MDD brain network analysis based on EEG functional connectivity and graph theory

Randomized EEG functional brain networks in major depressive disorders with greater resilience and lower rich-club coefficient

Resting-state fNIRS (R-fNIRS) imaging data has proven to be a valuable technique to quantitatively characterize functional architectures of human brain network. However, whether the brain network metrics derived using weighted brain network model is test-retest (TRT) reliable remains largely unknown. Here, we firstly constructed weighted brain networks on a group of 18 participants, and then applied graph-theory approach to quantify topological parameters of each weighted network. The intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of network metrics. We found that the reliability of the weighted network metrics is threshold-sensitive, and most of these network metrics showed fair to excellent reliability. Specifically, the global network metrics, e.g., clustering coefficient, path length, local efficiency and global efficiency were of excellent level reliability (ICC >0.75) on both HbO and HbR signals. The nodal network metrics, e.g., degree and efficiency, generally also showed excellent level reliability on both HbO and HbR signals, and the reliability of these two metrics was better than that of nodal betweenness. Overall, these findings demonstrated that most weighted network metrics derived from fNIRS are TRT reliable and can be used for brain network research.

Major depressive disorder (MDD) is a common and intractable mood disorder. Electroconvulsive therapy (ECT) is a common means of brain electrical stimulation for the treatment of MDD, but the neurobiological mechanism of its clinical symptom relief effect is still to be explored. This study aims to explore how ECT plays a role in depression remissions by investigating the changes of static and dynamic brain network characteristics in MDD patients before and after ECT. Resting-state functional magnetic resonance imaging (fMRI) scans were obtained from nine MDD patients twice before and after a full course of ECT, all of whom responded to ECT as defined by at least a 50% reduction from baseline Hamilton Depression Scale (HAMD) scores. Both static and dynamic characteristics of the functional brain network were compared between the pre- and post-ECT scans for all participants, and the correlations between changes in clinical symptoms and altered network metrics were also investigated. The clustering coefficient and local efficiency in static brain networks were increased significantly, while the global flexibility of dynamic brain networks was decreased significantly after ECT. Several regions of interest (ROIs) that changed significantly at the local level were also identified, which involved regions of the cerebellum, hippocampus as well as frontal and temporal cortices. Although not significant, the decrease of HAMD scores were associated with trends of changed network metrics after ECT. Our results suggest that ECT may alleviate the depressive symptoms of MDD by decreasing the randomness of the brain network as reflected by changes in both static and dynamic network properties and that the temporal gyrus, frontal gyrus, hippocampus, and cerebellar regions may play key roles in such mechanisms. These findings have important implications for our understandings of ECT and depression. However, this study is limited by a relatively small sample size and the results should be confirmed in larger samples.

Disrupted intrinsic functional brain network in patients with late-life depression: Evidence from a multi-site dataset

Sustained attention is crucial for daily tasks, yet individualized prediction based on accessible neuroimaging like electroencephalography (EEG) remains scarce. To address this gap, this study developed an individualized predictive framework using functional connectivity (FC) and brain network metrics extracted from EEG during a psychomotor vigilance test (PVT). Connectome-based predictive modeling (CPM) was employed for FC and stepwise regression for network metrics, followed by a decision-level fusion strategy to integrate both features. Results demonstrated that models based on negative FC (r = 0.241) and network metrics (r = 0.345) both significantly predicted sustained attention measures, while the fusion model achieved superior performance. Contributing features were dominated by left-hemispheric, low-frequency (i.e., delta and theta) connectivity. Notably, poorer attention correlated with increased delta-band small-worldness and local efficiency, suggesting a neural compensatory mechanism. Overall, the work validates a multimodal feature fusion approach for EEG-based cognitive prediction and provides quantitative evidence of neural mechanisms underlying sustained attention.

Topological characteristics of the brain can be analyzed using structural brain networks constructed by diffusion tensor imaging (DTI). When a brain network is constructed by the existing parcellation method, the structure of the network changes depending on the scale of parcellation and arbitrary thresholding. To overcome these issues, we propose to construct brain networks using the improved $\varepsilon $-neighbor construction, which is a parcellation free network construction technique. We acquired DTI from 14 control subjects and 15 subjects with autism. We examined the differences in topological properties of the brain networks constructed using the proposed method and the existing parcellation between the two groups. As the number of nodes increased, the connectedness of the network decreased in the parcellation method. However, for brain networks constructed using the proposed method, connectedness remained at a high level even with an increase in the number of nodes. We found significant differences in several topological properties of brain networks constructed using the proposed method, whereas topological properties were not significantly different for the parcellation method. The brain networks constructed using the proposed method are considered as more realistic than a parcellation method with respect to the stability of connectedness. We found that subjects with autism showed the abnormal characteristics in the brain networks. These results demonstrate that the proposed method may provide new insights to analysis in the structural brain network. We proposed the novel brain network construction method to overcome the shortcoming in the existing parcellation method.

