Functional connectivity maps of theta/alpha and beta coherence within the subthalamic nucleus region.

The subthalamic nucleus (STN) is a primary target for deep brain stimulation in Parkinson's disease (PD). Although small in size, the STN is commonly partitioned into sensorimotor, cognitive/associative, and limbic subregions based on its structural connectivity profile to cortical areas. We investigated whether such a regional specialization is also supported by functional connectivity between local field potential recordings and simultaneous magnetoencephalography. Using a novel data set of 21 PD patients, we replicated previously reported cortico-STN coherence networks in the theta/alpha and beta frequency ranges, and looked for the spatial distribution of these networks within the STN region. Although theta/alpha and beta coherence peaks were both observed in on-medication recordings from electrode contacts at several locations within and around the STN, sites with theta/alpha coherence peaks were situated at significantly more inferior MNI coordinates than beta coherence peaks. Sites with only theta/alpha coherence peaks, i.e. without distinct beta coherence, were mostly located near the border of sensorimotor and cognitive/associative subregions as defined by a tractography-based atlas of the STN. Peak coherence values were largely unaltered by the medication state of the subject, however, theta/alpha peaks were more often identified in recordings obtained after administration of dopaminergic medication. Our findings suggest the existence of a frequency-specific topography of cortico-STN coherence within the STN, albeit with considerable spatial overlap between functional networks. Consequently, optimization of deep brain stimulation targeting might remain a trade-off between alleviating motor symptoms and avoiding adverse neuropsychiatric side effects.

Low-beta cortico-pallidal coherence decreases during movement and correlates with overall reaction time.

Beta band oscillations (13-30 Hz) are a hallmark of cortical and subcortical structures that are part of the motor system. In addition to local population activity, oscillations also provide a means for synchronization of activity between regions. Here we examined the role of beta band coherence between the internal globus pallidus (GPi) and (motor) cortex during a simple reaction time task performed by nine patients with idiopathic dystonia. We recorded local field potentials from deep brain stimulation (DBS) electrodes implanted in bilateral GPi in combination with simultaneous whole-head magneto-encephalography (MEG). Patients responded to visually presented go or stop-signal cues by pressing a button with left or right hand. Although coherence between signals from DBS electrodes and MEG sensors was observed throughout the entire beta band, a significant movement-related decrease prevailed in lower beta frequencies (∼13-21 Hz). In addition, patients' absolute coherence values in this frequency range significantly correlated with their median reaction time during the task (r = 0.89, p = 0.003). These findings corroborate the recent idea of two functionally distinct frequency ranges within the beta band, as well as the anti-kinetic character of beta oscillations.

Application of decorrelation-independent component analysis to biomagnetic multi-channel measurements.

Biomagnetic multi-channel recordings are typically a superposition of signals from several biological sources of interest and from biological and technical noise sources. Besides averaging, source localization, and spectral analysis to name only a few methods, independent component analysis is an established tool to resolve the superposition present in raw biomagnetic data on a purely statistical basis. Here the time-delayed decorrelation-independent component analysis algorithm is applied to exemplary magnetocardiographic and magnetoencephalographic data and the successful signal separation is demonstrated.

Linking neuroimaging signals to behavioral responses in single cases: Challenges and opportunities.

Despite rapid progress both in psychology and neuroimaging, there is still a convergence gap between the results of these two scientific disciplines. This is particularly unsatisfactory, as the variability between single subjects needs to be understood both for basic science and for patient diagnostics in, for example, the field of age-related cognitive changes. Active and passive behaviors are the observables in psychology and can be studied alone or in combination with the neuroimaging approach. Various physical signatures of brain activity are the observables in neuroimaging and can be measured concurrent with behaviors. Despite the intrinsic relationship between behaviors and the corresponding neuroimaging patterns and the obvious advantages in integrating behavioral and neuroimaging measurements, the results of combined studies can be difficult to interpret. Experiments are often optimized to yield either a novel behavioral or a novel physiological result, but rarely designed for a better match between the two. Since integrating the results is probably a key to future progress in clinical psychology and basic research, an attempt is made here to identify some difficulties and to provide some ideas for future research.

Fetal magnetocardiography measurements with an array of microfabricated optically pumped magnetometers.

Following the rapid progress in the development of optically pumped magnetometer (OPM) technology for the measurement of magnetic fields in the femtotesla range, a successful assembly of individual sensors into an array of nearly identical sensors is within reach. Here, 25 microfabricated OPMs with footprints of 1 cm(3) were assembled into a conformal array. The individual sensors were inserted into three flexible belt-shaped holders and connected to their respective light sources and electronics, which reside outside a magnetically shielded room, through long optical and electrical cables. With this setup the fetal magnetocardiogram of a pregnant woman was measured by placing two sensor belts over her abdomen and one belt over her chest. The fetal magnetocardiogram recorded over the abdomen is usually dominated by contributions from the maternal magnetocardiogram, since the maternal heart generates a much stronger signal than the fetal heart. Therefore, signal processing methods have to be applied to obtain the pure fetal magnetocardiogram: orthogonal projection and independent component analysis. The resulting spatial distributions of fetal cardiac activity are in good agreement with each other. In a further exemplary step, the fetal heart rate was extracted from the fetal magnetocardiogram. Its variability suggests fetal activity. We conclude that microfabricated optically pumped magnetometers operating at room temperature are capable of complementing or in the future even replacing superconducting sensors for fetal magnetocardiography measurements.

