Mental Abnormality And Criminal Responsibility

This is a study in criminal law. The problem probed is the relationship between mental abnormality and criminal responsibility. The subject is yet an unsolved area in criminal jurisprudence. It is of great interest to many jurists lawyers philosophers and psychiatrists. The study lays special emphasis on the Indian law .Comparative assessment wherever found necessary,especially of positions in England ,United states and Germany is made. The thesis is in six parts and sixteen chapters.

Bone marrow cells differentiation to neurons in the rat brain using Serotonin and GABA:Their role on glutamate receptors, IP3, cAMP and cGMP functional regulation in unilateral Parkinsonism induced by 6-hydroxydopamine

Parkinson's disease is a chronic progressive neurodegenerative movement disorder characterized by a profound and selective loss of nigrostriatal dopaminergic neurons. Our findings demonstrated that glutamatergic system is impaired during PD. The evaluations of these damages have important implications in understanding the molecular mechanism underlying motor, cognitive and memory deficits in PD. Our results showed a significant increase of glutamate content in the brain regions of 6- OHDA infused rat compared to control. This increased glutamate content caused an increase in glutamatergic and NMDA receptors function. Glutamate receptor subtypes- NMDAR1, NMDA2B and mGluR5 have differential regulatory role in different brain regions during PD. The second messenger studies confirmed that the changes in the receptor levels alter the IP3, cAMP and cGMP content. The alteration in the second messengers level increased the expression of pro-apoptotic factors - Bax and TNF-α, intercellular protein - α-synuclein and reduced the expression of transcription factor - CREB. These neurofunctional variations are the key contributors to motor and cognitive abnormalities associated with PD. Nestin and GFAP expression study confirmed that 5-HT and GABA induced the differentiation and proliferation of the BMC to neurons and glial cells in the SNpc of rats. We also observed that activated astrocytes are playing a crucial role in the proliferation of transplanted BMC which makes them significant for stem cell-based therapy. Our molecular and behavioural results showed that 5-HT and GABA along with BMC potentiates a restorative effect by reversing the alterations in glutamate receptor binding, gene expression and behaviour abnormality that occur during PD. The therapeutic significance in Parkinson’s disease is of prominence.

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