ALPHA2 Adrenergic and High Affinity Serotonergic Receptor Changes in the Brain Stem of Streptozotocin induced Diabetic Rats

The brain stems (13S) of streptozotocin (STZ)-diabetic rats were studied lo see the changes in neurotransmitter content and their receptor regulation. The norepinephrine (NE) content determined in the diabetic brain stems did ^ control. an E showed la while PI turnover hri content increased significantly compared N^r eNveFa o the recep significant increase. The alpha2 adrenergic receptor IneP utisoulinntreat d ratsetheNE contentt dec^ sled was significantly reduced during diabetes. in versedcto reanorm sed ulcrea e tK reatment the state. while EPI content remained increased as in die diabetic B,, for a]pha2 adrenergic receptors slw^nificantly while Unlabelled clonidine inhibited [31-I]NE binding in BS of control, diabetic and insulin treated ulations bindi diabetic rats showed that alpha2 adrenergicre^ punks cojnidiabetic animal the ligand bound sites with Hill slopes significantly away from unity. weaker to the low affinity site than in controls. Insulin treatment reversed[ this allumbmn to control levels. The displacement analysis using (-)-epinephrine age in control and diabetic animals revealed two populations of receptor affinidtyo=tat ss. In control animals, when GTP analogue added with epinephrine, the curve nagnlde caofnfitnroit yS model; but in the diabetic BS this effect `not aobserved. In bintact oth the diabetic data thus showlthat the effects of monovalent cations on affinity alphaz adrenergic receptors have a reduced affinity v due in stem ialtered Itscppeomson(5- regulation. The serotonin (5-HT) coat hydroxy) tryptophan (5-HTP) showed an increase and its breakdown metabolite (5-hydroxy) indoleacetic acid (5-I{IAA) showed a significant decrease. This showed that in serotonergic which l nerves there is a disturbance in both synthetic and breankduomwnbers pretma'med ana increased 5-HT. The high affinity serotonin receptor um ese serotonerg decrease in the receptor affinity. The insulin ^treatmentsturtiy showsha decreased serotonergic receptor kinetic parameters to control level. receptor function. These changes in adrenergic and serotonergic receptor function were suggested to be important in insulin function during STZ diabetes.

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions