Global Brain Network Research Articles

α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor density underlies intraregional and interregional functional centrality

Local and global functional connectivity densities (lFCD and gFCD, respectively), derived from functional magnetic resonance imaging (fMRI) data, represent the degree of functional centrality within local and global brain networks. While these methods are well-established for mapping brain connectivity, the molecular and synaptic foundations of these connectivity patterns remain unclear. Glutamate, the principal excitatory neurotransmitter in the brain, plays a key role in these processes. Among its receptors, the α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) is crucial for neurotransmission, particularly in cognitive functions such as learning and memory. This study aimed to examine the association of the AMPAR density and FCD metrics of intraregional and interregional functional centrality. Using [11C]K-2, a positron emission tomography (PET) tracer specific for AMPARs, we measured AMPAR density in the brains of 35 healthy participants. Our findings revealed a strong positive correlation between AMPAR density and both lFCD and gFCD-lFCD across the entire brain. This correlation was especially notable in key regions such as the anterior cingulate cortex, posterior cingulate cortex, pre-subgenual frontal cortex, Default Mode Network, and Visual Network. These results highlight that postsynaptic AMPARs significantly contribute to both local and global functional connectivity in the brain, particularly in network hub regions. This study provides valuable insights into the molecular and synaptic underpinnings of brain functional connectomes.

Neural Correlates of Psychedelic, Sleep, and Sedated States Support Global Theories of Consciousness.

Understanding neural mechanisms of consciousness remains a challenging question in neuroscience. A central debate in the field concerns whether consciousness arises from global interactions that involve multiple brain regions or focal neural activity, such as in sensory cortex. Additionally, global theories diverge between the Global Neuronal Workspace (GNW) hypothesis, which emphasizes frontal and parietal areas, and the Integrated Information Theory (IIT), which focuses on information integration within posterior cortical regions. To disentangle the global vs. local and frontoparietal vs. posterior dilemmas, we measured global functional connectivity and local neural synchrony with functional magnetic resonance imaging (fMRI) data across a spectrum of conscious states in humans induced by psychedelics, sleep, and deep sedation. We found that psychedelic states are associated with increased global functional connectivity and decreased local neural synchrony. In contrast, non-REM sleep and deep sedation displayed the opposite pattern, suggesting that consciousness arises from global brain network interactions rather than localized activity. This mirror-image pattern between enhanced and diminished states was observed in both anterior-posterior (A-P) and posterior-posterior (P-P) brain regions but not within the anterior part of the brain alone. Moreover, anterior transmodal regions played a key role in A-P connectivity, while both posterior transmodal and posterior unimodal regions were critical for P-P connectivity. Overall, these findings provide empirical evidence supporting global theories of consciousness in relation to varying states of consciousness. They also bridge the gap between two prominent theories, GNW and IIT, by demonstrating how different theories can converge on shared neuronal mechanisms.

Brain networks in newborns and infants with and without sensorineural hearing loss: A functional near-infrared spectroscopy study.

Understanding the impact of early sensory deficits on brain development is essential for understanding developmental processes and developing potential interventions. While previous studies have looked into the impact of prenatal experiences on language development, there is a lack of research on how these experiences affect early language and brain function development in individuals with sensorineural hearing loss (SNHL). To investigate SNHL effects on early brain development and connectivity in 4-month-olds vs healthy newborns and controls. The research involved analyzing the functional brain networks of 65 infants, categorized into three groups: 28 healthy newborns, 22 4-month-old participants with SNHL, and 15 age-matched healthy participants. The resting-state functional connectivity was measured and compared between the groups using functional near-infrared spectroscopy and graph theory to assess the brain network properties. Significant differences were found in resting-state functional connectivity between participants with SNHL and age-matched controls, indicating a developmental lag in brain connectivity for those with SNHL. Surprisingly, SNHL participants showed better connectivity development compared to healthy newborns, with connectivity strengths of 0.13 ± 0.04 for SNHL, 0.16 ± 0.08 for controls, and 0.098 ± 0.04 for newborns. Graph theory analysis revealed enhanced global brain network properties for the SNHL group, suggesting higher communication efficiency at 4 months. No significant differences were noted in network properties between 4-month-old SNHL participants and neonates. A unique pattern of central hubs was observed in the SNHL group, with 2 hubs in the left hemisphere compared to 6 in controls. 4-month-old infants with SNHL have a distinct brain network pattern with efficient long-distance information transmission but less effective local communication compared to age-matched controls.

