Rajanikant Panda

Bio: Rajanikant Panda is an academic researcher from University of Liège. The author has contributed to research in topics: Resting state fMRI & Consciousness. The author has an hindex of 13, co-authored 42 publications receiving 535 citations. Previous affiliations of Rajanikant Panda include Academy of Technology & National Institute of Mental Health and Neurosciences.

Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction

Abstract: Feature extraction and classification of electroencephalogram (EEGs) signals for (normal and epileptic) is a challenge for engineers and scientists. Various signal processing techniques have already been proposed for classification of non-linear and non- stationary signals like EEG. In this work, SVM (support vector machine) based classifier was employed to detect epileptic seizure activity from background electro encephalographs (EEGs). Five types of EEG signals (healthy subject with eye open condition, eye close condition, epileptic, seizure signal from hippocampal region) were selected for the analysis. Signals were preprocessed, decomposed by using discrete wavelet transform DWT till 5th level of decomposition tree. Various features like energy, entropy and standard deviation were computed and consequently used for classification of signals. The results show the promising classification accuracy of nearly 91.2% in detection of abnormal from normal EEG signals. This proposed classifier can be used to design expert system for epilepsy diagnosis purpose in various hospitals.

Recovery of resting brain connectivity ensuing mild traumatic brain injury

Abstract: Brains reveal amplified plasticity as they recover from an injury. We aimed to define time dependent plasticity changes in patients recovering from mild traumatic brain injury (mTBI). Twenty-five subjects with mild head injury were longitudinally evaluated within 36 h, 3 and 6 months using resting state functional connectivity (RSFC). Region of interest (ROI) based connectivity differences over time within the patient group and in comparison with a healthy control group were analyzed at p < 0.005. We found 33 distinct ROI pairs that revealed significant changes in their connectivity strength with time. Within 3 months, the majority of the ROI pairs had decreased connectivity in mTBI population, which increased and became comparable to healthy controls at 6 months. Within this diffuse decreased connectivity in the first 3 months, there were also few regions with increased connections. This hyper connectivity involved the salience network and default mode network within 36 h, and lingual, inferior frontal and fronto-parietal networks at 3 months. Our findings in a fairly homogenous group of patients with mTBI evaluated during the 6 month window of recovery defines time varying brain connectivity changes as the brain recovers from an injury. A majority of these changes were seen in the frontal and parietal lobes between 3 and 6 months after injury. Hyper connectivity of several networks supported normal recovery in the first 6 months and it remains to be seen in future studies whether this can predict an early and efficient recovery of brain function.

Role of altered cerebello-thalamo-cortical network in the neurobiology of essential tremor

Abstract: Essential tremor (ET) is the most common movement disorder among adults. Although ET has been recognized as a mono-symptomatic benign illness, reports of non-motor symptoms and non-tremor motor symptoms have increased its clinical heterogeneity. The neural correlates of ET are not clearly understood. The aim of this study was to understand the neurobiology of ET using resting state fMRI. Resting state functional MR images of 30 patients with ET and 30 age- and gender-matched healthy controls were obtained. The functional connectivity of the two groups was compared using whole-brain seed-to-voxel-based analysis. The ET group had decreased connectivity of several cortical regions especially of the primary motor cortex and the primary somatosensory cortex with several right cerebellar lobules compared to the controls. The thalamus on both hemispheres had increased connectivity with multiple posterior cerebellar lobules and vermis. Connectivity of several right cerebellar seeds with the cortical and thalamic seeds had significant correlation with an overall score of Fahn-Tolosa-Marin tremor rating scale (FTM-TRS) as well as the subscores for head tremor and limb tremor. Seed-to-voxel resting state connectivity analysis revealed significant alterations in the cerebello-thalamo-cortical network in patients with ET. These alterations correlated with the overall FTM scores as well as the subscores for limb tremor and head tremor in patients with ET. These results further support the previous evidence of cerebellar pathology in ET.

Temporal Dynamics of the Default Mode Network Characterize Meditation-Induced Alterations in Consciousness.

Abstract: Current research suggests that human consciousness is associated with complex, synchronous interactions between multiple cortical networks. In particular, the default mode network (DMN) of the resting brain is thought to be altered by changes in consciousness, including the meditative state. However, it remains unclear how meditation alters the fast and ever-changing dynamics of brain activity within this network. Here we addressed this question using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to compare the spatial extents and temporal dynamics of the DMN during rest and meditation. Using fMRI, we identified key reductions in the posterior cingulate hub of the DMN, along with increases in right frontal and left temporal areas, in experienced meditators during rest and during meditation, in comparison to healthy controls (HCs). We employed the simultaneously recorded EEG data to identify the topographical microstate corresponding to activation of the DMN. Analysis of the temporal dynamics of this microstate revealed that the average duration and frequency of occurrence of DMN microstate was higher in meditators compared to HCs. Both these temporal parameters increased during meditation, reflecting the state effect of meditation. In particular, we found that the alteration in the duration of the DMN microstate when meditators entered the meditative state correlated negatively with their years of meditation experience. This reflected a trait effect of meditation, highlighting its role in producing durable changes in temporal dynamics of the DMN. Taken together, these findings shed new light on short and long-term consequences of meditation practice on this key brain network.

Statistical Parametric Mapping The Analysis Of Functional Brain Images

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Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks.

Abstract: The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.

EEG seizure detection and prediction algorithms: a survey

Abstract: Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.