Resting-state Functional Brain Networks Research Articles

Mechanism of Cerebralcare Granule® for Improving Cognitive Function in Resting-State Brain Functional Networks of Sub-healthy Subjects.

Cerebralcare Granule® (CG), a Chinese herbal medicine, has been used to ameliorate cognitive impairment induced by ischemia or mental disorders. The ability of CG to improve health status and cognitive function has drawn researchers' attention, but the relevant brain circuits that underlie the ameliorative effects of CG remain unclear. The present study aimed to explore the underlying neurobiological mechanisms of CG in ameliorating cognitive function in sub-healthy subjects using resting-state functional magnetic resonance imaging (fMRI). Thirty sub-healthy participants were instructed to take one 2.5-g package of CG three times a day for 3 months. Clinical cognitive functions were assessed with the Chinese Revised Wechsler Adult Intelligence Scale (WAIS-RC) and Wechsler Memory Scale (WMS), and fMRI scans were performed at baseline and the end of intervention. Functional brain network data were analyzed by conventional network metrics (CNM) and frequent subgraph mining (FSM). Then 21 other sub-healthy participants were enrolled as a blank control group of cognitive functional. We found that administrating CG can improve the full scale of intelligence quotient (FIQ) and Memory Quotient (MQ) scores. At the same time, following CG treatment, in CG group, the topological properties of functional brain networks were altered in various frontal, temporal, occipital cortex regions, and several subcortical brain regions, including essential components of the executive attention network, the salience network, and the sensory-motor network. The nodes involved in the FSM results were largely consistent with the CNM findings, and the changes in nodal metrics correlated with improved cognitive function. These findings indicate that CG can improve sub-healthy subjects' cognitive function through altering brain functional networks. These results provide a foundation for future studies of the potential physiological mechanism of CG.

Differences on Brain Connectivity in Adulthood Are Present in Subjects with Iron Deficiency Anemia in Infancy.

Iron deficiency continues to be the most prevalent micronutrient deficit worldwide. Since iron is involved in several processes including myelination, dopamine neurotransmission and neuronal metabolism, the presence of iron deficiency anemia (IDA) in infancy relates to long-lasting neurofunctional effects. There is scarce data regarding whether these effects would extend to former iron deficient anemic human adults. Resting state functional magnetic resonance imaging (fMRI) is a novel technique to explore patterns of functional connectivity. Default Mode Network (DMN), one of the resting state networks, is deeply involved in memory, social cognition and self-referential processes. The four core regions consistently identified in the DMN are the medial prefrontal cortex, posterior cingulate/retrosplenial cortex and left and right inferior parietal cortex. Therefore to investigate the DMN in former iron deficient anemic adults is a particularly useful approach to elucidate de long term effects on functional brain. We conducted this research to explore the connection between IDA in infancy and altered patterns of resting state brain functional networks in young adults. Resting-state fMRI studies were performed to 31 participants that belong to a follow-up study since infancy. Of them, 14 participants were former iron deficient anemic in infancy and 17 were controls, with mean age of 21.5 years (±1.5) and 54.8% were males. Resting-state fMRI protocol was used and the data was analyzed using the seed based connectivity statistical analysis to assess the DMN. We found that compared to controls, former iron deficient anemic subjects showed posterior DMN decreased connectivity to the left posterior cingulate cortex (PCC), whereas they exhibited increased anterior DMN connectivity to the right PCC. Differences between groups were also apparent in the left medial frontal gyrus, with former iron deficient anemic participants having increased connectivity with areas included in DMN and dorsal attention networks. These preliminary results suggest different patterns of functional connectivity between former iron deficient anemic and control young adults. Indeed, IDA in infancy, a common nutritional problem among human infants, may turn out to be important for understanding the mechanisms of cognitive alterations, common in adulthood.

Elevated Body Mass Index is Associated with Increased Integration and Reduced Cohesion of Sensory-Driven and Internally Guided Resting-State Functional Brain Networks.

