A methodological pipeline for serial-section imaging and tissue realignment for whole-brain functional and connectivity assessment

Lilia Mesina, Aaron A. Wilber, Benjamin J. Clark, Sutherland Dube, Alexis J. Demecha, Craig E.L. Stark, Bruce L. McNaughton
2016 Journal of Neuroscience Methods  
Background-Understanding the neurobiological basis of cognition and behavior, and disruptions to these processes following injury and disease, requires a large-scale assessment of neural populations, and knowledge of their patterns of connectivity. New Method-We present an analysis platform for large-scale investigation of functional and neuroanatomical connectivity in the rodents. Retrograde tracers were injected and in a subset of animals behavioral tests to drive immediate-early gene
more » ... on were administered. This approach allows users to perform whole-brain assessment of function and connection in a semiautomated quantitative manner. Brains were cut in the coronal plane, and an image of the block face was acquired. Wide-field fluorescent scans of whole sections were acquired and analyzed using Matlab software. Results- The toolkit utilized open-source and custom platforms to accommodate a largely automated analysis pipeline in which neuronal boundaries are automatically segmented, the position of segmented neurons are co-registered with a corresponding image acquired during vibratome sectioning, and a 3-D representation of neural tracer (and other products) throughout the entire brain is generated. Comparison with Existing Methods-Current whole brain connectivity measures primarily target mice and use anterograde tracers. Our focus on segmented units of interest (e.g., NeuN labeled neurons) and restricting measures to these units produces a flexible platform for a variety of whole brain analyses (measuring activation, connectivity, markers of disease, etc.). Conclusions-This open-source toolkit allows an investigator to visualize and quantify whole brain data in 3-D, and additionally provides a framework that can be rapidly integrated with userspecific analyses and methodologies.
doi:10.1016/j.jneumeth.2016.03.021 pmid:27039972 pmcid:PMC5695690 fatcat:hk2cdnxefnbx3abdjgxbtnuykm