Challenges and opportunities in mining neuroscience data

Abstract

Understanding the brain requires a broad range of approaches and methods from the domains of biology, psychology, chemistry, physics, and mathematics. The fundamental challenge is to decipher the "neural choreography" associated with complex behaviors and functions, including thoughts, memories, actions, and emotions. This demands the acquisition and integration of vast amounts of data of many types, at multiple scales in time and in space. Here we discuss the need for neuroinformatics approaches to accelerate progress, using several illustrative examples. The nascent field of "connectomics" aims to comprehensively describe neuronal connectivity at either a macroscopic level (in long-distance pathways for the entire brain) or a microscopic level (among axons, dendrites, and synapses in a small brain region). The Neuroscience Information Framework (NIF) encompasses all of neuroscience and facilitates the integration of existing knowledge and databases of many types. These examples illustrate the opportunities and challenges of data mining across multiple tiers of neuroscience information and underscore the need for cultural and infrastructure changes if neuroinformatics is to fulfill its potential to advance our understanding of the brain.

Fig. 1

Schematic illustration of online data mining capabilities envisioned for the Human Connectome Project. Investigators will be able to pose a wide range of queries (e.g., connectivity patterns of a particular brain region of interest averaged across a group of individuals based on behavioral criteria) and view the search results interactively on 3-D brain models. Datasets of interest will be freely available for downloading and additional offline analysis.

Fig 2

Current contents of the NIF. The NIF navigation bar displays the current contents of the NIF data federation organized by data type and level of the nervous system. The number of records in each category is displayed in parentheses.