Faruque Reza

Altered plasticity of the human motor cortex in Parkinson's disease

Interventional paired associative stimulation (IPAS) to the contralateral peripheral nerve and cerebral cortex can enhance the primary motor cortex (M1) excitability with two synchronously arriving inputs. This study investigated whether dopamine contributed to the associative long‐term potentiation–like effect in the M1 in Parkinsons disease (PD) patients. Eighteen right‐handed PD patients and 11 right‐handed age‐matched healthy volunteers were studied. All patients were studied after 12 hours off medication with levodopa replacement (PD‐off). Ten patients were also evaluated after medication (PD‐on). The IPAS comprised a single electric stimulus to the right median nerve at the wrist and subsequent transcranial magnetic stimulation of the left M1 with an interstimulus interval of 25 milliseconds (240 paired stimuli every 5 seconds for 20 minutes). The motor‐evoked potential amplitude in the right abductor pollicis brevis muscle was increased by IPAS in healthy volunteers, but not in PD patients. IPAS did not affect the motor‐evoked potential amplitude in the left abductor pollicis brevis. The ratio of the motor‐evoked potential amplitude before and after IPAS in PD‐off patients increased after dopamine replacement. Thus, dopamine might modulate cortical plasticity in the human M1, which could be related to higher order motor control, including motor learning. Ann Neurol 2006

Brain-derived neurotrophic factor-mediated retrograde signaling required for the induction of long-term potentiation at inhibitory synapses of visual cortical pyramidal neurons

High-frequency stimulation (HFS) induces long-term potentiation (LTP) at inhibitory synapses of layer 5 pyramidal neurons in developing rat visual cortex. This LTP requires postsynaptic Ca2+ rise for induction, while the maintenance mechanism is present at the presynaptic site, suggesting presynaptic LTP expression and the necessity of retrograde signaling. We investigated whether the supposed signal is mediated by brain-derived neurotrophic factor (BDNF), which is expressed in pyramidal neurons but not inhibitory interneurons. LTP did not occur when HFS was applied in the presence of the Trk receptor tyrosine kinase inhibitor K252a in the perfusion medium. HFS produced LTP when bath application of K252a was started after HFS or when K252a was loaded into postsynaptic cells. LTP did not occur in the presence of TrkB-IgG scavenging BDNF or function-blocking anti-BDNF antibody in the medium. In cells loaded with the Ca2+ chelator BAPTA, the addition of BDNF to the medium enabled HFS to induce LTP without affecting baseline synaptic transmission. These results suggest that BDNF released from postsynaptic cells activates presynaptic TrkB, leading to LTP. Because BDNF, expressed activity dependently, regulates the maturation of cortical inhibition, inhibitory LTP may contribute to this developmental process, and hence experience-dependent functional maturation of visual cortex.

Involvement of T-type Ca2+ channels in the potentiation of synaptic and visual responses during the critical period in rat visual cortex

Neocortical neuronal circuits are refined by experience during the critical period of early postnatal life. The shift of ocular dominance in the visual cortex following monocular deprivation has been intensively studied to unravel the mechanisms underlying the experience‐dependent modification. Synaptic plasticity is considered to be involved in this process. We previously showed in layer 2/3 pyramidal neurons of rat visual cortex that low‐frequency stimulation‐induced long‐term potentiation (LTP) at excitatory synapses, which requires the activation of Ni2+‐sensitive (R‐type or T‐type) voltage‐gated Ca2+ channels (VGCCs) for induction, shared a similar age and experience dependence with ocular dominance plasticity. In this study, we examined whether this LTP is involved in ocular dominance plasticity. In visual cortical slices, LTP was blocked by mibefradil, kurtoxin and R‐(−)‐efonidipine, T‐type VGCC blockers, but not by SNX‐482, an R‐type VGCC blocker, indicating that LTP induction requires T‐type VGCC activation. Mibefradil did not affect synaptic transmission even at a dose about 30 times higher than that required for LTP blockade. Therefore, this drug was used to test the effect of T‐type VGCC blockade on ocular dominance shift produced by 6 days of monocular deprivation during the critical period using visual evoked potentials (VEPs). Although this monocular deprivation commonly produced both depression of deprived eye responses and potentiation of nondeprived eye responses, only the former change occurred when mibefradil was infused into the visual cortex during monocular deprivation. Mibefradil infusion produced no acute effects on VEPs. These results suggest that T‐type VGCC‐dependent LTP contributes to the experience‐dependent enhancement of visual responses.

Simultaneous EEG-fMRI for working memory of the human brain

Memory plays an important role in human life. Memory can be divided into two categories, i.e., long term memory and short term memory (STM). STM or working memory (WM) stores information for a short span of time and it is used for information manipulations and fast response activities. WM is generally involved in the higher cognitive functions of the brain. Different studies have been carried out by researchers to understand the WM process. Most of these studies were based on neuroimaging modalities like fMRI, EEG, MEG etc., which use standalone processes. Each neuroimaging modality has some pros and cons. For example, EEG gives high temporal resolution but poor spatial resolution. On the other hand, the fMRI results have a high spatial resolution but poor temporal resolution. For a more in depth understanding and insight of what is happening inside the human brain during the WM process or during cognitive tasks, high spatial as well as high temporal resolution is desirable. Over the past decade, researchers have been working to combine different modalities to achieve a high spatial and temporal resolution at the same time. Developments of MRI compatible EEG equipment in recent times have enabled researchers to combine EEG-fMRI successfully. The research publications in simultaneous EEG-fMRI have been increasing tremendously. This review is focused on the WM research involving simultaneous EEG-fMRI data acquisition and analysis. We have covered the simultaneous EEG-fMRI application in WM and data processing. Also, it adds to potential fusion methods which can be used for simultaneous EEG-fMRI for WM and cognitive tasks.

