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Jinhui Wang

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15 papers

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YNIMG Journal 2025 Journal Article

A novel method for functional brain networks based on static cerebral blood flow

Changwen Wu
Yu He
Junle Li
Xiaofan Qiu
Qihong Zou
Jinhui Wang

Cerebral blood flow (CBF) offers a quantitative and reliable measurement for brain activity and is increasingly used to study functional networks. However, current methods evaluate inter-regional relations mainly based on CBF temporal dynamics, which suffers from low signal-to-noise ratio and poor temporal resolution. Here we proposed a method to construct functional brain networks by estimating shape similarity (index by Jensen-Shannon divergence) in probability distributions of regional static CBF measured by arterial spin labeling perfusion imaging over a scanning period. Based on CBF data of 30 healthy participants from 10 visits, we found that the CBF networks exhibited non-trivial topological features (e.g., small-world organization, modular architecture, and hubs) and showed low-to-fair test-retest reliability and high between-subject consistency. We further found that interregional CBF similarities were depended on anatomical distance and differed between high- and lower-order subnetworks. Moreover, interregional CBF similarities within high-order subnetworks showed significantly lower reliability than those within low-order subnetworks. Finally, we showed that nodal degree of the CBF networks were related to regional sizes and CBF levels and spatially aligned with maps of the dopamine transporter and metabolic glutamate receptor 5 intensities, expression levels of genes primarily enriched in cholesterol-related pathways and endothelial cells, and meta-analytic activations related to memory, language, and executive function. Altogether, our proposed method provide a novel, relatively reliable, and neurobiologically meaningful means to study functional network organization of the human brain.

YNICL Journal 2025 Journal Article

Alterations of long-range association fibers in patients with anti-N-methyl-D-aspartate receptor encephalitis

Xiaodong Chen
Ling Fang
Yiying Huang
Yu Huang
Yi Lu
Jinhui Wang
Chunxin Liu
Huanquan Liao

BACKGROUND: Patients with anti-NMDAR encephalitis typically exhibit impaired cognitive integration, which relies on the integrity of long-range association fibers connecting diverse brain regions. However, the microstructural integrity of long-range association fibers in this population remains unknown. METHODS: Diffusion tensor imaging (DTI) data were collected from 32 patients with anti-NMDAR encephalitis and 30 healthy controls. Patients were further categorized into early and delayed immunotherapy subgroups based on a 2-week threshold for immunotherapy initiation. The diffusion properties of major long-range association fibers were quantified at both the bundle and node levels. RESULTS: Compared with healthy controls, patients exhibited widespread microstructural damage within long-range association fibers, with more severe alterations in the delayed immunotherapy subgroup (FDR-corrected p < 0.05). In this subgroup(n = 14), radial diffusivity (RD) of left inferior fronto-occipital fasciculus (IFOF), left inferior longitudinal fasciculus (ILF), left superior longitudinal fascicles (SLF), and bilateral arcuate fascicles correlated significantly with global cognition (MMSE, FDR-corrected p < 0.05). Notably, RD also strongly correlated with working memory in the delayed immunotherapy subgroup, showing bundle-wise associations for IFOF (left: r = -0.8315, p = 0.0112; right: r = -0.7044, p = 0.0295), ILF (left: r = -0.7473, p = 0.0243), SLF (left: r = -0.7562, p = 0.0243; right: r = -0.6599, p = 0.0391), and arcuate fasciculus (left: r = -0.7240, p = 0.0272; right: r = -0.6835, p = 0.0333), with left-hemisphere predominance confirmed by node-wise analyses of IFOF, ILF, SLF, and arcuate fasciculus (FDR-corrected p < 0.05). CONCLUSIONS: Our findings highlight widespread microstructural damage in long-range association fibers in patients with anti-NMDAR encephalitis, particularly in those with delayed immunotherapy. This damage may serve as the neurophysiological basis for cognitive impairments, with working memory being most affected.