Cardiac artifact subspace identification and elimination in cognitive MEG data using time-delayed decorrelation.

To reduce physiological artifacts in magnetoencephalographic (MEG) and electroencephalographic recordings, a number of methods have been applied in the past such as principal component analysis, signal-space projection, regression using secondary information, and independent component analysis. This method has become popular as it does not have constraints such as orthogonality between artifact and signal or the need for a priori information. Applying the time-delayed decorrelation algorithm to raw data from a visual stimulation MEG experiment, we show that several of the independent components can be attributed to the cardiac artifact. Calculating an average cardiac activity shows that physiologically different excitation states of the heart produce similar field distributions in the MEG sensor system. This is equivalent to differing spectral properties of cardiac field distributions in the raw data. As a consequence, the algorithm combines, e.g., the R peak and the T wave of the cardiac cycle into a single component and the one-to-one assignment of each independent component with a physiological source is not justified in this case. To improve the signal quality of visually evoked fields, the multidimensional cardiac artifact subspace is suppressed from the data. To assess the preservation of the evoked signal after artifact suppression, a geometrical and a temporal measure are introduced. The suppression of cardiac and alpha wave artifacts allows, in our experimental setting, the reduction of the number of epochs to one half while preserving the visually evoked signal.

Validity of subthalamic-cortical coherency observed in patients with Parkinson's disease.

Simultaneous magnetoencephalography (MEG) and local field potential (LFP) recordings in patients with Parkinson's disease (PD) undergoing deep brain stimulation (DBS) treatment is a promising tool for both clinical application and basic research. Recordings can be accomplished during the time interval between electrode insertion and its connection to the pulse generator while electrodes are being externalized. In nine PD patients, coherence (COH) between LFP and MEG signals was calculated from the data of a 5-min simultaneous MEG-LFP rest recording. For the observed COH patterns, a validation procedure is introduced based on time-shift principal component analysis (TSPCA), which was originally developed to suppress background signals from MEG. Here TSPCA is used as a regression of the MEG signal with filtered versions of the LFP signal to intentionally remove COH. The channel mean of the original COH is compared with the residual channel mean COH after TSPCA application. COH peaks are suppressed in the 15- to 30-Hz range; at lower frequencies, the results are less obvious due to the presence of an artifact caused by a weak remanent magnetization of the externalization wires. The COH suppression is statistically significant for four out of nine subjects, and validation has been achieved, as the COH suppression yields the hypothesized outcome.

BioSig: the free and open source software library for biomedical signal processing.

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

Magnetoencephalography discriminates modality-specific infraslow signals less than 0.1 Hz.

DC-magnetoencephalography (DC-MEG) technique has been refined and allows to record cortical activity in the infraslow frequency range less than 0.1 Hz noninvasively. Important questions however, remained, especially, how specific these infraslow activations can be recorded and whether different activations, for example, motor versus acoustic, can be separated. To clarify these questions, in the present DC-MEG study, cortical infraslow activity was investigated intraindividually in response to different activation modalities, that is, motor versus acoustic: in 13 individuals, 30-s periods of finger movement or listening to concert music, were interleaved for 60 min. DC-MEG was capable to resolve intermodal differences concerning the relative amplitudes, field patterns, and source localizations. These results clarify that DC-MEG allows to identify and to discriminate modality-specific infraslow cortical neuronal signals.

Cross-correlation of motor activity signals from dc-magnetoencephalography, near-infrared spectroscopy, and electromyography.

Neuronal and vascular responses due to finger movements were synchronously measured using dc-magnetoencephalography (dcMEG) and time-resolved near-infrared spectroscopy (trNIRS). The finger movements were monitored with electromyography (EMG). Cortical responses related to the finger movement sequence were extracted by independent component analysis from both the dcMEG and the trNIRS data. The temporal relations between EMG rate, dcMEG, and trNIRS responses were assessed pairwise using the cross-correlation function (CCF), which does not require epoch averaging. A positive lag on a scale of seconds was found for the maximum of the CCF between dcMEG and trNIRS. A zero lag is observed for the CCF between dcMEG and EMG. Additionally this CCF exhibits oscillations at the frequency of individual finger movements. These findings show that the dcMEG with a bandwidth up to 8 Hz records both slow and faster neuronal responses, whereas the vascular response is confirmed to change on a scale of seconds.

Recent advances in modeling and analysis of bioelectric and biomagnetic sources.

Determining the centers of electrical activity in the human body and the connectivity between different centers of activity in the brain is an active area of research. To understand brain function and the nature of cardiovascular diseases requires sophisticated methods applicable to non-invasively measured bioelectric and biomagnetic data. As it is difficult to solve for all unknown parameters at once, several strains of data analysis have been developed, each trying to solve a different part of the problem and each requiring a different set of assumptions. Current trends and results from major topics of electro- and magnetoencephalographic data analysis are presented here together with the aim of stimulating research into the unification of the different approaches. The following topics are discussed: source reconstruction using detailed finite element modeling to locate sources deep in the brain; connectivity analysis for the quantification of strength and direction of information flow between activity centers, preferably incorporating an inverse solution; the conflict between the statistical independence assumption of sources and a possible connectivity; the verification and validation of results derived from non-invasively measured data through animal studies and phantom measurements. This list already indicates the benefits of a unified view.