scholarly journals Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Shuihua Wang ◽  
Mengmeng Chen ◽  
Yang Li ◽  
Yudong Zhang ◽  
Liangxiu Han ◽  
...  
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Psychiatric Disorders ◽  
Dendritic Spines ◽  
Dendritic Spine ◽  
Experimental Result ◽  
High Interest ◽  
Parkinson’S Diseases ◽  
Dendrite Boundary ◽  
Mushroom Spines

Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer’s disease, Parkinson’s diseases, and autism). In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN) for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby). We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines.

An Image Analysis Algorithm for Dendritic Spines

2002 ◽  
Vol 14 (6) ◽  
pp. 1283-1310 ◽  
Author(s):  
Ingrid Y. Y. Koh ◽  
W. Brent Lindquist ◽  
Karen Zito ◽  
Esther A. Nimchinsky ◽  
Karel Svoboda
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Dendritic Spines ◽  
Dendritic Spine ◽  
Time Series Data ◽  
Neuronal Morphology ◽  
Laser Scanning Microscopy ◽  
Series Data ◽  
Spine Length ◽  
Spine Morphology

The structure of neuronal dendrites and their spines underlie the connectivity of neural networks. Dendrites, spines, and their dynamics are shaped by genetic programs as well as sensory experience. Dendritic structures and dynamics may therefore be important predictors of the function of neural networks. Based on new imaging approaches and increases in the speed of computation, it has become possible to acquire large sets of high-resolution optical micrographs of neuron structure at length scales small enough to resolve spines. This advance in data acquisition has not been accompanied by comparable advances in data analysis techniques; the analysis of dendritic and spine morphology is still accomplished largely manually. In addition to being extremely time intensive, manual analysis also introduces systematic and hard-to-characterize biases. We present a geometric approach for automatically detecting and quantifying the three-dimensional structure of dendritic spines from stacks of image data acquired using laser scanning microscopy. We present results on the measurement of dendritic spine length, volume, density, and shape classification for both static and timelapse images of dendrites of hippocampal pyramidal neurons. For spine length and density, the automated measurements in static images are compared with manual measurements. Comparisons are also made between automated and manual spine length measurements for a time-series data set. The algorithm performs well compared to a human analyzer, especially on time-series data. Automated analysis of dendritic spine morphology will enable objective analysis of large morphological data sets. The approaches presented here are generalizable to other aspects of neuronal morphology.

scholarly journals Sex differences in dendritic spine density and morphology in auditory and visual cortices in adolescence and adulthood

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Emily M. Parker ◽  
Nathan L. Kindja ◽  
Claire E. J. Cheetham ◽  
Robert A. Sweet
Keyword(s):  
Sex Differences ◽  
Dendritic Spines ◽  
Dendritic Spine ◽  
Brain Regions ◽  
Spine Density ◽  
Female Mice ◽  
Dendritic Spine Density ◽  
Layer 4 ◽  
Visual Cortices ◽  
Mushroom Spines

AbstractDendritic spines are small protrusions on dendrites that endow neurons with the ability to receive and transform synaptic input. Dendritic spine number and morphology are altered as a consequence of synaptic plasticity and circuit refinement during adolescence. Dendritic spine density (DSD) is significantly different based on sex in subcortical brain regions associated with the generation of sex-specific behaviors. It is largely unknown if sex differences in DSD exist in auditory and visual brain regions and if there are sex-specific changes in DSD in these regions that occur during adolescent development. We analyzed dendritic spines in 4-week-old (P28) and 12-week-old (P84) male and female mice and found that DSD is lower in female mice due in part to fewer short stubby, long stubby and short mushroom spines. We found striking layer-specific patterns including a significant age by layer interaction and significantly decreased DSD in layer 4 from P28 to P84. Together these data support the possibility of developmental sex differences in DSD in visual and auditory regions and provide evidence of layer-specific refinement of DSD over adolescent brain development.