YNIMG Journal 2025 Journal Article

Mapping subtype-specific disease epicenters and brain aging characteristics in major depressive disorder through normative model-driven analysis of brain structural alterations

Peng Wang
Yuhong Zheng
Li Sun
Yang Xiao
Xuelian Zang
Jinghua Wang
Jinhui Wang
Shao-Wei Xue

Major depressive disorder (MDD), a prevalent mental health condition, manifests intricate alterations in brain structure that evolve gradually over time and across various brain regions. Despite significant research efforts, two fundamental questions remain unsettled: the precise brain origins of MDD and whether MDD contributes to accelerates brain aging. To this end, we conducted a comprehensive investigation leveraging data from 830 MDD patients and 853 matched healthy controls (HC). Normative models, established on HC gray matter volume (GMV) data, were utilized to quantify individual deviations in GMV among MDD patients. Employing k-means clustering to these deviation profiles, we successfully discerned two clinically distinct subtypes. Subtype 1 is characterized by GMV atrophy, coupled with indications of accelerated brain aging processes. In contrast, subtype 2 exhibits increased GMV without significant acceleration of aging phenomena. Intriguingly, both subtypes converge on the default mode network as a common disease epicenter, highlighting a shared neurophysiological underpinning. However, subtype-specific epicenters diverge, with subtype 1 featuring unique foci primarily in the hippocampus and amygdala, whereas subtype 2 distinguishes itself with epicenters primarily located in the accumbens. This nuanced examination of subtype-specific brain alterations, incorporating their intricate spatiotemporal dynamics, provides profound insights into the heterogeneity and complexity inherent in MDD.

YNIMG Journal 2024 Journal Article

Enhanced cerebral blood flow similarity of the somatomotor network in chronic insomnia: Transcriptomic decoding, gut microbial signatures and phenotypic roles

Xiaofen Ma
Junle Li
Yuping Yang
Xiaofan Qiu
Jintao Sheng
Ningke Han
Changwen Wu
Guang Xu

Chronic insomnia (CI) is a complex disease involving multiple factors including genetics, gut microbiota, and brain structure and function. However, there lacks a unified framework to elucidate how these factors interact in CI. By combining data of clinical assessment, sleep behavior recording, cognitive test, multimodal MRI (structural, functional, and perfusion), gene, and gut microbiota, this study demonstrated that enhanced cerebral blood flow (CBF) similarities of the somatomotor network (SMN) acted as a key mediator to link multiple factors in CI. Specifically, we first demonstrated that only CBF but not morphological or functional networks exhibited alterations in patients with CI, characterized by increases within the SMN and between the SMN and higher-order associative networks. Moreover, these findings were highly reproducible and the CBF similarity method was test-retest reliable. Further, we showed that transcriptional profiles explained 60.4 % variance of the pattern of the increased CBF similarities with the most correlated genes enriched in regulation of cellular and protein localization and material transport, and gut microbiota explained 69.7 % inter-individual variance in the increased CBF similarities with the most contributions from Negativicutes and Lactobacillales. Finally, we found that the increased CBF similarities were correlated with clinical variables, accounted for sleep behaviors and cognitive deficits, and contributed the most to the patient-control classification (accuracy = 84.4 %). Altogether, our findings have important implications for understanding the neuropathology of CI and may inform ways of developing new therapeutic strategies for the disease.

YNIMG Journal 2024 Journal Article

Mapping morphological cortical networks with joint probability distributions from multiple morphological features

