The future of human cerebral cartography: a novel approach.

Cerebral cartography can be understood in a limited, static, neuroanatomical sense. Temporal information from electrical recordings contributes information on regional interactions adding a functional dimension. Selective tagging and imaging of molecules adds biochemical contributions. Cartographic detail can also be correlated with normal or abnormal psychological or behavioural data. Modern cerebral cartography is assimilating all these elements. Cartographers continue to collect ever more precise data in the hope that general principles of organization will emerge. However, even detailed cartographic data cannot generate knowledge without a multi-scale framework making it possible to relate individual observations and discoveries. We propose that, in the next quarter century, advances in cartography will result in progressively more accurate drafts of a data-led, multi-scale model of human brain structure and function. These blueprints will result from analysis of large volumes of neuroscientific and clinical data, by a process of reconstruction, modelling and simulation. This strategy will capitalize on remarkable recent developments in informatics and computer science and on the existence of much existing, addressable data and prior, though fragmented, knowledge. The models will instantiate principles that govern how the brain is organized at different levels and how different spatio-temporal scales relate to each other in an organ-centred context

Lost in translation

Translation in cognitive neuroscience remains beyond the horizon, brought no closer by supposed major advances in our understanding of the brain. Unless our explanatory models descend to the individual level-a cardinal requirement for any intervention-their real-world applications will always be limited. Drawing on an analysis of the informational properties of the brain, here we argue that adequate individualisation needs models of far greater dimensionality than has been usual in the field. This necessity arises from the widely distributed causality of neural systems, a consequence of the fundamentally adaptive nature of their developmental and physiological mechanisms. We discuss how recent advances in high-performance computing, combined with collections of large-scale data, enable the high-dimensional modelling we argue is critical to successful translation, and urge its adoption if the ultimate goal of impact on the lives of patients is to be achieved

Extent of Pseudocapacitance in High‐Surface Area Vanadium Nitrides

Early transition‐metal nitrides, especially vanadium nitride (VN), have shown promise for use in high energy density supercapacitors due to their high electronic conductivity, areal specific capacitance, and ability to be synthesized in high surface area form. Their further development would benefit from an understanding of their pseudocapacitive charge storage mechanism. In this paper, the extent of pseudocapacitance exhibited by vanadium nitride in aqueous electrolytes was investigated using cyclic voltammetry and electrochemical impedance spectroscopy. The pseudocapacitance contribution to the total capacitance in the nitride material was much higher than the double‐layer capacitance and ranged from 85 % in basic electrolyte to 87 % in acidic electrolyte. The mole of electrons transferred per VN material during pseudocapacitive charge storage was also evaluated. This pseudocapacitive charge‐storage is the key component in the full utilization of the properties of early‐transition metal nitrides for high‐energy density supercapacitors.Double‐layer capacitance vs. pseudocapacitance: the electrostatic double‐layer and pseudocapacitive charge storage mechanisms in high‐surface‐area vanadium nitride are investigated. The magnitude of the pseudocapacitive charge storage capacity and mole of electrons transferred are reported. The pseudocapacitive charge‐storage mechanism is the key component in maximizing the energy density of supercapacitors based on transition‐metal nitrides.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146597/1/batt201800050.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146597/2/batt201800050_am.pd