The impact of indirect structure on functional connectivity in schizophrenia using a multiplex brain network.

It is known that abnormal functional connectivity (FC) in schizophrenia (SZ) is closely related to structural connectivity (SC). We speculate that indirect SC also have an impact on FC in SZ patients. Conventional single-layer network has limitations for studying the relationship between indirect SC and FC. Thus, this study constructed a multiplex network based on structural connectivity and functional connectivity (SC-FC). The SC-FC bandwidth and SC-FC cost are used to analyze the impact of indirect SC on FC. Moreover, this paper proposed mediation ability, mediation cost, mediated strength and mediated cost to quantify the effects of mediator nodes and mediated nodes on indirect SC. The results show that SZ patients exhibit lower SC-FC bandwidth and SC-FC cost compared to healthy controls (HC), which could be caused by the limbic and subcortical network (LSN), default mode network (DMN) and visual network (VN). The mediator and mediated nodes in indirect SC of SZ patients also showed diminished effects. These findings suggest that functional communication ability and cost in SZ patients are influenced by indirect SC. This study provides new perspectives for understanding the relationship between indirect SC and FC, and provides strong evidence for interpreting the physiological mechanisms of SZ patients.

Abnormal Dynamic Reconfiguration of Multilayer Temporal Networks in Patients with Bipolar Disorder.

BACKGROUND: Multilayer networks have been used to identify abnormal dynamic reconfiguration in bipolar disorder (BD). However, these studies ignore the differences in information interactions between adjacent layers when constructing multilayer networks, and the analysis of dynamic reconfiguration is not comprehensive enough; Methods: Resting-state functional magnetic resonance imaging data were collected from 46 BD patients and 54 normal controls. A multilayer temporal network was constructed for each subject, and inter-layer coupling of different nodes was considered using network similarity. The promiscuity, recruitment, and integration coefficients were calculated to quantify the different dynamic reconfigurations between the two groups; Results: The global inter-layer coupling, recruitment, and integration coefficients were significantly lower in BD patients. These results were further observed in the attention network and the limbic/paralimbic and subcortical network, reflecting reduced temporal stability, intra- and inter-subnetwork communication abilities in BD patients. The whole-brain promiscuity was increased in BD patients. The same results were observed in the somatosensory/motor and auditory network, reflecting more functional interactions; Conclusions: This study discovered abnormal dynamic interactions of BD from the perspective of dynamic reconfiguration, which can help to understand the pathological mechanisms of BD.

Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data.

Multivariate entropy algorithms have proven effective in the complexity dynamic analysis of electroencephalography (EEG) signals, with researchers commonly configuring the variables as multi-channel time series. However, the complex quantification of brain dynamics from a multi-frequency perspective has not been extensively explored, despite existing evidence suggesting interactions among brain rhythms at different frequencies. In this study, we proposed a novel algorithm, termed multi-frequency entropy (mFreEn), enhancing the capabilities of existing multivariate entropy algorithms and facilitating the complexity study of interactions among brain rhythms of different frequency bands. Firstly, utilizing simulated data, we evaluated the mFreEn's sensitivity to various noise signals, frequencies, and amplitudes, investigated the effects of parameters such as the embedding dimension and data length, and analyzed its anti-noise performance. The results indicated that mFreEn demonstrated enhanced sensitivity and reduced parameter dependence compared to traditional multivariate entropy algorithms. Subsequently, the mFreEn algorithm was applied to the analysis of real EEG data. We found that mFreEn exhibited a good diagnostic performance in analyzing resting-state EEG data from various brain disorders. Furthermore, mFreEn showed a good classification performance for EEG activity induced by diverse task stimuli. Consequently, mFreEn provides another important perspective to quantify complex dynamics.

Abnormal Dynamic Reconstruction of Overlapping Communities in Schizophrenia Patients.

OBJECTIVE: This study aims to explore the changes in dynamic overlapping communities in the brains of schizophrenia (SZ) patients and further investigate the dynamic restructuring patterns of overlapping communities in SZ patients. MATERIALS AND METHODS: A total of 43 SZ patients and 49 normal controls (NC) were selected for resting-state functional MRI (rs-fMRI) scans. Dynamic functional connectivity analysis was conducted separately on SZ patients and NC using rs-fMRI and Jackknife Correlation techniques to construct dynamic brain network models. Based on these models, a dynamic overlapping community detection method was utilized to explore the abnormal overlapping community structure in SZ patients using evaluation metrics such as the structural stability of overlapping communities, nodes' functional diversity, and activity level of overlapping communities. RESULTS: The stability of communities in SZ patients showed a decreasing trend. The changes in the overlapping community structure of SZ patients may be related to a decrease in the diversity of overlapping node functions. Additionally, compared to the NC group, the activity level of overlapping communities of SZ patients was significantly reduced. CONCLUSION: The structure or organization of the brain functional network in SZ patients is abnormal or disrupted, and the activity of the brain network in information processing and transmission is weakened in SZ patients.

