BACKGROUND:Persistent post-stroke ankle impairment hinders functional recovery. Brain-computer interface (BCI)-controlled ankle robot show rehabilitation potential, but their efficacy and underlying neuroplasticity remain unclear. OBJECTIVE:To assess BCI-controlled ankle robot training on post-stroke lower-limb motor recovery and neuroplasticity using quantitative EEG (qEEG). METHODS:Thirty-two stroke patients were randomized to BCI (n = 16, 40-minute BCI-robot training) or control (n = 16, 40-minute ankle-robot training) groups, receiving 5 sessions/week for 2 weeks. Outcomes included Fugl-Meyer Assessment-Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Functional Ambulatory Category (FAC), Modified Ashworth Scale (MAS), active range of motion (AROM), and muscle strength. QEEG assessed the relative power of the delta (rδ), theta (rθ), alpha (rα), beta (rβ) bands, spectral power ratios, pairwise-derived Brain Symmetry Index (pdBSI), and functional connectivity. RESULTS:Both groups showed significant within-group improvements in dorsiflexion AROM, dorsiflexor strength, FMA-LE, BBS, and FAC (P < .05). The BCI group demonstrated significantly greater FMA-LE improvement than controls (∆FMA-LE, P = .007) and reduced calf spasticity (MAS; P = .038). QEEG analysis in the BCI group revealed decreased rδ (P = .005), increased rα (P = .017), reduced DAR and DTABR (P < .05), reduced interhemispheric asymmetry (pdBSI-δ; P = .018), and enhanced Cz-parietal connectivity in α and β bands (P < .05). CONCLUSION:BCI-controlled ankle robot training significantly improved lower-limb motor function and reduced spasticity post-stroke. Associated neurophysiological changes, characterized by reduced slow-wave power and asymmetry, increased alpha power, and functional connectivity, indicated beneficial neuroplastic reorganization.Clinical trial registration number: China Clinical Trail Registry (ChiCTR2300074381; URL: http://www.chictr.org.cn).

Abstract Background and Objective Normative modeling is a key tool for understanding brain alterations in neurodegenerative diseases, such as cerebellar-type multiple system atrophy. However, existing methods lack interpretability and fail to capture clinically meaningful pathological changes. This study presents DINMC, a Deep Interpretable Normative Model Construction framework, which combines autoencoder-based learning with statistical hypothesis testing to better capture and interpret disease-specific neu-roanatomical changes. Methods The DINMC framework constructs normative models using neuroimaging data from multi-site large healthy cohorts. It utilizes a U-shaped convolutional autoencoder to train these models, which are then applied to reconstruct brain features from both patients and healthy controls within the same study cohort. Pathological confidence values are derived by fusing original and deviation feature spaces, offering a measure of disease-related pathology reflected in each dimension of the features. The framework was validated through statistical analysis and prognostic classification and regression tasks. Results The pathological confidence provides valuable insights into the neuroanatomical regions most affected by the disease, as well as the correlation between changes in these regions and clinical assessment scales. Our optimal model outperform traditional methods in prognostic prediction tasks, with an AUC of 0.972 for classification tasks and an R 2 of 0.432 for regression tasks. Conclusion DINMC provides a novel and interpretable framework for neuroimaging analysis. By combining deep learning and statistical hypothesis testing, this framework offers a unique solution to improving both the interpretability and performance of normative models in neuroimaging. The approach is scalable to other neuroimaging datasets, offering a versatile tool for broader biomedical applications.

The ability to distinguish speakers based on speech signals is a fundamental human ability essential for social communication, yet the neural mechanisms underlying this process remain poorly understood. The present study investigated the temporal dynamics of neural activity during speaker discrimination using event-related potentials (ERPs). Twenty-four native Mandarin speakers completed two tasks: an oddball session, in which participants passively listened to speech stimuli from standard and deviant speakers, and a voice line-up session, in which participants explicitly judged whether two consecutively presented speech stimuli were produced by the same or different speakers. In the oddball session, deviant stimuli elicited robust mismatch negativity (MMN) and P3a components compared to standard stimuli, indicating pre-attentive detection of speaker changes. In the voice line-up session, the different-speaker condition elicited more negative N1 and N400 amplitudes and more positive P2 amplitudes than the same-speaker condition, suggesting that speaker discrimination engages both early sensory processing and later cognitive integration. No significant differences were observed between the P300 and P600 components. These findings reveal distinct neural signatures associated with speaker-related processing across multiple temporal stages, with the MMN and P3a reflecting automatic detection of speaker-related acoustic changes, and the N1, P2, and N400 reflecting explicit speaker discrimination processes. While the present paradigm cannot fully isolate identity-level representations from low-level acoustic discrimination, the results provide novel ERP evidence on the temporal architecture engaged when listeners process speaker-specific information, contributing to a deeper understanding of speaker-related processing in the broader context of speaker identification research.