Adolescent major depressive disorder (MDD) is prevalent globally but often goes unnoticed due to differences in symptoms compared to adult criteria. Analyzing the brain from a network perspective provides new insights into higher-level brain functions and its pathophysiology. This study aimed to investigate changes in the topological organization of functional networks in adolescents with first-episode, treatment-naïve MDD. The study included 23 adolescents with depression and 27 matched healthy controls (HCs). Resting-state functional MRI (rs-fMRI) was conducted, and whole-brain functional networks were constructed. Graph theory analysis was used to evaluate network topological properties. A machine-learning multivariate diagnostic model was developed using network metrics associated with depression severity. Both the MDD and HC groups displayed small-world topology, with male MDD patients showing reduced global clustering efficiency (Cp). The nodal Cp (NCp) and local efficiency (NLE) in the bilateral pallidum were significantly positively correlated with depression severity. In contrast, nodal efficiency (NE) in the left medial orbital superior frontal gyri (ORBsupmed) showed a negative correlation with disease severity. A machine-learning multivariate model using regional network topological features produced an AUROC of 0.71 (95% CI: 0.54-0.92) and an F1 score of 0.65, successfully differentiating adolescent MDD from HCs. Our findings suggest disruptions in small-world topology in both global and local brain networks in adolescent depression. These abnormal nodal properties may serve as novel neural markers of the disorder.

Brain Connectivity: A Journal of Clinical Neurology, Neuroscience, and Neuroimaging Advancing the Field of Neurology.

Functional restoration of the cerebral cortex relies on activity-dependent neuroplasticity following stroke. However, optimizing robotic rehabilitation for hand recovery remains a significant challenge. This proof-of-concept study investigates the feasibility of a configurable soft pneumatic robot in modulating the brain network among ten participants with hand motor impairments after stroke. The programmable robotic intervention was administered in a randomized sequence for resting-state assessment, slow-mode, and fast-mode robotic therapy by adjusting working mode, action time, and interaction duration. Functional near-infrared spectroscopy (fNIRS) was employed to measure cortical activity and functional connectivity. Additionally, graph theory metrics, including clustering coefficient, average path length, small-world index, global efficiency, degree centrality, and eigenvector centrality, were derived from fNIRS-based functional connectivity matrices. The results demonstrated that robotic intervention significantly improved clustering coefficient (P = 0.034), average path length (P = 0.007), and global efficiency (P = 0.001). While small-world index, degree centrality, and eigenvector centrality showed an increasing trend, these differences did not reach statistical significance. Moreover, fast-mode therapy induced more substantial changes in clustering coefficient compared to slow-mode therapy, suggesting a potentially stronger effect on neural reorganization. These findings preliminarily support the use of soft robotics with adjustable paradigms to enhance brain network connectivity and facilitate neuroplasticity following stroke. The observed improvements in global efficiency and small-world properties indicate that robotic therapy optimizes cortical organization, promoting functional recovery. Further studies with larger sample sizes and personalized intervention protocols are needed to confirm these results and explore their long-term effects.

The human brain operates as a complex network, and understanding its functional connectivity is a core challenge in neuroscience. Functional near-infrared spectroscopy (fNIRS) offers a non-invasive, portable method for studying brain activity and connectivity, providing valuable insights into the brain's network dynamics. In this study, we used fNIRS to examine the functional connectivity of the human brain during the Dual n-back task, a cognitive challenge that varies in memory load (0-back, 1-back, and 2-back). Data were collected from 24 channels in the frontal cortex and pre-processed with discrete wavelet transform. Functional connectivity matrices for each task level were calculated using correlation analysis, and graph theory metrics such as clustering coefficient and local and global efficiency were assessed. Statistical comparisons (t-tests and ANOVA) revealed significant differences in these metrics across memory load levels, with higher memory loads leading to altered brain connectivity patterns (p < 0.05 for clustering coefficient and local efficiency, p < 0.04 for global efficiency). These findings suggest that as cognitive demand increases, the functional connectivity of the brain's frontal network changes, reflecting the dynamic nature of brain activity during complex tasks. This research highlights the potential of fNIRS for exploring brain network functions and has broader implications for understanding cognitive processes and developing neurocognitive diagnostics and interventions.

