GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

Alterations in Topological Structure and Modular Interactions in Pediatric Patients with Complete Spinal Cord Injury: A Functional Brain Network Study

Traumatic complete spinal cord injury (CSCI) leads to severe impairment of sensory-motor function, and patients often suffer from neuropsychological deficits such as anxiety, depression, and cognitive deficits, which involve different brain functional modules. However, the alterations in modular organization and the interactions between these modules in pediatric patients with CSCI remain unclear. In this study, a total of 70 participants, including 34 pediatric CSCI patients and 36 healthy controls (HCs) aged 6 to 12 years, underwent whole-brain resting-state functional MRI. The functional networks were analyzed via a graph theory approach based on the 90-region Automated Anatomical Labeling (AAL 90) atlas, generating a 90 × 90 correlation matrix. Metrics for nodal, global, and modular scales were calculated to evaluate alterations in the network's topology. Between-group comparisons and partial correlation analysis were performed. Compared to HCs, pediatric CSCI patients exhibited significant decreases in nodal metrics, particularly in subcortical networks (SN) like the bilateral thalamus. Besides, the distribution of core nodes changed, with five newly added core nodes primarily located in the regions of the default mode network (DMN). For modular interactions, patients group presented increased connectivity within the DMN and between the DMN and the attention network (AN) but reduced connectivity between DMN and SN, DMN and vision network (VN), and AN and SN. Notably, the participation coefficient (Pc) of the TPOmid.L (left temporal pole: middle temporal gyrus) was positively correlated with motor scores, suggesting its potential as an indicator for evaluating the motor function in pediatric CSCI patients. Additionally, the patients demonstrated a different modular structure with significantly lower modularity. These findings suggest that functional network and modular alterations chiefly occur in emotional cognition and vision-associated regions, emphasizing the importance to focus on their psychocognitive well-being and providing evidence for visual-feedback related rehabilitation strategies.

Individualized morphological covariation network aberrance associated with seizure relapse after antiseizure medication withdrawal

This study intents to detect graphical network features associated with seizure relapse following antiseizure medication (ASM) withdrawal. Twenty-four patients remaining seizure-free (SF-group) and 22 experiencing seizure relapse (SR-group) following ASM withdrawal as well as 46 matched healthy participants (Control) were included. Individualized morphological similarity network was constructed using T1-weighted images, and graphic metrics were compared between groups. Relative to the Control, the SF-group exhibited lower local efficiency, while the SR-group displayed lower global and local efficiency and longer characteristic path length. Both patient groups displayed reduced centrality in certain subcortical and cortical nodes than the Control, with a more pronounced reduction in the SR-group. Additionally, the SR-group exhibited lower centrality of the right pallidum than the SF-group. Decreased subcortical-cortical connectivity was found in both patient groups than the Control, with a more extensive decrease in the SR-group. Furthermore, an edge connecting the right pallidum and left middle temporal gyrus exhibited decreased connectivity in the SR-group than in the SF-group. A weaker small-worldization network upon medication withdrawal, potentially underpinned by node decentralization and subcortical-cortical decoupling, may elevate the risk of seizure relapse.

The association between anhedonia and prefrontal cortex activation in patients with major depression: a functional near-infrared spectroscopy study

This study investigated cerebral blood flow characteristics using functional nearinfrared spectroscopy (fNIRS) in agomelatine-treated depressed patients with anhedonia. The level of anhedonia was assessed by the Snaith Hamilton Rating Scale (SHAPS) and Montgomery Åsberg Depression Rating Scale 5-item (MADRS 5-item) score. All 84 patients were evaluated on the day of the study initiation and followed at week 1, 4 and 8 after the study initiation. Graph theory-based network analysis showed 2-back task-modulated global efficiency (adjusted B = 0.055, 95% CI = 0.043 - 0.066) and local efficiency (adjusted B = 0.066, 95% CI = 0.050 - 0.081) were significantly increased 1 week after treatment compared with the baseline. Furthermore, the increased oxy-hemoglobin (oxy-Hb) values from the baseline to the one week after treatment was positively related to the total MADRAS 5-item score reductions in the left-hemispheric orbitofrontal cortex (r = - 0.307, p = 0.005). Our findings suggest that abnormal functional connectivity over OFC may reflect the pathophysiological characteristics of anhedonia and serve as a clinical biomarker for anhedonia.

Association Between White Matter Hyperintensity and Cognitive Impairment in Cerebral Small Vessel Disease: The Frequency-dependent Role of Brain Functional Activity

BACKGROUND

Cognitive dysfunction in cerebral small vessel disease (CSVD) patients is associated with white matter hyperintensity (WMH), which demonstrates frequency-dependent correlations with brain functional activities. However, the neural mechanisms underlying the relationship between these structural and functional abnormalities and cognitive impairment remain unclear.

METHODS

We recruited 34 CSVD patients (mean age 63.74 ± 4.85 years, 19 males) and 45 age-matched healthy controls (mean age 63.69 ± 6.15 years, 15 males). All participants underwent magnetic resonance imaging (MRI) scanning and comprehensive cognitive assessments, including three behavioral tasks and a cognitive questionnaire battery. Regional brain activity and network topological properties were separately compared between the two groups for each of the three frequency bands (slow-4, slow-5, and typical band) using two-sample t-tests. Simple and multiple mediation analyses were performed to examine the relationships among WMH, functional brain measures, and global cognition.

RESULTS

CSVD patients exhibited frequency-specific alterations in regional activity and reduced global functional organization in the slow-4 band. Frequency-dependent functional measures in the slow-4 band significantly mediated the relationship between deep WMH and cognitive performance.

CONCLUSION

Our findings demonstrate the frequency-specific mediating role of abnormal brain functions in the pathophysiological pathway linking WMHs to cognitive impairment. This study provides new insight into the pathological mechanisms underlying WMH-related cognitive dysfunction.

CLINICAL TRIAL REGISTRATION

ChiCTR2100043346, 02 November 2021, https://www.chictr.org.cn/showproj.html?proj=52285.

Sex-specific brain morphological and network differences in patients showing Parkinson's disease with and without possible rapid eye movement sleep behavior disorder

Background

Sex is a crucial determinant in the clinical manifestations of diseases. However, previous studies have not clarified whether altered brain morphology shows sex-specific patterns in patients with Parkinson's disease (PD) with or without possible rapid eye movement sleep behavior disorder (RBD). This study aimed to investigate sex-specific differences in the patterns of morphological changes among different subgroups of PD.

Methods

High-resolution T1-weighted magnetic resonance imaging and clinical scale data were collected from 278 participants in the Parkinson's disease Progression Marker Initiative database: 93 patients with PD-pRBD (60 males, 33 females), 114 patients showing PD without RBD (PDnon-pRBD group; 68 males, 46 females), and 71 healthy controls (HCs; 44 males, 17 females). The Computational Anatomy Toolbox (CAT) 12 was utilized to collect data on gray matter volume (GMV) and cortical morphological metrics. Subsequently, individual-level morphological similarity networks were constructed on the basis of these cortical metrics. Finally, the topological properties of the network were analyzed using graph theoretical methods.

Results

In the PD-pRBD group, the GMV in the frontal and temporal lobes of males was lower than that of females. In contrast, the gyrification index (GI) of the frontal lobe in males was lower than that in females within the PDnon-pRBD group. Network analyses based on graph theory revealed that male PD-pRBD patients showed lower network information integration than female patients, particularly in terms of the global properties of fractal dimension (FD) networks. Moreover, in the PD-pRBD group, male patients showed a strong correlation between morphological network metrics and cognitive performance, as measured by the Hopkins Verbal Learning Test-Revised (HVLT-R) memory scores.

Conclusion

The presence of more significant sex-related differences in brain morphological changes in the PD-pRBD group in comparison with the PDnon-pRBD group highlights the importance of considering sex-related differences in the diagnosis and management of patients with PD-pRBD.

Longitudinal alterations in morphological brain networks and cognitive function in common-type COVID-19: a 3-month follow-up study

Purpose

To investigate the morphological network and cognitive function of patients with common-type coronavirus disease 2019 (COVID-19) during the acute phase, and examine dynamic changes at 3-month follow-up.

Methods

At baseline, high-resolution T1-weighted imaging was conducted in 35 patients with COVID-19 and 40 healthy controls; 22 patients were reassessed at 3 months. All patients underwent cognitive assessments. Individual morphological brain networks were constructed using grey matter volume similarity, and topological properties were analyzed using graph theory. We used an independent sample t-test at baseline and a paired sample t-test to compare the 3-month follow-up with the acute phase, with false discovery rate corrections (p < 0.05).

Results

In the acute phase, patients exhibited increased subcortical network (SCN) connectivity, and reduced connectivity between the frontoparietal network (FPN) and limbic network (LN), the SCN and dorsal/ventral attention network (DAN/VAN), and the LN and DAN. At follow-up, SCN connectivity remained elevated, with partial recovery in SCN-DAN/VAN and LN-DAN connectivity, and significant FPN-LN improvements. Enhanced global efficiency and reduced path length indicated improved network integration. Additionally, digit symbol substitution test and verbal fluency test scores improved over time.

Conclusion

COVID-19 induces short-term disruptions in cognition-related morphological subnetworks, with subcortical networks compensating for these changes. Significant recovery in FPN-LN connectivity and partial restoration of other networks highlight the plasticity of the brain and suggest that FPN-LN connectivity is a potential neuroimaging marker for cognitive recovery.