Histone H1x in mouse ventral hippocampus associates with, but does not cause behavioral adaptations to stress.

Prior research has identified differential protein expression levels of linker histone H1x within the ventral hippocampus (vHipp) of stress-susceptible versus stress-resilient mice. These mice are behaviorally classified based on their divergent responses to chronic social stress. Here, we sought to determine whether elevated vHipp H1x protein levels directly contribute to these diverging behavioral adaptations to stress. First, we demonstrated that stress-susceptible mice uniquely express elevated vHipp H1x protein levels following chronic stress. Given that linker histones coordinate heterochromatin compaction, we hypothesize that elevated levels of H1x in the vHipp may impede pro-resilience transcriptional adaptations and prevent development of the resilient phenotype following social stress. To test this, 8-10-week-old male C57BL/6 J mice were randomly assigned to groups undergoing 10 days of chronic social defeat stress (CSDS) or single housing, respectively. Following CSDS, mice were classified as susceptible versus resilient based on their social interaction behaviors. We synthesized a viral overexpression (OE) vector for H1x and transduced all stressed and single housed mice with either H1x or control GFP within vHipp. Following viral delivery, we conducted social, anxiety-like, and memory-reliant behavior tests on distinct cohorts of mice. We found no behavioral adaptations following H1x OE compared to GFP controls in susceptible, resilient, or single housed mice. In sum, although we confirm elevated vHipp protein levels of H1x associate with susceptibility to social stress, we observe no significant behavioral consequence of H1x OE. Thus, we conclude elevated levels of H1x are associated with, but are not singularly sufficient to drive development of behavioral adaptations to stress.

A bibliometric and visual analysis of cognitive function in bipolar disorder from 2012 to 2022.

INTRODUCTION: Bipolar disorder (BD) is a chronic psychiatric disorder that combines hypomania or mania and depression. The study aims to investigate the research areas associated with cognitive function in bipolar disorder and identify current research hotspots and frontier areas in this field. METHODOLOGY: Publications related to cognitive function in BD from 2012 to 2022 were searched on the Web of Science Core Collection (WoSCC) database. VOSviewer, CiteSpace, and Scimago Graphica were used to conduct this bibliometric analysis. RESULTS: A total of 989 articles on cognitive function in BD were included in this review. These articles were mainly from the United States, China, Canada, Spain and the United Kingdom. Our results showed that the journal "Journal of Affective Disorders" published the most articles. Apart from "Biploar disorder" and "cognitive function", the terms "Schizophrenia", "Meta analysis", "Rating scale" were also the most frequently used keywords. The research on cognitive function in bipolar disorder primarily focused on the following aspects: subgroup, individual, validation and pathophysiology. CONCLUSIONS: The current concerns and hotspots in the filed are: "neurocognitive impairment", "subgroup", "1st degree relative", "mania", "individual" and "validation". Future research is likely to focus on the following four themes: "Studies of the bipolar disorder and cognitive subgroups", "intra-individual variability", "Validation of cognitive function tool" and "Combined with pathology or other fields".

Multi-Stimulus Least-Squares Transformation With Online Adaptation Scheme to Reduce Calibration Effort for SSVEP-Based BCIs.

