Explore the vast range of titles available.

In recent years numerous attempts to understand the human brain were undertaken from a network point of view. A network framework takes into account the relationships between the different parts of the system and enables to examine how global and complex functions might emerge from network topology. Previous work revealed that the human brain features 'small world' characteristics and that cortical hubs tend to interconnect among themselves. However, in order to fully understand the topological structure of hubs, and how their profile reflect the brain's global functional organization, one needs to go beyond the properties of a specific hub and examine the various structural layers that make up the network. To address this topic further, we applied an analysis known in statistical physics and network theory as k-shell decomposition analysis. The analysis was applied on a human cortical network, derived from MRI\\DSI data of six participants. Such analysis enables us to portray a detailed account of cortical connectivity focusing on different neighborhoods of inter-connected layers across the cortex. Our findings reveal that the human cortex is highly connected and efficient, and unlike the internet network contains no isolated nodes. The cortical network is comprised of a nucleus alongside shells of increasing connectivity that formed one connected giant component, revealing the human brain's global functional organization. All these components were further categorized into three hierarchies in accordance with their connectivity profile, with each hierarchy reflecting different functional roles. Such a model may explain an efficient flow of information from the lowest hierarchy to the highest one, with each step enabling increased data integration. At the top, the highest hierarchy (the nucleus) serves as a global interconnected collective and demonstrates high correlation with consciousness related regions, suggesting that the nucleus might serve as a platform for consciousness to emerge.

The human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics. We utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques. Our results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.

Little is known about the changes of brain structural and functional connectivity networks underlying the pathophysiology in migraine. We aimed to investigate how the cortical network reorganization is altered by frequent cortical overstimulation associated with migraine. Gray matter volumes and resting-state functional magnetic resonance imaging signal correlations were employed to construct structural and functional networks between brain regions in 43 female patients with migraine (PM) and 43 gender-matched healthy controls (HC) by using graph theory-based approaches. Compared with the HC group, the patients showed abnormal global topology in both structural and functional networks, characterized by higher mean clustering coefficients without significant change in the shortest absolute path length, which indicated that the PM lost optimal topological organization in their cortical networks. Brain hubs related to pain-processing revealed abnormal nodal centrality in both structural and functional networks, including the precentral gyrus, orbital part of the inferior frontal gyrus, parahippocampal gyrus, anterior cingulate gyrus, thalamus, temporal pole of the middle temporal gyrus and the inferior parietal gyrus. Negative correlations were found between migraine duration and regions with abnormal centrality. Furthermore, the dysfunctional connections in patients' cortical networks formed into a connected component and three dysregulated modules were identified involving pain-related information processing and motion-processing visual networks. Our results may reflect brain alteration dynamics resulting from migraine and suggest that long-term and high-frequency headache attacks may cause both structural and functional connectivity network reorganization. The disrupted information exchange between brain areas in migraine may be reshaped into a hierarchical modular structure progressively.

Knowing which of correlated traits are more strongly targeted by selection is crucial to understand the evolutionary process. For example, it could help in understanding how behavioural and cognitive adaptations to social living have evolved. Social competence is the ability of animals to optimize their social behaviours according to the demands of their social environment. It is a behavioural performance trait that expresses how well a whole organism performs complex social tasks, such as choosing mates, raising offspring, participating in dominance hierarchies, solving conflicts or forming social bonds. Non‐social competence, on the other hand, is the ability of animals to optimize their non‐social behaviours according to the demands of their non‐social environment, such as finding food or avoiding predators. Social and non‐social cognition are correlated lower‐level traits of social and non‐social competence, respectively, encompassing the underlying psychological and neural mechanisms of behaviour that allow animals to acquire, encode, store and recall information about their social and non‐social environments. Here, we employ the theoretical framework that selection acts on performance traits first and on lower‐level traits only secondarily, to propose a new approach to the study of the evolution of social cognition. We hypothesize that when selection favours social competence, the cognitive system becomes more adapted to the social domain, making species biased for social information, and increasing their degree of sociality. The opposite can happen when selection favours non‐social competence. The level of specialization that the cognitive system can attain depends on whether social and non‐social competence are correlated with the same cognitive lower‐level traits. This in turn will determine whether species will evolve a type of social cognition that is general—that contributes with cognitive abilities that can be used in both social and non‐social environments—or modular—that contributes with cognitive abilities that are specific to the social environment. A free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Summary can be found within the Supporting Information of this article.

Quantum physics is a linear theory, so it is somewhat puzzling that it can underlie very complex systems such as digital computers and life. This paper investigates how this is possible. Physically, such complex systems are necessarily modular hierarchical structures, with a number of key features. Firstly, they cannot be described by a single wave function: only local wave functions can exist, rather than a single wave function for a living cell, a cat, or a brain. Secondly, the quantum to classical transition is characterised by contextual wave-function collapse shaped by macroscopic elements that can be described classically. Thirdly, downward causation occurs in the physical hierarchy in two key ways: by the downward influence of time dependent constraints, and by creation, modification, or deletion of lower level elements. Fourthly, there are also logical modular hierarchical structures supported by the physical ones, such as algorithms and computer programs, They are able to support arbitrary logical operations, which can influence physical outcomes as in computer aided design and 3-d printing. Finally, complex systems are necessarily open systems, with heat baths playing a key role in their dynamics and providing local arrows of time that agree with the cosmological direction of time that is established by the evolution of the universe.