Stroke remains a significant cause of disability globally, with a noticeable prevalence in China. Post-stroke rehabilitation, particularly through brain-computer interface (BCI) methods, plays a vital role in enhancing motor function recovery. However, the efficacy of BCI rehabilitation might be hindered by challenges in individualized program of prognosis prediction. This study aimed to develop prognostic prediction models for unilateral hemiplegia after BCI rehabilitation, utilizing both clinical and functional magnetic resonance imaging (fMRI) data, in order to enhance treatment efficiency and optimize patient outcomes. The study included 40 stroke patients (22 left hemisphere affected and 18 right hemisphere affected) who underwent BCI rehabilitation training at the Beijing Tsinghua Changgung Hospital (Beijing, China). Data related to patients' demographics, disease duration, and assessment scores were collected. Based on the improvement in the Fugl-Meyer assessment of the upper extremity (FMA-UE) rating scale, patients were categorized into responder and non-responder groups. Linear regression and its variants, including multivariate logistic regression and optimal subset regression, were utilized to predict the post-treatment scores based on both fMRI and clinical data. The accuracy and R-squared value of the models were assessed using leave-one-out cross-validation (LOOCV). The linear regression model using imaging data exhibited a remarkable performance with a classification accuracy of 100% and R2 (LOOCV) exceeding 0.94. In contrast, the model relying solely on clinical data achieved a classification accuracy of <80%. These results clearly demonstrated the potential of employing imaging data and machine learning methods to effectively predict the effectiveness of BCI rehabilitation. This study assessed the effectiveness of neuroimaging in predicting the efficacy of BCI rehabilitation for unilateral stroke patients. The developed model could serve as a foundation for enhancing our comprehension of rehabilitation outcomes, especially in uniqueness of left and right stroke, and ultimately improving patient well-being. The findings underscored the potential of neuroimaging data in optimizing BCI rehabilitation, leading to the enhanced recovery of motor function in unilateral stroke patients.

Background The clinical efficacy of brain–computer interface (BCI)–based rehabilitation after stroke remains uncertain, particularly in the subacute phase, where evidence is limited and clinically meaningful improvement is seldom evaluated. This study aimed to investigate the therapeutic efficacy and electrophysiological mechanisms of BCI-assisted pneumatic hand training in subacute stroke. Methods In this prospective randomized controlled trial, 129 patients with stroke were assigned to a BCI group ( n = 72) or a control group ( n = 57). Upper limb function was assessed using FMA-UE, ARAT, WMFT, and MBI. Clinically meaningful improvement was evaluated based on minimal clinically important difference (MCID). Resting-state EEG was analyzed for spectral power and topographic distribution. Results The BCI group demonstrated greater improvement in FMA-UE scores than the control group (11.57 ± 7.98 vs 5.94 ± 6.13, p = 0.01). A higher proportion of patients in the BCI group achieved MCID in FMA-UE, ARAT, and WMFT. EEG analysis revealed increased beta-band power following BCI intervention, predominantly over central and parietal regions. Between-group comparisons showed greater beta-band changes in the BCI group. Post-treatment, no significant differences were observed between the BCI group and healthy controls, suggesting a normalization trend. Conclusion BCI-assisted pneumatic hand training enhances upper limb recovery in subacute stroke and is associated with increased beta-band activity, indicating potential neurophysiological mechanisms of motor recovery.