Steady-state visual evoked potential (SSVEP), one of the most popular electroencephalography (EEG)-based brain-computer interface (BCI) paradigms, can achieve high performance using calibration-based recognition algorithms. As calibration-based recognition algorithms are time-consuming to collect calibration data, the least-squares transformation (LST) has been used to reduce the calibration effort for SSVEP-based BCI. However, the transformation matrices constructed by current LST methods are not precise enough, resulting in large differences between the transformed data and the real data of the target subject. This ultimately leads to the constructed spatial filters and reference templates not being effective enough. To address these issues, this paper proposes multi-stimulus LST with online adaptation scheme (ms-LST-OA). METHODS: The proposed ms-LST-OA consists of two parts. Firstly, to improve the precision of the transformation matrices, we propose the multi-stimulus LST (ms-LST) using cross-stimulus learning scheme as the cross-subject data transformation method. The ms-LST uses the data from neighboring stimuli to construct a higher precision transformation matrix for each stimulus to reduce the differences between transformed data and real data. Secondly, to further optimize the constructed spatial filters and reference templates, we use an online adaptation scheme to learn more features of the EEG signals of the target subject through an iterative process trial-by-trial. RESULTS: ms-LST-OA performance was measured for three datasets (Benchmark Dataset, BETA Dataset, and UCSD Dataset). Using few calibration data, the ITR of ms-LST-OA achieved 210.01±10.10 bits/min, 172.31±7.26 bits/min, and 139.04±14.90 bits/min for all three datasets, respectively. CONCLUSION: Using ms-LST-OA can reduce calibration effort for SSVEP-based BCIs.

A High Threshold of Biotherapeutic Aggregate Numbers is Needed to Induce an Immunogenic Response In Vitro, In Vivo, and in the Clinic.

BACKGROUND AND PURPOSE: There is concern that subvisible aggregates in biotherapeutic drug products pose a risk to patient safety. We investigated the threshold of biotherapeutic aggregates needed to induce immunogenic responses. METHODS AND RESULTS: Highly aggregated samples were tested in cell-based assays and induced cellular responses in a manner that depended on the number of particles. The threshold of immune activation varied by disease state (cancer, rheumatoid arthritis, allergy), concomitant therapies, and particle number. Compared to healthy donors, disease state patients showed an equal or lower response at the late phase (7 days), suggesting they may not have a higher risk of responding to aggregates. Xeno-het mice were used to assess the threshold of immune activation in vivo. Although highly aggregated samples (~ 1,600,000 particles/mL) induced a weak and transient immunogenic response in mice, a 100-fold dilution of this sample (~ 16,000 particles/mL) did not induce immunogenicity. To confirm this result, subvisible particles (up to ~ 18,000 particles/mL, containing aggregates and silicone oil droplets) produced under representative administration practices (created upon infusion of a drug product through an IV catheter) did not induce a response in cell-based assays or appear to increase the rate of adverse events or immunogenicity during phase 3 clinical trials. CONCLUSION: The ability of biotherapeutic aggregates to elicit an immune response in vitro, in vivo, and in the clinic depends on high numbers of particles. This suggests that there is a high threshold for aggregates to induce an immunogenic response which is well beyond that seen in standard biotherapeutic drug products.

A zinc finger transcription factor enables social behaviors while controlling transposable elements and immune response in prefrontal cortex.

The neurobiological origins of social behaviors are incompletely understood. Here we utilized synthetic biology approaches to reprogram the function of ZFP189, a transcription factor whose expression and function in rodent prefrontal cortex was previously demonstrated to be protective against stress-induced social deficits. We created novel synthetic ZFP189 transcription factors including ZFP189VPR, which activates the transcription of target genes and therefore exerts opposite functional control from the endogenous, transcriptionally repressive ZFP189WT. Following viral delivery of these synthetic ZFP189 transcription factors to mouse prefrontal cortex, we observe that ZFP189-mediated transcriptional control promotes mature dendritic spine morphology on transduced pyramidal neurons. Interestingly, inversion of ZFP189-mediated transcription in this brain area, achieved by viral delivery of synthetic ZFP189VPR, precipitates social behavioral deficits in terms of social interaction, motivation, and the cognition necessary for the maintenance of social hierarchy, without other observable behavioral deficits. RNA sequencing of virally manipulated prefrontal cortex tissues reveals that ZFP189 transcription factors of opposing regulatory function (ZFP189WT versus ZFP189VPR) have opposite influence on the expression of genetic transposable elements as well as genes that participate in adaptive immune functions. Collectively, this work reveals that ZFP189 function in the prefrontal cortex coordinates structural and transcriptional neuroadaptations necessary for complex social behaviors while regulating transposable element-rich regions of DNA and the expression of immune-related genes. Given the evidence for a co-evolution of social behavior and the brain immune response, we posit that ZFP189 may have evolved to augment brain transposon-associated immune function as a way of enhancing an animal's capacity for functioning in social groups.

Aberrant Functional Connectivity in Core-Periphery Structure Based on WSBM in ADHD.