Floating cities have emerged as an efficient long-term solution over unsustainable practiced solutions to combat the rising seas problem; nevertheless, the world lacks an international, official, and comprehensive framework regarding floating cities. Although previous research approached modular floating city design; however, resulted in configurations with various critical design restrictions mainly regarding interlocking capabilities and space utilization. The purpose of this paper is to offer a new systematic strategy for configuring modular and expandable floating cities without such restrictions. This paper explores Euclidean tilings as a strategy to offer numerous configurations based on regular, semi-regular, and demi-regular tilings. Selecting the ideal configuration is complicated; therefore, both quantitative and qualitative data methods were implemented to attain the objectives. Via an extensive literature review, this research derives key factors for configuring floating cities, then sets a brainstorming session with experts for group decision making before providing findings upon calculations via analytic hierarchy process, one of the most used quantitative data methods of multiple-criteria decision analysis. Through comprehensive literature review: seakeeping, modularity, zoning and circulation, and feasibility have been identified as the most significant criteria in floating city research. It explores the qualities and limitations of triangular, squared, hexagonal, octagonal, and dodecagonal platforms. Regarding criteria, seakeeping was the most significant criterion for platform selection by 53.6%. Regarding platforms, the hexagonal platform scored the highest with 25.31%. Relying on this method and the design considerations presented, numerous dynamic configurations can be offered and assessed through specific contexts without any of the past restrictions.

Achievement of the fusion for different modalities is a critical issue for multimodal emotion recognition. Feature-level fusion methods cannot deal with missing or corrupted data, while decision-level fusion methods may lose the correlation information between different modalities. To solve the above problems, a hierarchy modular neural network (HMNN) is proposed and is applied for multimodal emotion recognition. First, an HMNN is constructed to mimic the hierarchy modular architecture as demonstrated in the human brain. Each module contains several submodules dealing with features from different modalities. Connections are built between submodules within the same module and between corresponding submodules from different modules. Then, a learning algorithm based on Hebbian learning is used to train the connection weights in HMNN, which simulates the learning mechanism of the human brain. HMNN recognizes the label based on the activity level of each module and adopts the winner-take-all strategy. Finally, the proposed HMNN is applied on a public dataset for multimodal emotion recognition. Experimental results show that the proposed HMNN improves the recognition results, when compared with other decision-fusion methods, including support vector machine, as well as neural networks such as back-propagation and radial basis function neural networks. Furthermore, the inter-submodule connections in one module realizes information integration from different modalities and improves the performance of HMNN. Besides, the experiments suggest the effectiveness of HMNN on dealing with missing/corrupted data.

Structurally and functionally, the human brain’s visual cortex inspires convolutional neural networks (CNN). The visual cortex consists of different connected cortical regions. When a cortical area receives an input, it extracts meaningful information and forwards it to its neighboring region. CNN imitates the hierarchical structure of the visual cortex by multiple feature extraction layers. In neurosciences, it is believed that the modular structure of the human brain is the source of its cognitive abilities. This work contributes to the problem of domain decomposition, information routing control in the network, and module integration for image classification by proposing a novel framework to induce modularity in a pretrained CNN. We decompose the input domain of the CNN by employing novel Confusion Matrix driven Centroid Based Clustering (CMCBC) to create functional modules comprised of different pathways. CMCBC is an unsupervised clustering technique that utilizes the k-Medoid algorithm. This approach uses a confusion matrix to find similarities between each pair of classes and medoid for every cluster instead of using a distance function. The proposed framework is evaluated on two benchmark datasets, MNIST and CIFAR10, and the results achieved are promising. On the MNIST dataset, we achieved 98.51% accuracy using our proposed Modular CNN compared to the baseline accuracy of 99.39%. But at the same time, we saved 53% multiplications in the network, which significantly reduced the complexity. Similarly, on the CIFAR10 dataset, our model achieves 78.01% accuracy, 6% less than the baseline accuracy (84%). But when we retrain the network to align the weights further, our model outperformed the baseline model accuracy by 2.78% and achieved 86.78% accuracy.

Integrating structural and functional connectomes remains challenging because their relationship is non-linear and organized over nested modular hierarchies. We propose a hierarchical multiscale structure-function coupling framework for connectome integration that jointly learns individualized modular organization and hierarchical coupling across structural connectivity (SC) and functional connectivity (FC). The framework includes: (i) Prototype-based Modular Pooling (PMPool), which learns modality-specific multiscale communities by selecting prototypical ROIs and optimizing a differentiable modularity-inspired objective; (ii) an Attention-based Hierarchical Coupling Module (AHCM) that models both within-hierarchy and cross-hierarchy SC-FC interactions to produce enriched hierarchical coupling representations; and (iii) a Coupling-guided Clustering loss (CgC-Loss) that regularizes SC and FC community assignments with coupling signals, allowing cross-modal interactions to shape community alignment across hierarchies. We evaluate the model's performance across four cohorts for predicting brain age, cognitive score, and disease classification. Our model consistently outperforms baselines and other state-of-the-art approaches across three tasks. Ablation and sensitivity analyses verify the contributions of key components. Finally, the visualizations of learned coupling reveal interpretable differences, suggesting that the framework captures biologically meaningful structure-function relationships.

Many of the amazing functional capabilities of the brain are collective properties stemming from the interactions of large sets of individual neurons. In particular, the most salient collective phenomena in brain activity are oscillations, which require the synchronous activation of many neurons. Here, we analyse parsimonious dynamical models of neural synchronisation running on top of synthetic networks that capture essential aspects of the actual brain anatomical connectivity such as a hierarchical-modular and core-periphery structure. These models reveal the emergence of complex collective states with intermediate and flexible levels of synchronisation, halfway in the synchronous-asynchronous spectrum. These states are best described as broad Griffiths-like phases, i.e. an extension of standard critical points that emerge in structurally heterogeneous systems. We analyse different routes (bifurcations) to synchronisation and stress the relevance of 'hybrid-type transitions' to generate rich dynamical patterns. Overall, our results illustrate the complex interplay between structure and dynamics, underlining key aspects leading to rich collective states needed to sustain brain functionality.