Elevated body mass index (BMI) is associated with increased multi-morbidity and mortality. The investigation of the relationship between BMI and brain organization has the potential to provide new insights relevant to clinical and policy strategies for weight control. Here, we quantified the association between increasing BMI and the functional organization of resting-state brain networks in a sample of 496 healthy individuals that were studied as part of the Human Connectome Project. We demonstrated that higher BMI was associated with changes in the functional connectivity of the default-mode network (DMN), central executive network (CEN), sensorimotor network (SMN), visual network (VN), and their constituent modules. In siblings discordant for obesity, we showed that person-specific factors contributing to obesity are linked to reduced cohesiveness of the sensory networks (SMN and VN). We conclude that higher BMI is associated with widespread alterations in brain networks that balance sensory-driven (SMN, VN) and internally guided (DMN, CEN) states which may augment sensory-driven behavior leading to overeating and subsequent weight gain. Our results provide a neurobiological context for understanding the association between BMI and brain functional organization while accounting for familial and person-specific influences.

Comparison of Local Information Indices Applied in Resting State Functional Brain Network Connectivity Prediction.

Anatomical distance has been widely used to predict functional connectivity because of the potential relationship between structural connectivity and functional connectivity. The basic implicit assumption of this method is “distance penalization.” But studies have shown that one-parameter model (anatomical distance) cannot account for the small-worldness, modularity, and degree distribution of normal human brain functional networks. Two local information indices–common neighbor (CN) and preferential attachment index (PA), are introduced into the prediction model as another parameter to emulate many key topological of brain functional networks in the previous study. In addition to these two indices, many other local information indices can be chosen for investigation. Different indices evaluate local similarity from different perspectives. Currently, we still have no idea about how to select local information indices to achieve higher predicted accuracy of functional connectivity. Here, seven local information indices are chosen, including CN, hub depressed index (HDI), hub promoted index (HPI), Leicht-Holme-Newman index (LHN-I), Sørensen index (SI), PA, and resource allocation index (RA). Statistical analyses were performed on eight network topological properties to evaluate the predictions. Analysis shows that different prediction models have different performances in terms of simulating topological properties and most of the predicted network properties are close to the real data. There are four topological properties whose average relative error is less than 5%, including characteristic path length, clustering coefficient, global efficiency, and local efficiency. CN model shows the most accurate predictions. Statistical analysis reveals that five properties within the CN-predicted network do not differ significantly from the real data (P > 0.05, false-discovery rate method corrected for seven comparisons). PA model shows the worst prediction performance which was first applied in models of growth networks. Our results suggest that PA is not suitable for predicting connectivity in a small-world network. Furthermore, in order to evaluate the predictions rapidly, prediction power was proposed as an evaluation metric. The current study compares the predictions of functional connectivity with seven local information indices and provides a reference of method selection for construction of prediction models.

Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults.

Atlas-guided diffuse optical tomography (atlas-DOT) is a computational means to image changes in cortical hemodynamic signals during human brain activities. Graph theory analysis (GTA) is a network analysis tool commonly used in functional neuroimaging to study brain networks. Atlas-DOT has not been analyzed with GTA to derive large-scale brain connectivity/networks based on near-infrared spectroscopy (NIRS) measurements. We introduced an automated voxel classification (AVC) method that facilitated the use of GTA with atlas-DOT images by grouping unequal-sized finite element voxels into anatomically meaningful regions of interest within the human brain. The overall approach included volume segmentation, AVC, and cross-correlation. To demonstrate the usefulness of AVC, we applied reproducibility analysis to resting-state functional connectivity measurements conducted from 15 young adults in a two-week period. We also quantified and compared changes in several brain network metrics between young and older adults, which were in agreement with those reported by a previous positron emission tomography study. Overall, this study demonstrated that AVC is a useful means for facilitating integration or combination of atlas-DOT with GTA and thus for quantifying NIRS-based, voxel-wise resting-state functional brain networks.

Lateralized Resting-State Functional Brain Network Organization Changes in Heart Failure.

