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Key Points Understanding the network organization of the brain has been a long-standing challenge for neuroscience. In the past decade, developments in graph theory have provided many new methods for topologically analysing complex networks, some of which have already been translated to the characterization of anatomical and functional brain networks. Anatomical networks at whole-brain and cellular scales in several species consistently demonstrate conservation of wiring costs and small-world topology (high clustering and short path length). Human brain anatomical networks, derived from MRI or diffusion tensor imaging data, have high-degree cortical 'hubs' and modular and hierarchical properties. Functional networks also demonstrate small-world properties at whole-brain and cellular spatial scales. Additionally, complex network properties including small-worldness and the existence of hubs are conserved over different frequency scales in functional MRI and electrophysiological data. Convergent experimental and computational data suggest that there is interdependence in the organization of structural and functional networks. The topology, synchronizability and other dynamic properties of functional networks are strongly affected by small-world and other metrics of structural connectivity. Conversely, over a slower timescale the dynamics can modulate structural network topology. Neuropsychiatric disorders can be thought of as dysconnectivity syndromes, and graph theory has already been used to quantify abnormality of structural and functional network properties in schizophrenia, Alzheimer's disease and other disorders. Graph theory can help us to understand the vulnerability of brain networks to lesions and could in future be used to provide markers of genetic risk for disorders or to measure therapeutic effects of drug treatments on functional networks. The network organization of the brain, as it is beginning to be revealed by graph theory, is compatible with the hypothesis that the brain, perhaps in common with other complex networks, has evolved both to maximize the efficiency of information transfer and to minimize connection cost, at all scales of space and time. Key issues for future work include clarifying the relationship between the brain's network properties and its emergent cognitive behaviours in health and disease. In recent years, the principles of network science have increasingly been applied to the study of the brain's structural and functional organization. Bullmore and Sporns review this growing field of research and discuss its contributions to our understanding of brain function. Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks — such as small-world topology, highly connected hubs and modularity — both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

OpenAI’s debut of its impressive Sora text-to-video tool has raised important questions. OpenAI’s debut of its impressive Sora text-to-video tool has raised important questions. Credit: OpenAI A still from a video generated by OpenAI's Sora of a young man reading a book while sitting on a cloud.

Before any invasive procedure, physicians have a legal obligation to inform patients. Traditionally, this involves a discussion with a physician, supplemented by written leaflet information directed at the specific procedure. Comparison of the use and effectiveness of computer-based visualization opposed to standardized conversation for providing patients with information of forthcoming procedures (coronary catheters or endoscopy procedures). Prospective, randomized trial with 56 participants allocated in two different groups: Visualization Group (standardized information supported by a tool for displaying two-dimensional pictures to explain medical facts as well as informative leaflet) or Control Group (standardized information and informative leaflet only). Detailed information was given about the indication, the probable complications and the details of the forthcoming procedures (coronary catheters or endoscopy procedures). All participants had to reach a Karnofsky Score of 70 points and be able to understand German or English. Main outcome measures were patient's satisfaction with physician-patient conversation, patient's acquired knowledge and duration of the intervention as described above. Patients of the Visualization Group were more satisfied with the conversation and had higher knowledge scores after the conversation. A Mann-Whitney-U-Test between the two groups showed that these differences in satisfaction (P<0.001) and knowledge (P= or <0.006) were statistically significant. Length of time needed for the conversation was slightly higher in the Visualization Group, but this difference was not statistically significant (25 versus 23 min; P= 0.441). No differences could be found due to differing age or educational level in the results of the Visualization and the Control Group. Using computerized visualization increased the satisfaction and knowledge of the patients. The presentation of the visualized information in the Visualization Group did not demand significantly more time than the standard conversation in the Control Group.

At a conference in San Francisco, a group drafted proposals to add more planets, instruments and other science icons to the keyboard.

AI researchers are excited about using generative networks to train image-recognition software. More widely, Cranmer says, AIs that generate scientific data might help astronomers and other researchers to prune noise from large data sets, and so better understand patterns within them.

In July last year, neurobiologist Björn Brembs published a paper about how fruit flies walk. Nine months on, his paper looks different: another group has fed its data into the article, altering one of the figures.

[...]the system combines multiple high-speed camera images to estimate the ball's trajectory and position when it hits the ground. With public opinion key to decisions on topics such as climate change, nuclear power and genetic modification, calls to spread awareness of uncertainty and probability in science are increasing, even if they are often drowned out by the comforting simplifications of certainty that characterize mainstream reporting.

When reading bioscience journal articles, many researchers focus attention on the figures and their captions. This observation led to the development of the BioText literature search engine, a freely available Web-based application that allows biologists to search over the contents of Open Access Journals, and see figures from the articles displayed directly in the search results. This article presents a qualitative assessment of this system in the form of a usability study with 20 biologist participants using and commenting on the system. 19 out of 20 participants expressed a desire to use a bioscience literature search engine that displays articles' figures alongside the full text search results. 15 out of 20 participants said they would use a caption search and figure display interface either frequently or sometimes, while 4 said rarely and 1 said undecided. 10 out of 20 participants said they would use a tool for searching the text of tables and their captions either frequently or sometimes, while 7 said they would use it rarely if at all, 2 said they would never use it, and 1 was undecided. This study found evidence, supporting results of an earlier study, that bioscience literature search systems such as PubMed should show figures from articles alongside search results. It also found evidence that full text and captions should be searched along with the article title, metadata, and abstract. Finally, for a subset of users and information needs, allowing for explicit search within captions for figures and tables is a useful function, but it is not entirely clear how to cleanly integrate this within a more general literature search interface. Such a facility supports Open Access publishing efforts, as it requires access to full text of documents and the lifting of restrictions in order to show figures in the search interface.