OBJECTIVE: Altered brain functional connectivity has been proposed as the neurobiological underpinnings of attention-deficit/hyperactivity disorder (ADHD), and the default mode interference hypothesis is one of the most popular neuropsychological models. Here, we explored whether this hypothesis is supported in adults with ADHD and the association with high-risk genetic variants and treatment outcomes.
METHODS: Voxel-based whole-brain connectome analysis was conducted on resting-state functional MRI data from 84 adults with ADHD and 89 healthy controls to identify functional connectivity substrates corresponding to ADHD-related alterations. The candidate genetic variants and 12-week cognitive behavioral therapy data were leveraged from the same population to assess these associations.
RESULTS: We detected breakdowns of functional connectivity in the precuneus and left middle temporal gyrus in adults with ADHD, with exact contributions from decreased connectivity within the default mode, dorsal and ventral attention networks, as well as increased connectivity among them with the middle temporal gyrus serving as a crucial \'bridge\'. Additionally, significant associations between the altered functional connectivity and genetic variants in both MAOA and MAOB were detected. Treatment restored brain function, with the amelioration of connectivity of the middle temporal gyrus, accompanied by improvements in ADHD core symptoms.
CONCLUSIONS: These findings support the interference of default mode on attention in adults with ADHD and its association with genetic risk variants and clinical management, providing insights into the underlying pathogenesis of ADHD and potential biomarkers for treatment evaluation.

OBJECTIVE: Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD.
METHODS: We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored.
RESULTS: Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing \"widely weaker\" FNC, but lower in States 2 and 4, representing \"locally stronger\" and \"widely stronger\" FNC, respectively. In State 1, representing \"widely moderate\" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration.
CONCLUSIONS: These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.

White matter (WM) functional activity has been reliably detected through functional magnetic resonance imaging (fMRI). Previous studies have primarily examined WM bundles as unified entities, thereby obscuring the functional heterogeneity inherent within these bundles. Here, for the first time, we investigate the function of sub-bundles of a prototypical visual WM tract-the optic radiation (OR). We use the 7T retinotopy dataset from the Human Connectome Project (HCP) to reconstruct OR and further subdivide the OR into sub-bundles based on the fiber\'s termination in the primary visual cortex (V1). The population receptive field (pRF) model is then applied to evaluate the retinotopic properties of these sub-bundles, and the consistency of the pRF properties of sub-bundles with those of V1 subfields is evaluated. Furthermore, we utilize the HCP working memory dataset to evaluate the activations of the foveal and peripheral OR sub-bundles, along with LGN and V1 subfields, during 0-back and 2-back tasks. We then evaluate differences in 2bk-0bk contrast between foveal and peripheral sub-bundles (or subfields), and further examine potential relationships between 2bk-0bk contrast and 2-back task d-prime. The results show that the pRF properties of OR sub-bundles exhibit standard retinotopic properties and are typically similar to the properties of V1 subfields. Notably, activations during the 2-back task consistently surpass those under the 0-back task across foveal and peripheral OR sub-bundles, as well as LGN and V1 subfields. The foveal V1 displays significantly higher 2bk-0bk contrast than peripheral V1. The 2-back task d-prime shows strong correlations with 2bk-0bk contrast for foveal and peripheral OR fibers. These findings demonstrate that the blood oxygen level-dependent (BOLD) signals of OR sub-bundles encode high-fidelity visual information, underscoring the feasibility of assessing WM functional activity at the sub-bundle level. Additionally, the study highlights the role of OR in the top-down processes of visual working memory beyond the bottom-up processes for visual information transmission. Conclusively, this study innovatively proposes a novel paradigm for analyzing WM fiber tracts at the individual sub-bundle level and expands understanding of OR function.