Disrupted coordination between primary and high-order cognitive networks in Parkinson's disease based on morphological and functional analysis

Patients with Parkinson's disease (PD) exhibit structural and functional alterations in both primary and high-order cognitive networks, but the interactions within aberrant functional networks and relevant structural foundation remains unexplored. In this study, the functional networks (FN) and the morphometric similarity networks (MSN) were constructed respectively based on the time-series data and gray matter volume from the MRI data of PD patients and controls. The efficiency, average controllability and k-shell values of the FN and MSN were calculated to evaluate their ability of information transmission and identify structural and functional abnormalities in PD. The abnormal regions were categorized into five types: regions with MSN abnormalities, regions with FN abnormalities, regions with both MSN and FN abnormalities, regions with abnormalities only in MSN but not in FN and regions with abnormalities only in FN but not in MSN. Further, the dynamic causal model (DCM) was used to evaluate the causal relationship of information flow between the identified regions. In the network property analysis of the FN, PD patients showed decreased global efficiency and connectivity in the visual network (VIS) and increased global efficiency in higher-order cognitive networks, including the ventral attention network (VAN), default mode network (DMN), and the limbic network (LIM) but no difference in MSN. In the DCM analysis of the regions, PD patients exhibited increased excitatory transition from the visual areas to the superior frontal gyrus, whereas had disturbed information flow from the visual areas to the insula and the orbitofrontal cortex. These findings suggest changes in structural and functional brain of PD patients, and advance our understanding of PD pathogenesis from different neural dimensions.

EEG-Based Micro-Expression Recognition: Flexible Brain Network Reconfiguration Supporting Micro-Expressions Under Positive Emotion

Purpose

Micro-expression recognition is valuable in clinical, security, judicial, economic, educational, and human-computer interaction fields. Electroencephalography (EEG)-based micro-expression recognition has gained attention for its objectivity and resistance to interference, unlike image-based methods. However, the neural mechanisms of micro-expressions remain unclear, limiting the development of EEG-based recognition technology.

Methods

We explored the brain reorganization mechanisms of micro-expressions (compared with macro-expressions and neutral expressions) under positive emotions across global networks, functional network modules, and hub brain regions using EEG, graph theory analysis, and functional connectivity.

Results

In global network, micro-expressions demonstrated higher network efficiency, clustering coefficient, and local efficiency, along with shorter average path lengths. In functional network modules, micro-expressions enhanced connectivity between the bilateral superior frontal gyrus (SFG), anterior cingulate cortex, and ventromedial prefrontal cortex (cognitive control), as well as between the left orbitofrontal cortex (OFC), temporal pole (TP), and inferior frontal gyrus (emotional processing). In hub brain regions, micro-expressions increased the hub centrality, information transmission efficiency, and local clustering of bilateral SFG, left OFC, left TP, and left Broca's area.

Conclusion

Micro-expressions require more efficient global communication and specialized emotion and cognitive control modules. Key hub regions supporting positive micro-expressions include the bilateral SFG (inhibitory control), left OFC and TP (emotion processing), and left Broca's area (language processing).

Impact of age-related hearing loss on decompensation of left DLPFC during speech perception in noise: a combined EEG-fNIRS study

Understanding speech-in-noise is a significant challenge for individuals with age-related hearing loss (ARHL). Evidence suggests that increased activity in the frontal cortex compensates for impaired speech perception in healthy aging older adults. However, whether older adults with ARHL still show preserved compensatory function and the specific neural regulatory mechanisms underlying such compensation remains largely unclear. Here, by utilizing a synchronized EEG-fNIRS test, we investigated the neural oscillatory characteristics of the theta band and synchronous hemodynamic changes in the frontal cortex during a speech recognition task in noise. The study included healthy older adults (n = 26, aged 65.4 ± 2.8), those with mild hearing loss (n = 26, aged 66.3 ± 3.8), and those with moderate to severe hearing loss (n = 26, aged 67.5 ± 3.7). Results showed that, relative to healthy older adults, older adults with ARHL exhibited lower activation and weakened theta band neural oscillations in the left dorsolateral prefrontal cortex (DLPFC) under noisy conditions, and this decreased activity correlated with high-frequency hearing loss. Meanwhile, we found that the connectivity of the frontoparietal network was significantly reduced, which might depress the top-down articulatory prediction function affecting speech recognition performance in ARHL older adults. The results suggested that healthy aging older adults might exhibit compensatory attentional resource recruitment through a top-down auditory-motor integration mechanism. In comparison, older adults with ARHL reflected decompensation of the left DLPFC involving the frontoparietal integration network during speech recognition tasks in noise.

Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder

INTRODUCTION

Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.

METHODS

A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging. Cortical thickness, surface area, and subcortical volumes were measured using FreeSurfer software. Common and classic machine learning models were utilized to identify distinct morphometric alterations between BD and MDD.

RESULTS

Significant morphological differences were observed in both common and distinct brain regions between BD, MDD, and HC. Specifically, abnormalities in the amygdala, thalamus, medial orbitofrontal cortex and fusiform were observed in both BD and MDD compared with HC. Relative to HC, unique differences in BD were identified in the lateral occipital and inferior/middle temporal regions, whereas MDD exhibited differences in nucleus accumbens and middle temporal regions. BD exhibited larger surface area in right middle temporal gyrus and greater right nucleus accumbens volume compared to MDD. The integration of two-stage models, including deep neural network (DNN) and support vector machine (SVM), achieved an accuracy rate of 91.2% in discriminating individuals with BD from MDD.

CONCLUSION

These findings demonstrate that structural MRI combined with machine learning techniques can accurately discriminate individuals with BD from MDD, and provide a foundation supporting the potential of this approach to improve diagnostic accuracy.

Impaired brain ability of older adults to transit and persist to latent states with well-organized structures at wakeful rest

The intrinsic brain functional network organization continuously changes with aging. By integrating spatial and temporal information, the process of how brain networks temporally reconfigure and remain well-organized spatial structure largely reflects the brain function, thereby holds the potential to capture its age-related declines. In this study, we examined the spatiotemporal brain dynamics from resting-state functional Magnetic Resonance Imaging (fMRI) data of healthy young and older adults using a Hidden Markov Model (HMM). Six brain states were generated by HMM, with the young group showing higher fractional occupancy and mean dwell time in states 1, 3, and 4 (SY1, SY2 and SY3), and the older group in states 2, 5, and 6 (SO1, SO2 and SO3). Importantly, comparisons of transition probabilities revealed that the older group showed a reduced brain ability to transition into states dominated by the younger group, as well as a diminished capacity to persist in them. Moreover, graph analysis revealed that these young-specific states exhibited higher modularity and k-coreness. Collectively, these findings suggested that the older group showed impaired brain ability of both transition into and sustain well spatially organized states. This emphasized that the temporal changes in brain state organization, rather than its static mode, could be a key biomarker for detecting age-related functional decline. These insights may pave the way for targeted interventions aimed at mitigating cognitive decline in the aging population.

The Neural Association Between Symptom and Cognition in Major Depressive Disorder: A Network Control Theory Study

Major depressive disorder (MDD) is characterized by intercorrelated clinical symptoms and cognitive deficits, whose neural mechanisms in relation to these disturbances remain unclear. To elucidate this, we applied the relatively new approach of Network Control Theory (NCT), which considers how network topology informs brain dynamics based on white matter connectivity data. We used the NCT parameter of average controllability (AC) to assess the potential control that brain network nodes have on brain-state transitions associated with clinical and cognitive symptoms in MDD. DTI and high-resolution T1-weighted anatomical imaging were performed on 170 MDD patients (mean age 31.6 years; 72 males, 98 females) and 137 healthy controls (HC; mean age 33.4 years; 64 males, 73 females). We used an NCT approach to compare AC between the groups. We then performed partial Spearman's rank correlation and moderation/mediation analyses for AC and cognition and clinical symptom scores. Compared with HC, MDD patients had lower AC in the left precuneus and superior parietal lobule and higher AC in the right precentral gyrus (preCG) and superior frontal gyrus (SFG), predominantly in the default-mode, somatomotor, and attention networks. In the HC group, AC of right preCG was positively associated with processing speed. While in the MDD group, AC of right SFG was negatively associated with memory function and also negatively moderated the association between memory and anxiety symptoms. The current study highlighted that the altered brain controllability may provide a novel understanding of the neural substrate underlying cognitive control in MDD. Disrupted control of right SFG during state transitions may partially explain the variable relationship between memory and anxiety symptoms in MDD.

Differences in Topography of Individual Amyloid Brain Networks by Amyloid PET Images in Healthy Control, Mild Cognitive Impairment, and Alzheimer's Disease

Amyloid plaques, implicated in Alzheimer's disease, exhibit a spatial propagation pattern through interconnected brain regions, suggesting network-driven dissemination. This study utilizes PET imaging to investigate these brain connections and introduces an innovative method for analyzing the amyloid network. A modified version of a previously established method is applied to explore distinctive patterns of connectivity alterations across cognitive performance domains. PET images illustrate differences in amyloid accumulation, complemented by quantitative network indices. The normal control group shows minimal amyloid accumulation and preserved network connectivity. The MCI group displays intermediate amyloid deposits and partial similarity to normal controls and AD patients, reflecting the evolving nature of cognitive decline. Alzheimer's disease patients exhibit high amyloid levels and pronounced disruptions in network connectivity, which are reflected in low levels of global efficiency (Eg) and local efficiency (Eloc). It is mostly in the temporal lobe where connectivity alterations are found, particularly in regions related to memory and cognition. Network connectivity alterations, combined with amyloid PET imaging, show potential as discriminative markers for different cognitive states. Dataset-specific variations must be considered when interpreting connectivity patterns. The variability in MCI and AD overlap emphasizes the heterogeneity in cognitive decline progression, suggesting personalized approaches for neurodegenerative disorders. This study contributes to understanding the evolving network characteristics associated with normal cognition, MCI, and AD, offering valuable insights for developing diagnostic and prognostic markers.

