Signatures of Higher Functions of Brain Dynamics in Electroencephalogram: A Nonlinear Analysis

The brain with its highly complex structure made up of simple units,imterconnected information pathways and specialized functions has always been an object of mystery and sceintific fascination for physiologists,neuroscientists and lately to mathematicians and physicists. The stream of biophysicists are engaged in building the bridge between the biological and physical sciences guided by a conviction that natural scenarios that appear extraordinarily complex may be tackled by application of principles from the realm of physical sciences. In a similar vein, this report aims to describe how nerve cells execute transmission of signals ,how these are put together and how out of this integration higher functions emerge and get reflected in the electrical signals that are produced in the brain.Viewing the E E G Signal through the looking glass of nonlinear theory, the dynamics of the underlying complex system-the brain ,is inferred and significant implications of the findings are explored.

Brain Adrenergic and Serotonergic Receptor Function in Streptozotocin-Diabetic Rats

Department of Biotechnology, Cochin University of Science and Technology

Muscarinic M1, M3, Nicotinic,GABAA and GABAB Receptor Subtypes Gene Expression in Insulin Induced Hypoglycemic Rat Brain Regions: Functional Regulation through Phospholipase C and CREB Protein

In the present study, a detailed investigation on the alterations of muscarinic M1, M3, α7 nicotinic acetylcholine receptor (α7 nAchR), GABA receptors and its subtypes; GABAAα1 and GABAB in the brain regions of streptozotocin induced diabetic and insulin induced hypoglycemic rats were carried out. Gene expression of acetylcholine esterase (AChE), choline acetyltransferase (ChAT), GAD, GLUT3, Insulin receptor, superoxide dismutase (SOD), Bax protein, Phospholipase C and CREB in hypoglycemic and hyperglycemic rat brain were studied. Muscarinic M1, M3 receptors, AChE, ChAT, GABAAα1, GABAB, GAD, Insulin receptor, SOD, Bax protein and Phospholipase C expression in pancreas was also carried out. The molecular studies on the CNS and PNS damage will elucidate the therapeutic role in the corrective measures of the damage to the brain during hypoglycemia and hyperglycemia.

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.

Dopamine D1 and D2 Receptor Functional Regulation:Glutamate Receptor Gene Expression in the Brain of Hypoglycaemic and Hyperglycaemic Rats

In the present study a detailed investigation on the alterations of dopamine and its receptors in the brain regions of streptozotocin induced diabetic and insulin induced hypoglycaemic rats were carried out. Glutamate receptor, NMDARI gene expression in the hypoglycaemic and hyperglycaemic brain was also studied. EEG recording in hypoglycaemic and hyperglycaemic will be carried out to measure brain activity. in vitro studies on glucose uptake and insulin secretion, with and without specific antagonists were carried out to confirm the specific receptor subtypes - DA D1, DA D2 and NMDA involved in the functional regulation during hyperglycaemic and hypoglycaemic brain damage. The molecular studies on the brain damage through dopaminergic and glutamergic receptors will elucidate the therapeutic role in the corrective measures of the damage to the brain during hypoglycaemia and hyperglycaemia. This has importance in the management of diabetes and antidiabetic treatment for better intellectual functioning of the individual.

Neuronal degeneration in streptozotocin induced diabetic rats: Effect of Aegle marmelose and pyridoxine in pancreatic cell proliferation and neuronal Surviva

This thesis Entitled Neuronal degeneration in streptozotocin induced diabetic rats: effect of aegle marmelose and pyridoxine in pancreatic B cell proliferation and neuronal survival. Diabetes mellitus, a chronic metabolic disorder results in neurological dysfunctions and structural changes in the CNS. Antioxidant therapy is a challenging but necessary dimension in the management of diabetes and neurodegenerative changes associated with it. Our results showed regional variation and imbalance in the expression pattern of dopaminergic receptor subtypes in diabetes and its role in imbalanced insulin signaling and glucose regulation. Disrupted dopaminergic signaling and increased hyperglycemic stress in diabetes contributed to the neuronal loss. Neuronal loss in diabetic rats mediated through the expression of pattern of GLUT-3, CREB, IGF-1, Akt-1, NF,B, second messengers- cAMP, cGMP, IP3 and activation of apoptotic factors factors- TNF-a,caspase-8. Disrupted dopaminergic receptor expressions and its signaling in pancreas contributed defective insulin secretion in diabetes. Activation of apoptotic factors- TNF- a,caspase-8 and defective functioning of neuronal survival factors, disrupted second messenger signaling modulated neuronal viability in diabetes. Hyperglycemic stress activated the expression of TNF-a,caspase-8, BAX and differential expression of anti oxidant enzymes- SOD and GPx in liver lead to apoptosis. Treatment of diabetic rats with insulin, Aegle marmelose and pyridoxine significantly reversed the altered dopaminergic neurotransmission, GLUT3, GLUT2, IGF-1 and second messenger signaling. Antihyperglycemic and antioxidant activity of Aegle marmelose and pyridoxine enhanced pancreatic B cell proliferation, increased insulin synthesis and secretion in diabetic rats. Thus our results conclude the neuroprotective and regenerating ability of Aegle marmelose and pyridoxine which in turn has a novel therapeutic role in the management of diabetes.

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.

“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.

“Adrenergic receptors and monoamines in the brain and pancreatic islets of streptozotoein diabetic rats: Role in insulin secretion as a function of age”

I) To study the changes in the content of brain rrrorroamirres in streptozotocirr-irrduced tliabetes as a lirnction of age and to lirrd the role oliadrenal lrornroncs in diabetic state. 2) To assess the adrenergic receptor function in the brain stem ofstreptozotocin-induced diabetic rats ofdillerent ages. 3) To study the changes in the basal levels of second messenger cAMP in the brain stenr ofstreptozotocin-induced diabetic rats as a function of age. 4) To study the changes occurring in the content ofmorroamines and their metabolites in whole pancreas and isolated pancreatic islets of streptozotocin-diabetic rats as a function ofage and the effect of adrenal hormones. 5) To study the adrenergic receptors and basal levels of cAMP in isolated pancreatic islets in young and old streptozotoein-diabetic rats. 6) The in virro study of CAMP content in pancreatic islets of young and old rats and its ellect on glucose induced insulin secretion. 7) 'lhe in vitro study on the involvement of dopamine and corticosteroids in glucose induced insulin secretion in pancreatic islets as a function of age.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Level Identification of Brain MR Images using Histogram of a LBP variant

Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper, we compare Histogram based Features of LBP (HF/LBP), against Histogram based Features of MOD-LBP (HF/MOD-LBP) in retrieving similar axial brain images. We show that replacing local histogram with a local distance transform based similarity metric further improves the performance of MOD-LBP based image retrieval

Content-Based Image Retrieval of Axial Brain Slices Using a Novel LBP with a Ternary Encoding

Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variancebased local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MODLTP and iteratively reweighting the moment features of MOD-LTP based on the user’s feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R.S. (2010) Local structure-based region-of-interest retrieval in brain MR images. IEEE Trans. Inf. Technol. Biomed., 14, 897–903.) in retrieving the first 10 relevant images

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