Characterizing Autism Spectrum Disorder through Fusion of Local Cortical Activation and Global Functional Connectivity using Game-Based Stimuli and a Mobile EEG System.

The deficit in social interaction skills among individuals with autism spectrum disorder (ASD) is strongly influenced by personal experiences and social environments. Neuroimaging studies have previously highlighted the link between social impairment and brain activity in ASD. This study aims to develop a method for assessing and identifying ASD using a social cognitive game-based paradigm combined with electroencephalography (EEG) signaling features. Typically developing (TD) participants and autistic preadolescents and teenagers were recruited to participate in a social game while 12-channel EEG signals were recorded. The EEG signals underwent preprocessing to analyze local brain activities, including event-related potentials (ERPs) and time-frequency features. Additionally, the global brain network's functional connectivity between brain regions was evaluated using phase-lag indices (PLIs). Subsequently, machine learning models were employed to assess the neurophysiological features. Results indicated pronounced ERP components, particularly the late positive potential (LPP), in parietal regions during social training. Autistic preadolescents and teenagers exhibited lower LPP amplitudes and larger P200 amplitudes compared to TD participants. Reduced theta synchronization was also observed in the ASD group. Aberrant functional connectivity within certain time intervals was noted in the ASD group. Machine learning analysis revealed that support-vector machines achieved a sensitivity of 100%, specificity of 91.7%, and accuracy of 95.8% as part of the performance evaluation when utilizing ERP and brain oscillation features for ASD characterization. These findings suggest that social interaction difficulties in autism are linked to specific brain activation patterns. Traditional behavioral assessments face challenges of subjectivity and accuracy, indicating the potential use of social training interfaces and EEG features for cognitive assessment in ASD.

Meso-scale reorganization of local–global brain networks under mild sedation of propofol anesthesia

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

Exploring complex and integrated information during sleep.

The Integrated Information Theory is a theoretical framework that aims to elucidate the nature of consciousness by postulating that it emerges from the integration of information within a system, and that the degree of consciousness depends on the extent of information integration within the system. When consciousness is lost, the core complex of consciousness proposed by the Integrated Information Theory disintegrates, and Φ measures, which reflect the level of integrated information, are expected to diminish. This study examined the predictions of the Integrated Information Theory using the global brain network acquired via functional magnetic resonance imaging during various tasks and sleep. We discovered that the complex located within the frontoparietal network remained constant regardless of task content, while the regional distribution of the complex collapsed in the initial stages of sleep. Furthermore, Φ measures decreased as sleep progressed under limited analysis conditions. These findings align with predictions made by the Integrated Information Theory and support its postulates.

Smoking affects global and regional brain entropy in depression patients regardless of depression: Preliminary findings

ObjectiveThis study examines the effect of smoking on global and regional brain entropy in patients with Major Depressive Disorder (MDD), aiming to elucidate the relationship between smoking habits and brain network complexity in depression. MethodsThe study enrolled 24 MDD patients, divided into smokers and non-smokers, from Alanya Alaaddin Keykubat University and Istanbul Medipol University. Resting-state fMRI data were acquired and processed. The complexity of neuronal activity was assessed using dispersion entropy, with statistical significance determined by a suite of tests including Kolmogorov–Smirnov, Student's t-test, and Mann–Whitney U test. ResultsThe smoking cohort exhibited higher global brain entropy compared to the non-smoking group (p = 0.033), with significant differences in various brain networks, indicating that smoking may alter global brain activity and network dynamics in individuals with MDD. ConclusionThe study provides evidence that smoking is associated with increased brain entropy in MDD patients, suggesting that chronic smoking may influence cognitive and emotional networks. This underscores the importance of considering smoking history in the treatment and prognosis of MDD. The findings call for further research to understand the mechanistic links between smoking, brain entropy, and depression.