BACKGROUND: The different symptoms of major depressive disorder (MDD) in adolescents compared to adults suggested there may be differences in the pathophysiology between adolescents and adults with MDD. However, despite the amygdala being considered critical in the pathophysiology, there was limited knowledge about the commonalities and differences in the resting-state functional connectivity (rsFC) of amygdala subregions in MDD patients of different age groups.
METHODS: In the current study, 65 adolescents (46 with MDD and 19 controls) and 91 adults (35 with MDD and 56 controls) were included. A seed-based functional connectivity analysis was performed for each of the amygdala subregions. A 2 × 2 ANOVA was used to analyze the main effect of age, diagnosis, and their interaction on the rsFC of each subregion.
RESULTS: A significant main effect of age was revealed in the rsFC of bilateral centromedial (CM) subregions and right laterobasal (LB) subregion with several brain regions in the limbic system and frontoparietal network. The significant main effect of diagnosis showed MDD patients of different ages showed higher connectivity than controls between the right LB and left middle frontal gyrus (MFG).
CONCLUSIONS: The rsFC of specific amygdala subregions with brain regions in the limbic system and frontoparietal network is affected by age, indicating a distinct amygdala connectivity profile in adolescents. The decreased rsFC between the right LB and the left MFG in adolescents and adults with MDD could serve as a diagnostic biomarker and a target of nonpharmacological treatment for MDD.

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.

BACKGROUND: Anxious depression (AD) is a common subtype of major depressive disorder (MDD). Neuroimaging studies of AD have revealed inconsistent and heterogeneous brain alterations with the use of single-model methods. Therefore, it is necessary to explore the pathogenesis of AD using multi-model imaging analyses to obtain more homogeneous and robust results.
METHODS: One hundred and eighty-two patients with MDD and 64 matched healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) was used to estimate the gray matter volume (GMV) of all subjects. The GMV differences between the AD and non-anxious depression (NAD) participants were used as regions of interest (ROIs) for subsequent resting state functional connectivity (rs-FC) analyses. Correlation analysis was used to evaluate the associations between clinical symptoms and abnormal function in specific brain areas.
RESULTS: Decreased GMV in the medial frontal gyrus (MFG) and the superior frontal gyrus (SFG) was observed in the AD group compared to the NAD group. Taking the MFG and SFG as ROIs, the rs-FC analysis revealed decreased FC between the left SFG and left temporal pole and between the left SFG and right MFG in the AD group compared to the NAD group. Finally, the FC between the left SFG and left temporal pole was negatively correlated with HAMD-17 scores in the AD group.
CONCLUSIONS: By combining the GMV and rs-FC models, this study revealed that structural and functional disruption of the affective network may be an important pathophysiology underlying AD. The structural impairment may serve as the foundation of the functional impairment.

Modular dynamic graph theory metrics effectively capture the patterns of dynamic information interaction during human brain development. While existing research has employed modular algorithms to examine the overall impact of dynamic changes in community structure throughout development, there is a notable gap in understanding the cross-community dynamic changes within different functional networks during early childhood and their potential contributions to the efficiency of brain information transmission. This study seeks to address this gap by tracing the trajectories of cross-community structural changes within early childhood functional networks and modeling their contributions to information transmission efficiency. We analyzed 194 functional imaging scans from 83 children aged 2 to 8 years, who participated in passive viewing functional magnetic resonance imaging sessions. Utilizing sliding windows and modular algorithms, we evaluated three spatiotemporal metrics-temporal flexibility, spatiotemporal diversity, and within-community spatiotemporal diversity-and four centrality metrics: within-community degree centrality, eigenvector centrality, between-community degree centrality, and between-community eigenvector centrality. Mixed-effects linear models revealed significant age-related increases in the temporal flexibility of the default mode network (DMN), executive control network (ECN), and salience network (SN), indicating frequent adjustments in community structure within these networks during early childhood. Additionally, the spatiotemporal diversity of the SN also displayed significant age-related increases, highlighting its broad pattern of cross-community dynamic interactions. Conversely, within-community spatiotemporal diversity in the language network exhibited significant age-related decreases, reflecting the network\'s gradual functional specialization. Furthermore, our findings indicated significant age-related increases in between-community degree centrality across the DMN, ECN, SN, language network, and dorsal attention network, while between-community eigenvector centrality also increased significantly for the DMN, ECN, and SN. However, within-community eigenvector centrality remained stable across all functional networks during early childhood. These results suggest that while centrality of cross-community interactions in early childhood functional networks increases, centrality within communities remains stable. Finally, mediation analysis was conducted to explore the relationships between age, brain dynamic graph metrics, and both global and local efficiency based on community structure. The results indicated that the dynamic graph metrics of the SN primarily mediated the relationship between age and the decrease in global efficiency, while those of the DMN, language network, ECN, dorsal attention network, and SN primarily mediated the relationship between age and the increase in local efficiency. This pattern suggests a developmental trajectory in early childhood from global information integration to local information segregation, with the SN playing a pivotal role in this transformation. This study provides novel insights into the mechanisms by which early childhood brain functional development impacts information transmission efficiency through cross-community adjustments in functional networks.

