Informatics

cloudSLEAP – PROJECT SUMMARY/ABSTRACT Understanding how the brain produces complex behavior is a central goal of neuroscience, but quantifying behavior is technically challenging, particularly in unrestrained and naturalistic settings. Tools that are able to overcome these limitations leverage deep learning to achieve robust markerless motion capture, enabling characterization of behavior through precise positional tracking of body parts from standard videos of behavior.

Summary: Behavioral data is central to biomedical research, including both synchronous measures (e.g. brain activation and button-presses from a reading task in an fMRI scan), and those performed independently (e.g. a literacy questionnaire.) Compared to neurophysiology and brain imaging data, behavioral data is often relatively small, with file sizes in the megabytes rather than terabytes for both experimental scripts and resulting datasets.

Humans make rich inferences about the relationships between entities in the world from scarce information. For example, we can find a novel destination after seeing a few street numbers, or find a page in a dictionary by glancing at a few words in other pages. Theoretical considerations suggest that the brain makes such inferences by constructing "internal models" of the relationships in the environment (relationships between actions and states of the world), and by mentally simulating those models. However, the neural substrates and mechanisms of mental simulation are not understood.

The ability to predict when external events will occur, such as anticipating the actions of a predator or the availability of food, is critical for survival. Converging computational and experimental work suggests that dynamically changing patterns of neural activity, including neural sequences, underlie temporal prediction and temporal processing.

Serotonergic neuromodulation is a crucial factor in regulating several aspects of brain function, from mood disorders to appetite, reward and motivation, and in maintaining balance of sensory perception. However, the network mechanisms by which it modulates brain dynamics are elusive. In this project, we will develop and experimentally test a mechanistic theory explaining the observed modulations of cortical activity induced by the serotonergic activation via hallucinogenic agonists.

A brief glimpse at a face quickly reveals rich multi-dimensional information about the person in front of us. How is this impressive computational feat accomplished? A recently revised neural framework for face processing suggests perception of face form information, i.e. face invariant features such as gender, age, and identity, are processed through the ventral visual pathway, comprising the occipital face area, fusiform face area, and anterior temporal lobe face area.

Fast glutamatergic synaptic transmission is based on a precise and complex molecular organization which requires the control of the number of AMPA-type glutamate receptors (AMPARs) at the postsynaptic sites of glutamatergic synapses on dendritic spines. The number of AMPARs varies as a function of pre- and postsynaptic activation history of the synapse. It is now well described that synapses can change their number of AMPARs and therefore, their response properties through biochemical mechanisms of synaptic plasticity. In this way, information is stored in the brain.

Numerous neuroscience and clinical applications exist for a noninvasive neuromodulation technology that can reach deep in the brain with high resolution. One compelling clinical application is the treatment of drug addiction, a major public health challenge in the US. In humans, the neural targets for treatment are 1-4 mm3, and thus a critical goal is to stimulate deep in the brain with higher resolutions than currently available with any noninvasive stimulation modality.

Today, the most widespread tool for measuring whole-brain activity noninvasively is functional magnetic resonance imaging (fMRI). Although fMRI tracks neural activity indirectly through measuring the associated changes in blood flow, volume and oxygenation, recent evidence has suggested that these active hemodynamic changes in the brain are far more precisely coordinated than previously believed, perhaps at the fine spatial scale of the basic modules of functional architecture: cerebral cortical columns and layers.