OBJECTIVE: Brain network studies have revealed that the community structure of ADHD is altered. However, these studies have only focused on modular community structure, ignoring the core-periphery community structure. METHOD: This paper employed the weighted stochastic block model to divide the functional connectivity (FC) into 10 communities. And we adopted core score to define the core-periphery structure of FC. Finally, connectivity strength (CS) and disruption index (DI) were used to evaluate the changes of core-periphery structure in ADHD. RESULTS: The core community of visual network showed reduced CS and a positive value of DI, while the CS of periphery community was enhanced. In addition, the interaction between core communities (involving the sensorimotor and visual network) and periphery community of attention network showed increased CS and a negative valve of DI. CONCLUSION: Anomalies in core-periphery community structure provide a new perspective for understanding the community structure of ADHD.

Enhanced Dynamic Laterality Based on Functional Subnetworks in Patients with Bipolar Disorder.

An ocean of studies have pointed to abnormal brain laterality changes in patients with bipolar disorder (BD). Determining the altered brain lateralization will help us to explore the pathogenesis of BD. Our study will fill the gap in the study of the dynamic changes of brain laterality in BD patients and thus provide new insights into BD research. In this work, we used fMRI data from 48 BD patients and 48 normal controls (NC). We constructed the dynamic laterality time series by extracting the dynamic laterality index (DLI) at each sliding window. We then used k-means clustering to partition the laterality states and the Arenas-Fernandez-Gomez (AFG) community detection algorithm to determine the number of states. We characterized subjects' laterality characteristics using the mean laterality index (MLI) and laterality fluctuation (LF). Compared with NC, in all windows and state 1, BD patients showed higher MLI in the attention network (AN) of the right hemisphere, and AN in the left hemisphere showed more frequent laterality fluctuations. AN in the left hemisphere of BD patients showed higher MLI in all windows and state 3 compared to NC. In addition, in the AN of the right hemisphere in state 1, higher MLI in BD patients was significantly associated with patient symptoms. Our study provides new insights into the understanding of BD neuropathology in terms of brain dynamic laterality.

Histone H1x in mouse ventral hippocampus correlates with, but does not cause behavioral adaptations to stress.

Prior research has identified differential protein expression levels of linker histone H1x within the ventral hippocampus (vHipp) of stress-susceptible versus stress-resilient mice. These mice are behaviorally classified based on their divergent responses to chronic social stress. Here, we sought to determine whether elevated vHipp H1x protein levels directly contribute to these diverging behavioral adaptations to stress. First, we demonstrate that stress-susceptible mice uniquely express elevated vHipp H1x protein levels following chronic stress. Given that linker histones coordinate heterochromatin compaction, we hypothesize that elevated levels of H1x in the vHipp may impede pro-resilience transcriptional adaptations and prevent development of the resilient phenotype following social stress. To test this, 8-10-week-old male C57BL/6J mice were randomly assigned to stressed and unstressed groups undergoing 10 days of chronic social defeat stress (CSDS) or single housing respectively. Following CSDS, mice were classified as susceptible versus resilient based on their social interaction behaviors. We synthesized a viral overexpression (OE) vector for H1x and transduced experimental mice with either H1x or control GFP within vHipp. Following viral delivery, we conducted social, anxiety-like, and memory-reliant behavior tests on distinct cohorts of mice. We found no behavioral adaptations following H1x OE compared to GFP controls in susceptible, resilient, or unstressed mice. In sum, although we confirm vHipp protein levels of H1x correlate with susceptibility to social stress, we observe no significant behavioral consequence of H1x OE. Thus, we conclude elevated levels of H1x are correlated with, but are not singularly sufficient to drive development of behavioral adaptations to stress.

High-Quality Reconstruction for Laplace NMR Based on Deep Learning.

Laplace nuclear magnetic resonance (NMR) exploits relaxation and diffusion phenomena to reveal information regarding molecular motions and dynamic interactions, offering chemical resolution not accessible by conventional Fourier NMR. Generally, the applicability of Laplace NMR is subject to the performance of signal processing and reconstruction algorithms involving an ill-posed inverse problem. Here, we propose a proof-of-concept of a deep-learning-based method for rapid and high-quality spectra reconstruction from Laplace NMR experimental data. This reconstruction method is performed based on training on synthetic exponentially decaying data, which avoids a vast amount of practically acquired data and makes it readily suitable for one-dimensional relaxation and diffusion measurements by commercial NMR instruments.

