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.

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

Decreased 5-HT2C receptor binding in the cerebral cortex and brain stem during pancreatic regeneration in rats

The purpose of this study was to investigate the role of central 5-HT2C receptor binding in rat model of pancreatic regeneration using 60-70% pancreatectomy. The 5-HT and 5-HT2c receptor kinetics were studied in cerebral cortex and brain stem of sham operated, 72 h pancreatectomised and 7 days pancreatectomised rats. Scatchard analysis with [3H] mesulergine in cerebral cortex showed a significant decrease (p < 0.05) in maximal binding (B^,ax) without any change in Kd in 72 h pancreatectomised rats compared with sham. The decreased Bmax reversed to sham level by 7 days after pancreatectomy. In brain stem , Scatchard analysis showed a significant decrease (p < 0.01) in Bax with a significant increase (p < 0.01) in Kd. Competition analysis in brain stem showed a shift in affinity towards a low affinity. These parameters were reversed to sham level by 7 days after pancreatectomy. Thus the results suggest that 5-HT through the 5-HT2C receptor in the brain has a functional regulatory role in the pancreatic regeneration. (Mol Cell Biochem 272: 165-170, 2005)

Enhanced /3-adrenergic receptors in the brain and pancreas during pancreatic regeneration in weanling rats

Adrenergic stimulation has an inyortant role in the pancreatic It-cell proliferation and insulin secretion. In the present study. we have investigaled how sympathetic system mgulales the panrrealic n I rnerui nr ht an:ilyiing I'pinephi inn 1111 ), Norepinephrinc (NE) and /1-adrenergic receptor changes in the brain as (%eli is in the I swirls. Fill and NII showed a significant decrease in the brain regions, pancreas and plasma :rt 72Ius iller partial prurcrealectonty. We observed an increase in the circulating insulin levels at 72 hrs. Scatchard analysis using I CHI propranolol showed a significant increase in the number of loth the low affinity and high affinity t-adrenergic receplors in cerebral cortex and hypothalamus of partially pancreatectornised rats during peak DNA synthesis. The affinity of the receptors decrea,ed significantly in the low and high affinity receptors of cerebral cortex and the high affinity hypothalamic receptors. In file brain stein, low affinity receptors were increased significantly during regeneration whereas there was no change in the high affinity receptors. The pancreatic ff-adrenergic receptors were also up regulated at 72 firs after partial panerealectony. In vitro studies showed that /i-adrenergic receptors are positive regulators of islet cell proliferation and insulin secretion. Thus our results suggest that the t-adrenergic receptors are functionally enhanced during pancreatic regeneration, which in turn increases pancreatic ft-cell proliferation an(hilisulin secretion in wean hug rats.

Decreased a2-adrenergic receptor in the brain stem and pancreatic islets during pancreatic regeneration in weanling rats

Sympathetic stimulation inhibits insulin secretion. a2-Adrenergic receptor is known to have a regulatory role in the sympathetic function. We investigated the changes in the a2-adrenergic receptors in the brain stein and pancreatic islets using [3H]Yohimbine during pancreatic regeneration in weanling rats. Brain stem and pancreatic islets of experimental rats showed a significant decrease (p<0.001) in norepinephrine (NE) content at 72 h after partial pancreatectomy. The epinephrine (EPI) content showed a significant decrease (p<0.001) in pancreatic islets while it was not detected in brain stem at 72 h after partial pancreatectomy. Scatchard analysis of [3H]Yohimbine showed a significant decrease (p<0.05) and Kd at 72 h after partial pancreatectomy in the brain stem. In the pancreatic islets, Scatchard analysis of [3H]Yohimbine showed a signiinfiBca'nnatx decrease (p<0.001) in B,nax and Kd (p<0.05) at 72 h after partial pancreatectomy. The binding parameters reversed to near sham by 7 days after pancreatectomy both in brain stein and pancreatic islets. This shows that pancreatic insulin secretion is influenced by central nervous system inputs from the brain stem. In vitro studies with yohimbine showed that the a2-adrenergic receptors are inhibitory to islet DNA synthesis and insulin secretion. Thus our results suggest that decreased a2-adrenergic receptors during pancreatic regeneration functionally regulate insulin secretion and pancreatic 13-cell proliferation in weanling rats.

Decreased (x1-Adrenergic Receptor Binding in the Cerebral Cortex and Brain Stem during Pancreatic Regeneration in Rats

purpose of this study was to investigate the role of brain al-adrenergic receptor binding in the rat model of pancreatic regeneration using 60-70% pancre:dectorny. The a, -adrenergic receptors kinetics was studied in the cerebral cor:cx and brain stem of sham operated . 72 It pan- crea(ectoinised and 7 days pancreatectomised rats. Scar chard analysis with I `I I lprazocin in cerebral cartes and brain stein showed a significant decrease (/' < 0.01). (P < 0.05) in maximal binding ( 1),,,,,) with it significant decrease (P < 0.001 ), ( P < 0.01) in the K,,in 72 It pancreatecto- raised rats compared with sham , respectively . Competition analysis in cerebral cortex and brain stem showed it shift in affinity during pancreatic regeneration . The sympathetic activity was decreased as indicated by the significantly de- increased norepinephrine level in the plasma (P < 0.001), cerebral cortex (P < 0.01) and brain stem (P < 0.001) of 72 h pancreatectomised rats compared to sham . Thus, from our results it is suggested that the central a, -adrenergic receptors have a functional role in the pancreatic regenera- Lion mediated through the sympathetic pathway.