Recent efforts on text-to-audio (TTA) generation are starting to explore fine-grained controllability, e.g., precise timing control, with innovations on conditioning techniques or training-free latent manipulations. However, constrained by data scarcity, their generation performance at scale is still limited. In this study, we recast high-controllability TTA generation as a multi-task learning problem, and introduce a progressive diffusion modeling approach, ControlAudio. Our method adeptly fits distributions conditioned on fine-grained information, including text, timing, and phoneme features, through a step-by-step strategy. First, we propose a data construction method spanning both annotation and simulation, augmenting condition information in the sequence of text, timing, and phoneme. Second, at the model training stage, we pretrain a scalable diffusion transformer (DiT) on large-scale text-audio pairs, achieving high-fidelity TTA generation, and then incrementally integrate the timing and phoneme features, expanding controllability. Finally, at the inference stage, we propose progressively guided generation, which sequentially emphasizes more fine-grained information, aligning inherently with the coarse-to-fine sampling nature of DiT. Extensive experiments show that ControlAudio achieves state-of-the-art performance in terms of temporal accuracy and speech clarity, significantly outperforming existing methods on both objective and subjective evaluations. Demo samples are available at: https://control-audio.github.io/Control-Audio.

Abstract Deep brain systems involved in arousal, autonomic regulation, sensory integration, and homeostatic control remain underrepresented in conventional whole-brain neuroimaging frameworks. In particular, diencephalic and brainstem nuclei are often insufficiently represented in cortex-centered analyses, limiting the normative references needed to interpret systems-level variation in health and disease. To address this gap, we developed a unified multiscale framework with explicit representation of deep nuclei. By integrating cerebral, cerebellar, diencephalic, and brainstem atlases in standard space, we constructed a 220-region whole-brain parcellation and extracted complementary features at three analytical scales: nodal properties, edge-wise connectivity, and persistent-homology-based topological descriptors. We applied this framework to healthy adults from the Human Connectome Project-Aging cohort to characterize normative multiscale organization and test sex- and age-related variation. Applied to this cohort, our framework revealed pronounced heterogeneity across anatomical systems. Brainstem and diencephalic nuclei showed multiscale feature profiles distinct from those of cerebral and cerebellar regions across nodal, edge-wise, and higher-order topological scales. Sex comparisons identified selective differences across different scales, whereas age modeling revealed widespread but feature- and system-dependent variation across adulthood. Together, these findings show that normative whole-brain organization in this deep-system-aware space is structured by system-specific rather than globally uniform patterns. These findings establish a normative multiscale framework for characterizing brainstem-diencephalic-cerebellar-cerebral organization in healthy adults and provide a quantitative reference for future translational studies of disease-related abnormalities in deep regulatory systems.

Text-to-audio (TTA) generation has made significant strides, yet achieving precise and consistent audio editing remains a major challenge. However, existing methods struggle to balance temporal consistency with background preservation. In this paper, we propose FreeSonic, a training-free framework leveraging the state-of-the-art Rectified Flow-based TangoFlux model. FreeSonic utilizes an optimized inversion-reverse process and joint text-audio attention maps for precise target segment extraction. For content editing, a novel scheduled attention decoupling confines modifications to target regions while preserving original acoustic context. Furthermore, task-oriented noise injection enhances versatility for tasks such as audio removal and non-rigid replacement. Extensive experimental results demonstrate that FreeSonic achieves a superior balance by providing a high-fidelity and efficient solution for precise and consistent audio editing. Project and demos: https://free-sonic.github.io/

Changes in brain functional asymmetry are important physiological characteristics for evaluating neurorehabilitation. The characteristics of brain networks can be used to assess the brain functional asymmetry. The multiplex networks is defined as a multilayer networks that the interlayer connections are not present, apart from those between replica nodes. How to integrate different single layer networks information to assess the asymmetry for enhancing the assessment accuracy of the neurorehabilitation is a problem that attracts our attention. BAMN, a brain asymmetry analysis method based on multiplex network is presented in this paper. The proposed method extends the attributes of graph theory of single layer to multilayer, calculates their differences between the left and right hemisphere of the brain. It has been validated by using clinical EEG data of after anterior cruciate ligament reconstruction (ACLR) patients and healthy controls, discovering the distinct asymmetry features between these two groups. Some of the signifcant difference features are significantly correlated with the clinical scores and they may be used in the future assessment of the neurorehabilitation.

Predicting stroke recovery outcomes is crucial yet challenging, and different models yield varied predictions. For addressing the fusion of different models to provide more reliable prediction, a Dempster-Shafer Theory (DST)-based multi-model fusion method for different machine learning models is proposed in this paper to provide a robust prediction for the recovery outcomes. The Shannon entropy is applied as a measure of uncertainty and for performing DST fusion. It is well-suited for multi-model fusion, even though the output distribution of sub-models is unknown. The EEG-based Motor Imagery Brain-Computer Interface (MI-BCI) training is used in the experiments and validations on 13 stroke patients, with the recovery outcomes prediction accuracy of 92.3% and uncertainty of 0.24 of the multi-model fusion, demonstrating higher accuracy and lower uncertainty compared to any single prediction model.

