From sample to knowledge: Towards an integrated approach for neuroscience discovery

TL;DR

This paper advocates for integrated sample-to-knowledge pipelines in neuroscience to optimize data acquisition and processing, enabling more effective extraction of neuroanatomical insights from large datasets.

Contribution

It introduces a novel framework for feedback-driven optimization across all pipeline stages, enhancing knowledge discovery in neuroscience imaging data.

Findings

Proposes a sample-to-knowledge pipeline with feedback loops

Demonstrates multimodal approach combining X-ray microtomography and electron microscopy

Provides an experimental paradigm for analyzing synaptic distributions

Abstract

Imaging methods used in modern neuroscience experiments are quickly producing large amounts of data capable of providing increasing amounts of knowledge about neuroanatomy and function. A great deal of information in these datasets is relatively unexplored and untapped. One of the bottlenecks in knowledge extraction is that often there is no feedback loop between the knowledge produced (e.g., graph, density estimate, or other statistic) and the earlier stages of the pipeline (e.g., acquisition). We thus advocate for the development of sample-to-knowledge discovery pipelines that one can use to optimize acquisition and processing steps with a particular end goal (i.e., piece of knowledge) in mind. We therefore propose that optimization takes place not just within each processing stage but also between adjacent (and non-adjacent) steps of the pipeline. Furthermore, we explore the existing categories of knowledge representation and models to motivate the types of experiments and analysis needed to achieve the ultimate goal. To illustrate this approach, we provide an experimental paradigm to answer questions about large-scale synaptic distributions through a multimodal approach combining X-ray microtomography and electron microscopy.