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.

Brain 5HT2A Receptor Regulation by Tryptophan Supplementation in Streptozotocin Diabetic Rats

5-HT2A receptor binding parameters were studied in the cerebral cortex and brain stem of control, diabetic, insulin, insulin + tryptophan and tr3yptophan treated streptozotocin diabetic rats. Scatchard analysis using selective antagonist, [-H](±)2,3-dimethoxyphenyl-l-[2-(4-piperidine)- methanol] ([3H]MDL100907) in cerebral cortex of diabetic rats showed a significant decrease in dissociation constant (Kd) without any change in maximal binding (Bm). Competition binding studies in cerebral cortex using ketanserin against [3H]MDL100907 showed the appearance of an additional site in the low affinity region during diabetes. In the brain stem, Scatchard analysis showed a significant increase in Bmax and Kd. Displacement studies showed a shift in the receptor affinity towards a low affinity state. All these altered parameters in diabetes were reversed to control level by insulin, insulin + tryptophan and tryptophan treatments. Tryptophan treatment is suggested to reverse the altered 5-HT2Abinding and blood glucose level to control status by increasing the brain 5-HT content.

Enhancement of [m-methoxy 3H]MDL100907 binding to 5HT 2A receptors in cerebral cortex and brain stem of streptozotocin induced diabetic rats

5-Hydroxytryptamine2A (5-HT2A) receptor kinetics was studied in cerebral cortex and brain stem of streptozotocin (STZ) induced diabetic rats. Scatchard analysis with [3H] (±) 2,3dimethoxyphenyl-l-[2-(4-piperidine)-methanol] ([3H]MDL100907) in cerebral cortex showed no significant change in maximal binding (Bmax) in diabetic rats compared to controls. Dissociation constant (K) of diabetic rats showed a significant decrease (p < 0.05) in cerebral cortex, which was reversed to normal by insulin treatment. Competition studies of [3H]MDL100907 binding in cerebral cortex with ketanserin showed the appearance of an additional low affinity site for 5-HT2A receptors in diabetic state, which was reversed to control pattern by insulin treatment. In brain stem, scatchard analysis showed a significant increase (p < 0.05) in Bmax accompanied by a significant increase (p < 0.05) in Kd. Competition analysis in brain stem also showed a shift in affinity towards a low affinity State for 5-HT2A receptors. All these parameters were reversed to control level by insulin treatment. These results show that in cerebral cortex there is an increase in affinity of 5-HT2A receptors without any change in its number and in the case of brain stem there is an increase in number of 5HT2A receptors accompanied by a decrease in its affinity during diabetes. Thus, from the results we suggest that the increase in affinity of 5-HT2A receptors in cerebral cortex and upregulation of 5-HT2A receptors in brain stem may lead to altered neuronal function in diabetes.

Complexity quantification of dense array EEG using sample entropy analysis

n this paper, a time series complexity analysis of dense array electroencephalogram signals is carried out using the recently introduced Sample Entropy (SampEn) measure. This statistic quantifies the regularity in signals recorded from systems that can vary from the purely deterministic to purely stochastic realm. The present analysis is conducted with an objective of gaining insight into complexity variations related to changing brain dynamics for EEG recorded from the three cases of passive, eyes closed condition, a mental arithmetic task and the same mental task carried out after a physical exertion task. It is observed that the statistic is a robust quantifier of complexity suited for short physiological signals such as the EEG and it points to the specific brain regions that exhibit lowered complexity during the mental task state as compared to a passive, relaxed state. In the case of mental tasks carried out before and after the performance of a physical exercise, the statistic can detect the variations brought in by the intermediate fatigue inducing exercise period. This enhances its utility in detecting subtle changes in the brain state that can find wider scope for applications in EEG based brain studies.

Significance of Time Scales in Nonlinear Dynamical Analysis of Electroencephalogram Signals

We propose to show in this paper, that the time series obtained from biological systems such as human brain are invariably nonstationary because of different time scales involved in the dynamical process. This makes the invariant parameters time dependent. We made a global analysis of the EEG data obtained from the eight locations on the skull space and studied simultaneously the dynamical characteristics from various parts of the brain. We have proved that the dynamical parameters are sensitive to the time scales and hence in the study of brain one must identify all relevant time scales involved in the process to get an insight in the working of brain.

Nonlinear Analysis of an Electroencephalogram (ECG) During Epileptic Seizure

A mathematical analysis of an electroencephalogram of a human Brain during an epileptic seizure shows that the K2 entropy decreases as compared to a clinically normal brain while the dimension of the attractor does not show significant deviation.

“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.