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Graph theory is now becoming a standard tool in system‐level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function. Graph theory is now becoming a standard tool in system‐level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function. ​

Identifying brain topology alterations in chronic pain is a crucial step in understanding its pathophysiology. The primary objective of this systematic review and meta-analysis was to assess alterations in resting-state functional and structural global network properties in patients with chronic pain. Following the preregistration (PROSPERO CRD42024542390), databases were searched for studies comparing connectivity-based whole-brain global network properties between patients with chronic pain and healthy controls. Risk of bias was assessed using an adapted Newcastle-Ottawa scale. Random-effect meta-analyses were conducted for each global network property separately. A total of 32 functional topology studies and 17 structural topology studies were included in the qualitative review, with 27 functional topology studies and 17 structural topology studies eligible for meta-analysis across nine unique structural and functional global network properties. The number of participants per meta-analysis ranged from 178 to 1,592. There was low-certainty evidence that chronic pain patients showed impairments in local efficiency of resting-state functional whole-brain topology (SMD: -0.50, 95%-CI: -0.81 to -0.19, 95%-PI: -1.38 to 0.38), and low to very low-certainty evidence that structural whole-brain topology was not altered in chronic pain across nine global network properties. The heterogeneity was high in the majority of functional (I : 1-76%) and structural (I : 68-97%) topology studies. Most functional (50%) and structural (65%) topology studies showed some concern regarding the risk of bias. The meta-analyses indicate that functional but not structural whole-brain topological reorganisation is involved in the pathophysiology of chronic pain.

•Strokes, traumatic lesions, tumors and neurodegenerative disorders are all examples of neurological conditions that can severely impair the normal functioning of the human brain.•Given the same amount of damage, a lot of variability exists in individual outcomes, a phenomenon often attributed to so-called “brain resilience”.•By simulating brain lesions in a large dataset of twins, we investigated whether brain resilience is mainly influenced by genetic factor or by environmental factors.•Results imply both heritable and environmental components of brain resilience, which may be important for designing interventions to help build individual resilience. Resilience is the capacity of complex systems to persist in the face of external perturbations and retain their functional properties and performance. In the present study, we investigated how individual variations in brain resilience, which might influence response to stress, aging and disease, are influenced by genetics and/or the environment, with potential implications for the implementation of resilience-boosting interventions. Resilience estimates were derived from in silico lesioning of either brain regions or functional connections constituting the connectome of healthy individuals belonging to two different large and unique datasets of twins, specifically: 463 individual twins from the Human Connectome Project and 453 individual twins from the Colorado Longitudinal Twin Study. As has been reported previously, moderate heritability was found for several topological indexes of brain efficiency and modularity. Importantly, evidence of heritability was found for resilience measures based on removal of brain connections rather than specific single regions, suggesting that genetic influences on resilience are preferentially directed toward region-to-region communication rather than local brain activity. Specifically, the strongest genetic influence was observed for moderately weak, long-range connections between a specific subset of functional brain networks: the Default Mode, Visual and Sensorimotor networks. These findings may help identify a link between brain resilience and network-level alterations observed in neurological and psychiatric diseases, as well as inform future studies investigating brain shielding interventions against physiological and pathological perturbations.

Cerebral dynamics emerge from the brain’s substrate due to the anatomical patterns of its physical connections, which we know are not a fixed structure but are subject to temporal and local modifications. This circumstance makes it possible for a more or less fixed number of neurons to generate a range of complex networks. By studying the topological space associated with these physical connections and their geometric dynamics, we can use Differential Geometry to study the foundations of the brain’s connectome.

The brain topology highly reflects the complex cognitive functions of the biological brain after million-years of evolution. Learning from these biological topologies is a smarter and easier way to achieve brain-like intelligence with features of efficiency, robustness, and flexibility. Here we proposed a brain topology-improved spiking neural network (BT-SNN) for efficient reinforcement learning. First, hundreds of biological topologies are generated and selected as subsets of the Allen mouse brain topology with the help of the Tanimoto hierarchical clustering algorithm, which has been widely used in analyzing key features of the brain connectome. Second, a few biological constraints are used to filter out three key topology candidates, including but not limited to the proportion of node functions (e.g., sensation, memory, and motor types) and network sparsity. Third, the network topology is integrated with the hybrid numerical solver-improved leaky-integrated and fire neurons. Fourth, the algorithm is then tuned with an evolutionary algorithm named adaptive random search instead of backpropagation to guide synaptic modifications without affecting raw key features of the topology. Fifth, under the test of four animal-survival-like RL tasks (i.e., dynamic controlling in Mujoco), the BT-SNN can achieve higher scores than not only counterpart SNN using random topology but also some classical ANNs (i.e., long-short-term memory and multi-layer perception). This result indicates that the research effort of incorporating biological topology and evolutionary learning rules has much in store for the future.

Executive functions (EF) are a set of higher-order cognitive abilities that enable goal-directed behavior by controlling lower-level operations. In the brain, those functions have been traditionally associated with activity in the Frontoparietal Network, but recent neuroimaging studies have challenged this view in favor of more widespread cortical involvement. In the present study, we aimed to explore whether the network that serves as critical hubs at rest, which we term network reliance, differentiate individuals as a function of their level of EF. Furthermore, we investigated whether such differences are driven by genetic as compared to environmental factors. For this purpose, resting-state functional magnetic resonance imaging data and the behavioral testing of 453 twins from the Colorado Longitudinal Twins Study were analyzed. Separate indices of EF performance were obtained according to a bifactor unity/diversity model, distinguishing between three independent components representing: Common EF, Shifting-specific and Updating-specific abilities. Through an approach of step-wise in silico network lesioning of the individual functional connectome, we show that interindividual differences in EF are associated with different dependencies on neural networks at rest. Furthermore, these patterns show evidence of mild heritability. Such findings add knowledge to the understanding of brain states at rest and their connection with human behavior, and how they might be shaped by genetic influences.

