mode of a network can be easily changed by removing few selected edges. Our methods provide the ability to design the desired control mode for different control scenarios, which may be useful in many applications.

Understanding structural controllability of a complex network requires to identify a Minimum Input nodes Set (MIS) of the network. It has been suggested that finding an MIS is equivalent to computing a maximum matching of the network, where the unmatched nodes constitute an MIS. However, maximum matching of a network is often not unique, and finding all MISs may provide deep insights to the controllability of the network. Finding all possible input nodes, which form the union of all MISs, is computationally challenging for large networks. Here we present an efficient enumerative algorithm for the problem. The main idea is to modify a maximum matching algorithm to make it efficient for finding all possible input nodes by computing only one MIS. We rigorously proved the correctness of the new algorithm and evaluated its performance on synthetic and large real networks. The experimental results showed that the new algorithm ran several orders of magnitude faster than the existing method on large real networks.