Functional and Structural Network Alterations in HIV-Associated Asymptomatic Neurocognitive Disorders: Evidence for Functional Disruptions Preceding Structural Changes

Purpose

This study focuses on the asymptomatic neurocognitive impairment (ANI) stage of HIV-associated neurocognitive disorders (HAND). Using multimodal MRI and large-scale brain network analysis, we aimed to investigate alterations in functional networks, structural networks, and functional-structural coupling in persons with ANI.

Patients and Methods

A total of 95 participants, including 48 healthy controls and 47 persons with HIV-ANI, were enrolled. Resting-state fMRI and diffusion tensor imaging were used to construct functional and structural connectivity matrices. Graph-theoretical analysis was employed to assess inter-group differences in global metrics, nodal characteristics, and functional-structural coupling patterns. Furthermore, machine learning classifiers were used to construct and evaluate classification models based on imaging features from both groups. The performance of different models was compared to identify the optimal diagnostic model for detecting HIV-ANI.

Results

Structural network analysis showed no significant changes in the global or local topological properties of persons with ANI. In contrast, functional networks exhibited significant reorganization in key regions, including the visual, executive control, and default mode networks. Functional-structural coupling was significantly enhanced in the occipital and frontal networks. These changes correlated with immune status, infection duration, and cognitive performance. Furthermore, the classification model integrating graph-theoretical topological features and functional connectivity achieved the best performance, with an area under the curve (AUC) of 0.962 in the test set.

Conclusion

Functional network reorganization and enhanced functional-structural coupling may reflect early synaptic and dendritic damage in persons with ANI, serving as potential early warning signals for HAND progression. These findings provide sensitive biomarkers and valuable perspectives for early diagnosis and intervention.

Graph theory analysis of a human body metabolic network: A systematic and organ-specific study

PURPOSES

Positron emission tomography (PET) imaging is widely used to detect focal lesions or diseases and to study metabolic abnormalities between organs. However, analyzing organ correlations alone does not fully capture the characteristics of the metabolic network. Our work proposes a graph-based analysis method for quantifying the topological properties of the network, both globally and at the nodal level, to detect systemic or single-organ metabolic abnormalities caused by diseases such as lung cancer.

METHODS

We used whole-body 18F-fluorodeoxyglucose (18F-FDG) standardized uptake value (SUV) images from 32 lung cancer patients and 20 healthy controls to construct two-organ glucose metabolism correlation networks at the population level. We calculated five global measures and three nodal centralities for these networks to explore the small-world, rich-club and modular organization in the metabolic network. Additionally, we analyzed the preference for connections significantly affected by lung cancer by dividing organs according to system level and spatial location.

RESULTS

In lung cancer patients, functional segregation in metabolic networks increased (increasedC p ${{C}_p}$ ,E loc ${{E}_{{\mathrm{loc}}}}$ , and Q $Q$ , t < 0), whereas functional integration decreased (increasedL p ${{L}_p}$ , t < 0, and decreasedE glob ${{E}_{{\mathrm{glob}}}}$ , t > 0), indicating more localized and dispersed metabolic activities. At the nodal level, certain organs, such as the pancreas, liver, heart, and right kidney, were no longer hubs in lung cancer patients (decreased nodal centralities, t > 0), whereas the left adrenal gland, left kidney, and left lung showed significantly increased centralities (increased nodal centralities, t < 0). This change suggests compensatory effects between organs. Connections between the nervous and urinary systems, as well as between the upper and middle organs, were more strongly affected by lung cancer (p < 0.05).

CONCLUSIONS

Our study demonstrates the utility of graph theory in analyzing PET imaging data to uncover metabolic network abnormalities. We identified significant topological changes and shifts in nodal roles in lung cancer patients, indicating a shift toward localized and segregated metabolic activities. These findings emphasize the need to consider systemic interactions and specific organ connections affected by disease. The impact on connections between the nervous and urinary systems and between the upper and middle regions underscores the modular nature of organ interactions, offering insights into disease mechanisms and potential therapeutic targets.

Altered internetwork functional connectivity and graph analysis of occipital regions in patients with chronic rhinosinusitis accompanied by olfactory dysfunction

This study assessed whole-brain functional connectivity and network graph theory indices in patients with chronic rhinosinusitis with (CRSwOD) and without (CRSsOD) olfactory dysfunction. We also analyzed correlations between the abnormal network metrics and clinical indices. We acquired resting-state functional magnetic resonance images from 31 patients with CRSsOD, 26 with CRSwOD, and 25 healthy controls (HCs). Functional connectivity was computed and graph theory metrics were evaluated based on the Dosenbach-160 Atlas; relationships between neuroimaging indicators and clinical scales were assessed using Pearson correlation analysis. The results showed that CRSsOD patients had 11 edges with greater strength than HCs, CRSwOD patients had 1 greater edge than HCs, and CRSsOD patients had 5 greater edges than CRSwOD patients. Nodal degree centrality and efficiency in the right posterior occipital region were significantly altered in patients with CRSsOD compared with those in CRSwOD and in HCs. Five and two edges correlated with clinical scales in patients with CRSsOD and CRSwOD, respectively, whereas no correlations in global and nodal indicators were found. These results imply that distinct brain network patterns, particularly in the occipital cortex, could be a valid neuroimaging marker for related diagnosis and prognosis of CRSsOD and CRSwOD patients, and contribute to our better understanding of the central neural mechanisms of CRSwOD, providing new ideas for the clinical management of CRSwOD.

Altered Muscle-Brain Connectivity During Left and Right Biceps Brachii Isometric Contraction Following Sleep Deprivation: Insights from PLV and PDC

Insufficient sleep causes muscle fatigue, impacting performance. The mechanism of brain-muscle signaling remains uncertain. In this study, we examined the impact of sleep deprivation on muscle endurance during isometric contractions and explored the changes in brain-muscle connectivity.

METHODS

The research involved 35 right-handed male participants who took part in an exercise test that included isometric contractions of the left and right biceps in both sleep-deprived and well-rested states. Muscle contraction duration and electroencephalogram (EEG) and electromyography (EMG) signals were recorded. Functional connectivity between brain regions was assessed using the phase locking value (PLV), while partial directed coherence (PDC) was used to analyze signal directionality between motor centers and muscles.

RESULTS

The connectivity strength between Brodmann areas (BAs) 1-5 and the right BA6, 8 regions was significantly decreased in the isometric contractions after sleep deprivation. Insufficient sleep enhanced the PDC signals from the motor center of the right brain to the left biceps, and it decreased the PDC signals from both biceps to their opposite motor centers.

CONCLUSIONS

Sleep deprivation shortened muscle isometric contraction duration by affecting the interaction between the somatosensory motor cortex and the right premotor cortex, reducing biceps feedback signal connectivity to the contralateral motor center in the brain.

Brain network alterations in mobile phone use problem severity: A multimodal neuroimaging analysis

Background and aims

Problematic mobile phone use can disrupt social interaction and well-being, potentially influencing cognitive processes. This study investigated whether mobile phone use problem severity is associated with alterations in the topological organization of brain networks.

Methods

Rs-fMRI and DTI data were collected from 81 healthy participants. Graph theory analyses were applied. The Mobile Phone Problem Use Scale-10 (MPPUS-10) was used to assess mobile phone use problem severity. Correlation analyses were conducted between each graph metric and questionnaire scores.

Results

MPPUS-10 scores correlated with global fMRI metrics: higher scores linked to longer shortest path length (reduced integration) and lower global efficiency (reduced information transfer). Conversely, higher MPPUS-10 scores were correlated with a greater clustering coefficient and higher local efficiency, which reflect increased local connectivity. Furthermore, higher MPPUS-10 scores were associated with a higher sigma value from DTI, indicating altered structural network properties. Some specific brain regions also showed significant correlations with MPPUS-10 scores.

Discussion and conclusion

These findings indicate that higher mobile phone use problem severity is associated with decreased integration and increased segregation of functional networks, alongside enhanced small-worldness in structural networks. Reduced integration aligns with addiction theories suggesting digital overload worsens network dysfunction, disrupting brain connectivity. Additionally, higher severity was correlated with altered connectivity in multiple regions, such as the precentral gyrus, supplementary motor area, and postcentral gyrus. These regions are associated with motor control, sensorimotor processing, and memory function. Further research is needed to explore whether these findings reflect shifts in the integration and integrity of brain information-processing modules.

Functional network hubs in vestibular migraine: a neuroimaging perspective

OBJECTIVE

This study utilizes resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory analysis to identify key brain regions in vestibular migraine (VM), explore their associations with clinical symptoms, and examine the role of these functional network hubs in the pathophysiology of VM, offering novel insights and a theoretical basis for understanding its neural mechanisms and improving its clinical diagnosis and treatment.

METHODS

We enrolled patients diagnosed with VM, individuals with Migraine without Aura (MwoA), and healthy control subjects, collecting both clinical and sociodemographic data alongside MRI data. Employing graph theory analysis, we focused on identifying critical hub nodes and networks within VM patients, using metrics like degree, betweenness centrality, and eigenvector centrality for our analysis.

