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ADHD presents a considerable social burden in adult life, and until recently was thought to be exclusive of children and adolescents. Techniques for an optimized diagnosis (based on electrophysiology) have been proven useful. In this paper, we present a new method for processing chirp-evoked, paired auditory late responses within the original time-domain, through two-dimensional image processing,...
In vivo dopaminergic imaging through [123I]FP-CIT Single Photon Emission Computerized Tomography (SPECT) provide useful information which enhances the accuracy of diagnosis of PD. Such imaging techniques have given rise to new class of subjects termed scans without evidence of dopaminergic Deficit (SWEDD) subjects, who are clinically diagnosed as PD but show normal dopaminergic scans. Although it...
A Weighted Dynamic Inverse Problem Solution is proposed for electroencephalographic current density reconstruction. The method considers physiologically based models that takes both spatial and temporal dynamics into account and a weighting stage to obtain the covariance of the measurement equation from observations. The calculated weighting matrix is included in the cost function used to solve the...
Electrocorticogram (ECoG) signals are acquired from electrodes that are surgically implanted into the subdural space of the brain. Although this procedure is usually performed for clinical purposes such as defining seizure locations and/or brain mapping, ECoG signals can also be used for characterizing the electrophysiology underlying various behaviors or for brain-computer interface applications...
People can learn how to use a new tool with repeated practice. It has been suggested that such motor learning is closely related to specific changes in brain activity in humans. Here, we used functional near-infrared spectroscopy (fNIRS), a non-invasive neuroimaging technology, and examined whether it can detect learning-related activity. Subjects performed a cursor-tracking movement task with rotational...
Regional brain atrophy is a typical structural symptom of Alzheimer's disease (AD). Magnetic resonance imaging (MRI) scans capture brain structure with high resolution and are often processed with automated segmentation and parcellation algorithm (e.g. Freesurfer) to generate regional measures, like cortical volume, cortical thickness and surface area, which are widely used as inputs in classification...
In the present study, we investigated the performance of a recently proposed sparse source imaging algorithm, i.e. wavelet-based sparse source imaging (W-SSI), in estimating epileptic sources using magnetoencephalography (MEG) interictal spikes. Three patients with medically refractory partial epilepsy were studied. Spontaneous MEG data were recorded using 148-channel magnetometers. Realistic boundary...
One possible way to examine brain connectivity is to study correlations between signals recorded from different areas. The recent trends couple the signal processing based on data-driven mathematical methods with graph analysis performed on the connectivity matrix. The connectivity matrices can be evaluated by using several methods, and different toolboxes are available. The aim of the proposed platform...
Epilepsy is a neurological illness which may be controlled by medication but not fully cured. In the case of pharmacoresistant epilepsy, seizures may prove fatal and surgery becomes an option. Compared with EEG (electro-encephalography), MEG (magenetoencephalography) offers greater accuracy in epilepsy localization owing to higher spatial resolution. However, streaming data from MEG system for surgical...
Recent studies on Alzheimer's disease (AD) have shown disrupted topological organizations of both functional and anatomical brain networks in AD patients, suggesting that AD might be a disease related to disconnection between brain regions. White matter (WM)-based anatomical network can be weighted by fractal anisotropy (FA), which reflects the integrity of axons and myelin sheaths. Thus, study on...
Methods to interpret data obtained from resting state functional magnetic imaging (rs-fMRI) must be developed to more thoroughly understand how network structure of the brain supports the body and the mind. To this end, we examine the use of agglomerative clustering (AC) as a method for rs-fMRI analysis. AC is a data driven approach for organizing spatially distinct clusters of temporally similar...
We present a measurement system for 256-channel in vitro recordings of brain tissue electrophysiological activity. The system consists of: brain tissue life support system, Microelectrode Array (MEA), 4 Application Specific Integrated Circuits (ASIC's) for signals conditioning, Digitizer and PC Application for measurement, data presentation and storage. The life support system keeps brain tissue samples...
Corticomuscular coherence between human cortical rhythms and surface electromyography (sEMG) is commonly observed within the beta (13–35 Hz) and gamma (35–60 Hz) band frequency ranges, but is typically absent within the alpha band (8–12 Hz) in healthy subjects. A recent study has shown that significant alpha band corticomuscular coherence can be mechanically induced in healthy subjects using a spring...
Network-wide synchronous firing ('network burst') is interesting as they are thought to be the unit of information processing in neural networks. The investigation of network bursts has been mainly carried out by microelectrode array technology. Recently, calcium imaging technique has been used to interrogate neural circuits in large-scale. Here we implemented an automated algorithm for network burst...
Functional connectivities constructed via resting state fMRI (R-fMRI) data have been widely used to study the brain's functional activities and to characterize the brain's states. However, the temporal dynamic transition patterns of the brain's functional states have been rarely investigated before. In this paper, we present a novel algorithmic framework to cluster and label the brain's functional...
This study used independent component analysis (ICA) to decompose the surrogate image data by concatenating in space multiple subjects' anatomical images after spatial normalization and smoothing to make all images in the same space. The multiple-subject anatomical image data included three different subject/patient populations, namely the Parkinson's disease (PD) and essential tremor (ET) as well...
We present the first topographical characterization of seizures induced by ultrabrief pulse width right unilateral electroconvulsive therapy (ECT) and magnetic seizure therapy (MST). Topographical electroencephalogram (EEG) was acquired during treatments in a randomized controlled trial contrasting the efficacy and safety of ECT and MST. EEG power topography within delta, theta, alpha, and beta frequency...
Muscle synergies have been proposed as building blocks that simplify the construction of movements, and as a method to study motor behavior. However, the pre-processing of the EMG signals and the factorization algorithm may impact on their extraction and meaning. This preliminary work aimed at investigating the influence of the selection of the muscles on muscle synergies analysis. In particular,...
This study introduces a novel and computationally efficient metric for assessment of accuracy of decomposition of high-density surface EMG signals. The metric, so called Pulse-to-Noise Ratio (PNR), builds on the results of the previously published Convolution Kernel Compensation (CKC) decomposition technique and is applied to every identified motor unit (MU), without any significant computational...
We study the electrical influence of an electrode over an axon of nonzero thickness in time using a two-dimensional finite element formulation. Although our inspiration comes from the practice of peripheral nerve stimulation, other types of neural tissue excitation can benefit from the model. Our formulation combines a Hodgkin-Huxley model to account for nerve dynamics with electrostatic intra- and...
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