Multimodal learning has gained significant attention in recent years for combining information from different modalities using Deep Neural Networks (DNNs). However, existing approaches often overlook the varying importance of modalities and neglect uncertainty estimation, leading to limited generalization and unreliable predictions. In this paper, we propose a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to address these challenges by integrating multimodal information at both the Basic Belief Assignment (BBA) level and multimodal level, for enhancing accuracy, robustness, and reliability. The proposed DDEF approach utilizes the Dirichlet framework and BBA methods to connect neural network outputs with Dirichlet distribution parameters, enabling effective uncertainty estimation, and the Dempster-Shafer Theory (DST) is used for dual-level fusion, facilitating the fusion of evidence from two BBA methods and multiple modalities. It has been validated by two experiments on synthetic digit classification, and real-world medical prognosis after brain-computer interface (BCI) treatment, and by demonstrating superior performance compared to existing methods. Our findings emphasize the importance of considering multimodal integration and uncertainty estimation for reliable decision-making in deep learning.

Accurate prognostic prediction in patients with disorders of consciousness (DOC) is a core clinical concern and a formidable challenge in neuroscience. Resting-state EEG has shown promise in identifying electrophysiological prognostic markers and may be easily deployed at the bedside. However, the lack of brain dynamic modeling and the spatial mixture of signals in scalp EEG have constrained our exploration of biomarkers and comprehension of the mechanisms underlying consciousness recovery. Here, we introduce EEG source space analysis and brain dynamics to investigate the brain networks of patients with DOC (n = 178) with different outcomes (six-month follow-up), followed by graph theory and high-order topological analysis to explore the relationship between network structure and prognosis, and finally assess the importance of features. We show that a positive prognosis is associated with large-scale lower levels of low-frequency hypersynchrony. Moreover, we provide evidence that this pattern is driven not by all brain states but only by specific states. Analyses reveal that the positive prognosis is attributed to the network retaining lower segregation, higher integration, and stronger stability compared to the negative prognosis. Furthermore, our results highlight the importance of brain networks derived from brain dynamics in prognosis. The prognosis models based on clinical and neural features can achieve acceptable and even excellent performance under different outcome definitions (AUC = 0.714-0.893). Overall, our study offers new perspectives for the identification of prognostic biomarkers and provides avenues for profound insights into the mechanisms underlying consciousness improvement or recovery.

Network neuroscience has emerged as an indispensable tool for studying brain structure and function. Currently, the network-based statistic (NBS) procedure is widely used for dealing with massive multiple testing/comparison problems in brain networks. However, the NBS requires choosing a hard cluster-forming threshold, lacking objective rules. A powerful and flexible statistical framework is urgently needed with growing interest in finer-grained network explorations across modalities and scales. Here, we introduce a permutation-based framework—”Threshold-Free Network-Oriented Statistics” (TFNOS). It integrates two “threshold-free” pathways: traversing all cluster-forming thresholds (TT) and using predefined clusters (PC). The TT procedure, building upon the threshold-free network-based statistics, requires setting additional parameters. The PC procedures comprise six variants given the degree of freedom in pooling data, null distribution construction, and controlled error rate. Using numerical simulations, we evaluated the performance of the TT procedure under 600 parameter combinations, then benchmarked TFNOS procedures and baselines across different topologies of effects, sample sizes, and effect sizes, and finally provided illustrative examples with real data. We offer recommended parameter values that allow the TT procedure to stably maintain leading power, while empirically controlling the false discovery rate (FDR) beyond only weakly controlling the familywise error rate (FWER). Notably, the relevant parameters commonly employed in the field appear overly liberal. Furthermore, for the PC procedures, FDR-controlling variants showed improved power compared to FWER-controlling variants, and some of them are simple but do not compromise power. The nonparametric PC procedures allow the selection of any test statistics considered appropriate. Overall, the TFNOS is a generalized framework for inference on edges/nodes of undirected/directed brain networks. We provide empirical and principled criteria for selecting appropriate procedures and may enhance the reproducibility and sensitivity of future brain research.### Competing Interest StatementThe authors have declared no competing interest.