Heart failure (HF) patients show brain injury in autonomic, affective, and cognitive sites, which can change resting-state functional connectivity (FC), potentially altering overall functional brain network organization. However, the status of such connectivity or functional organization is unknown in HF. Determination of that status was the aim here, and we examined region-to-region FC and brain network topological properties across the whole-brain in 27 HF patients compared to 53 controls with resting-state functional MRI procedures. Decreased FC in HF appeared between the caudate and cerebellar regions, olfactory and cerebellar sites, vermis and medial frontal regions, and precentral gyri and cerebellar areas. However, increased FC emerged between the middle frontal gyrus and sensorimotor areas, superior parietal gyrus and orbito/medial frontal regions, inferior temporal gyrus and lingual gyrus/cerebellar lobe/pallidum, fusiform gyrus and superior orbitofrontal gyrus and cerebellar sites, and within vermis and cerebellar areas; these connections were largely in the right hemisphere (p<0.005; 10,000 permutations). The topology of functional integration and specialized characteristics in HF are significantly changed in regions showing altered FC, an outcome which would interfere with brain network organization (p<0.05; 10,000 permutations). Brain dysfunction in HF extends to resting conditions, and autonomic, cognitive, and affective deficits may stem from altered FC and brain network organization that may contribute to higher morbidity and mortality in the condition. Our findings likely result from the prominent axonal and nuclear structural changes reported earlier in HF; protecting neural tissue may improve FC integrity, and thus, increase quality of life and reduce morbidity and mortality.

Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks

We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson's correlation to estimate functional connectivity implies that the range of fluctuations of functional connections over short time-scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are unexpectedly strong or weak. We apply this method to data recorded from N=80 participants, and show that the number of unexpectedly strong/weak connections fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with default mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form distinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivity.

Reconfiguration of the Brain Functional Network Associated with Visual Task Demands.

Neuroimaging studies have demonstrated that the topological properties of resting-state brain functional networks are modulated through task performances. However, the reconfiguration of functional networks associated with distinct degrees of task demands is not well understood. In the present study, we acquired fMRI data from 18 healthy adult volunteers during resting-state (RS) and two visual tasks (i.e., visual stimulus watching, VSW; and visual stimulus decision, VSD). Subsequently, we constructed the functional brain networks associated with these three conditions and analyzed the changes in the topological properties (e.g., network efficiency, wiring-cost, modularity, and robustness) among them. Although the small-world attributes were preserved qualitatively across the functional networks of the three conditions, changes in the topological properties were also observed. Compared with the resting-state, the functional networks associated with the visual tasks exhibited significantly increased network efficiency and wiring-cost, but decreased modularity and network robustness. The changes in the task-related topological properties were modulated according to the task complexity (i.e., from RS to VSW and VSD). Moreover, at the regional level, we observed that the increased nodal efficiencies in the visual and working memory regions were positively associated with the increase in task complexity. Together, these results suggest that the increased efficiency of the functional brain network and higher wiring-cost were observed to afford the demands of visual tasks. These observations provide further insights into the mechanisms underlying the reconfiguration of the brain network during task performance.

Is the Internet gaming‐addicted brain close to be in a pathological state?

Internet gaming addiction (IGA) is becoming a common and widespread mental health concern. Although IGA induces a variety of negative psychosocial consequences, it is yet ambiguous whether the brain addicted to Internet gaming is considered to be in a pathological state. We investigated IGA-induced abnormalities of the brain specifically from the network perspective and qualitatively assessed whether the Internet gaming-addicted brain is in a state similar to the pathological brain. Topological properties of brain functional networks were examined by applying a graph-theoretical approach to analyzing functional magnetic resonance imaging data acquired during a resting state in 19 IGA adolescents and 20 age-matched healthy controls. We compared functional distance-based measures, global and local efficiency of resting state brain functional networks between the two groups to assess how the IGA subjects' brain was topologically altered from the controls' brain. The IGA subjects had severer impulsiveness and their brain functional networks showed higher global efficiency and lower local efficiency relative to the controls. These topological differences suggest that IGA induced brain functional networks to shift toward the random topological architecture, as exhibited in other pathological states. Furthermore, for the IGA subjects, the topological alterations were specifically attributable to interregional connections incident on the frontal region, and the degree of impulsiveness was associated with the topological alterations over the frontolimbic connections. The current findings lend support to the proposition that the Internet gaming-addicted brain could be in the state similar to pathological states in terms of topological characteristics of brain functional networks.