Yuqi Wang
Junle Li
Suhui Jin
Jing Wang
Yating Lv
Qihong Zou
Jinhui Wang

Morphological features sourced from structural magnetic resonance imaging can be used to infer human brain connectivity. Although integrating different morphological features may theoretically be beneficial for obtaining more precise morphological connectivity networks (MCNs), the empirical evidence to support this supposition is scarce. Moreover, the incorporation of different morphological features remains an open question. In this study, we proposed a method to construct cortical MCNs based on multiple morphological features. Specifically, we adopted a multi-dimensional kernel density estimation algorithm to fit regional joint probability distributions (PDs) from different combinations of four morphological features, and estimated inter-regional similarity in the joint PDs via Jensen-Shannon divergence. We evaluated the method by comparing the resultant MCNs with those built based on different single morphological features in terms of topological organization, test-retest reliability, biological plausibility, and behavioral and cognitive relevance. We found that, compared to MCNs built based on different single morphological features, MCNs derived from multiple morphological features displayed less segregated, but more integrated network architecture and different hubs, had higher test-retest reliability, encompassed larger proportions of inter-hemispheric edges and edges between brain regions within the same cytoarchitectonic class, and explained more inter-individual variance in behavior and cognition. These findings were largely reproducible when different brain atlases were used for cortical parcellation. Further analysis of macaque MCNs revealed weak, but significant correlations with axonal connectivity from tract-tracing, independent of the number of morphological features. Altogether, this paper proposes a new method for integrating different morphological features, which will be beneficial for constructing MCNs.

YNIMG Journal 2023 Journal Article

Multimodal and multiscale evidence for network-based cortical thinning in major depressive disorder

Junle Li
Rui Wang
Ning Mao
Manli Huang
Shijun Qiu
Jinhui Wang

BACKGROUND: Major depressive disorder (MDD) is associated with widespread, irregular cortical thickness (CT) reductions across the brain. However, little is known regarding mechanisms that govern spatial distribution of the reductions. METHODS: We combined multimodal MRI and genetic, cytoarchitectonic and chemoarchitectonic data to examine structural covariance, functional synchronization, gene co-expression, cytoarchitectonic similarity and chemoarchitectonic covariance between regions atrophied in MDD. RESULTS: Regions atrophied in MDD were associated with significantly higher structural covariance, functional synchronization, gene co-expression and chemoarchitectonic covariance. These results were robust against methodological variations in brain parcellation and null model, reproducible in patients and controls, and independent of age at onset of MDD. Despite no significant differences in the cytoarchitectonic similarity, MDD-related CT reductions were susceptible to specific cytoarchitectonic class of association cortex. Further, we found that nodal shortest path lengths to disease epicenters derived from structural (right supramarginal gyrus) and chemoarchitectonic covariance (right sulcus intermedius primus) networks of healthy brains were correlated with the extent to which a region was atrophied in MDD, supporting the transneuronal spread hypothesis that regions closer to the epicenters are more susceptible to MDD. Finally, we showed that structural covariance and functional synchronization among regions atrophied in MDD were mainly related to genes enriched in metabolic and membrane-related processes, driven by genes in excitatory neurons, and associated with specific neurotransmitter transporters and receptors. CONCLUSIONS: Altogether, our findings provide empirical evidence for and genetic and molecular insights into connectivity-constrained CT thinning in MDD.

YNIMG Journal 2023 Journal Article

Single-subject cortical morphological brain networks: Phenotypic associations and neurobiological substrates

Zhen Li
Junle Li
Ningkai Wang
Yating Lv
Qihong Zou
Jinhui Wang

Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.

YNIMG Journal 2022 Journal Article

Separate neural subsystems support goal-directed speech listening

Liu-Fang Zhou
Dan Zhao
Xuan Cui
Bingbing Guo
Fangwei Zhu
Chunliang Feng
Jinhui Wang
Ming Meng

How do humans excel at tracking the narrative of a particular speaker with a distracting noisy background? This feat places great demands on the collaboration between speech processing and goal-related regulatory functions. Here, we propose that separate subsystems with different cross-task dynamic activity properties and distinct functional purposes support goal-directed speech listening. We adopted a naturalistic dichotic speech listening paradigm in which listeners were instructed to attend to only one narrative from two competing inputs. Using functional magnetic resonance imaging with inter- and intra-subject correlation techniques, we discovered a dissociation in response consistency in temporal, parietal and frontal brain areas as the task demand varied. Specifically, some areas in the bilateral temporal cortex (SomMotB_Aud and TempPar) and lateral prefrontal cortex (DefaultB_PFCl and ContA_PFCl) always showed consistent activation across subjects and across scan runs, regardless of the task demand. In contrast, some areas in the parietal cortex (DefaultA_pCunPCC and ContC_pCun) responded reliably only when the task goal remained the same. These results suggested two dissociated functional neural networks that were independently validated by performing a data-driven clustering analysis of voxelwise functional connectivity patterns. A subsequent meta-analysis revealed distinct functional profiles for these two brain correlation maps. The different-task correlation map was strongly associated with language-related processes (e.g., listening, speech and sentences), whereas the same-task versus different-task correlation map was linked to self-referencing functions (e.g., default mode, theory of mind and autobiographical topics). Altogether, the three-pronged findings revealed two anatomically and functionally dissociated subsystems supporting goal-directed speech listening.

