Informatics

Project Summary Advances in neural recording technology now provide access to neural activity at high temporal resolutions, from many brain areas, and during complex and naturalistic behavior. Interpreting these types of high-dimensional and unconstrained neural recordings is still a major challenge in neuroscience. The aim of this project is to develop innovative methods for distilling high-dimensional neural activity patterns into simpler low-dimensional formats that can be effectively compared across time, conditions, or even across species.

Project Summary: Organisms' actions and decisions are guided by experience. Models of such behavior often appeal to the formalism of probabilistic inference, in which expectations about the world build up sequentially due to past observations. These models can account for typical response patterns of subjects performing cog- nitive tasks. However, a theory grounded in biophysical principles of neural circuit architecture and activity is lacking.

Abstract The field of neuroscience is experiencing unprecedented growth in the ability to record from and manipulate brain circuits in humans and in animal models. MEG/EEG are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it is still extremely difficult to interpret the underlying cellular and circuit level generators of these `macro-scale' signals without simultaneous invasive recordings.

Project Summary Abstract Learning new tasks and exposure to new environments lead to changes in the dynamics of brain circuits, as observed in various recent experiments. The ability to embed the statistics of the environment within brain circuits is essential for animals ability to thrive and survive in changing environments. However, the mechanisms by which circuits dynamics are implemented and learned are not well understood, and pose significant theoretical challenges. Recent work in both theoretical and experimental labs has highlighted the importance of circuit dynamics.

Diagnosis of Alzheimer's Disease Using Dynamic High- Order Brain Networks Abstract Alzheimer's disease (AD) is the most common form of dementia with no known disease-modifying treatment. Current clinical diagnosis and monitoring of the disease are primarily based on subjective neuropsychological and neurobehavioral assessments, which are generally susceptible to large variability.

Abstract The twin study design in brain imaging offers a very effective way of determining heritability of the human brain. The difference in variability between monozygotic (MZ) and same-sex dizygotic (DZ) twins can be used in determining heritability. We propose to determine the extent of heritability of both structural and functional brain networks at the voxel-level using 200 pairs of twin (400 individuals) of fMRI/DTI and MRI.

Principal investigator/Program Director (Last, first, middle): Hanson, Stephen, José RFA-EB-15-006 Project Summary/Abstract Since the earliest days of neuroscience research, core methods have focused on matching specific functions to local brain structure and neural activity. The relationship between brain structure and function has been a key motivation for the development and application of novel methods and discovery. Despite the apparent success of this program in identifying brain areas associated with memory, attention, executive control, action- perception, language, etc..

ABSTRACT Neuronal spike-trains and various other signals in the central nervous system have a discrete, impulsive nature that is well characterized with point process statistical models. In several neuroscience applications, such impulsive signals are transformed upon interaction with biological processes or measurement artifacts, and are consequently observed as filtered point process data.

Summary Progress from description to quantification is essential as a science matures. Yet numerical analysis of the elementary unit of brain circuitry—the individual neuron—continues to pose methodological challenges. Even the definition of a measurement yardstick (a metric) for the tree shape of a neuron remains an open research problem. Without such metrics, researchers cannot accurately classify neurons into cell types, an essential step toward understanding the circuit components and how they work together.

Project Summary Even in the absence of changing sensory inputs, many networks in the brain exhibit emer- gent dynamics: that is, they display patterns of neural activity that are shaped by the intrinsic structure of the network, rather than modulated by an external input. Such dynamics are be- lieved to underlie central pattern generators (CPGs) for locomotion, oscillatory activity in cortex and hippocampus, and the complex interplay between sensory-driven responses and ongoing spontaneous activity.