Small vessel disease and cognitive reserve oppositely modulate global network redundancy and cognitive function: A study in middle-to-old aged community participants.

Cerebral small vessel disease (SVD) can disrupt the global brain network and lead to cognitive impairment. Conversely, cognitive reserve (CR) can improve one's cognitive ability to handle damaging effects like SVD, partly by optimizing the brain network's organization. Understanding how SVD and CR collectively influence brain networks could be instrumental in preventing cognitive impairment. Recently, brain redundancy has emerged as a critical network protective metric, providing a nuanced perspective of changes in network organization. However, it remains unclear how SVD and CR affect global redundancy and subsequently cognitive function. Here, we included 121 community-dwelling participants who underwent neuropsychological assessments and a multimodal MRI examination. We visually examined common SVD imaging markers and assessed lifespan CR using the Cognitive Reserve Index Questionnaire. We quantified the global redundancy index (RI) based on the dynamic functional connectome. We then conducted multiple linear regressions to explore the specific cognitive domains related to RI and the associations of RI with SVD and CR. We also conducted mediation analyses to explore whether RI mediated the relationships between SVD, CR, and cognition. We found negative correlations of RI with the presence of microbleeds (MBs) and the SVD total score, and a positive correlation of RI with leisure activity-related CR (CRI-leisure). RI was positively correlated with memory and fully mediated the relationships between the MBs, CRI-leisure, and memory. Our study highlights the potential benefits of promoting leisure activities and keeping brain redundancy for memory preservation in older adults, especially those with SVD.

Metastability indexes global changes in the dynamic working point of the brain following brain stimulation.

Several studies have shown that coordination among neural ensembles is a key to understand human cognition. A well charted path is to identify coordination states associated with cognitive functions from spectral changes in the oscillations of EEG or MEG. A growing number of studies suggest that the tendency to switch between coordination states, sculpts the dynamic repertoire of the brain and can be indexed by a measure known as metastability. In this article, we characterize perturbations in the metastability of global brain network dynamics following Transcranial Magnetic Stimulation that could quantify the duration for which information processing is altered. Thus allowing researchers to understand the network effects of brain stimulation, standardize stimulation protocols and design experimental tasks. We demonstrate the effect empirically using publicly available datasets and use a digital twin (a whole brain connectome model) to understand the dynamic principles that generate such observations. We observed a significant reduction in metastability, concurrent with an increase in coherence following single-pulse TMS reflecting the existence of a window where neural coordination is altered. The reduction in complexity was validated by an additional measure based on the Lempel-Ziv complexity of microstate labeled EEG data. Interestingly, higher frequencies in the EEG signal showed faster recovery in metastability than lower frequencies. The digital twin shed light on how the phase resetting introduced by the single-pulse TMS in local cortical networks can propagate globally, giving rise to changes in metastability and coherence.

Abstract 70: Rich-Club Behaviour and Analyses in the Human Brain Connectome as Novel Early Biomarkers of Cognitive Decline

Introduction: Transient ischemic attack (TIA) patients have an increased risk of early dementia, but the mechanism is unclear. One potential mechanism is the damaging effects of microvascular disease on brain networks. Rich club is a phenomenon where highly connected nodes form a “rich club” amongst themselves, thus playing an important role in global brain network communication. In this study we aim to determine 1) whether the prominence of the rich club effect declines greater in TIA patients than controls over time, and 2) the relationship of rich club prominence with cognition. Methods: TIA patients (n=47) and controls (n=43) underwent DTI imaging and cognitive assessment at baseline, 1-year, 3-year, and 5-year. Whole-brain tractography utilizing constrained spherical deconvolution was performed to construct whole-brain connectomes for both groups at all available timepoints. The maximum normalized rich club coefficient (Φ max ) was used as an indicator of the prominence of the rich club effect. Independent-sample T-tests were used to compare Φ max cross-sectionally, while a linear mixed effect (LME) model was used to examine the longitudinal change in Φ max in TIA patients vs. controls, while controlling for covariates including age, sex, WMH volume, whole-brain volume, and peak width of skeletonized mean diffusivity. Results: TIA patients had a mean age of 63.94 ( SD =8.62) and 20 females. Controls had a mean age of 60.73 (8.16) and 29 females. The prominence of the rich club was not significantly different between TIA patients and controls at baseline or 1-year ( p &gt;0.05). However, TIA patients showed a significantly less prominent rich club at 3-year and 5-year compared to controls ( p =0.047 and 0.018, respectively). TIA patients exhibited a significantly greater decrease in rich club prominence over time, relative to controls ( p =0.023), after accounting for covariates. Longitudinal LME models showed no association between changes in rich club prominence and changes in cognition ( p &gt;0.05 for all cognitive domains). Conclusion: In this study, we show a decrease in rich club prominence in TIA patients compared to controls which might identify their increased risk of cognitive impairment, and the potential benefits of aggressive vascular risk reduction.