An EEG-Based Biometric System Using Eigenvector Centrality in Resting State Brain Networks

Recently, there has been a growing interest in the use of brain activity for biometric systems. However, so far these studies have focused mainly on basic features of the Electroencephalography. In this study we propose an approach based on phase synchronization, to investigate personal distinctive brain network organization. To this end, the importance, in terms of centrality, of different regions was determined on the basis of EEG recordings. We hypothesized that nodal centrality enables the accurate identification of individuals. EEG signals from a cohort of 109 64-channels EEGs were band-pass filtered in the classical frequency bands and functional connectivity between the sensors was estimated using the Phase Lag Index. The resulting connectivity matrix was used to construct a weighted network, from which the nodal Eigenvector Centrality was computed. Nodal centrality was successively used as feature vector. Highest recognition rates were observed in the gamma band (equal error rate ( ${\rm EER}) = 0.044$ ) and high beta band ( ${\rm EER} = 0.102$ ). Slightly lower recognition rate was observed in the low beta band ( ${\rm EER} = 0.144$ ), while poor recognition rates were observed for the others frequency bands. The reported results show that resting-state functional brain network topology provides better classification performance than using only a measure of functional connectivity, and may represent an optimal solution for the design of next generation EEG based biometric systems. This study also suggests that results from biometric systems based on high-frequency scalp EEG features should be interpreted with caution.

Effect of Chinese tuina massage therapy on resting state brain functional network of patients with chronic neck pain

ObjectiveCervical disease, a type of chronic pain, can greatly impact quality of life. Traditional Chinese tuina, a form of therapeutic massage and manipulation, has been shown to be effective in relieving pain and other symptoms in patients with chronic neck pain. This study applied functional magnetic resonance imaging (fMRI) to explore the features of the resting state network of patients with chronic neck pain caused by cervical radiculopathy, and how tuina affects the causality between intrinsic brain networks. MethodsUsing Granger causality analysis, effective connectivity of brain networks of 10 patients with chronic neck pain was compared with 10 healthy control subjects. Resting state fMRI data were using magnetic resonance scanning. Cervical spondylosis symptom scores were evaluated before and after 4 weeks of tuina therapy. Independent component analysis was applied to extract the specific networks related to sensation, execution, and cognition, including sensorimotor network (SMN), visual network (VN), auditory network (AN), anterior and posterior default mode network (aDMN, pDMN), left frontoparietal network and right frontoparietal network. ResultsCompared with the control group, data from the treatment group revealed two major findings: before tuina therapy, SMN had a profound influence on aDMN and AN greatly affected pDMN; however, after 4 weeks of tuina therapy, aDMN and SMN showed reversed causality. ConclusionChronic neck pain caused by cervical radiculopathy may influence the DMN, which plays an important role in emotion, cognition, and memory, by stimulating the sensory afferent network. Tuina not only significantly relieves pain and discomfort, but also reverses the causality between aDMN and SMN.

A combined study of GSK3β polymorphisms and brain network topological metrics in major depressive disorder

GSK3β genotypes may interact with major depressive disorder (MDD) and may have a role in determining regional gray matter volume differences from healthy comparison subjects. However, any associations of GSK3β genotypes with MDD related to abnormal functional brain activity have yet to be elucidated. In the present study, resting state functional brain networks were constructed by thresholding partial correlation matrices of 90 regions. Differences in the network features of GSK3β-rs6438552 genotypes were tested, and a 2×2 analysis of variance was performed to identify the main effects of genotypes, disease status, and their interactions in MDD. Compared with CC carriers, T+ carriers with MDD showed increased nodal centralities in many brain regions—mainly the limbic system, thalamus and parts of the parietal, temporal, occipital, and frontal regions. Decreased nodal centralities predominantly occurred in the sensorimotor area and parts of the frontal, occipital, and temporal lobes. Significant interactions between genotypes and disease status were found in the left thalamus, left superior occipital gyrus, and left inferior parietal lobe. Only altered nodal centrality in the left angular gyrus was negatively correlated with scores on the Hamilton Depression Rating Scale. Our results suggest the GSK3β genotypic effect of rs6438552 and its interaction with disease status may contribute to the altered topological organization of resting state functional brain networks in MDD patients.

Fluid intelligence and brain functional organization in aging yoga and meditation practitioners.

