Dissection of spatiotemporal activity from large-scale, multi-modal, multi-resolution hippocampal-neocortical recordings.

PROJECT SUMMARY/ ABSTRACT
Advances in neurotechnologies are producing large and complex datasets at unprecedented rate. Large-scale
electrophysiological and optical imaging recordings provide an urging need to develop novel theoretical and
analytical approaches to analyze and interpret these multi-scale, multi-resolution brain recordings. In response
to the call of BRAIN Initiative 2.0 report, our project will develop new analytic tools and computational
framework to understand complex and dynamic nature of large-scale spatiotemporal brain activity and the
associated brain functions in a brain state-dependent manner. These tools will establish an analysis pipeline
for large-scale electrophysiological and calcium imaging data. We will develop innovative supervised and
unsupervised machine learning methods, and disseminate these analytic tools and software to the
neuroscience community. Our team is uniquely positioned to not only develop these novel tools for basic
neuroscience investigations, but also apply them in intracranial ECoG recordings where epilepsy patients
underwent spatial or non-spatial memory tasks. In Aim 1, we will develop analytic tools and software for
decoding representations of spatial or task information in large-scale hippocampal and neocortical recordings.
In Aim 2, we will develop computational theories and framework for testing the task dimensionality of
hippocampal population codes. In Aim 3, we will develop analytic methods and software for assessing and
interpreting concurrent spatiotemporal neural patterns between multi-region, multi-scale, multi-resolution brain
recordings. Overall, seamless integration of data analytics, theory and modeling as well as applications of
these tools to address important basic/clinical neuroscience questions are highly significant. Accomplishment
of these aims will not only establish an analysis pipeline for large-scale electrophysiological and calcium
imaging data, but also empower experimental neuroscientists in their hypothesis-driven investigative studies
and drive future data collection.

Grant Number: 1RF1DA056394-01
NIH Institute/Center: NIH
Principal Investigator: Zhe Chen