YNIMG Journal 2021 Journal Article

Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability

Yinzhi Li
Ningkai Wang
Hao Wang
Yating Lv
Qihong Zou
Jinhui Wang

Morphological brain networks, in particular those at the individual level, have become an important approach for studying the human brain connectome; however, relevant methodology is far from being well-established in their formation, description and reproducibility. Here, we extended our previous study by constructing and characterizing single-subject morphological similarity networks from brain volume to surface space and systematically evaluated their reproducibility with respect to effects of different choices of morphological index, brain parcellation atlas and similarity measure, sample size-varying stability and test-retest reliability. Using the Human Connectome Project dataset, we found that surface-based single-subject morphological similarity networks shared common small-world organization, high parallel efficiency, modular architecture and bilaterally distributed hubs regardless of different analytical strategies. Nevertheless, quantitative values of all interregional similarities, global network measures and nodal centralities were significantly affected by choices of morphological index, brain parcellation atlas and similarity measure. Moreover, the morphological similarity networks varied along with the number of participants and approached stability until the sample size exceeded ~70. Using an independent test-retest dataset, we found fair to good, even excellent, reliability for most interregional similarities and network measures, which were also modulated by different analytical strategies, in particular choices of morphological index. Specifically, fractal dimension and sulcal depth outperformed gyrification index and cortical thickness, higher-resolution atlases outperformed lower-resolution atlases, and Jensen-Shannon divergence-based similarity outperformed Kullback-Leibler divergence-based similarity. Altogether, our findings propose surface-based single-subject morphological similarity networks as a reliable method to characterize the human brain connectome and provide methodological recommendations and guidance for future research.

YNIMG Journal 2021 Journal Article

The coupling of BOLD signal variability and degree centrality underlies cognitive functions and psychiatric diseases

Jintao Sheng
Liang Zhang
Junjiao Feng
Jing Liu
Anqi Li
Wei Chen
Yuedi Shen
Jinhui Wang

Brain signal variability has been consistently linked to functional integration; however, whether this coupling is associated with cognitive functions and/or psychiatric diseases has not been clarified. Using multiple multimodality datasets, including resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP: N = 927) and a Beijing sample (N = 416) and cerebral blood flow (CBF) and rsfMRI data from a Hangzhou sample (N = 29), we found that, compared with the existing variability measure (i. e. , SDBOLD), the mean-scaled (standardized) fractional standard deviation of the BOLD signal (mfSDBOLD) maintained very high test-retest reliability, showed greater cross-site reliability and was less affected by head motion. We also found strong reproducible couplings between the mfSDBOLD and functional integration measured by the degree centrality (DC), both cross-voxel and cross-subject, which were robust to scanning and preprocessing parameters. Moreover, both mfSDBOLD and DC were correlated with CBF, suggesting a common physiological basis for both measures. Critically, the degree of coupling between mfSDBOLD and long-range DC was positively correlated with individuals’ cognitive total composite scores. Brain regions with greater mismatches between mfSDBOLD and long-range DC were more vulnerable to brain diseases. Our results suggest that BOLD signal variability could serve as a meaningful index of local function that underlies functional integration in the human brain and that a strong coupling between BOLD signal variability and functional integration may serve as a hallmark of balanced brain networks that are associated with optimal brain functions.

