Thomas Macrina will present his FPO "Precise Alignment of Serial Section Electron Microscopy Images and Analysis of Neural Circuits" on Monday, May 2, 2022 at 6:00 PM via Zoom.
The members of his committee are as follows:
Examiners: H. Sebastian Seung (Adviser), Mala Murthy, Felix Heide
Readers: Kai Li, Stephan Saalfeld (HHMI Janelia)
A copy of his thesis is available upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis.
Everyone is invited to attend his talk.
Abstract follows below:
Neural circuits provide a means to test how the structure of a brain is related to its function, but reconstructing complete circuits in a whole mammalian brain is currently not possible.
A promising path is to image a brain with serial section electron microscopy and use automatic methods to reconstruct circuits from the data. The imaging process introduces significant physical distortion that causes the automatic methods to make errors, so it’s crucial to remove the distortion in a step called alignment. There are existing approaches to alignment, and by augmenting them with human-in-the-loop intervention we were able to produce a very accurate alignment of a five teravoxel mouse cortex dataset. This approach failed to correct distortion caused by cracks and folds, and the amount of human effort involved made it prohibitively expensive to process larger datasets. To overcome these limitations, our group developed a new alignment pipeline that uses deep optic flow models to predict more precise distortion measurements. Using this new alignment pipeline, we have been able to accurately align much larger datasets, including a 100 teravoxel whole fly brain and a one petavoxel cubic millimeter of mouse cortex. These alignments enabled high quality automatic reconstructions that are now being proofread with manageable human effort to produce some of the largest neural circuits to date. With improvements in the reconstruction technology, like our new alignment pipeline, neural circuits are becoming more accessible, and there will be more effort put toward analyzing them. We analyzed a neural circuit reconstructed from the terascale mouse cortex dataset, finding that sizes of synapses in a shared layer 2/3 pyramidal neuron connection have a correlated binary component, and that the network of these pyramidal neurons is well-described by a configuration model. Both results may have implications for how to constrain biological models of memory or neural network organization.