RESULTS

The study included 30 VM patients, 28 MwoA subjects, and 31 healthy controls. Analysis of rich-club coefficients across different levels of network sparsity indicated significantly lower normalized rich-club coefficients for VM and MwoA groups compared to healthy controls at a 65% sparsity threshold, particularly within a node degree range of 91 to 94. Notably, the temporal lobes, limbic system, and frontal lobes were predominant regions for rich-club nodes in the VM group, with significant increases in centrality metrics observed in the right posterior parahippocampal gyrus. These metrics in the hippocampus and parahippocampal gyrus showed a positive correlation with the intensity, duration, and progression of headache episodes in VM patients.

CONCLUSIONS

In vestibular migraine patients, critical hub nodes such as the hippocampus and parahippocampal gyrus are identified, potentially associated with emotional regulation, pain perception, and the memory of pain.

Graph theory analysis reveals functional brain network alterations in HIV-associated asymptomatic neurocognitive impairment in virally suppressed homosexual males

BACKGROUND

This study aimed to investigate the global and nodal functional network alterations, abnormal connections of brain regions, and potential imaging biomarkers in virally suppressed people living with HIV (PLH) with asymptomatic neurocognitive impairment (ANI) using graph theory analysis.

METHODS

The study included 64 men with ANI (mean age 32.45 years) and 64 healthy controls (HC) (mean age 31.31 years). The functional network was established through the graph theory method and Automated Anatomic Labeling (AAL) 90 atlas, which provides a cerebrum parcellation framework. Moreover, hub regions were identified based on betweenness centrality (Bc). Functional connectivity (FC) differences were investigated between the two groups, these connections were located in the resting-state network (RSN). Neuropsychological (NP) tests were performed, and relationships between graph theory measures, clinical data, and NP tests were analyzed. Multiple comparisons were used to correct for false-positive findings.

RESULTS

On the global level, small-worldness, global efficiency (Eg), and local efficiency (Eloc) were significantly decreased in ANI subjects. On a nodal level, brain regions in the frontal and subcortical regions showed significantly decreased nodal measures, while regions in the parietal, temporal, and occipital lobes showed increased nodal measures. Increased FCs were found between brain regions in the visual, frontoparietal, and somatomotor networks. Hub regions overlapped highly between the two groups. Age was negatively correlated with graph theory measures.

CONCLUSION

Our findings demonstrate the global and nodal alterations in the functional network of virally suppressed homosexual males in the ANI stage. Frontal and subcortical brain regions may be important for finding the imaging biomarkers for HIV-associated neurocognitive disorder.

The role of cerebellar-cortical connectivity in modulating attentional abilities: insight from football athletes

Neuroplasticity, a phenomenon present throughout the lifespan, is thought to be influenced by physical training. However, the relationship between neuroplastic differences and attentional abilities remains unclear. This study explored the differences in brain function and attentional abilities between professional football athletes and novices, and further investigated the relationship between the two. To address this question, we included 49 football athletes and 63 novices in our study, collecting data on resting-state functional connectivity and Attention Network Test (ANT). Behavioral results from the ANT indicated that football experts had superior orienting attention but weaker alerting functions compared to novices, with no difference in executive control attention. fMRI results revealed that football experts exhibited higher fractional Amplitude of Low-Frequency Fluctuations (fALFF) values in the bilateral anterior cerebellar lobes, bilateral insula, and left superior temporal gyrus. Functional connectivity analysis showed increased connectivity between the left anterior cerebellar lobe and various cortical regions, including the right supramarginal gyrus, left precuneus, left superior frontal gyrus, bilateral posterior cerebellar lobes, and bilateral precentral gyri in experts compared to novices. More importantly, in the expert group but not in novice group, functional connectivity differences significantly predicted attentional orienting scores. Graph theoretical analysis showed that experts exhibited higher betweenness centrality and node efficiency in the right cerebellar lobule III (Cerebelum_3_R) node. Our findings demonstrate that long-term professional football training may significantly affect neuroplasticity and attentional functions. Importantly, our analysis reveals a substantive connection between these two aspects, suggesting that the integration of neuroplastic and attentional changes is likely mediated by cerebellar-cortical connectivity.

Translational network neuroscience: Nine roadblocks and possible solutions

Translational network neuroscience aims to integrate advanced neuroimaging and data analysis techniques into clinical practice to better understand and treat neurological disorders. Despite the promise of technologies such as functional MRI and diffusion MRI combined with network analysis tools, the field faces several challenges that hinder its swift clinical translation. We have identified nine key roadblocks that impede this process: (a) theoretical and basic science foundations; (b) network construction, data interpretation, and validation; (c) MRI access, data variability, and protocol standardization; (d) data sharing; (e) computational resources and expertise; (f) interdisciplinary collaboration; (g) industry collaboration and commercialization; (h) operational efficiency, integration, and training; and (i) ethical and legal considerations. To address these challenges, we propose several possible solution strategies. By aligning scientific goals with clinical realities and establishing a sound ethical framework, translational network neuroscience can achieve meaningful advances in personalized medicine and ultimately improve patient care. We advocate for an interdisciplinary commitment to overcoming translational hurdles in network neuroscience and integrating advanced technologies into routine clinical practice.

Over-integration of visual network in major depressive disorder and its association with gene expression profiles

Major depressive disorder (MDD) is a common psychiatric condition associated with aberrant functional connectivity in large-scale brain networks. However, it is unclear how the network dysfunction is characterized by imbalance or derangement of network modular interaction in MDD patients and whether this disruption is associated with gene expression profiles. We included 262 MDD patients and 297 healthy controls, embarking on a comprehensive analysis of intrinsic brain activity using resting-state functional magnetic resonance imaging (R-fMRI). We assessed brain network integration by calculating the Participation Coefficient (PC) and conducted an analysis of intra- and inter-modular connections to reveal the dysconnectivity patterns underlying abnormal PC manifestations. Besides, we explored the potential relationship between the above graph theory measures and clinical symptoms severity in MDD. Finally, we sought to uncover the association between aberrant graph theory measures and postmortem gene expression data sourced from the Allen Human Brain Atlas (AHBA). Relative to the controls, alterations in systemic functional connectivity were observed in MDD patients. Specifically, increased PC within the bilateral visual network (VIS) was found, accompanied by elevated functional connectivities (FCs) between VIS and both higher-order networks and Limbic network (Limbic), contrasted by diminished FCs within the VIS and between the VIS and the sensorimotor network (SMN). The clinical correlations indicated positive associations between inter-VIS FCs and depression symptom, whereas negative correlations were noted between intra-VIS FCs with depression symptom and cognitive disfunction. The transcriptional profiles explained 21-23.5% variance of the altered brain network system dysconnectivity pattern, with the most correlated genes enriched in trans-synaptic signaling and ion transport regulation. These results highlight the modular connectome dysfunctions characteristic of MDD and its linkage with gene expression profiles and clinical symptomatology, providing insight into the neurobiological underpinnings and holding potential implications for clinical management and therapeutic interventions in MDD.

Disrupted small-world architecture and altered default mode network topology of brain functional network in college students with subclinical depression

BACKGROUND

Subclinical depression (ScD), serving as a significant precursor to depression, is a prevalent condition in college students and imposes a substantial health service burden. However, the brain network topology of ScD remains poorly understood, impeding our comprehension of the neuropathology underlying ScD.

METHODS

Functional networks of individuals with ScD (n = 26) and healthy controls (HCs) (n = 33) were constructed based on functional magnetic resonance imaging data. These networks were then optimized using a small-worldness and modular similarity-based network thresholding method to ensure the robustness of functional networks. Subsequently, graph-theoretic methods were employed to investigated both global and nodal topological metrics of these functional networks.

RESULTS

Compared to HCs, individuals with ScD exhibited significantly higher characteristic path length, clustering coefficient, and local efficiency, as well as a significantly lower global efficiency. Additionally, significantly lower nodal centrality metrics were found in the default mode network (DMN) regions (anterior cingulate cortex, superior frontal gyrus, precuneus) and occipital lobe in ScD, and the nodal efficiency of the left precuneus was negatively correlated with the severity of depression.

CONCLUSIONS

Altered global metrics indicate a disrupted small-world architecture and a typical shift toward regular configuration of functional networks in ScD, which may result in lower efficiency of information transmission in the brain of ScD. Moreover, lower nodal centrality in DMN regions suggest that DMN dysfunction is a neuroimaging characteristic shared by both ScD and major depressive disorder, and might serve as a vital factor promoting the development of depression.

The cerebellar glucose metabolism in moyamoya vasculopathy and its correlation with neurocognitive performance after cerebral revascularization surgery: a [18F]FDG PET study

BACKGROUND

The vascular cognitive impairment (VCI) is quite common in moyamoya vasculopathy (MMV). However, the abnormality of cerebellar glucose metabolism in MMV and its relationship with patients' neurocognitive performance were few reported.

OBJECTIVE

In this study, we aimed to investigate the relationship between neurocognitive performance and cerebellar glucose metabolism. Furthermore, the cerebellar glucose metabolism changes after combined revascularization surgery were also researched.

METHODS

We retrospectively analyzed the 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography ([18F]FDG PET) images and their neuropsychological scales in 93 eligible MMV patients by comparing their cerebellar standardized uptake values ratio (SUVR) and metabolic covariant network (MCN) among different neurocognitive groups. Then, forty-two MMV patients with VCI who underwent combined revascularization surgery were prospectively observed. According to their neuropsychological performance at 6-month follow-up, these patients were assigned to cognitive improved group (n = 22) and non-improved group (n = 20). The cerebellar SUVR and MCN changes were also analyzed.

