Cholinergic receptor subtypes functional regulation in spinal cord injured monoplegic rats

The present study deals with the Cholinergic Receptor subtypes functional regulation in spinal cord injured monoplegic rats: Effect of 5-HT GABA and bone marrow cells.Spinal cord injury causes permanent and irrevocable motor deficits and neurodegeneration. Disruption of the spinal cord leads to diminished transmission of descending control from the brain to motor neurons and ascending sensory information. Behavioural studies showed deficits in motor control and coordination in SCI rats. Cholinergic system plays an important role in SCI, the evaluation of which provides valuable insight on the underlying mechanisms of motor deficit that occur during SCI. The cholinergic transmission was studied by assessing the muscarinic and nicotinic receptors; cholinergic enzymes- ChAT and AChE; second messenger enzyme PLC; transcription factor CREB and second messengers - IP3, cAMP and cGMP. We observed a decrease in the cholinergic transmission in the brain and spinal cord of SCI rats. The disrupted cholinergic system is the indicative of motor deficit and neuronal degeneration in the spinal cord and brain regions. SCI mediated oxidative stress and apoptosis leads to neuronal degeneration in SCI rats. The decreased expression of anti oxidant enzymes – SOD, GPx and neuronal cell survival factors - BDNF, GDNF, IGF-1, Akt and cyclin D2 along with increased expression of apoptotic factors – Bax, caspase-8, TNFa and NF-kB augmented the neuronal degeneration in SCI condition. BMC administration in combination with 5-HT and GABA in SCI rats showed a reversal in the impaired cholinergic neurotransmission and reduced the oxidative stress and apoptosis. It also enhanced the expression of cell survival factors in the spinal cord region. In SCI rats treated with 5-HT and GABA, the transplanted BMC expressed NeuN confirming that 5-HT and GABA induced the differentiation and proliferation of BMC to neurons in the spinal cord. Neurotrophic factors and anti-apoptotic elements in SCI rats treated with 5-HT and GABA along with BMC rendered neuroprotective effects accompanied by improvement in behavioural deficits. This resulted in a significant reversal of altered cholinergic neurotransmission in SCI. The restorative and neuro protective effects of BMC in combination with 5-HT and GABA are of immense therapeutic significance in the clinical management of SCI.

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.

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.