OBJECTIVE: Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia.
METHODS: A predictive model used connectome-based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre-treatment functional connectivity (FC) data from 59 patients with MDD. Node-based FC analysis was employed to compare differences in FC patterns between melancholic and non-melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients.
RESULTS: CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non-melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non-melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method.
CONCLUSIONS: This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non-melancholic MDD patients. These findings provide guidance for clinical treatment.

UNASSIGNED: Patients with temporal lobe epilepsy (TLE) often exhibit neurocognitive disorders; however, we still know very little about the pathogenesis of cognitive impairment in patients with TLE. Therefore, our aim is to detect changes in the structural connectivity networks (SCN) of patients with TLE.
UNASSIGNED: Thirty-five patients with TLE were compared with 47 normal controls (NC) matched according to age, gender, handedness, and education level. All subjects underwent thin-slice T1WI scanning of the brain using a 3.0 T MRI. Then, a large-scale structural covariance network was constructed based on the gray matter volume extracted from the structural MRI. Graph theory was then used to determine the topological changes in the structural covariance network of TLE patients.
UNASSIGNED: Although small-world networks were retained, the structural covariance network of TLE patients exhibited topological irregularities in regular architecture as evidenced by an increase in the small world properties (p < 0.001), normalized clustering coefficient (p < 0.001), and a decrease in the transfer coefficient (p < 0.001) compared with the NC group. Locally, TLE patients showed a decrease in nodal betweenness and degree in the left lingual gyrus, right middle occipital gyrus and right thalamus compared with the NC group (p < 0.05, uncorrected). The degree of structural networks in both TLE (Temporal Lobe Epilepsy) and control groups was distributed exponentially in truncated power law. In addition, the stability of random faults in the structural covariance network of TLE patients was stronger (p = 0.01), but its fault tolerance was lower (p = 0.03).
UNASSIGNED: The objective of this study is to investigate the potential neurobiological mechanisms associated with temporal lobe epilepsy through graph theoretical analysis, and to examine the topological characteristics and robustness of gray matter structural networks at the network level.

The fragmentation of the functional brain network has been identified through the functional connectivity (FC) analysis in studies investigating anesthesia-induced loss of consciousness (LOC). However, it remains unclear whether mild sedation of anesthesia can cause similar effects. This paper aims to explore the changes in local-global brain network topology during mild anesthesia, to better understand the macroscopic neural mechanism underlying anesthesia sedation. We analyzed high-density EEG from 20 participants undergoing mild and moderate sedation of propofol anesthesia. By employing a local-global brain parcellation in EEG source analysis, we established binary functional brain networks for each participant. Furthermore, we investigated the global-scale properties of brain networks by estimating global efficiency and modularity, and examined the changes in meso-scale properties of brain networks by quantifying the distribution of high-degree and high-betweenness hubs and their corresponding rich-club coefficients. It is evident from the results that the mild sedation of anesthesia does not cause a significant change in the global-scale properties of brain networks. However, network components centered on SomMot L show a significant decrease, while those centered on Default L, Vis L and Limbic L exhibit a significant increase during the transition from wakefulness to mild sedation (p<0.05). Compared to the baseline state, mild sedation almost doubled the number of high-degree hubs in Vis L, DorsAttn L, Limbic L, Cont L, and reduced by half the number of high-degree hubs in SomMot R, DorsAttn R, SalVentAttn R. Further, mild sedation almost doubled the number of high-betweenness hubs in Vis L, Vis R, Limbic R, Cont R, and reduced by half the number of high-betweenness hubs in SomMot L, SalVentAttn L, Default L, and SomMot R. Our results indicate that mild anesthesia cannot affect the global integration and segregation of brain networks, but influence meso-scale function for integrating different resting-state systems involved in various segregation processes. Our findings suggest that the meso-scale brain network reorganization, situated between global integration and local segregation, could reflect the autonomic compensation of the brain for drug effects. As a direct response and adjustment of the brain network system to drug administration, this spontaneous reorganization of the brain network aims at maintaining consciousness in the case of sedation.