RESULTS

SUVR of right Lobule VI/Crus II/VIII decreased when cognitive impairment progression (P < 0.05, Least-Significant Difference [LSD] post hoc analysis). The cerebellar glucose metabolic pattern can be divided into two parts, in which the cerebellar posterior lobe was positively related to patients' neurocognitive performance, while the vermis and anterior lobe showed negative relationship with the neurocognitions (P < 0.001). Further MCN analysis expound that the degree of right Lobule VI/Crus II/VIII displayed decreased tendency as cognitive impairment worsened (P < 0.05, LSD post hoc analysis). After revascularization surgery, the SUVR of right cerebellar posterior lobe significantly promoted in improved group (P < 0.001). Besides, we also witnessed the SUVR improvement in left cerebral hemisphere, thalamus, and red nucleus (P < 0.001). The MCN analysis revealed that the posterior connective strength improvement among right Lobule VI and several cerebral regions significantly correlated with memory and executive screening (MES) score (P < 0.001, false discovery rate corrected).

CONCLUSION

We found that the hypometabolism of cerebellar posterior lobe, especially in the right Lobule VI, was associated with MMV patients' neuropsychological performance, while the anterior lobe and vermis showed opposites tendencies. Combined revascularization surgery improved the posterior cerebellar metabolism and was associated with favorable neurocognitive outcomes, which might be related to the activation of cortico-rubral-cerebellar pathway.

Impact of hearing loss on brain signal variability in older adults under different auditory load conditions

Introduction

The moment-by-moment variability in brain signals, a newly recognized indicator, demonstrates both the adaptability of an individual's brain as a unique trait and the distribution of neural resources within that individual in response to constantly shifting task requirements. This study aimed to explore brain signal variability in older adults using oxyhemoglobin (HbO) variability derived from fNIRS during tasks with increasing signal-to-noise ratio (SNR) loads and to assess the effects of varying degrees of hearing loss on speech recognition performance and related brain signal variability patterns.

Methods

Eighty-one participants were categorized into three groups: healthy controls (n = 30, aged 65.5 ± 3.4), mild hearing loss (n = 25, aged 66.0 ± 3.7), and moderate to severe hearing loss (n = 26, aged 67.5 ± 3.7). Speech perception was tested under quiet, 5 dB SNR, and 0 dB SNR conditions.

Results

Results revealed that the brain signal variability increased with higher SNR loads in healthy older adults, indicating enhanced neural resource allocation with the SNR load. In contrast, we found that hearing loss reduced brain signal variability during speech recognition tasks, especially in noisy conditions, in the mild hearing loss and moderate to severe hearing loss groups, possibly indicating decreased neural processing efficiency. Additionally, a positive correlation between brain signal variability and speech recognition performance was observed in healthy control participants across all SNR conditions, suggesting that brain signal variability could dynamically respond to the precise level of auditory environment demands. However, this relationship was only significant at the 5 dB SNR condition in hearing loss groups.

Discussion

Taken together, this study underscores the significant impact of hearing loss on brain signal variability modulation in auditory cognitive tasks and highlights the need for further research to understand the underlying neural mechanisms.

Altered Directed-Connectivity Network in Temporal Lobe Epilepsy: A MEG Study

Temporal lobe epilepsy (TLE) is considered a network disorder rather than a localized lesion, making it essential to study the network mechanisms underlying TLE. In this study, we constructed directed brain networks based on clinical MEG data using the Granger Causality Analysis (GCA) method, aiming to provide new insights into the network mechanisms of TLE. MEG data from 13 lTLE and 21 rTLE patients and 14 healthy controls (HCs) were analyzed. The preprocessed MEG data were used to construct directed brain networks using the GCA method and undirected brain networks using the Pearson Correlation Coefficient (PCC) method. Graph theoretical analysis extracted global and local topologies from the binary matrix, and SVM classified topologies with significant differences (p < 0.05). Comparative studies were performed on connectivity strengths, graph theory metrics, and SVM classifications between GCA and PCC, with an additional analysis of GCA-weighted network connectivity. The results show that TLE patients showed significantly increased functional connectivity based on GCA compared to the control group; similarities of the hub brain regions between lTLE and rTLE patients and the cortical-limbic-thalamic-cortical loop were identified; TLE patients exhibited a significant increase in GCA-based Global Clustering Coefficient (GCC) and Global Local Efficiency (GLE); most brain regions with abnormal local topological properties in TLE patients overlapped with their hub regions. The directionality of brain connectivity has played a significantly more pivotal role in research on TLE. GCA may be a potential tool in MEG analysis to distinguish TLE patients and HC effectively.

Cerebellar output neurons can impair non-motor behaviors by altering development of extracerebellar connectivity

The capacity of the brain to compensate for insults during development depends on the type of cell loss, whereas the consequences of genetic mutations in the same neurons are difficult to predict. We reveal powerful compensation from outside the mouse cerebellum when the excitatory cerebellar output neurons are ablated embryonically and demonstrate that the main requirement for these neurons is for motor coordination and not basic learning and social behaviors. In contrast, loss of the homeobox transcription factors Engrailed1/2 (EN1/2) in the cerebellar excitatory lineage leads to additional deficits in adult learning and spatial working memory, despite half of the excitatory output neurons being intact. Diffusion MRI indicates increased thalamo-cortico-striatal connectivity in En1/2 mutants, showing that the remaining excitatory neurons lacking En1/2 exert adverse effects on extracerebellar circuits regulating motor learning and select non-motor behaviors. Thus, an absence of cerebellar output neurons is less disruptive than having cerebellar genetic mutations.

Abnormal Alterations of the White Matter Structural Network in Patients with Herpes Zoster and Postherpetic Neuralgia

PHN is one of the most common clinical complications of herpes zoster (HZ), the pathogenesis of which is unclear and poorly treated clinically, and many studies now suggest that postherpetic neuralgia (PHN) pain may be related to central neurologic mechanisms. This study aimed to investigate the white matter structural networks and changes in the organization of the rich-club in HZ and PHN. Diffusion imaging (DTI) data from 89 PHN patients, 76 HZ patients, and 66 healthy controls (HCs) were used to construct corresponding structural networks. Using graph-theoretic analysis, changes in the overall and local characteristics of the structural networks and rich-club organization were analyzed, and their correlations with clinical scales were analyzed. Compared with HCs, PHN patients had reduced global efficiency (Eg), reduced local efficiency (Eloc), a reduced clustering coefficient (Cp), a longer characteristic path length (Lp), and reduced nodal efficiency (Ne) in several brain regions, including the right posterior cingulate gyrus, the right supraoccipital gyrus, the bilateral postcentral gyrus, and the right precuneus; HZ patients had reduced Eg, a longer Lp, and reduced right orbital frontalis suprachiasmatic Ne. Moreover, HZ and PHN patients showed a significant reduction in the strength of rich-club connections. Compared with HZ patients, the intensities of the rich-club and feeder connections were lower in the PHN patients. Moreover, the changes in the structural networks and rich-club organization topology indices of the patients in the HZ and PHN patients were significantly correlated with disease duration, pain scores, and emotional changes. The structural networks of HZ and PHN patients exhibited reduced network transmission efficiency and rich-club connectivity, possibly due to structural damage to the white matter, and this was more obvious in PHN patients. The rich-club connectivity of HZ patients showed incomplete compensation in the acute pain stage.

Case report: Monitoring consciousness with fNIRS in a patient with prolonged reduced consciousness following hemorrhagic stroke undergoing adjunct taVNS therapy

Disorders of consciousness (DoC) resulting from severe brain injury present substantial challenges in rehabilitation due to disruptions in brain network connectivity, particularly within the frontal-parietal network critical for awareness. Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a promising non-invasive intervention; however, the precise mechanisms through which it influences cortical function in DoC patients remain unclear. This study describes the effects of taVNS on fronto-parietal network connectivity and arousal in a 77-year-old female patient with unresponsive wakefulness syndrome (UWS). The patient received bilateral taVNS for 1 h daily over 3 months, with functional connectivity (FC) in the frontoparietal network assessed using functional near-infrared spectroscopy (fNIRS) and behavioral responsiveness evaluated through the Coma Recovery Scale-Revised (CRS-R). After taVNS intervention, mean FC was enhanced from 0.06 (SD = 0.31) to 0.33 (SD = 0.28) in the frontal-parietal network. The frontal-parietal were subdivided into 12 regions of interest (ROIs) and it was determined that the FC between the left dorsolateral prefrontal cortex (DLPFC) and the left prefrontal ROIs was 0.06 ± 0.41 before the intervention and 0.55 ± 0.24 after the intervention. Behavioral improvements were evidenced by an increase in CRS-R scores from 2 to 14, marking the patient's transition from UWS to minimally conscious state plus (MCS+). Additionally, regions associated with auditory and sensory processing showed increased cortical engagement, supporting the positive impact of taVNS on cortical responsiveness. This suggests its value as a non-invasive adjunctive therapy in the rehabilitation of DoC patients. Further studies are necessary to confirm these effects in a wider patient population and to refine the strategy for clinical application of taVNS.