Effects of cranial irradiation on dendritic spine complexity in the rat hippocampus and to induce memory decline.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e13545-e13545
Author(s):  
Shengjun Ji
Keyword(s):  
Dendritic Spines ◽  
Dendritic Spine ◽  
Cranial Irradiation ◽  
Cytoskeletal Proteins ◽  
Spine Density ◽  
Memory Dysfunction ◽  
Dendritic Spine Density ◽  
Basal Dendrites ◽  
Radiation Induced ◽  
Mushroom Spines

e13545 Background: Cognitive deficit was the most serious complication of cranial irradiation in brain metastatic carcinoma. However, the underlying mechanisms remain obscure. Dendrites are the anatomic bases of synaptic contact and action potential propagation. Alterations of dendritic architecture may contribute to radiation-induced memory dysfunction. Methods: 21-day-old Sprague-Dawley rats received 10Gy cranial irradiation. 1 and 3 months later, Morris Water Maze, Fear Conditioning test and novel object recognition were used to test the memory function. Golgi staining was used to assess changes in dendritic spine density and morphology. Moreover, cytoskeletal proteins PSD-95 were analyzed with Western blot. Results: Our data showed that 10Gy cranial irradiation induced significant decline in the spatial memory and memory retention of rats and accompanied the morphological changes in dendritic spines. The result revealed significant reductions in spine density at 1 month (40.58%) and 3 months (28.92%) in the DG. In CA1 basal dendrites, irradiation resulted in a significant reduction (33.29%) in spine density only at 1month postirradiation. Compared to control, mushroom spines reduced at 1 month (7.17%, 10.01%) and 3 months (9.29%, 11.94%) post irradiation in DG and CA1 basal dendrites, respectively. Also, we found PSD-95 depletion coincided in time with alteration in dendritic spines. Conclusions: These data suggest that cranial irradiation decreased the dendritic spine density and mushroom spines, which may be associated with radiation-induced memory dysfunction. Acknowledgment: This study was supported by the National Natural Science Foundation of China 81402517 and the Suzhou Science and Technology Project SYS201651.

Fluoxetine Protects against Dendritic Spine Loss in Middle-aged APPswe/PSEN1dE9 Double Transgenic Alzheimer’s Disease Mice

2020 ◽  
Vol 17 (1) ◽  
pp. 93-103 ◽  
Author(s):  
Jing Ma ◽  
Yuan Gao ◽  
Wei Tang ◽  
Wei Huang ◽  
Yong Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dendritic Spines ◽  
Dendritic Spine ◽  
Early Stage ◽  
Learning Ability ◽  
Middle Aged ◽  
Protein Levels ◽  
Spine Pathology ◽  
The Impact

Background: Studies have suggested that cognitive impairment in Alzheimer’s disease (AD) is associated with dendritic spine loss, especially in the hippocampus. Fluoxetine (FLX) has been shown to improve cognition in the early stage of AD and to be associated with diminishing synapse degeneration in the hippocampus. However, little is known about whether FLX affects the pathogenesis of AD in the middle-tolate stage and whether its effects are correlated with the amelioration of hippocampal dendritic dysfunction. Previously, it has been observed that FLX improves the spatial learning ability of middleaged APP/PS1 mice. Objective: In the present study, we further characterized the impact of FLX on dendritic spines in the hippocampus of middle-aged APP/PS1 mice. Results: It has been found that the numbers of dendritic spines in dentate gyrus (DG), CA1 and CA2/3 of hippocampus were significantly increased by FLX. Meanwhile, FLX effectively attenuated hyperphosphorylation of tau at Ser396 and elevated protein levels of postsynaptic density 95 (PSD-95) and synapsin-1 (SYN-1) in the hippocampus. Conclusion: These results indicated that the enhanced learning ability observed in FLX-treated middle-aged APP/PS1 mice might be associated with remarkable mitigation of hippocampal dendritic spine pathology by FLX and suggested that FLX might be explored as a new strategy for therapy of AD in the middle-to-late stage.

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

Processing of Sonar Image Based on Compressive Sensing

2011 ◽  
Vol 301-303 ◽  
pp. 719-723 ◽  
Author(s):  
Zhi Jing Xu ◽  
Huan Lei Dai ◽  
Pei Pei Cao
Keyword(s):  
Wavelet Transform ◽  
Matching Pursuit ◽  
Experimental Result ◽  
Underwater Acoustic Channel ◽  
Sonar Image ◽  
Underwater Image ◽  
Reconstruction Image ◽  
Observation Matrix ◽  
Efficient Transmission

The particularity of the underwater acoustic channel has put forward a higher request for collection and efficient transmission of the underwater image. In this paper, based on the characteristics of sonar image, wavelet transform is used to sparse decompose the image, and selecting Gaussian random matrix as the observation matrix and using the orthogonal matching pursuit (OMP) algorithm to reconstruct the image. The experimental result shows that the quality of the reconstruction image and PSNR have gained great ascension compared to the traditional compression and processing of image based on the wavelet transform while they have the same measurement numbers in the coding portion. It provides a convenient for the sonar image’s underwater transmission.

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