Showing papers by "Rajanikant Panda published in 2018"

Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy.

Abstract: Purpose Quasi-stable electrical distribution in EEG called microstates could carry useful information on the dynamics of large scale brain networks. Using machine learning techniques we explored if abnormalities in microstates can identify patients with Temporal Lobe Epilepsy (TLE) in the absence of an interictal discharge (IED). Method 4 Classes of microstates were computed from 2 min artefact free EEG epochs in 42 subjects (21 TLE and 21 controls). The percentage of time coverage, frequency of occurrence and duration for each of these microstates were computed and redundancy reduced using feature selection methods. Subsequently, Fishers Linear Discriminant Analysis (FLDA) and logistic regression were used for classification. Result FLDA distinguished TLE with 76.1% accuracy (85.0% sensitivity, 66.6% specificity) considering frequency of occurrence and percentage of time coverage of microstate C as features. Conclusion Microstate alterations are present in patients with TLE. This feature might be useful in the diagnosis of epilepsy even in the absence of an IED.

Novel Findings in Obstetric Brachial Plexus Palsy: A Study of Corpus Callosum Volumetry and Resting-State Functional Magnetic Resonance Imaging of Sensorimotor Network.

Abstract: Background The response of the brain to obstetric brachial plexus palsy (OBPP) is not clearly understood. We propose that even a peripheral insult at the developmental stage may result in changes in the volume of white matter of the brain, which we studied using corpus callosum volumetry and resting-state functional magnetic resonance imaging (rsfMRI) of sensorimotor network. Objective To study the central neural effects in OBPP. Methods We performed an MRI study on a cohort of 14 children who had OBPP and 14 healthy controls. The mean age of the test subjects was 10.07 ± 1.22 yr (95% confidence interval). Corpus callosum volumetry was compared with that of age-matched healthy subjects. Hofer and Frahm segmentation was used. Resting-state fMRI data were analyzed using the FSL software (FMRIB Software Library v5.0, Oxford, United Kingdom), and group analysis of the sensorimotor network was performed. Results Statistical analysis of corpus callosum volume revealed significant differences between the OBPP cohort and healthy controls, especially in the motor association areas. Independent t-test revealed statistically significant volume loss in segments I (prefrontal), II (premotor), and IV (primary sensory area). rsfMRI of sensorimotor network showed decreased activation in the test hemisphere (the side contralateral to the injured brachial plexus) and also decreased activation in the ipsilateral hemisphere, when compared with healthy controls. Conclusion OBPP occurs in an immature brain and causes central cortical changes. There is secondary corpus callosum atrophy which may be due to retrograde transneuronal degeneration. This in turn may result in disruption of interhemispheric coactivation and consequent reduction in activation of sensorimotor network even in the ipsilateral hemisphere.

Decreased integration of EEG source-space networks in disorders of consciousness

Abstract: Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients functional brain networks. Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n=6), MCS+ (n=29), MCS- (n=17) and UWS (n=9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate group differences between healthy volunteers and patient groups. Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness.

Neural correlates of modified subjective state of consciousness induced by hypnosis using EEG-connectivity approach

Abstract: Methods: Ten healthy subjects (7 females, mean age 24±3years) underwent high density EEG recordings in both eye close awake resting state and hypnosis state. The hypnotic state instruction involved a 3-min induction procedure with muscle relaxation and eye fixation. After preprocessing EEG data, both hypothesis and data driven analysis were conducted using connectivity approach. Classical power spectral analysis was performed for delta (1-4Hz), theta (4.1-8Hz), alpha (8.1-12Hz), beta1 (12.1-20Hz) and beta2 (20.1-30Hz) frequency bands. Connectivity between every pair of electrodes was assessed using weighted Phase Lag Index. Hypothesis-based connectivity was computed for frontal, parietal and midline regions [2]. Data-driven graph theory connectivity was carried out to measure brain connectivity network properties and altered hub regions [3].

Optimization of EDM Machining Parameters to Machine INCONEL – 825

Abstract: It is observed that in recent trend INCONEL material has tremendous application in aeronautical, aerospace industry and automobile engineering because of its favorable properties. Therefore “Inconel 825” material has been chosen to machine by EDM. But EDM has a disadvantage of lower MRR. So an experimental investigation has been carried out to study machining parameters of EDM to improve MRR and reducing TWR, surface roughness and kerf width. Here heat treatment process has been studied and applied to improve tool life by reducing tool wear rate. In this experiment Copper tool has modified by changing its grain growth structure by step hardening process and results in both heat treated and without heat treated tools are compared. Keywords-EDM (Electro Discharge Machine), Without Heat treated tool, With Heat Treated Tool, MRR (Material Removal Rate), TWR (Tool Wear Rate), SR (Surface Roughness)

Large-Scale Brain Network Modelling Using Graph-Theory Approach in Neuroscience

Abstract: From decade, neuroscientist trying to understand the brain functional dynamics of healthy individuals and the patients suffering with neuro-psychiatric disorders. In this study, we propose a large-scale brain network modelling using graph theory approach to understand the brain dynamics of healthy individual’s by exploring the ageing. We used resting-state functional magnetic resonance imaging (rsfMRI) data. We selected two groups of subject’s 1) young healthy subjects (mean age: 23 ±6) and 2) old healthy subject (mean age: 40±5). The rsfMRI data pre-processed for ac-pc baseline correction, realignment, registration, segmentation, normalization and band pass filtering. The preprocessed data were parcellated using ‘Dosenbach-160’ atlas and connectivity matrix computed using Pearson correlation. Large-scale brain network computed by network segregation (i.e. clustering coefficient), integration (i.e. participation coefficient), efficiency and small-worldness. Individual nodal graph measures were computed through integrated nodal clustering coefficient and participation coefficient. Finally, statistical analysis were carried out using two-sample t-test with FDR correction. We found the older healthy individuals have lower clustering coefficient, small-worldness and higher participation coefficient. Our finding suggests graph theory connectivity measure is a potential technique to understand the neural dynamics of ageing, cognitive processes in healthy individual and may be a potential methods to study the alter brain functions in patients with neuro-psychiatric disorders.