Feature fusion analysis approach based on synchronous EEG-fNIRS signals: application in etomidate use disorder individuals

Etomidate is commonly used for induction of anesthesia, but prolonged use can affect brain neurovascular mechanisms, potentially leading to use disorders. However, limited research exists on the impact of etomidate on brain function, and accurately and noninvasively extracting and analyzing neurovascular brain features remains a challenge. This study introduces a novel feature fusion approach based on whole-brain synchronous Electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) signals aimed at addressing the difficulty of jointly analyzing neural and hemodynamic signals and features in specific locations, which is critical for understanding neurovascular mechanism changes in etomidate use disorder individuals. To address the challenge of optimizing the accuracy of neurovascular coupling analysis, we proposed a multi-band local neurovascular coupling (MBLNVC) method. This method enhances spatial precision in NVC analysis by integrating multi-modal brain signals. We then mapped the different brain features to the Yeo 7 brain networks and constructed feature vectors based on these networks. This multilayer feature fusion approach resolves the issue of analyzing complex neural and vascular signals together in specific brain locations. Our approach revealed significant neurovascular coupling enhancement in the sensorimotor and dorsal attention networks (p < 0.05, FDR corrected), corresponding with different frequency bands and brain networks from single-modal features. These features of the intersection of bands and networks showed high sensitivity to etomidate using machine learning classifiers compared to other features (accuracy: support vector machine (SVM) - 82.10%, random forest (RF) - 80.50%, extreme gradient boosting (XGBoost) - 78.40%). These results showed the potential of the proposed feature fusion analysis approach in exploring changes in brain mechanisms and provided new insights into the effects of etomidate on resting neurovascular brain mechanisms.

Brain connectomes in youth at risk for serious mental illness: a longitudinal perspective

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for prevention and early intervention. In the current study, changes in whole brain structural and functional connectomes were investigated in youth at transdiagnostic risk over a one-year period. Based on clinical assessments, participants were assigned to one of 5 groups: healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9). Constrained spherical deconvolution was used to generate whole brain tractography maps, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional magnetic resonance imaging (fMRI) signal between pairs of brain regions. Linear mixed models revealed structural and functional abnormalities in global metrics of small world lambda, and resting state networks involving the fronto-parietal, default mode, and deep grey matter networks, along with the visual and dorsal attention networks. Machine learning analysis additionally identified changes in nodal metrics of betweenness centrality in the angular gyrus and bilateral temporal gyri as potential features which can discriminate between the groups. Our findings further support the view that abnormalities in large scale networks (particularly those involving fronto-parietal, temporal, default mode, and deep grey matter networks) may underlie transdiagnostic risk for SMIs.

Ginsenoside Rg1 ameliorates stress-exacerbated Parkinson's disease in mice by eliminating RTP801 and α-synuclein autophagic degradation obstacle

Emerging evidence shows that psychological stress promotes the progression of Parkinson's disease (PD) and the onset of dyskinesia in non-PD individuals, highlighting a potential avenue for therapeutic intervention. We previously reported that chronic restraint-induced psychological stress precipitated the onset of parkinsonism in 10-month-old transgenic mice expressing mutant human α-synuclein (αSyn) (hαSyn A53T). We refer to these as chronic stress-genetic susceptibility (CSGS) PD model mice. In this study we investigated whether ginsenoside Rg1, a principal compound in ginseng notable for soothing the mind, could alleviate PD deterioration induced by psychological stress. Ten-month-old transgenic hαSyn A53T mice were subjected to 4 weeks' restraint stress to simulate chronic stress conditions that worsen PD, meanwhile the mice were treated with Rg1 (40 mg· kg-1 ·d-1, i.g.), and followed by functional magnetic resonance imaging (fMRI) and a variety of neurobehavioral tests. We showed that treatment with Rg1 significantly alleviated both motor and non-motor symptoms associated with PD. Functional MRI revealed that Rg1 treatment enhanced connectivity between brain regions implicated in PD, and in vivo multi-channel electrophysiological assay showed improvements in dyskinesia-related electrical activity. In addition, Rg1 treatment significantly attenuated the degeneration of dopaminergic neurons and reduced the pathological aggregation of αSyn in the striatum and SNc. We revealed that Rg1 treatment selectively reduced the level of the stress-sensitive protein RTP801 in SNc under chronic stress conditions, without impacting the acute stress response. HPLC-MS/MS analysis coupled with site-directed mutation showed that Rg1 promoted the ubiquitination and subsequent degradation of RTP801 at residues K188 and K218, a process mediated by the Parkin RING2 domain. Utilizing αSyn A53T+; RTP801-/- mice, we confirmed the critical role of RTP801 in stress-aggravated PD and its necessity for Rg1's protective effects. Moreover, Rg1 alleviated obstacles in αSyn autophagic degradation by ameliorating the RTP801-TXNIP-mediated deficiency of ATP13A2. Collectively, our results suggest that ginsenoside Rg1 holds promise as a therapeutic choice for treating PD-sensitive individuals who especially experience high levels of stress and self-imposed expectations.

Graph theory-driven structural and functional connectivity analyses revealing regulatory mechanisms of brain network in patients with classic trigeminal neuralgia

A specific regulatory mechanism underlying classical trigeminal neuralgia (cTN) remains unknown. The present study posits that the initiation and advancement of cTN may be attributed to a self-regulatory and compensatory mechanism within the brain's limbic system. A sample size of thirty-three patients diagnosed with cTN and twenty-one normal controls were recruited for this investigation. Functional magnetic resonance imaging data were collected from all participants. Graph-theoretic analysis was employed to identify abnormal nodes induced by cTN in the brain atlas, followed by determining the brain network function in conjunction with the outcomes of regional homogeneity (ReHo) and functional connectivity (FC). During data processing, relatively strict thresholds were set for all corrections. The findings indicated that the discrepancy in small-worldness characteristics between the two cohorts primarily stemmed from the characteristic path length. Additionally, there was an overlap between brain regions exhibiting markedly reduced node efficiency in cTN patients and those exhibiting markedly reduced ReHo signal. The FC analysis of the whole brain revealed nine brain regions with reduced connectivity in the cTN group, corresponding to brain regions with diminished node efficiency. Notably, most of these abnormal brain regions were located in the limbic system, providing evidence of the compensatory mechanism of the limbic system.

The negative relationship between brain-age gap and psychological resilience defines the age-related neurocognitive status in older people

Biological brain age is a brain-predicted age using machine learning to indicate brain health and its associated conditions. The presence of an older predicted brain age relative to the actual chronological age is indicative of accelerated aging processes. Consequently, the disparity between the brain's chronological age and its predicted age (brain-age gap) and the factors influencing this disparity provide critical insights into cerebral health dynamics during aging. In this study, we employed a Lasso regression model and analyzed multimodal imaging data from 124 participants aged 53 to 76 to formulate and predict brain age. Additionally, we conducted partial correlation analyses to explore the complex relationship between the brain-age gap and network metrics, cognitive assessments, and emotional evaluations, while controlling for chronological age, gender, and education. Our findings highlight psychological resilience as a significant mitigating factor against premature brain aging. It is established that psychological resilience significantly influences the modulation of the brain-age gap. Moreover, psychological resilience and the brain-age gap exhibit a high accuracy (above 0.72) in segregating Montreal Cognitive Assessment score-based cohorts. This observation underscores significant insight into the potential of utilizing the brain-age gap as a diagnostic tool for the early detection of accelerated aging. It advocates for the timely application of interventions, including the development of programs aimed at bolstering psychological resilience.

Reduced white matter integrity and disrupted brain network in children with type 2 and 3 spinal muscular atrophy

BACKGROUND

Spinal muscular atrophy (SMA) is caused by reduced expression of survival motor neuron (SMN) protein. Previous studies indicated SMA causes not only lower motor neuron degeneration but also extensive brain involvement. This study aimed to investigate the changes of brain white matter and structural network using diffusion tensor imaging (DTI) in children with type 2 and 3 SMA.

METHODS

Forty-two type 2 and 3 pediatric SMA patients and 42 age- and gender-matched healthy controls (HC) were prospectively enrolled in this study. The tract-based spatial statistics (TBSS) was used to assess white matter integrity and the structural network properties were calculated based on DTI white matter fiber tracking and the graph theory approach. A partial correlation was performed to explore the relationship between white matter parameters and clinical characteristics.

RESULTS

In total, 42 patients (mean age, 10.86 ± 4.07 years; 23 men) were included. TBSS analysis revealed widespread white matter changes in SMA patients. The SMA patients showed changes in multiple small-world and network efficiency parameters. Compared to the HC group, SMA showed increased characteristic path length (Lp), normalized clustering coefficient (γ), small-world characteristic (σ), and decreased global efficiency (Eglob) (all p < 0.05). In the node properties, right supramarginal gyrus, right orbital part of superior frontal gyrus, right supplementary motor area, and left median cingulate and paracingulate gyri changed in SMA patients. A decreased axial diffusivity (AD) value was associated with lower Hammersmith Functional Motor Scale-Expanded scores (r = 0.45, p = 0.02), which means that the symptoms of SMA patients are more severe.

CONCLUSIONS

This study found white matter and DTI-based brain network abnormalities in SMA patients, suggesting SMN protein deficiency may affect white matter development.

Reorganization of gray matter networks in patients with Moyamoya disease

Patients with Moyamoya disease (MMD) exhibit significant alterations in brain structure and function, but knowledge regarding gray matter networks is limited. The study enrolled 136 MMD patients and 99 healthy controls (HCs). Clinical characteristics and gray matter network topology were analyzed. Compared to HCs, MMD patients exhibited decreased clustering coefficient (Cp) (P = 0.006) and local efficiency (Eloc) (P = 0.013). Ischemic patients showed decreased Eloc and increased characteristic path length (Lp) compared to asymptomatic and hemorrhagic patients (P < 0.001, Bonferroni corrected). MMD patients had significant regional abnormalities, including decreased degree centrality (DC) in the left medial orbital superior frontal gyrus, left orbital inferior frontal gyrus, and right calcarine fissure and surrounding cortex (P < 0.05, FDR corrected). Increased DC was found in bilateral olfactory regions, with higher betweenness centrality (BC) in the right median cingulate, paracingulate fusiform gyrus, and left pallidum (P < 0.05, FDR corrected). Ischemic patients had lower BC in the right hippocampus compared to hemorrhagic patients, while hemorrhagic patients had decreased DC in the right triangular part of the inferior frontal gyrus compared to asymptomatic patients (P < 0.05, Bonferroni corrected). Subnetworks related to MMD and white matter hyperintensity volume were identified. There is significant reorganization of gray matter networks in patients compared to HCs, and among different types of patients. Gray matter networks can effectively detect MMD-related brain structural changes.

