Theory & Data Analysis Tools

The brain uses its own previous activity to adapt to an ever-changing environment. This history dependent adaptation takes place at all scales of organization of the nervous system. The objective of this project is to develop a common theoretical formalism to be applied to multiple history dependent phenomena, from the biochemical reactions that underlie synaptic plasticity, to the emergent patterns in complex neural networks. At the core of this formalism is the recognition that most models of neuronal activity are based on the classical reaction-diffusion equation.

Abstract: Causality is central to neuroscience. For example, we might ask about the causal effect of a neuron on another neuron, or its influence on perception, action, or cognition. Moreover, any medical approaches aim at producing a causal effect – effecting improvements for patients. Randomized controlled trials (RCTs) are the gold standard to establish causality, but they are not always practical. For example, while we can electrically or optogenetically activate entire areas, large-scale targeted stimulation of individual neurons is hard.

The massive neural activity data collected through transformative new technologies present an incredible opportunity in facilitating the mission of the BRAIN Initiative and the wider neuroscience community: to understand brain function.

Project Summary A central goal of systems neuroscience is to relate behavior to its underlying circuit dynamics. This task is complicated by the complex and circuitous paths along which information flows as it is encoded and processed in the many steps between sensory inputs and motor outputs. Currently, we understand little regarding the organization and dynamics of interactions between brain areas. For example, we do not know the degree to which specific brain areas have separate representations versus when information is encoded jointly across brain areas.

PROJECT SUMMARY/ABSTRACT To develop maps at multiple scales of neuronal circuits in the human brain and study the brain dynamics, there is a need for non-invasive functional brain imaging with high spatiotemporal resolution operating in natural environments. Among non-invasive functional brain imaging methods, magnetoencephalography (MEG) is the only technology that can map cortical activity down to millimeter spatial resolution with millisecond time resolution. Current cryogenic MEG systems employ superconducting quantum interference device (SQUID) magnetometers.

Project Summary/Abstract Functional near-infrared spectroscopy (fNIRS) is a well-established neuroimaging method which enables neuroscientists to study brain activity by non-invasively monitoring hemodynamic changes in the cerebral cortex. In the last decade, the use of fNIRS has increased significantly with the formation of a society, with an exponential growth of users and publications, and with an increasing number of available commercial instruments.

ABSTRACT The overarching goal of this project is to develop, validate and implement a new modality for noninvasive functional imaging of neural currents deep in the human brain through the skull at unprecedented spatial and temporal resolution. Transcranial Acoustoelectric Brain Imaging (tABI) is a disruptive technology that exploits an ultrasound (US) beam to transiently interact with physiologic current, producing a radiofrequency signature detected by one or more surface electrodes.

Research applications of brain Positron Emission Tomography (PET) have been in place for over 40 years. The combination of quantitative PET systems with novel radiotracers has led to a numerous imaging para- digms to understand normal brain physiology including neurotransmitter dynamics and receptor pharmacology at rest and during activation. Brain-dedicated PET systems offer important advantages over currently available PET systems in terms of sensitivity and resolution. However, the state-of-the-art for brain PET has not progressed beyond the 20-year-old HRRT.

A theory of population coding in the cerebellum In order to move accurately, the brain relies on internal models that predict the sensory consequences of motor commands. Evidence for this idea comes from human behavioral experiments [1-7] and animal lesion studies [8- 11], suggesting that the critical structure for forming internal models is the cerebellum.

Abstract How is connectivity between neurons related to patterns of activity exhibited by these neurons in vivo? This question of structure-function relations in brain circuits is of fundamental importance. Answering it in a quantitative manner would have far-reaching consequences both for our theories of how brain works and for applications ranging from better disease treatments to new tools for artificial intelligence.