Papers

Here we highlight collaborative projects supported by the Consortium

Corticospinal neurons encode complex motor signals that are broadcast to dichotomous striatal circuits
Anders Nelson, Brenda Abdelmesih, Rui M Costa
BioRxiv July 21 (2020)

Animals interact with their environments through movement, the product of precisely patterned activation of spinal circuits that control body muscles, as well as the myriad brain regions that are integral to controlling these circuits. How is it that widespread and diverse brain regions can act together and not send conflicting information to the spinal cord? One idea is that a specialized neuronal pathway broadcasts a signal to all of these brain and spinal regions simultaneously, in effect telling these downstream regions to keep their activity in lock step. A good contender for this specialized role is the corticospinal pathway, because it contains neurons that send long-range projections not only to the spinal cord, but also to most of the other brain regions that are involved in generating movements. While corticospinal neurons have been known for well over a century, neuroscientists understand remarkably little about how they are organized, what types of cells they communicate with in downstream brain regions, and what information they encode in their activity. Our paper uses a range of advanced techniques to map the projections of corticospinal neurons and identify the cell types that they target in different brain and spinal regions. We identified unexpected complexities in this pathway, like the fact that different groups of corticospinal neurons target different cell types in downstream brain and spinal regions. We also showed that these neurons encode surprisingly complex information like sequences of movements, rather than only muscle activity. Altogether, our results reveal the biological principles that underlie the corticospinal pathway, and move us closer to understanding how these unique cells coordinate the brain and spinal structures that are necessary for behavior.

Application of the hierarchical bootstrap to multi-level data in neuroscience
Saravanan V, Berman GJ, Sober SJ
Neurons, Behavior, Data Analysis and Theory (NBDT) July 21 (2020)

Neuroscience experiments yield a dizzying array of data types, including anatomical, electrophysiological, optical, genetic, and behavioral datasets. What neuroscience datasets typically share, however, is a hierarchical structure, as when we collect repeated measurements from multiple subjects or across different experimental conditions.

For example, we might analyze electrophysiological recordings of 100 neurons from each of 5 animals in response to 2 different stimuli, or count dendritic spines on 100 neurons in 5 animals treated with one drug and 5 animals treated with another drug. Because of this hierarchy, the individual measurements of neural activity or spine number are not independent, since both the activity and spine data are more likely to be similar within individual subjects than between them.

The common practice of analyzing hierarchical data with statistical methods that assume - and in fact rely on - independent data sampling often results in errors. As an illustration, in the hypothetical datasets described above

If we treat all measurements for a given stimulus or drug condition as independent, we will massively inflate our false-positive (type 1 error) rate.

If we average within each animal to compensate for cross-animal differences, we will massively inflate our false-negative (type 2 error) rate.

The “hierarchical bootstrap” (Efron and Tibshirani, 1994) is a robust and easy-to-implement approach that accounts for multi-level data structure when computing statistical uncertainty. Although widely used in other fields, the hierarchical bootstrap is seldom applied to neuroscience datasets. Our paper presents a guide to applying this technique to neuroscience data and provides examples of performing the hierarchical bootstrap on both synthetic neural population activity and real-world behavioral datasets from songbirds and fruit flies.