YNIMG Journal 2014 Journal Article

Different topological organization of human brain functional networks with eyes open versus eyes closed

Pengfei Xu
Ruiwang Huang
Jinhui Wang
Nicholas T. Van Dam
Teng Xie
Zhangye Dong
Chunping Chen
Ruolei Gu

Opening and closing the eyes are fundamental behaviors for directing attention to the external versus internal world. However, it remains unclear whether the states of eyes-open (EO) relative to eyes-closed (EC) are associated with different topological organizations of functional neural networks for exteroceptive and interoceptive processing (processing the external world and internal state, respectively). Here, we used resting-state functional magnetic resonance imaging and neural network analysis to investigate the topological properties of functional networks of the human brain when the eyes were open versus closed. The brain networks exhibited higher cliquishness and local efficiency, but lower global efficiency during the EO state compared to the EC state. These properties suggest an increase in specialized information processing along with a decrease in integrated information processing in EO (vs. EC). More importantly, the “exteroceptive” network, including the attentional system (e. g. , superior parietal gyrus and inferior parietal lobule), ocular motor system (e. g. , precentral gyrus and superior frontal gyrus), and arousal system (e. g. , insula and thalamus), showed higher regional nodal properties (nodal degree, efficiency and betweenness centrality) in EO relative to EC. In contrast, the “interoceptive” network, composed of visual system (e. g. , lingual gyrus, fusiform gyrus and cuneus), auditory system (e. g. , Heschl's gyurs), somatosensory system (e. g. , postcentral gyrus), and part of the default mode network (e. g. , angular gyrus and anterior cingulate gyrus), showed significantly higher regional properties in EC vs. EO. In addition, the connections across sensory modalities were altered by volitional eye opening. The synchronicity between the visual system and the motor, somatosensory and auditory systems, characteristic of EC, was attenuated in EO. Further, the connections between the visual system and the attention, arousal and subcortical systems were increased in EO. These results may indicate that EO leads to a suppression of sensory modalities (other than visual) to allocate resources to exteroceptive processing. Our findings suggest that the topological organization of human brain networks dynamically switches corresponding to the information processing modes as we open or close our eyes.

YNIMG Journal 2012 Journal Article

Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3)

Zhengjia Dai
Chaogan Yan
Zhiqun Wang
Jinhui Wang
Mingrui Xia
Kuncheng Li
Yong He

Increasing attention has recently been directed to the applications of pattern recognition and brain imaging techniques in the effective and accurate diagnosis of Alzheimer's disease (AD). However, most of the existing research focuses on the use of single-modal (e. g. , structural or functional MRI) or single-level (e. g. , brain local or connectivity metrics) biomarkers for the diagnosis of AD. In this study, we propose a methodological framework, called multi-modal imaging and multi-level characteristics with multi-classifier (M3), to discriminate patients with AD from healthy controls. This approach involved data analysis from two imaging modalities: structural MRI, which was used to measure regional gray matter volume, and resting-state functional MRI, which was used to measure three different levels of functional characteristics, including the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and regional functional connectivity strength (RFCS). For each metric, we computed the values of ninety regions of interest derived from a prior atlas, which were then further trained using a multi-classifier based on four maximum uncertainty linear discriminant analysis base classifiers. The performance of this method was evaluated using leave-one-out cross-validation. Applying the M3 approach to the dataset containing 16 AD patients and 22 healthy controls led to a classification accuracy of 89. 47% with a sensitivity of 87. 50% and a specificity of 90. 91%. Further analysis revealed that the most discriminative features for classification are predominantly involved in several default-mode (medial frontal gyrus, posterior cingulate gyrus, hippocampus and parahippocampal gyrus), occipital (fusiform gyrus, inferior and middle occipital gyrus) and subcortical (amygdale and pallidum of lenticular nucleus) regions. Thus, the M3 method shows promising classification performance by incorporating information from different imaging modalities and different functional properties, and it has the potential to improve the clinical diagnosis and treatment evaluation of AD.