Connectome-Based Neurosurgery in Primary Intra-Axial Neoplasms: Beyond the Traditional Modular Conception of Brain Architecture for the Preservation of Major Neurological Domains and Higher-Order Cognitive Functions.

Despite the therapeutical advancements in the surgical treatment of primary intra-axial neoplasms, which determined both a significative improvement in OS and QoL and a reduction in the incidence of surgery-induced major neurological deficits, nowadays patients continue to manifest subtle post-operative neurocognitive impairments, preventing them from a full reintegration back into social life and into the workforce. The birth of connectomics paved the way for a profound reappraisal of the traditional conception of brain architecture, in favour of a model based on large-scale structural and functional interactions of a complex mosaic of cortical areas organized in a fluid network interconnected by subcortical bundles. Thanks to these advancements, neurosurgery is facing a new era of connectome-based resections, in which the core principle is still represented by the achievement of an ideal onco-functional balance, but with a closer eye on whole-brain circuitry, which constitutes the foundations of both major neurological functions, to be intended as motricity; language and visuospatial function; and higher-order cognitive functions such as cognition, conation, emotion and adaptive behaviour. Indeed, the achievement of an ideal balance between the radicality of tumoral resection and the preservation, as far as possible, of the integrity of local and global brain networks stands as a mandatory goal to be fulfilled to allow patients to resume their previous life and to make neurosurgery tailored and gentler to their individual needs.

Multi-Modal Learning for Predicting the Genotype of Glioma.

The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrating focal tumor image and geometric features with brain network features derived from MRI. Convolutional neural networks show reasonable performance in predicting IDH mutation, which, however, cannot learn from non-Euclidean data, e.g., geometric and network data. In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks. To mitigate the limited availability of diffusion MRI, we develop a self-supervised approach to generate brain networks from anatomical multi-sequence MRI. Moreover, to extract tumor-related features from the brain network, we design a hierarchical attention module for the brain network encoder. Further, we design a bi-level multi-modal contrastive loss to align the multi-modal features and tackle the domain gap at the focal tumor and global brain. Finally, we propose a weighted population graph to integrate the multi-modal features for genotype prediction. Experimental results on the testing set show that the proposed model outperforms the baseline deep learning models. The ablation experiments validate the performance of different components of the framework. The visualized interpretation corresponds to clinical knowledge with further validation. In conclusion, the proposed learning framework provides a novel approach for predicting the genotype of glioma.

Alterations in the global brain network in older adults with poor sleep quality: A resting-state fMRI study