Protocol for a nested case-control study: identifying neuroimaging biomarkers for the progression of subclinical depression and qi-stagnation constitution to major depressive disorder in adolescents

Background

Major depressive disorder (MDD) frequently results in suboptimal treatment outcomes and elevated recurrence rate, with patients frequently engaging in self-harm and suicidal behavior, thereby placing a heavy burden on families and society. Specifically, MDD in adolescents is linked to an elevated suicide risk. Thus, early identification and intervention is crucial for adolescents at high risk for developing MDD. Subclinical depression (SD), characterized by depressive symptoms that do not meet the full criteria for MDD, substantially increases the risk of developing MDD. According to Traditional Chinese Medicine body constitution theory, Qi-stagnation constitution (QSC) is also considered a significant risk factor for the progression to MDD. This study protocol aims to identify neuroimaging biomarkers for the progression from adolescents with SD and QSC to those with MDD, facilitating early intervention strategies.

Methods and analysis

This nested case-control study includes both longitudinal follow-up and cross-sectional comparison. Three hundred first-year senior high school students diagnosed with SD and QSC will be recruited. The 300 adolescents will undergo rs-fMRI scans at baseline and again after one year. We then divide the 300 adolescents with SD and QSC into two groups based on whether they progress to MDD after one year. Functional brain networks will be constructed based on 400 regions of interest (ROIs). Neuroimaging measures, including regional homogeneity and low-frequency fluctuation for each ROI, as well as graph-based global efficiency, nodal efficiency, and nodal centrality from the binary networks, will then be calculated. Finally, differences in these neuroimaging measures between the two groups at baseline will be analyzed to identify biomarkers that can predict the progression from adolescents with SD and QSC to those with MDD.

Study registration

This study protocol does not involve clinical interventions and is classified as an observational study, so it was not subject to prior registration.

Connectional differences between humans and macaques in the MT+ complex

MT+ is pivotal in the dorsal visual stream, encoding tool-use characteristics such as motion speed and direction. Despite its conservation between humans and monkeys, differences in MT+ spatial location and organization may lead to divergent, yet unexplored, connectivity patterns and functional characteristics. Using diffusion tensor imaging, we examined the structural connectivity of MT+ subregions in macaques and humans. We also employed graph-theoretical analyses on the constructed homologous tool-use network to assess their functional roles. Our results revealed location-dependent connectivity in macaques, with MST, MT, and FST predominantly connected to dorsal, middle, and ventral surfaces, respectively. Humans showed similar connectivity across all subregions. Differences in connectivity between MST and FST are more pronounced in macaques. In humans, the entire MT+ region, especially MST, exhibited stronger information transmission capabilities. Our findings suggest that the differences in tool use between humans and macaques may originate earlier than previously thought, particularly within the MT+ region.

Common and distinct neural underpinnings of the association between childhood maltreatment and depression and aggressive behavior

BACKGROUND

Although childhood maltreatment (CM) is widely recognized as a transdiagnostic risk factor for various internalizing and externalizing psychological disorders, the neural basis underlying this association remain unclear. The potential reasons for the inconsistent findings may be attributed to the involvement of both common and specific neural pathways that mediate the influence of childhood maltreatment on the emergence of psychopathological conditions.

METHODS

This study aimed to delineate both the common and distinct neural pathways linking childhood maltreatment to depression and aggression. First, we employed Network-Based Statistics (NBS) on resting-state functional magnetic resonance imaging (fMRI) data to identify functional connectivity (FC) patterns associated with depression and aggression. Mediation analyses were then conducted to assess the role of these FC patterns in the relationship between childhood maltreatment and each outcome.

RESULTS

The results demonstrated that FC within the default mode network (DMN) and between the cingulo-opercular network (CON) and dorsal attention network (DAN) mediated the association between childhood maltreatment and aggression, whereas FC within the reward system and between the CON and the reward system mediated the link between childhood maltreatment and depression.

CONCLUSIONS

We speculate that the control system may serve as a transdiagnostic neural basis accounting for the sequela of childhood maltreatment, and the attention network and the reward network may act as specific neural basis linking childhood maltreatment to depression and aggression, respectively.

Executive function, limbic circuit dynamics and repetitive and restricted behaviors in children with autism spectrum disorder

Objective

Repetitive and restricted behaviors (RRBs) are a core symptom of autism spectrum disorder (ASD), but effective treatment approaches are still lacking. Executive function (EF) has been identified as a promising target, as research increasingly shows a link between EF deficits and the occurrence of RRBs. However, the neural mechanisms that connect the two remain unclear. Since the orbitofrontal cortex (OFC) plays a role in both EF and RRBs, its functional connectivity dynamics could offer valuable insights into this relationship.

Methods

This study analyzed data from the Autism Brain Imaging Data Exchange (ABIDE) II database to explore brain function in 93 boys with ASD and 110 typically developing (TD) boys. Time-varying functional connectivity was analyzed between eight OFC subregions and other brain areas. By employing linear regression, the study assessed how atypical connectivity dynamics and EF influence RRBs. Additionally, mediation analysis with bootstrapping was used to determine how EF mediates the relationship between atypical connectivity and RRBs.

Results

We found significant differences in the variance of FC between ASD and TD groups, specifically in the OFC subregion in L-prefrontal and the left amygdala (t = 5.00, FDR q < 0.01). Regression analyses revealed that increased variance of this FC and EF significantly impacted RRBs, with inhibition, emotional control, and monitor showing strong associations (standardized β = 0.60 to 0.62, p < 0.01), which also had significant indirect effects on the relationship between the above dynamic FC and RRBs, which accounted for 59% of the total effect.

Conclusion

This study highlights the critical role of EFs as a key mechanism in addressing RRBs in ASD. Specifically, it points out that EFs mediate the influence of atypical time-varying interactions within the OFC-amygdala circuit on RRBs.

Adverse childhood experiences, brain efficiency, and the development of pain symptoms in youth

BACKGROUND

Adverse childhood experiences (ACEs) are often reported by youths with chronic pain, and both ACEs and chronic pain disrupt how information is processed. However, it is unknown whether changes to shared neural networks underlie the relationship between ACEs and the development of pain symptoms. This study explored the relationships between ACEs, brain efficiency, and pain symptomology in youth.

METHODS

A community sample of youths aged 14-18 years underwent MRIs, answered trauma and pain questionnaires, and underwent pain sensory testing, twice, 3 months apart (Nbaseline = 44; Nfollow-up = 42). Sensory testing determined thresholds for mechanical and thermal stimuli. Global and local network efficiencies were evaluated using graph theory. Generalized estimating equations were applied to examine whether brain efficiency moderated the relationships between ACEs, pain intensity, and pain sensitivity (i.e., mechanical detection, heat pain, and temperature change thresholds).

RESULTS

There was a significant interaction between ACEs and global brain efficiency in association with pain intensity (β = -0.31, p = 0.02) and heat pain (β = -0.29, p = 0.004). Lower global brain efficiency exacerbated the relationship between ACEs and pain intensity (θX → Y|W = -1.16 = 0.37, p = 0.05), and heat pain sensitivity (θX → Y|W = -1.30 = 0.44, p = 0.05). Higher global brain efficiency ameliorated the relationship between ACEs and pain intensity (θX → Y|W = 1.75 = -0.53, p = 0.05).

CONCLUSIONS

The relationship between ACEs and pain symptomology was comparable to chronic pain phenotypes (i.e., higher pain intensity and pain thresholds) and may vary as a function of brain efficiency in youth. This stresses the importance of assessing for pain symptoms in trauma-exposed youth, as earlier identification and intervention are critical in preventing the chronification of pain.

SIGNIFICANCE

This article explores the relationship between ACEs, pain symptomology, and brain efficiency in youth. ACEs may affect how the brain processes information, including pain. Youths with lower brain efficiencies that were exposed to more ACEs have pain symptomology comparable to youths with chronic pain. Understanding this relationship is important for the earlier identification of pain symptoms, particularly in vulnerable populations such as youths exposed to trauma, and is critical for preventing the chronification of pain.

Lower functional connectivity state transitions during affective processing correlate with subsequent impairment in sustaining positive affect in subthreshold depression

Background

Diminished capacity for maintaining positive affect (PA) has been identified in subthreshold depression (StD). While recent studies have explored affective dynamics among StD, the relationship between early emotional processing impairments and the capacity to prolong PA remains uncertain. Furthermore, it is unclear how brain connectivity patterns observed in StD are associated with PA maintenance.

Methods

The experimental procedure comprised a baseline rs-fMRI scan, followed by a PA-inducing movie viewing task, and three further rs-fMRI sessions. Participants provided PA ratings following each session. PA maintenance was quantified through the slope of mood change between each session after movie viewing. We performed a dynamic functional connectivity analysis on movie viewing data, as well as a series of static functional connectivity (FC), analyses on data of all rs-fMRI sessions from 25 StD and 25 healthy controls (HC). Correlations between brain-related measures and slope of mood change were calculated.