Numerous studies have documented the normal age-related decline of neural structure, function, and cognitive performance. Preliminary evidence suggests that meditation may reduce decline in specific cognitive domains and in brain structure. Here we extended this research by investigating the relation between age and fluid intelligence and resting state brain functional network architecture using graph theory, in middle-aged yoga and meditation practitioners, and matched controls. Fluid intelligence declined slower in yoga practitioners and meditators combined than in controls. Resting state functional networks of yoga practitioners and meditators combined were more integrated and more resilient to damage than those of controls. Furthermore, mindfulness was positively correlated with fluid intelligence, resilience, and global network efficiency. These findings reveal the possibility to increase resilience and to slow the decline of fluid intelligence and brain functional architecture and suggest that mindfulness plays a mechanistic role in this preservation.

Dopamine precursor depletion impairs structure and efficiency of resting state brain functional networks

Spatial patterns of functional connectivity derived from resting brain activity may be used to elucidate the topological properties of brain networks. Such networks are amenable to study using graph theory, which shows that they possess small world properties and can be used to differentiate healthy subjects and patient populations. Of particular interest is the possibility that some of these differences are related to alterations in the dopamine system. To investigate the role of dopamine in the topological organization of brain networks at rest, we tested the effects of reducing dopamine synthesis in 13 healthy subjects undergoing functional magnetic resonance imaging. All subjects were scanned twice, in a resting state, following ingestion of one of two amino acid drinks in a randomized, double-blind manner. One drink was a nutritionally balanced amino acid mixture, and the other was tyrosine and phenylalanine deficient. Functional connectivity between 90 cortical and subcortical regions was estimated for each individual subject under each dopaminergic condition. The lowered dopamine state caused the following network changes: reduced global and local efficiency of the whole brain network, reduced regional efficiency in limbic areas, reduced modularity of brain networks, and greater connection between the normally anti-correlated task-positive and default-mode networks. We conclude that dopamine plays a role in maintaining the efficient small-world properties and high modularity of functional brain networks, and in segregating the task-positive and default-mode networks.This article is part of the Special Issue Section entitled ‘Neuroimaging in Neuropharmacology’.

Changes in MEG resting-state networks are related to cognitive decline in type 1 diabetes mellitus patients.

ObjectiveIntegrity of resting-state functional brain networks (RSNs) is important for proper cognitive functioning. In type 1 diabetes mellitus (T1DM) cognitive decrements are commonly observed, possibly due to alterations in RSNs, which may vary according to microvascular complication status. Thus, we tested the hypothesis that functional connectivity in RSNs differs according to clinical status and correlates with cognition in T1DM patients, using an unbiased approach with high spatio-temporal resolution functional network.MethodsResting-state magnetoencephalographic (MEG) data for T1DM patients with (n = 42) and without (n = 41) microvascular complications and 33 healthy participants were recorded. MEG time-series at source level were reconstructed using a recently developed atlas-based beamformer. Functional connectivity within classical frequency bands, estimated by the phase lag index (PLI), was calculated within eight commonly found RSNs. Neuropsychological tests were used to assess cognitive performance, and the relation with RSNs was evaluated.ResultsSignificant differences in terms of RSN functional connectivity between the three groups were observed in the lower alpha band, in the default-mode (DMN), executive control (ECN) and sensorimotor (SMN) RSNs. T1DM patients with microvascular complications showed the weakest functional connectivity in these networks relative to the other groups. For DMN, functional connectivity was higher in patients without microangiopathy relative to controls (all p < 0.05). General cognitive performance for both patient groups was worse compared with healthy controls. Lower DMN alpha band functional connectivity correlated with poorer general cognitive ability in patients with microvascular complications.DiscussionAltered RSN functional connectivity was found in T1DM patients depending on clinical status. Lower DMN functional connectivity was related to poorer cognitive functioning. These results indicate that functional connectivity may play a key role in T1DM-related cognitive dysfunction.

An abnormal resting-state functional brain network indicates progression towards Alzheimer's disease.

Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer's disease, and brain network-connection strength, network efficiency, and nodal attributes are abnormal. However, existing research has only analyzed the differences between these patients and normal controls. In this study, we constructed brain networks using resting-state functional MRI data that was extracted from four populations (normal controls, patients with early mild cognitive impairment, patients with late mild cognitive impairment, and patients with Alzheimer's disease) using the Alzheimer's Disease Neuroimaging Initiative data set. The aim was to analyze the characteristics of resting-state functional neural networks, and to observe mild cognitive impairment at different stages before the transformation to Alzheimer's disease. Results showed that as cognitive deficits increased across the four groups, the shortest path in the resting-state functional network gradually increased, while clustering coefficients gradually decreased. This evidence indicates that dementia is associated with a decline of brain network efficiency. In addition, the changes in functional networks revealed the progressive deterioration of network function across brain regions from healthy elderly adults to those with mild cognitive impairment and Alzheimer's disease. The alterations of node attributes in brain regions may reflect the cognitive functions in brain regions, and we speculate that early impairments in memory, hearing, and language function can eventually lead to diffuse brain injury and other cognitive impairments.

Small-worldness and modularity of the resting-state functional brain network decrease with aging

The human brain is a complex network that is known to be affected by normal aging. Graph-based analysis has been used to estimate functional brain network efficiency and effects of normal aging on small-worldness have been reported. This relationship is further investigated here along with network modularity, a statistic reflecting how well a network is organized into modules of densely interconnected nodes. Modularity has previously been observed to vary as a function of working memory capacity, therefore we hypothesized that both small-worldness and modularity would show age-related declines. We found that both small-worldness and modularity were negatively correlated with increasing age but that this decline was relatively slow.

Research on Functional Brain Network Metrics for Depression Patients Automatic Identification

Nowadays, Brain network as a means of emerging brain disease research has been fully recognized which is applied to the neurological diseases, such as major depressive disorder (MDD). It also can detect the exception of the whole brain network topological. But there is no evidence to prove that abnormal brain network topology metrics can be an effective feature in the classification model to distinguish the healthy control and MDD. So, we hypothesize the abnormal brain network topology metrics can be used as an valid classification feature. Resting state functional brain networks were constructed for 26 healthy controls and 34 MDD patients by thresholding partial correlation matrices of 90 regions. According to the theory-based approaches, the global and local metrics were calculated. Non-parametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in support vector machine algorithm. The current study demonstrate that MDD is associated with abnormal function brain network topological metrics and statistically significance network metrics can be successfully used for feature selection in classification algorithms.

Research into community structure of resting-state brain functional network

The community detecting algorithm was applied to human functional network to explore the mechanism of human brain.The brain functional data of 28 healthy subjects were collected by functional Magnetic Resonance Imaging(fMRI),and the brain functional network of human beings based on time series was constructed.A threshold range of vertices in the network was designated according to modularity and full connected network theory.The community structures were detected by using the hierarchical clustering algorithm and the greedy algorithm respectively,and the experimental results show that similar community structures have been obtained.Then different performances can be explored across the threshold by analyzing the modularity.An effective threshold range of vertices between 180 to 320 in brain network was proposed.Exploring the community structure is helpful to comprehend the mechanism of brain lesions,which provides a tool for diagnosis and treatment of brain diseases.

Abnormal Functional Brain Network Metrics for Machine Learning Classifier in Depression Patients Identification

Brain network is a widely used tool for identifying abnormal topological properties in whole-brain networks which has been applied to neurological disease diagnosis such as Major Depressive Disorder (MDD). But there is not any study showing that abnormal brain network topological metrics can be used in machine learning classification methods for the identification of MDD patients. In order to find an appropriate feature selection method, we hypothesize that MDD disrupts the topological organization of functional brain networks and the abnormal topological metrics could be used as effective features in constructing a classifier. Resting state functional brain networks were constructed for 26 healthy controls and 34 MDD patients by thresholding partial correlation matrices of 90 regions. The topological metrics, including global and local, were calculated using graph theory-based approaches. Non-parametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in support vector machine algorithm. Result showed that both the MDD and control groups showed small-world architecture in brain functional networks. However, some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia and medial temporal and prefrontal regions. Support vector machine with radial basis kernel function algorithm exhibited the highest average accuracy (86.01%) with 28 features (p<0.05). Overall, the current study suggested that MDD is associated with abnormal functional brain network topological metrics and statistically significant network metrics can be successfully used in classification algorithms as features.