ObjectivesOlder adults often experience more fragmented and less restful sleep than younger individuals. However, the mechanisms in the brain that underlie these phenomena have been rarely studied. The aim of this study was to identify the differences in global brain network properties between older adults with good and poor sleep quality and to explore the relationship between these network properties and symptoms of insomnia. MethodsWe performed a graph-theoretic analysis of resting-state functional magnetic resonance imaging data from two groups of older adults: 59 with good sleep quality and 59 with poor sleep quality. Functional connectivity among 264 brain regions of interest was estimated, and undirected graphs were constructed. Network topological properties were compared using one-way repeated-measures analysis of variance. Pearson's correlation analysis assessed the relationship between the mean scores of the global network properties across network sparsity and insomnia symptoms, as measured using the global Pittsburgh Sleep Quality Index score. ResultsOlder adults with poor sleep quality displayed increased small-world-ness, global efficiency and modularity in the functional brain network during rest. Conversely, they exhibited decreased local efficiency, characteristic path length and clustering coefficient. In the total population, we found associations between insomnia symptoms and global network characteristics, except for betweenness centrality and modularity. ConclusionsIn older adults with poor sleep quality, global network measures revealed a functional brain architecture that tended towards integration rather than segregation. Further research is needed to better understand what these findings mean about the effects of aging on poor sleep quality.

Functional brain network features specify DBS outcome for patients with treatment resistant depression.

Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus (SCG) aims to alter dysregulation between subcortical and cortex. However, the 50% response rates for SCG-DBS indicates that selection of appropriate patients is challenging. Since stimulation influences large-scale network function, we hypothesized that network features can be used as biomarkers to inform outcome. In this pilot project, we used resting-state EEG recorded longitudinally from 10 TRD patients with SCG-DBS (11 at baseline). EEGs were recorded before DBS-surgery, 1-3 months, and 6 months post surgery. We used graph theoretical analysis to calculate clustering coefficient, global efficiency, eigenvector centrality, energy, and entropy of source-localized EEG networks to determine their topological/dynamical features. Patients were classified as responders based on achieving a 50% or greater reduction in Hamilton Depression (HAM-D) scores from baseline to 12 months post surgery. In the delta band, false discovery rate analysis revealed that global brain network features (segregation, integration, synchronization, and complexity) were significantly lower and centrality of subgenual anterior cingulate cortex (ACC) was higher in responders than in non-responders. Accordingly, longitudinal analysis showed SCG-DBS increased global network features and decreased centrality of subgenual ACC. Similarly, a clustering method separated two groups by network features and significant correlations were identified longitudinally between network changes and depression symptoms. Despite recent speculation that certain subtypes of TRD are more likely to respond to DBS, in the SCG it seems that underlying brain network features are associated with ability to respond to DBS. SCG-DBS increased segregation, integration, and synchronizability of brain networks, suggesting that information processing became faster and more efficient, in those patients in whom it was lower at baseline. Centrality results suggest these changes may occur via altered connectivity in specific brain regions especially ACC. We highlight potential mechanisms of therapeutic effect for SCG-DBS.

Brain networks and epilepsy development in patients with Alzheimer disease.

This study aimed to investigate the association between brain networks and epilepsy development in patients with Alzheimer disease (AD). We enrolled patients newly diagnosed with AD at our hospital who underwent three-dimensional T1-weighted magnetic resonance imaging at the time of AD diagnosis and included healthy controls. We obtained the cortical, subcortical, and thalamic nuclei structural volumes using FreeSurfer and applied graph theory to obtain the global brain network and intrinsic thalamic network based on the structural volumes using BRAPH. We enrolled 25 and 56 patients with AD with and without epilepsy development, respectively. We also included 45 healthy controls. The global brain network differed between the patients with AD and healthy controls. The local efficiency (2.026 vs. 3.185, p=.048) and mean clustering coefficient (0.449 vs. 1.321, p=.024) were lower, whereas the characteristic path length (0.449 vs. 1.321, p=.048) was higher in patients with AD than in healthy controls. Both global and intrinsic thalamic networks were significantly different between AD patients with and without epilepsy development. In the global brain network, local efficiency (1.340 vs. 2.401, p=.045), mean clustering coefficient (0.314 vs. 0.491, p=.045), average degree (27.442 vs. 41.173, p=.045), and assortative coefficient (-0.041 vs. -0.011, p=.045) were lower, whereas the characteristic path length (2.930 vs. 2.118, p=.045) was higher in patients with AD with epilepsy development than in those without. In the intrinsic thalamic network, the mean clustering coefficient (0.646 vs. 0.460, p=.048) was higher, whereas the characteristic path length (1.645 vs. 2.232, p=.048) was lower in patients with AD with epilepsy development than in those without. We found that the global brain network differs between patients with AD and healthy controls. In addition, we demonstrated significant associations between brain networks (both global brain and intrinsic thalamic networks) and epilepsy development in patients with AD.