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

A noisy environment can considerably impact drivers' attention and fatigue, endangering driving safety. Consequently, this study designed a simulated driving experimental scenario to analyse the effects of noise generated during urban rail transit train operation on drivers' functional brain networks. The experiment recruited 16 participants, and the simulated driving scenario was conducted at noise levels of 50, 60, 70, and 80 dB. Functional connectivity between all electrode pairs across various frequency bands was evaluated using the weighted phase lag index (WPLI), and a brain network based on this was constructed. Graph theoretic analysis employed network global efficiency, degree, and clustering coefficient as metrics. Significant increases in the WPLI values of theta and alpha frequency bands were observed in high noise environments (70 dB, 80 dB), as well as enhanced brain synchronisation. Furthermore, concerning the topological metrics of brain networks, it was observed that the global efficiency of brain networks in theta and alpha frequency ranges, as well as the node degree and clustering coefficients, experienced substantial growth in high noise environments (70 dB, 80 dB) as opposed to 50 dB and 60 dB. This finding indicates that high-noise environments impact the reorganisation of functional brain networks, leading to a preference for network structures with improved global efficiency. Such findings may improve our understanding of the neural mechanisms of driving under noise exposure, and thus potentially reduce road accidents to some extent.

Several studies have been provided the principal activated regions and locations during action observation. However, it remains unclear that the dynamic link and information flows in different visual perspectives during the action observation tasks. The aim of this study was investigating the effective connectivity of different visual perspectives during action observation tasks and classify the perspectives. Eleven regions were selected as the regions of interest (ROI) by meta-analysis using the term of "action observation" for this study. We employed weighted and directed (WD) brain networks by Generalized partial directed coherence (GPDC) on the fMRI BOLD time series of action observation tasks from different visual perspectives. Afterward, the paired sample t-test was implemented to find the alteration of brain networks between the two perspectives. According to the results, global efficiency, local efficiency and cluster coefficient were statistically significantly different between first-person perspective (1PP) and third-person perspective (3PP) in Supplementary motor cortex (SMA) and Right inferior parietal lobe (RIPL) and Left inferior parietal lobe (LIPL). These three regions which we considered may provide the additional support for the visuospatial transformation from 3PP to 1PP. WD brain network topological measures of SMA, RIPL and LIPL were also selected to classify the 1PP and 3PP using support vector machine (SVM) with the best accuracy close to 90%.

Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding

ObjectiveUsing the phase synchronization of EEG signals, two different phases, PLI and PLV, were used to construct brain network analysis and graph convolutional neural network, respectively, to achieve automatic identification of Alzheimer’s disease (AD) and to assist in the early diagnosis of Alzheimer’s disease.MethodsIn this paper, we selected outpatients (16 AD subjects, 20 mild cognitive impairment (MCI) subjects and 21 healthy control (HC) subjects) from the outpatient clinic of Yangpu Mental Health Center in Shanghai, China, from January 2023 to December 2023, and collected resting-state EEG data. To collect resting-state EEG data, each patient was asked to sit down with eyes closed for 5 min. Firstly, the acquired EEG data were preprocessed to extract the data in the α-band at 8–13 Hz; secondly, the phase lag index (PLI) and phase-locked value (PLV) were used to construct the brain functional network, and the brain functional connectivity map was visualized by brain functional connectivity analysis. Finally, the constructed PLI and PLV were input into the graph convolutional neural network (GCN) model as node features for training and classification, respectively.ResultsHealthy controls had relatively strong mean brain functional connectivity in the PLV brain network compared to AD and MCI patients. MCI patients showed lower mean brain functional connectivity in the brain network of PLI, while all three groups showed significant differences in brain functional connectivity between parietal and occipital lobes. The GCN model improved classification accuracy by more than 10% compared to using a machine learning classifier. When PLV was used as the nodal feature in the GCN model, the model achieved an average classification accuracy of 77.80% for the three groups of AD, MCI and HC, which was an improvement over the accuracy of choosing raw EEG data and PLI as the nodal feature. The performance of the model was further validated.ConclusionsThe experimental results show that the GCN model can effectively identify the graph structure compared with the traditional machine learning model, the GCN-PLV model can better classify AD patients, and the alpha band is proved to be more suitable for AD resting-state EEG by tenfold cross-validation. The brain network map constructed based on PLI and PLV can further capture the local features of EEG signals and the intrinsic functional relationships between brain regions, and the combination of these two models has certain reference value for the diagnosis of AD patients.