SABRE Hyperpolarization of Exchangeable Protons in Methanol-d4 through Dynamic Covalent Bonds.

Utilizing para-hydrogen (p-H2)-induced hyperpolarization to increase the sensitivity of nuclear magnetic resonance, especially signal amplification by reversible exchange (SABRE), has been widely studied. Here, we achieved hyperpolarization of exchangeable protons in methanol-d4 by introducing dynamic covalent bonds as reversible exchange following the SABRE process. To release the hyperpolarized CD3OH, the pyridine-based ligands with aldehyde groups underwent acetal exchange between the aldehyde and hydroxyl groups of CD3OH after being first hyperpolarized by SABRE. Our mechanistic study highlights the importance of the reversible exchange of functional groups and chemical kinetics in realizing hyperpolarization of exchangeable protons in methanol-d4. Our work broadens SABRE's chemical system compatibility and possible applications.

A zinc finger transcription factor tunes social behaviors by controlling transposable elements and immune response in prefrontal cortex.

The neurobiological origins of social behaviors are incompletely understood. Here we utilized synthetic biology approaches to reprogram the function of ZFP189, a transcription factor whose expression and function in the rodent prefrontal cortex was previously determined to be protective against stress-induced social deficits. We created novel synthetic ZFP189 transcription factors including ZFP189VPR, which activates the transcription of target genes and therefore exerts opposite functional control from the endogenous, transcriptionally repressive ZFP189WT. Upon viral delivery of these synthetic ZFP189 transcription factors to mouse prefrontal cortex, we observe that ZFP189-mediated transcriptional control promotes mature dendritic spine morphology on transduced pyramidal neurons. Interestingly, dysregulation of ZFP189-mediated transcription in this brain area, achieved by delivery of synthetic ZFP189VPR, precipitates social behavioral deficits in terms of social interaction, motivation, and the cognition necessary for the maintenance of social hierarchy, without other observable behavioral deficits. By performing RNA sequencing in virally manipulated prefrontal cortex tissues, we discover that ZFP189 transcription factors of opposing regulatory function have opposite influence on the expression of genetic transposable elements as well as genes that participate in immune functions. Collectively, this work reveals that ZFP189 function in the prefrontal cortex coordinates structural and transcriptional neuroadaptations necessary for social behaviors by binding transposable element-rich regions of DNA to regulate immune-related genes. Given the evidence for a co-evolution of social behavior and the brain immune response, we posit that ZFP189 may have evolved to augment brain transposon-associated immune function as a way of enhancing an animal's capacity for functioning in social groups.

A flexible speller based on time-space frequency conversion SSVEP stimulation paradigm under dry electrode.

Introduction: Speller is the best way to express the performance of the brain-computer interface (BCI) paradigm. Due to its advantages of short analysis time and high accuracy, the SSVEP paradigm has been widely used in the BCI speller system based on the wet electrode. It is widely known that the wet electrode operation is cumbersome and that the subjects have a poor experience. In addition, in the asynchronous SSVEP system based on threshold analysis, the system flickers continuously from the beginning to the end of the experiment, which leads to visual fatigue. The dry electrode has a simple operation and provides a comfortable experience for subjects. The EOG signal can avoid the stimulation of SSVEP for a long time, thus reducing fatigue. Methods: This study first designed the brain-controlled switch based on continuous blinking EOG signal and SSVEP signal to improve the flexibility of the BCI speller. Second, in order to increase the number of speller instructions, we designed the time-space frequency conversion (TSFC) SSVEP stimulus paradigm by constantly changing the time and space frequency of SSVEP sub-stimulus blocks, and designed a speller in a dry electrode environment. Results: Seven subjects participated and completed the experiments. The results showed that the accuracy of the brain-controlled switch designed in this study was up to 94.64%, and all the subjects could use the speller flexibly. The designed 60-character speller based on the TSFC-SSVEP stimulus paradigm has an accuracy rate of 90.18% and an information transmission rate (ITR) of 117.05 bits/min. All subjects can output the specified characters in a short time. Discussion: This study designed and implemented a multi-instruction SSVEP speller based on dry electrode. Through the combination of EOG and SSVEP signals, the speller can be flexibly controlled. The frequency of SSVEP stimulation sub-block is recoded in time and space by TSFC-SSVEP stimulation paradigm, which greatly improves the number of output instructions of BCI system in dry electrode environment. This work only uses FBCCA algorithm to test the stimulus paradigm, which requires a long stimulus time. In the future, we will use trained algorithms to study stimulus paradigm to improve its overall performance.

