NIH: BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain (R01)

Theories, ideas and conceptual frameworks

• Theoretical insights into how circuit dynamics depend on the properties of single neurons and their connections. Identify conditions for which insights from small circuits scale to larger circuits. Determine which general rules of circuit function depend on specific biological details of neuronal, non-neuronal and synapse function.
• Theories of how information is encoded in the chemical and electrical activity of neurons to implicate behavior in both short and longer time scales.
• Theories of how ensembles of activity can produce collective state conditions and processes with emergent properties beyond the individual units of activity.
• Theories of how ongoing ensemble activity carries out effortful, deliberate cognitive processes requiring multiple or iterative steps, such as mental imagery, mental spatial navigation, mathematical processing, reasoning, or other cognitive abilities that are specially advanced in humans.
• Theories of how interactions within and between large neural systems and brain areas—encompassing inputs from multiple sensory systems, internal states, memories, goals, constraints, and preferences—drive behavior in humans and animals, including specialized animal models.

Models and the associated statistical, analytical and numerical methods to integrate information across large temporal and spatial scales in the nervous system. 

• Models and methods that integrate knowledge across multiple levels - connecting cellular properties with anatomical constraints, physiology, and behavior; linking mechanisms of neural activity with biophysical mechanisms; bridging mesoscale neural circuits with macroscale neural populations.
• Models of collective neuronal activity on spatial scales that span individual synapses, neurons, circuits, networks and systems; developing theories of dynamical activity that span timescales of synapses, action potentials, network activity (including attractors and persistent activity) and internal circuit states (including neuropeptides and neuromodulatory systems).
• Formal statistical inference frameworks to conduct network connectivity and causal-inference analyses from different types of neuroscience data such as fMRI, EEG, LFP and multi-site single neuron recordings.
• Uncertainty quantification of the data, parameters and outcomes of predictive multiscale models of the brain, e.g. as a result of sparse data and biological variation across subjects.
• New, interoperable simulation methods for multiscale models; e.g. to couple subcellular to the neuronal networks, to full-brain model scales.

New methods for complex data analysis

• Methods to extract fundamental features from large nonlinear, spatio-temporal data sets, including real-time data analysis, e.g. from physiological, behavioral and imaging data.
• Novel implementations of dynamic versions of principal component analysis, including novel implementations of independent component analysis, graphical models and compressed sensing that may be used to dynamically track structure in continuous data, point process data, and combinations of the two.
• Tools to address data dimensionality – correlating lower dimension neural activity among subsets of strategically sampled neuronal populations; analyzing higher dimension data resulting from increased behavioral and stimulus complexity.
• Data fusion and data assimilation methods to combine heterogeneous data and link sparse data with mechanisms.

Responsiveness Criteria

• Database curation, annotation and development is not be responsive to this FOA.  
• Projects to develop or improve computational infrastructure are not be responsive to this FOA
• Projects to develop theories and models that do not explicitly state how the theories and models proposed are informed by the underlying neurobiology will be considered non-responsive.