Results

Individuals with StD exhibited reduced capacity in sustaining PA, reflected in a decrease in PA in the early maintenance stage. StD also had a lower number of transitions between four brain states during movie viewing, which was related to subsequent impairment in sustaining PA. In addition, StD had weaker static FC between left inferior frontal gyrus and right middle occipital gyrus during the first resting-state session following movie viewing, which in turn was related to a steeper decline in PA.

Conclusions

These results highlight the brain features driving PA dysregulation in StD and provide a potential avenue for the development of future interventions.

Investigating the predictive models of efficacy of accelerated neuronavigation-guided rTMS for suicidal depression based on multimodal large-scale brain networks

Background

Accelerated neuronavigation-guided high-dose repetitive transcranial magnetic stimulation (NH-rTMS) can rapidly reduce suicidal ideation and alleviate depressive symptoms in one week. Exploring accelerated NH-rTMS-related biomarkers will enhance the precision of treatment decisions for patients with major depressive disorder (MDD). This study aimed to establish predictive models of treatment response to accelerated NH-rTMS in MDD based on multimodal large-scale brain networks.

Method

In this study, morphological, structural, and functional brain networks were constructed for untreated MDD patients with suicidal ideation before accelerated NH-rTMS treatment. Linear support vector regression methods were utilized to examine the ability of multimodal brain networks in predicting antidepressant and anti-suicidal effects of accelerated NH-rTMS.

Results

We found that both the morphological and structural networks predicted the percentage changes of total Beck Scale of Suicidal Ideation and 24-item Hamilton Depression Rating Scale (HAMD-24) scores. Additionally, the functional networks predicted the percentage changes of total HAMD-24 scores. Further analyses revealed that the structural networks outperformed the morphological and functional networks and the somatomotor module outperformed other subnetworks in the prediction.

Conclusions

In summary, our study provides brain connectome-based predictive models of treatment response to accelerated NH-rTMS in MDD patients with suicidal ideation.

Functional connectivity gradients and neurotransmitter maps among patients with mild cognitive impairment and depression symptoms

BACKGROUND

Both depressive symptoms and neurotransmitter changes affect the characteristics of functional brain networks in clinical patients. We sought to explore how brain functional grading is organized among patients with mild cognitive impairment and depressive symptoms (D-MCI) and whether changes in brain organization are related to neurotransmitter distribution.

METHODS

Using 3 T magnetic resonance imaging (MRI) we acquired functional MRI (fMRI) data from patients with D-MCI, patients with mild cognitive impairment without depression (nD-MCI), and healthy controls. We used resting-state fMRI and diffusion embedding to examine the pattern of functional connectivity gradients. We used analysis of covariance and post hoc t tests to compare the difference in functional connectivity gradients among the 3 groups. We examined the correlation between variations in functional connectivity gradients and neurotransmitter maps using the JuSpace toolbox.

RESULTS

We included 105 participants, including 31 patients with D-MCI, 40 patients with nD-MCI, and 34 healthy controls. Compared with healthy controls, both the nD-MCI and D-MCI groups showed abnormalities in the principal unimodal-transmodal gradient pattern. Compared with controls, the D-MCI group showed an increased secondary gradient in the default mode network. Differences in the functional connectivity gradients between the D-MCI and nD-MCI groups were significantly correlated with the distribution of 5-hydroxytryptamine receptor subtype 1A.

LIMITATIONS

The small sample size affects the generalizability of the results, and the neurotransmitter template is based on healthy participants, not patients.

CONCLUSION

Our results suggest that depressive symptoms cause abnormalities in the hierarchical segregation of functional brain organization among patients with MCI. Such abnormal changes may be related to the distribution of neurotransmitters.

Site- and electroencephalogram-frequency-specific effects of 800-nm prefrontal transcranial photobiomodulation on electroencephalogram global network topology in young adults

Significance

Transcranial photobiomodulation (tPBM) is an optical intervention that effectively enhances human cognition. However, limited studies have reported the effects of tPBM on electrophysiological brain networks.

Aim

We aimed to investigate the site- and electroencephalogram (EEG)-frequency-specific effects of 800-nm prefrontal tPBM on the EEG global network topology of the human brain, so a better understanding of how tPBM alters EEG brain networks can be achieved.

Approach

A total of 26 healthy young adults participated in the study, with multiple visits when either active or sham tPBM interventions were delivered to either the left or right forehead. A 19-channel EEG cap recorded the time series before and after the 8-min tPBM/sham. We used graph theory analysis (GTA) and formulated adjacency matrices in five frequency bands, followed by quantification of normalized changes in GTA-based global topographical metrics induced by the respective left and right tPBM/sham interventions.

Results

Statistical analysis indicated that the effects of 800-nm prefrontal tPBM on the EEG global topological networks are both site- and EEG-frequency-dependent. Specifically, our results demonstrated that the left 800-nm tPBM primarily enhanced the alpha network efficiency and information transmission, whereas the right 800-nm tPBM augmented the clustering ability of the EEG topological networks and improved the formation of small-worldness of the beta waves across the entire brain.

Conclusions

The study concluded that 800-nm prefrontal tPBM can enhance global connectivity patterns and information transmission in the human brain, with effects that are site- and EEG-frequency-specific. To further confirm and better understand these findings, future research should correlate post-tPBM cognitive assessments with EEG network analysis.

Limbic/paralimbic connection weakening in preschool autism-spectrum disorder based on diffusion basis spectrum imaging

This study aims to investigate the value of basal ganglia and limbic/paralimbic networks alteration in identifying preschool children with ASD and normal controls using diffusion basis spectrum imaging (DBSI). DBSI data from 31 patients with ASD and 30 NC were collected in Hunan Children's Hospital. All data were imported into the post-processing server. The most discriminative features were extracted from the connection, global and nodal metrics separately using the two-sample t-test. To effectively integrate the multimodal information, we employed the multi-kernel learning support vector machine (MKL-SVM). In ASD group, the value of global efficiency, local efficiency, clustering coefficient and synchronization were lower than NC group, while modularity score, hierarchy, normalized clustering coefficient, normalized characteristic path length, small-world, characteristic path length and assortativity were higher. Significant weaker connections are mainly distributed in the limbic/paralimbic networks. The model combining consensus connection, global and nodal graph metrics features can achieve the best performance in identifying ASD patients, with an accuracy of 96.72%.The most specific brain regions connection weakening associated with preschool ASD are predominantly located in limbic/paralimbic networks, suggesting their involvement in abnormal brain development processes. The effective combination of connection, global and nodal metrics information by MKL-SVM can effectively distinguish patients with ASD.

Cortical Morphological Networks Differ Between Gyri and Sulci

This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.

Reorganization of Dynamic Network in Stroke Patients and Its Potential for Predicting Motor Recovery

Objective: The investigation of brain functional network dynamics offers a promising approach to understanding network reorganization poststroke. This study aims to explore the dynamic network configurations associated with motor recovery in stroke patients and assess their predictive potential using multilayer network analysis. Methods: Resting-state functional magnetic resonance imaging data were collected from patients with subacute stroke within 2 weeks of onset and from matched healthy controls (HCs). Group-independent component analysis and a sliding window approach were utilized to construct dynamic functional networks. A multilayer network model was applied to quantify the switching rates of individual nodes, subnetworks, and the global network across the dynamic network. Correlation analyses assessed the relationship between switching rates and motor function recovery, while linear regression models evaluated the predictive potential of global network switching rate on motor recovery outcomes. Results: Stroke patients exhibited a significant increase in the switching rates of specific brain regions, including the medial frontal gyrus, precentral gyrus, inferior parietal lobule, anterior cingulate, superior frontal gyrus, and postcentral gyrus, compared to HCs. Additionally, elevated switching rates were observed in the frontoparietal network, default mode network, cerebellar network, and in the global network. These increased switching rates were positively correlated with baseline Fugl-Meyer assessment (FMA) scores and changes in FMA scores at 90 days poststroke. Importantly, the global network's switching rate emerged as a significant predictor of motor recovery in stroke patients. Conclusions: The reorganization of dynamic network configurations in stroke patients reveals crucial insights into the mechanisms of motor recovery. These findings suggest that metrics of dynamic network reorganization, particularly global network switching rate, may offer a robust predictor of motor recovery.

Altered neurovascular coupling and structure-function coupling in Moyamoya disease affect postoperative collateral formation

Chronic ischemia in moyamoya disease (MMD) impaired white matter microstructure and neural functional network. However, the coupling between cerebral blood flow (CBF) and functional connectivity and the association between structural and functional network are largely unknown. 38 MMD patients and 20 sex/age-matched healthy controls (HC) were included for T1-weighted imaging, arterial spin labeling imaging, resting-state functional MRI and diffusion tensor imaging. All patients had preoperative and postoperative digital subtraction angiography. Upon constructing the structural connectivity (SC) and functional connectivity (FC) networks, the SC-FC coupling was calculated. After obtaining the graph theoretical parameters, neurovascular coupling represented the spatial correlation between node degree centrality (DC) of functional networks and CBF. The CBF-DC coupling and SC-FC coupling were compared between MMD and HC groups. We further analyzed the correlation between coupling indexes and cognitive scores, as well as postoperative collateral formation. Compared with HC, CBF-DC coupling was decreased in MMD (p = 0.021), especially in the parietal lobe (p = 0.047). SC-FC coupling in MMD decreased in frontal, occipital, and subcortical regions. Cognitive scores were correlated with the CBF-DC coupling in frontal lobes (r = 0.394, p = 0.029) and SC-FC coupling (r = 0.397, p = 0.027). The CBF-DC coupling of patients with good postoperative collateral formation was higher (p = 0.041). Overall, neurovascular decoupling and structure-functional decoupling at the cortical level may be the underlying neuropathological mechanisms of MMD.