Two photon laser scanning microscopy (TPLSM) is a powerful tool for the examination of neural circuits. This thesis discusses some advancements in methodology necessary for applying its use to awake ...behaving mice. Chapters 1 and 2 discuss the challenge of motion artifact in TPLSM as it relates to imaging in head fixed mice atop a spherical treadmill. Chapter 1 describes a hidden Markov model (HMM) based motion correction algorithm which allowed our laboratory to be the first to image activity related fluorescence transients from calcium sensitive dyes in an awake mammal. Chapter 2 discusses measurements of the dynamics of brain motion with high spatial (50nm) and temporal (500Hz) resolution, which characterize brain motion at an unprecedented scale and serves as a ground truth measurement with which to compare the predictions of the HMM algorithm. We find that the spectral properties are such that most brain motion is in the 0-30 Hz range, that the maximal brain speed is ∼25 nm/ms, and that velocity distributions have exponentially distributed tails with speed constants ∼3nm/ms. Because these measurements are done simultaneously with TPLSM, they also directly demonstrate that the HMM model accurately predicts brain motion, avoiding over-fitting errors. Chapter 3 describes the construction of a virtual reality apparatus which can facilitate the study of complex navigation behaviors in head fixed animals. The technical considerations in designing a projection system using a toroidal screen and an angular amplification mirror are discussed. We find that ray tracing simulations suggest that the system is adequate for presenting realistic visual stimuli to mice. This system has facilitated the study of intracellular dynamics of hippocampal place cells, as well as imaging of the same phenomenon. Chapter 4 discusses the first biological feasibility tests of a theoretical idea called rate specific synchrony, in which a common noisy oscillation to a group of neurons causes their spike times to be synchronized when their firing rates are equal, but rapidly desynchronized when their rates are different. Using whole cell patch recordings from layer 2/3 cortical neurons, we injected complex noisy oscillations with varying amounts of steady depolarization, measured the resulting spike times, and quantified the synchronization observed between successive trials as a function of rate.
The advancement of single cell RNA-sequencing technologies has led to an explosion of cell type definitions across multiple organs and organisms. While standards for data and metadata intake are ...arising, organization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies. To facilitate cross-dataset comparison, the Allen Institute created the Common Cell type Nomenclature (CCN) for matching and tracking cell types across studies that is qualitatively similar to gene transcript management across different genome builds. The CCN can be readily applied to new or established taxonomies and was applied herein to diverse cell type datasets derived from multiple quantifiable modalities. The CCN facilitates assigning accurate yet flexible cell type names in the mammalian cortex as a step towards community-wide efforts to organize multi-source, data-driven information related to cell type taxonomies from any organism.
Morphology based analysis of cell types has been an area of great interest to the neuroscience community for several decades. Recently, high resolution electron microscopy (EM) datasets of the mouse ...brain have opened up opportunities for data analysis at a level of detail that was previously impossible. These datasets are very large in nature and thus, manual analysis is not a practical solution. Of particular interest are details to the level of post synaptic structures. This paper proposes a fully automated framework for analysis of post-synaptic structure based neuron analysis from EM data. The processing framework involves shape extraction, representation with an autoencoder, and whole cell modeling and analysis based on shape distributions. We apply our novel framework on a dataset of 1031 neurons obtained from imaging a 1mm x 1mm x 40 micrometer volume of the mouse visual cortex and show the strength of our method in clustering and classification of neuronal shapes.
Brain function results from communication between neurons connected by complex synaptic networks. Synapses are themselves highly complex and diverse signaling machines, containing protein products of ...hundreds of different genes, some in hundreds of copies, arranged in precise lattice at each individual synapse. Synapses are fundamental not only to synaptic network function but also to network development, adaptation, and memory. In addition, abnormalities of synapse numbers or molecular components are implicated in most mental and neurological disorders. Despite their obvious importance, mammalian synapse populations have so far resisted detailed quantitative study. In human brains and most animal nervous systems, synapses are very small and very densely packed: there are approximately 1 billion synapses per cubic millimeter of human cortex. This volumetric density poses very substantial challenges to proteometric analysis at the critical level of the individual synapse. The present work describes new probabilistic image analysis methods for single-synapse analysis of synapse populations in both animal and human brains.
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