Pre-processing is a fundamental step for any tasks reliant on scalp EEG data. The presence of various artifacts in acquired EEG data, which mask the expected features of brain activity, underscores the pivotal role of artifact removal in pre-processing. Recently, deep learning methods have demonstrated superior efficacy in artifact removal compared to conventional methods such as regression, classical filtering, and signal decomposition. While EEG signals, characterized as time series, differ from extensively explored modalities like photos or videos in multimodal machine learning, a suitable network architecture is necessary for the utilization of deep learning methods in EEG artifact removal tasks. Therefore, in this study, we propose a neural network architecture utilizing self-learned state distinction criteria for time series segmentation and tested its artifact removal performance on semi-simulated and real EEG datasets. Notably, our proposed network architecture outperforms the state-of-the-art network in artifact removal performance. Furthermore, the proposed model demonstrates acceptable real-time processing capabilities, thus highlighting its potential applications in real clinical and research settings as an online pre-processing step.

It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in $\text{R}^{{2}}$ , revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans.

Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting -state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain in-teractions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfi-guring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clin-ical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.

Effective treatment and accurate long-term prognostication of patients with disorders of consciousness (DOC) remain pivotal clinical issues and challenges in neuroscience. Previous studies have shown that zolpidem produces paradoxical recovery and induces similar change patterns in specific electrophysiological features in some DOC (∼6%). However, whether these specific features are neural markers of responders, and how neural features evolve over time remain unclear. Here, we capitalized on static and dynamic EEG analysis techniques to fully uncover zolpidem-induced alterations in eight patients with DOC and constructed machine-learning models to predict long-term outcomes at the single-subject level. We observed consistent patterns of change across all patients in several static features (e.g., decreased relative theta power and weakened alpha-band functional connectivity) after zolpidem administration, albeit none zolpidem responders. Based on the current evidence, previously published electrophysiological features are not neural markers for zolpidem responders. Moreover, we found that the temporal dynamics of the brain slowed down after zolpidem intake. Brain states before and after zolpidem administration could be completely characterized by the EEG features. Furthermore, long-term outcomes were accurately predicted using connectivity features. Our findings suggest that EEG neural signatures have huge potential to assess consciousness states and predict fine-grained outcomes. In summary, our results extend the understanding of the effects of zolpidem on the brain and open avenues for the application prospect of zolpidem and EEG in patients with DOC.

The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N = 31) and healthy populations (N = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.

Connectivity changes after spinal cord injury (SCI) appear as dynamic post-injury procedures. The present study aimed to investigate the alterations in the functional connectivity (FC) in different injury duration in complete SCI using resting-state functional magnetic resonance imaging (fMRI). A total of 30 healthy controls (HCs) and 27 complete SCI patients were recruited in this study. A seed-based connectivity analysis compared FC differences between HCs and SCI and among SCI subgroups (SCI patients with post-injury within 6 months (early stage, n = 13) vs. those with post-injury beyond 6 months (late stage, n = 14)). Compared to HCs, SCI patients showed an increase in FC between sensorimotor cortex and cognitive, visual, and auditory cortices. The FC between motor cortex and cognitive cortex increased over time after injury. The FC between sensory cortex and visual cortex increased within 6 months after SCI, while FC between the sensory cortex and auditory cortex increased beyond 6 months after injury. The FC between sensorimotor cortex and cognitive, visual, auditory regions increased in complete SCI patients. The brain FC changed dynamically, and rehabilitation might be adapted over time after SCI.

Resting-state fMRI has been widely applied in clinical research. Brain networks constructed by functional connectivity can reveal alterations related to disease and treatment. One of the major concerns of brain network application under clinical situations is how to analyze groups of data to find the potential biomarkers that can aid in diagnosis. In this paper, we briefly review common methods to construct brain networks from resting-state fMRI data, including different ways of the node definition and edge calculation. We focus on using a brain atlas to define nodes and estimate edges by static and dynamic functional connectivity. The directed connectivity method is also mentioned. We then discuss the challenges and pitfalls when analyzing groups of brain networks, including functional connectivity alterations, graph theory attributes analysis, and network-based statistics. Finally, we review the clinical application of resting-state fMRI in neurorehabilitation of spinal cord injury patients and stroke patients, the research on the mechanism and early diagnosis of neurodegenerative diseases, such as multiple system atrophy, as well as the research on brain functional network alteration of glioma patients.