The past decade has witnessed an explosive growth in our ability to observe and measure brain activity in animals and humans. The ability to ¡°understand the brain¡± has been the key to progress in neuroscience and for better management of various neurological and mental disorders. Though vast amounts of data have been generated using various techniques at multiple scales, there has been only limited progress in integrating functional activity data across the molecular, cellular, and system level. It is therefore important to develop principled methods, systems, and technologies which focus on the integrated picture of the data obtained at these various scales and to understand brain function as a whole. This challenge is fundamentally one in the domain of neurotechnology and neuroengineering, a discipline intersecting engineering sciences with neuroscience.
The development of methodologies to build an integrated picture of the multi-scale functional networks within the brain will have a marked impact on our understanding of both the healthy and diseased brain. Functional mapping techniques can be used to discern both the origin as well as the direction of information propagation within the cortex and can be used to analyze the complex pattern of interconnected neuronal networks. Characterization of these complex neural networks will enable a deeper understanding of the mechanisms by which the brain operates, leading to improved diagnosis of neural disorders such as stroke and epilepsy, better surgical planning and the creation and improvement of neural prosthesis in cases of brain injury or disease, and lead to better management of mental disorders such as schizophrenia, Alzheimer¡¯s disease, and depression, as well as pain. Innovative systems engineering theories, imaging tools, sensors, informatics, algorithms, and models are needed to tackle the grand challenges in brain research.