Maternal anxiety during pregnancy is associated with weaker prefrontal functional connectivity in adult offspring

BackgroundThe connectome, constituting a unique fingerprint of a person’s brain, may be influenced by its prenatal environment, potentially affecting later-life resilience and mental health.MethodsWe conducted a prospective resting-state functional Magnetic Resonance Imaging study in 28-year-old offspring (N = 49) of mothers whose anxiety was monitored during pregnancy. Two offspring anxiety subgroups were defined: “High anxiety” (n = 13) group versus “low-to-medium anxiety” (n = 36) group, based on maternal self-reported state anxiety at 12–22 weeks of gestation. To predict resting-state functional connectivity of 32 by 32 ROIs, maternal state anxiety during pregnancy was included as a predictor in general linear models for both ROI-to-ROI and graph theoretical metrics. Sex, birth weight and postnatal anxiety were included as covariates.ResultsHigher maternal anxiety was associated with weaker functional connectivity of medial prefrontal cortex with left inferior frontal gyrus (t = 3.45, pFDR < 0.05). Moreover, network-based statistics (NBS) confirmed our finding and revealed an additional association of weaker connectivity between left lateral prefontal cortex with left somatosensory motor gyrus in the offspring. While our results showed a general pattern of lower functional connectivity in adults prenatally exposed to maternal anxiety, we did not observe significant differences in global brain networks between groups.ConclusionsWeaker (medial) prefrontal cortex functional connectivity in the high anxiety adult offspring group suggests a long-term negative impact of prenatal exposure to high maternal anxiety, extending into adulthood. To prevent mental health problems at population level, universal primary prevention strategies should aim at lowering maternal anxiety during pregnancy.

Sensitive dynamics of brain cognitive networks and its resource constraints

It is well known that brain functions are closely related to the synchronization of brain networks, but the underlying mechanisms are still not completely understood. To study this problem, we here focus on the synchronization of cognitive networks, in contrast to that of a global brain network, as individual brain functions are in fact performed by different cognitive networks but not the global network. In detail, we consider four different levels of brain networks and two approaches, i.e., either with or without resource constraints. For the case of without resource constraints, we find that global brain networks have fundamentally different behaviors from that of the cognitive networks; i.e., the former has a continuous synchronization transition, while the latter shows a novel transition of oscillatory synchronization. This feature of oscillation comes from the sparse links among the communities of cognitive networks, resulting in coupling sensitive dynamics of brain cognitive networks. While for the case of resource constraints, we find that at the global level, the synchronization transition becomes explosive, in contrast to the continuous synchronization for the case of without resource constraints. At the level of cognitive networks, the transition also becomes explosive and the coupling sensitivity is significantly reduced, thus guaranteeing the robustness and fast switch of brain functions. Moreover, a brief theoretical analysis is provided.

Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment.