YNIMG Journal 2011 Journal Article

Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches

Jin Kang
Liang Wang
Chaogan Yan
Jinhui Wang
Xia Liang
Yong He

The cognitive activity of the human brain benefits from the functional connectivity of multiple brain regions that form specific, functional brain networks. Recent studies have indicated that the relationship between brain regions can be investigated by examining the temporal interaction (known as functional connectivity) of spontaneous blood oxygen level-dependent (BOLD) signals derived from resting-state functional MRI. Most of these studies plausibly assumed that inter-regional interactions were temporally stationary. However, little is known about the dynamic characteristics of resting-state functional connectivity (RSFC). In this study, we thoroughly examined this question within and between multiple functional brain networks. Twenty-two healthy subjects were scanned in a resting state. Several of the RSFC networks observed, including the default-mode, motor, attention, memory, auditory, visual, language and subcortical networks, were first identified using a conventional voxel-wise correlation analysis with predefined region of interests (ROIs). Then, a variable parameter regression model combined with the Kalman filtering method was employed to detect the dynamic interactions between each ROI and all other brain voxels within each of the RSFC maps extracted above. Experimental results revealed that the functional interactions within each RSFC map showed time-varying properties, and that approximately 10–20% of the voxels within each RSFC map showed significant functional connectivity to each ROI during the scanning session. This dynamic pattern was also observed for the interactions between different functional networks. In addition, the spatial pattern of dynamic connectivity maps obtained from neighboring time points had a high similarity. Overall, this study provides insights into the dynamic properties of resting-state functional networks.

YNIMG Journal 2011 Journal Article

Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study

Ying Han
Jinhui Wang
Zhilian Zhao
Baoquan Min
Jie Lu
Kuncheng Li
Yong He
Jianping Jia

Here we utilized resting-state functional magnetic resonance imaging (R-fMRI) to measure the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) in 24 patients with amnestic mild cognitive impairment (aMCI) and 24 age- and sex-matched healthy controls. Two different frequency bands (slow-5: 0. 01–0. 027 Hz; slow-4: 0. 027–0. 073 Hz) were analyzed. We showed that there were widespread differences in ALFF/fALFF between the two bands in many brain regions, predominantly including the medial prefrontal cortex (MPFC), posterior cingulate cortex/precuneus (PCC/PCu), basal ganglia, and hippocampus/parahippocampal gyrus (PHG). Compared to controls, the aMCI patients had decreased ALFF/fALFF values in the PCC/PCu, MPFC, hippocampus/PHG, basal ganglia, and prefrontal regions, and increased ALFF/fALFF values mainly in several occipital and temporal regions. Specifically, we observed that the ALFF/fALFF abnormalities in the PCC/PCu, PHG, and several occipital regions were greater in the slow-5 band than in the slow-4 band. Finally, our results of functional analysis were not significantly influenced by the gray matter loss in the MCI patients, suggesting that the results reflect functional differences between groups. Together, our data suggest that aMCI patients have widespread abnormalities in intrinsic brain activity, and the abnormalities depend on the studied frequency bands of R-fMRI data.

YNIMG Journal 2011 Journal Article

Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study

Lixia Tian
Jinhui Wang
Chaogan Yan
Yong He

We employed resting-state functional MRI (R-fMRI) to investigate hemisphere- and gender-related differences in the topological organization of human brain functional networks. Brain networks were first constructed by measuring inter-regional temporal correlations of R-fMRI data within each hemisphere in 86 young, healthy, right-handed adults (38 males and 48 females) followed by a graph-theory analysis. The hemispheric networks exhibit small-world attributes (high clustering and short paths) that are compatible with previous results in the whole-brain functional networks. Furthermore, we found that compared with females, males have a higher normalized clustering coefficient in the right hemispheric network but a lower clustering coefficient in the left hemispheric network, suggesting a gender–hemisphere interaction. Moreover, we observed significant hemisphere-related differences in the regional nodal characteristics in various brain regions, such as the frontal and occipital regions (leftward asymmetry) and the temporal regions (rightward asymmetry), findings that are consistent with previous studies of brain structural and functional asymmetries. Together, our results suggest that the topological organization of human brain functional networks is associated with gender and hemispheres, and they provide insights into the understanding of functional substrates underlying individual differences in behaviors and cognition.