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1.  Morphologic Evidence for Spatially Clustered Spines in Apical Dendrites of Monkey Neocortical Pyramidal Cells 
The Journal of comparative neurology  2012;520(13):2888-2902.
The general organization of neocortical connectivity in rhesus monkey is relatively well understood. However, mounting evidence points to an organizing principle that involves clustered synapses at the level of individual dendrites. Several synaptic plasticity studies have reported cooperative interaction between neighboring synapses on a given dendritic branch, which may potentially induce synapse clusters. Additionally, theoretical models have predicted that such cooperativity is advantageous, in that it greatly enhances a neuron’s computational repertoire. However, largely because of the lack of sufficient morphologic data, the existence of clustered synapses in neurons on a global scale has never been established. The majority of excitatory synapses are found within dendritic spines. In this study, we demonstrate that spine clusters do exist on pyramidal neurons by analyzing the three-dimensional locations of ~40,000 spines on 280 apical dendritic branches in layer III of the rhesus monkey prefrontal cortex. By using clustering algorithms and Monte Carlo simulations, we quantify the probability that the observed extent of clustering does not occur randomly. This provides a measure that tests for spine clustering on a global scale, whenever high-resolution morphologic data are available. Here we demonstrate that spine clusters occur significantly more frequently than expected by pure chance and that spine clustering is concentrated in apical terminal branches. These findings indicate that spine clustering is driven by systematic biological processes. We also found that mushroom-shaped and stubby spines are predominant in clusters on dendritic segments that display prolific clustering, independently supporting a causal link between spine morphology and synaptic clustering.
PMCID: PMC3573331  PMID: 22315181
clustering; dendritic spines; plasticity; morphology; image analysis
2.  Three-Dimensional Neuron Tracing by Voxel Scooping 
Journal of neuroscience methods  2009;184(1):169-175.
Tracing the centerline of the dendritic arbor of neurons is a powerful technique for analyzing neuronal morphology. In the various neuron tracing algorithms in use nowadays, the competing goals of computational efficiency and robustness are generally traded off against each other. We present a novel method for tracing the centerline of a neuron from confocal image stacks, which provides an optimal balance between these objectives. Using only local information, thin cross-sectional layers of voxels (‘scoops’) are iteratively carved out of the structure, and clustered based on connectivity. Each cluster contributes a node along the centerline, which is created by connecting successive nodes until all object voxels are exhausted. While data segmentation is independent of this algorithm, we illustrate the use of the ISODATA method to achieve dynamic (local) segmentation. Diameter estimation at each node is calculated using the Rayburst Sampling algorithm, and spurious end nodes caused by surface irregularities are then removed. On standard computing hardware the algorithm can process hundreds of thousands of voxels per second, easily handling the multi-gigabyte datasets resulting from high-resolution confocal microscopy imaging of neurons. This method provides an accurate and efficient means for centerline extraction that is suitable for interactive neuron tracing applications.
PMCID: PMC2753723  PMID: 19632273
Centerline Extraction; Neuron Tracing; Image Analysis; Medial Axis
The Journal of comparative neurology  2008;507(1):1141-1150.
Anatomical alterations in the medial prefrontal cortex (mPFC) are associated with hypothalamo-pituitary adrenal (HPA) axis dysregulation, altered stress hormone levels, and psychiatric symptoms of stress-related mental illnesses. Functional imaging studies reveal impairment and shrinkage of the mPFC in such conditions, and these findings are paralleled by experimental studies showing dendritic retraction and spine loss following repeated stress in rodents. Here we extend this characterization to how repeated stress affects dendritic spine morphology in mPFC through the utilization of an automated approach which rapidly digitizes, reconstructs 3-dimensionally, and calculates geometric features of neurons. Rats were perfused after being subjected to 3 weeks of daily restraint stress (6 hours/day), and intracellular injections of Lucifer Yellow were made in layers II/III pyramidal neurons in the dorsal mPFC. To reveal spines in all angles of orientation, deconvolved high-resolution confocal laser scanning microscopy image stacks of dendritic segments were reconstructed and analyzed for spine volume, surface area, and length using a Rayburst-based automated approach (8,091 and 8,987 spines for control and stress, respectively). We found that repeated stress results in an overall decrease in mean dendritic spine volume and surface area, which was most pronounced in the distal portion of apical dendritic fields. Moreover, we observed an overall shift in the population of spines, manifested by a reduction in large spines and increase in small spines. These results suggest a failure of spines to mature and stabilize following repeated stress, and are likely to have major repercussions on function, receptor expression, and synaptic efficacy.
PMCID: PMC2796421  PMID: 18157834
dendritic spine; morphometry; plasticity; prefrontal cortex; stress
4.  Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images 
PLoS ONE  2008;3(4):e1997.
A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) spine shapes with sufficient precision to distinguish morphologic types, and sufficient throughput for robust statistical analysis. The necessity to analyze large volumetric data sets accurately, efficiently, and in true 3D has been a major bottleneck in deriving reliable relationships between altered neuronal function and changes in spine morphology. We introduce a novel system for automated detection, shape analysis and classification of dendritic spines from laser scanning microscopy (LSM) images that directly addresses these limitations. The system is more accurate, and at least an order of magnitude faster, than existing technologies. By operating fully in 3D the algorithm resolves spines that are undetectable with standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering and Rayburst Sampling generate a profile of diameter estimates used to classify spines into morphologic types, while minimizing optical smear and quantization artifacts. The technique opens new horizons on the objective evaluation of spine changes with synaptic plasticity, normal development and aging, and with neurodegenerative disorders that impair cognitive function.
PMCID: PMC2292261  PMID: 18431482
5.  Dendritic vulnerability in neurodegenerative disease: insights from analyses of cortical pyramidal neurons in transgenic mouse models 
Brain structure & function  2010;214(2-3):181-199.
In neurodegenerative disorders, such as Alzheimer’s disease, neuronal dendrites and dendritic spines undergo significant pathological changes. Because of the determinant role of these highly dynamic structures in signaling by individual neurons and ultimately in the functionality of neuronal networks that mediate cognitive functions, a detailed understanding of these changes is of paramount importance. Mutant murine models, such as the Tg2576 APP mutant mouse and the rTg4510 tau mutant mouse have been developed to provide insight into pathogenesis involving the abnormal production and aggregation of amyloid and tau proteins, because of the key role that these proteins play in neurodegenerative disease. This review showcases the multidimensional approach taken by our collaborative group to increase understanding of pathological mechanisms in neurodegenerative disease using these mouse models. This approach includes analyses of empirical 3D morphological and electrophysiological data acquired from frontal cortical pyramidal neurons using confocal laser scanning microscopy and whole-cell patch-clamp recording techniques, combined with computational modeling methodologies. These collaborative studies are designed to shed insight on the repercussions of dystrophic changes in neocortical neurons, define the cellular phenotype of differential neuronal vulnerability in relevant models of neurodegenerative disease, and provide a basis upon which to develop meaningful therapeutic strategies aimed at preventing, reversing, or compensating for neurodegenerative changes in dementia.
PMCID: PMC3045830  PMID: 20177698
Alzheimer’s disease; Amyloid; Computational modeling; Dendritic spine; Tau; Whole-cell patch-clamp

Results 1-5 (5)