Accumulating evidence shows that cognitive impairment (CI) in chronic heart failure (CHF) patients is related to brain network dysfunction. This study investigated brain network structure and rich-club organization in chronic heart failure patients with cognitive impairment based on graph analysis of diffusion tensor imaging data. The brain structure networks of 30 CHF patients without CI and 30 CHF patients with CI were constructed. Using graph theory analysis and rich-club analysis, changes in global and local characteristics of the subjects' brain network and rich-club organization were quantitatively calculated, and the correlation with cognitive function was analyzed. Compared to the CHF patients in the group without CI group, the CHF patients in the group with CI group had lower global efficiency, local efficiency, clustering coefficient, the small-world attribute, and increased shortest path length. The CHF patients with CI group showed lower nodal degree centrality in the fusiform gyrus on the right (FFG.R) and nodal efficiency in the orbital superior frontal gyrus on the left (ORB sup. L), the orbital inferior frontal gyrus on the left (ORB inf. L), and the posterior cingulate gyrus on the right (PCG.R) compared with CHF patients without CI group. The CHF patients with CI group showed a smaller fiber number of edges in specific regions. In CHF patients with CI, global efficiency, local efficiency and the connected edge of the orbital superior frontal gyrus on the right (ORB sup. R) to the orbital middle frontal gyrus on the right (ORB mid. R) were positively correlated with Visuospatial/Executive function. The connected edge of the orbital superior frontal gyrus on the right to the orbital inferior frontal gyrus on the right (ORB inf. R) is positively correlated to attention/calculation. Compared with the CHF patients without CI group, the connection strength of feeder connection and local connection in CHF patients with CI group was significantly reduced, although the strength of rich-club connection in CHF patients complicated with CI group was decreased compared with the control, there was no statistical difference. In addition, the rich-club connection strength was related to the orientation (direction force) of the Montreal cognitive assessment (MoCA) scale, and the feeder and local connection strength was related to Visuospatial/Executive function of MoCA scale in the CHF patients with CI. Chronic heart failure patients with CI exhibited lower global and local brain network properties, reduced white matter fiber connectivity, as well as a decreased strength in local and feeder connections in key brain regions. The disrupted brain network characteristics and connectivity was associated with cognitive impairment in CHF patients. Our findings suggest that impaired brain network properties and decreased connectivity, a feature of progressive disruption of brain networks, predict the development of cognitive impairment in patients with chronic heart failure.

Engaging distributed cortical and cerebellar networks through motor execution, observation, and imagery.

When we interact with the environment around us, we are sometimes active participants, making directed physical motor movements and other times only mentally engaging with our environment, taking in sensory information and internally planning our next move without directed physical movement. Traditionally, cortical motor regions and key subcortical structures such as the cerebellum have been tightly linked to motor initiation, coordination, and directed motor behavior. However, recent neuroimaging studies have noted the activation of the cerebellum and wider cortical networks specifically during various forms of motor processing, including the observations of actions and mental rehearsal of movements through motor imagery. This phenomenon of cognitive engagement of traditional motor networks raises the question of how these brain regions are involved in the initiation of movement without physical motor output. Here, we will review evidence for distributed brain network activation during motor execution, observation, and imagery in human neuroimaging studies as well as the potential for cerebellar involvement specifically in motor-related cognition. Converging evidence suggests that a common global brain network is involved in both movement execution and motor observation or imagery, with specific task-dependent shifts in these global activation patterns. We will further discuss underlying cross-species anatomical support for these cognitive motor-related functions as well as the role of cerebrocerebellar communication during action observation and motor imagery.

Altered brain structural and functional connectivity in cannabis users

Cannabis is one of the most used and commodified illicit substances worldwide, especially among young adults. The neurobiology mechanism of cannabis is yet to be identified particularly in youth. The purpose of this study was to concurrently measure alterations in brain structural and functional connectivity in cannabis users using resting-state functional magnetic resonance images (rs-fMRI) and diffusion-weighted images (DWI) from a group of 73 cannabis users (age 22–36, 19 female) in comparison with 73 healthy controls (age 22–36, 14 female) from Human Connectome Project (HCP). Several significant differences were observed in local structural/functional network measures (e.g. degree and clustering coefficient), being prominent in the insular and frontal opercular cortex and lateral/medial temporal cortex. The rich-club organization of structural networks revealed a normal trend, distributed within bilateral frontal, temporal and occipital regions. However, minor differences were found between the two groups in the superior and inferior temporal gyri. Functional rich-club nodes were mostly located within parietal and posterior areas, with minor differences between the groups found mainly in the centro-temporal and parietal regions. Regional network measures of structural/functional networks were associated with times used cannabis (TUC) in several regions. Although the structural/functional network in both groups showed small-world property, no differences between cannabis users and healthy controls were found regarding the global network measures, showing no association with cannabis use. After FDR correction, all of the significant associations between network measures and TUC were found to be insignificant, except for the association between degree and TUC within the presubiculum region. To recap, our findings revealed alterations in local topological properties of structural and functional networks in cannabis users, although their global brain network organization remained intact.