Data mining the functional architecture of the brain's circuitry

TL;DR

This paper discusses how recent advances in neural recording and behavioral monitoring enable the exploration of the brain's functional architecture across multiple scales, moving beyond traditional localized studies.

Contribution

It highlights the transition from local to global neuroscience enabled by new technologies and emphasizes the importance of discovering the brain's functional architecture from large-scale data.

Findings

Advances in neural recording technology facilitate multi-scale brain analysis.

Understanding the brain's functional architecture can lead to better interventions for neurological diseases.

Challenges include data alignment and developing interpretable models.

Abstract

The brain is a highly complex organ consisting of a myriad of subsystems that flexibly interact and adapt over time and context to enable perception, cognition, and behavior. Understanding the multi-scale nature of the brain, i.e., how circuit- and moleclular-level interactions build up the fundamental components of brain function, holds incredible potential for developing interventions for neurodegenerative and psychiatric diseases, as well as open new understanding into our very nature. Historically technological limitations have forced systems neuroscience to be local in anatomy (localized, small neural populations in single brain areas), in behavior (studying single tasks), in time (focusing on specific stages of learning or development), and in modality (focusing on imaging single biological quantities). New developments in neural recording technology and behavioral monitoring now provide the data needed to break free of local neuroscience to global neuroscience: i.e., understanding how the brain's many subsystem interact, adapt, and change across the multitude of behaviors animals and humans must perform to thrive. Specifically, while we have much knowledge of the anatomical architecture of the brain (i.e., the hardware), we finally are approaching the data needed to find the functional architecture and discover the fundamental properties of the software that runs on the hardware. We must take this opportunity to bridge between the vast amounts of data to discover this functional architecture which will face numerous challenges from low-level data alignment up to high level questions of interpretable mathematical models of behavior that can synthesize the myriad of datasets together.