Altered higher-order coupling between brain structure and function with embedded vector representations of connectomes in schizophrenia.

It has been shown that the functional dependency of the brain exists in both direct and indirect regional relationships. Therefore, it is necessary to map higher-order coupling in brain structure and function to understand brain dynamic. However, how to quantify connections between not directly regions remains unknown to schizophrenia. The word2vec is a common algorithm through create embeddings of words to solve these problems. We apply the node2vec embedding representation to characterize features on each node, their pairwise relationship can give rise to correspondence relationships between brain regions. Then we adopt pearson correlation to quantify the higher-order coupling between structure and function in normal controls and schizophrenia. In addition, we construct direct and indirect connections to quantify the coupling between their respective functional connections. The results showed that higher-order coupling is significantly higher in schizophrenia. Importantly, the anomalous cause of coupling mainly focus on indirect structural connections. The indirect structural connections play an essential role in functional connectivity-structural connectivity (SC-FC) coupling. The similarity between embedded representations capture more subtle network underlying information, our research provides new perspectives for understanding SC-FC coupling. A strong indication that the structural backbone of the brain has an intimate influence on the resting-state functional.

Abnormal information interaction in multilayer directed network based on cross-frequency integration of mild cognitive impairment and Alzheimer's disease.

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) have been reported to result in abnormal cross-frequency integration. However, previous studies have failed to consider specific abnormalities in receiving and outputting information among frequency bands during integration. Here, we investigated heterogeneity in receiving and outputting information during cross-frequency integration in patients. The results showed that during cross-frequency integration, information interaction first increased and then decreased, manifesting in the heterogeneous distribution of inter-frequency nodes for receiving information. A possible explanation was that due to damage to some inter-frequency hub nodes, intra-frequency nodes gradually became new inter-frequency nodes, whereas original inter-frequency nodes gradually became new inter-frequency hub nodes. Notably, damage to the brain regions that receive information between layers was often accompanied by a strengthened ability to output information and the emergence of hub nodes for outputting information. Moreover, an important compensatory mechanism assisted in the reception of information in the cingulo-opercular and auditory networks and in the outputting of information in the visual network. This study revealed specific abnormalities in information interaction and compensatory mechanism during cross-frequency integration, providing important evidence for understanding cross-frequency integration in patients with MCI and AD.

Abnormal dynamic reconfiguration of the large-scale functional network in schizophrenia during the episodic memory task.

Episodic memory deficits are the core feature in schizophrenia (SCZ). Numerous studies have revealed abnormal brain activity associated with this disorder during episodic memory, however previous work has only relied on static analysis methods that treat the brain as a static monolithic structure, ignoring the dynamic features at different time scales. Here, we applied dynamic functional connectivity analysis to functional magnetic resonance imaging data during episodic memory and quantify integration and recruitment metrics to reveal abnormal dynamic reconfiguration of brain networks in SCZ. In the specific frequency band of 0.06-0.125 Hz, SCZ showed significantly higher integration during encoding and retrieval, and the abnormalities were mainly in the default mode, frontoparietal, and cingulo-opercular modules. Recruitment of SCZ was significantly higher during retrieval, mainly in the visual module. Interestingly, interactions between groups and task status in recruitment were found in the dorsal attention, visual modules. Finally, we observed that integration was significantly associated with memory performance in frontoparietal regions. Our findings revealed the time-varying evolution of brain networks in SCZ, while improving our understanding of cognitive decline and other pathophysiologies in brain diseases.

Gender effects on the controllability of hemispheric white matter networks.

Male and female adults exhibited significant group differences in brain white matter (WM) asymmetry and WM network controllability. However, gender differences in controllability of hemispheric WM networks between males and females remain to be determined. Based on 1 principal atlas and 1 replication atlas, this work characterized the average controllability (AC) and modal controllability (MC) of hemispheric WM network based on 1 principal dataset and 2 replication datasets. All results showed that males had higher AC of left hemispheric networks than females. And significant hemispheric asymmetry was revealed in regional AC and MC. Furthermore, significant gender differences in the AC asymmetry were mainly found in regions lie in the frontoparietal network, and the MC asymmetry was found in regions involving auditory and emotion process. Finally, we found significant associations between regional controllability and cognitive features. Taken together, this work could provide a novel perspective for understanding gender differences in hemispheric WM asymmetry and cognitive function between males and females.