Sleep is a fundamental physiological process necessary for efficient cognitive functioning especially in relation to memory consolidation and executive functions, such as attentional and switching abilities. The lack of sleep strongly alters the connectivity of some resting-state networks, such as default mode network and attentional network. In this study, by means of magnetoencephalography (MEG) and specific cognitive tasks, we investigated how brain topology and cognitive functioning are affected by 24 h of sleep deprivation (SD). Thirty-two young men underwent resting-state MEG recording and evaluated in letter cancellation task (LCT) and task switching (TS) before and after SD. Results showed a worsening in the accuracy and speed of execution in the LCT and a reduction of reaction times in the TS, evidencing thus a worsening of attentional but not of switching abilities. Moreover, we observed that 24 h of SD induced large-scale rearrangements in the functional network. These findings evidence that 24 h of SD is able to alter brain connectivity and selectively affects cognitive domains which are under the control of different brain networks.

It’s hard to be a sports expert, and even harder to grow from a sports expert to a sports elite. What is the difference between an expert and an elite, and how to grow from an expert to an elite, has always been a matter of great concern to professional athletes In order to explore the differences of brain neural mechanism between experts and elites in the process of motor behavior and reveal the internal connection between motor performance and brain activity, We collected and analyzed the electroencephalograph (EEG) of 14 national archers and 14 provincial archers during aiming and resting state, and constructed the EEG brain network of two groups of participants based on weighted phase lag index, the graph theory is used to analyze and compare the network characteristics from two aspects of local network topology and global network topology. The results showed that, compared with expert archer, elite archer had stronger functional coupling in beta1 and beta2 bands, and the difference was most obvious in frontal and central regions; the topological characteristics of global and local brain networks of elite archer were significantly stronger than that of expert archer; the brain network characteristics of expert archers showed a stronger correlation with archery performance. This reflects that elite archers show stronger functional coupling, higher integration efficiency of global and local information, and more independent performance in the archery process. These findings reveal the difference of brain electrical network topology between elite and expert archer in the preparation stage of shooting, and provide theoretical reference for further training, promotion and selection of professional athletes.

Abstract Background Although transcranial direct current stimulation (tDCS) has shown to potentially mitigate drug craving and attentional bias to drug-related stimuli, individual differences in such modulatory effects of tDCS are less understood. In this study, we aimed to investigate a source of the inter-subject variability in the tDCS effects that can be useful for tDCS-based treatments of individuals with methamphetamine (MA) use disorder (IMUD). Methods Forty-two IMUD (all male) were randomly assigned to receive a single-session of either sham or real bilateral tDCS (anodal right/cathodal left) over the dorsolateral prefrontal cortex. The tDCS effect on MA craving and biased attention to drug stimuli were investigated by quantifying EEG-derived P3 (a measure of initial attentional bias) and late positive potential (LPP; a measure of sustained motivated attention) elicited by these stimuli. To assess the association of changes in P3 and LPP with brain connectivity network (BCN) topology, the correlation between topology metrics, specifically those related to the efficiency of information processing, and the tDCS effect was investigated. Results The P3 amplitude significantly decreased following the tDCS session, whereas the amplitudes increased in the sham group. The changes in P3 amplitudes were significantly correlated with communication efficiency measured by BCN topology metrics (r = −0.47, P = .03; r = −0.49, P = .02). There was no significant change in LPP amplitude due to the tDCS application. Conclusions These findings validate that tDCS mitigates initial attentional bias, but not the sustained motivated attention, to MA stimuli. Importantly, however, results also show that the individual differences in the effects of tDCS may be underpinned by communication efficiency of the BCN topology, and therefore, these BCN topology metrics may have the potential to robustly predict the effectiveness of tDCS-based interventions on MA craving and attentional bias to MA stimuli among IMUD.

Plain language summary Alterations in the gut microbiota may influence language recovery in patients with poststroke aphasia Aphasia is one of the most common complications of stroke, affecting approximately one-third of stroke survivors. Poststroke aphasia (PSA) is associated with more severe strokes, higher mortality, slower functional recovery, and greater healthcare costs compared to stroke patients without PSA. The unique characteristics of human language, which cannot be fully replicated in animal models, present a significant barrier to exploring the pathogenesis of PSA. Consequently, understanding the underlying neural pathways and identifying novel therapeutic targets is critically important. Recent evidence highlights the bidirectional communication between the gut microbiota and the brain, referred to as the microbiota-gut-brain axis. Our prior studies have demonstrated gut microbiota imbalances in patients with acute ischemic stroke, which significantly influence outcomes and prognosis. Considering the marked differences in disease severity and long-term prognosis between PSA patients and non-PSA patients, we hypothesized that variations in the microbiota-gut-brain axis might be linked to language function. To test this hypothesis, we employed functional magnetic resonance imaging (fMRI), 16S rDNA sequencing and enzyme-linked immunosorbent assay (ELISA) to explore differences in gut microbiota composition, neuroendocrine-immune network (NEI) network indicators, and brain network topology among PSA patients, non-PSA patients, and healthy controls (HCs). Our findings revealed that PSA patients, compared to non-PSA patients and HCs, exhibited gut microbiota dysbiosis, increased inflammatory responses, abnormal secretion of brain-gut peptides, and early activation of homologous language-related regions in the right hemisphere. These results provide new insights into the role of the gut microbiota in language recovery in PSA and highlight the gut microbiota as a promising therapeutic target for this condition.