Optimization and development of concurrent EEG-fMRI data acquisition setup for understanding neural mechanisms of brain

Electroencephalography (EEG) and functional magnetic resonance (fMRI) both are considered as non-invasive neuroimaging modalities. Both are used for understanding brain functionalities in cognitive neuroscience as well as in clinical applications. EEG gives high temporal resolution and it has poor spatial resolution. On the other hand, fMRI has very high spatial resolution and poor temporal resolution. For deep understanding of neural mechanisms inside human brain, it is desirable to get the higher spatiotemporal resolution of human brain at the same time. Concurrent EEG-fMRI data recording solve the problem of higher spatiotemporal resolution. It can be also helpful to understand the neural mechanism inside human brain effectively. The concurrent EEG-fMRI recording requires MRI compatible EEG equipment which can be placed inside the higher magnetic field of MRI scanner and also synchronization is required to make setup concurrent. To get higher signal to noise ratio (SNR), optimization of data acquisition parameters plays a significant role. In this paper, we discussed the some real issues during data acquisition and their optimization. We have developed the concurrent EEG-fMRI setup and also successfully recorded the EEG-fMRI data concurrently by optimizing the data acquisition parameters involved. Artifacts have been removed from the data and further, data fusion framework is proposed for combine analysis of EEG and fMRI data.

Decoding of visual information from human brain activity: A review of fMRI and EEG studies.

Brain is the command center for the body and contains a lot of information which can be extracted by using different non-invasive techniques. Electroencephalography (EEG), Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are the most common neuroimaging techniques to elicit brain behavior. By using these techniques different activity patterns can be measured within the brain to decode the content of mental processes especially the visual and auditory content. This paper discusses the models and imaging techniques used in visual decoding to investigate the different conditions of brain along with recent advancements in brain decoding. This paper concludes that its not possible to extract all the information from the brain, however careful experimentation, interpretation and powerful statistical tools can be used with the neuroimaging techniques for better results.

A proposed frame work for real time epileptic seizure prediction using scalp EEG

Epilepsy is the brain disorder disease having more than 50 million people worldwide. The treatment for epilepsy is medication and surgery. Some patients are not cured with medicine and surgery. One third of the patients still remain with uncontrolled epilepsy. They need constant monitoring for epileptic seizures. Better treatment can be provided by the doctors or precautionary measures can be taken by the patients themselves if any abnormal brain activity or seizure is predicted before its occurrence. The pre-ictal period has some information about the occurrence of epileptic seizure in EEG signals. The brain behaves normal in inter-ictal and postictal periods. For epilepsy, long duration EEG recording are required from days to week. This keeps the patients to stay in the hospital for many days. Our proposed methodology is to predict the epileptic seizure and monitor the brain abnormality in real time. Still there is no epileptic seizure prediction algorithm using EEG available for clinical applications. Our aim is to study and develop a good epileptic seizure prediction algorithm/method with high value of sensitivity and specificity using scalp EEG i-e noninvasive approach. Also a comprehensive survey is done to find the limitations and research issues related to this. The proposed pattern recognition approach has great potential to be used in real time monitoring for epileptic patients and it can be helpful in improving the quality of life of the patients.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Discriminating the different human brain states with EEG signals using Fractal dimension: A nonlinear approach

EEG signals are measured on scalp of the human brain and are widely used to address the clinical as well as in modern application like brain computer interfacing (BCI) and gaming. Feature extraction plays a fundamental role for good classification purposes. EEG features commonly extracted are linear as well as nonlinear. Nonlinear approaches are used when the complexity of EEG signals increases. Nonlinear features like correlation dimension (CD), Lyapunov exponents, approximate entropy requires higher computational complexity. On other hand Fractal dimension (FD) requires less computations. Therefore, Fractal dimension are widely used in engineering and biological sciences. In our paper, Fractal dimension has been selected to discriminate the different brain states. EEG data from 08 healthy participants have been acquired during eyes open, eyes close and during IQ task. Fractal dimensions have been computed on the EEG data acquired. Using Fractal dimension, we have successfully discriminated the different brain conditions/states like eyes open, eyes close and IQ task. Results have shown better discrimination between mental task and active brain conditions with 91.66 % accuracy using SVM classifier as compared to other classifiers. This approach can be used for fast decision making and pattern matching based on the selected epoch of the EEG signal using nonlinear approach.

Prediction of Human Brain Activity Using Likelihood Ratio Based Score Fusion

Human brain has a complex structure with the billions of neurons, so it is a difficult and challenging task to predict the behavior of human brain. Different methods and classifiers are used to measure and classify the brain activities with higher accuracy and reliability. In this paper, instead of using mostly used classifier (support vector machine), prediction of the brain activity is done by estimating the match score densities. This method is based on likelihood ratio test which helps in finding the optimal combination of match scores. The distributions of match scores are modeled for different classes based on density score fusion in which the densities of different classes are estimated from the training data set and match scores are found by fusing the estimated densities with the testing data. The fusion is done with the data extracted from distributed activation patterns using multivariate pattern analysis (MVPA) against a visual task. MVPA is an intense strategy which helps in better understanding of the human brain. The match score-based technique is used in different biometric systems but never been used for the prediction of brain activity. In order to test the performance of proposed method, prediction accuracy is compared with the support vector machine using two data sets of different modalities, one is electroencephalography (EEG) and the other is functional magnetic resonance imaging (fMRI). The results show that the proposed method predicts the novel data with improved accuracy of 66.1% and 69.3% compared with support vector machine which have 64.15% and 65.7% for fMRI and EEG data sets, respectively.