Modeling sparse connectivity between underlying brain sources for EEG/MEG

We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model, (b) the demixing is estimated jointly with the source MVAR parameters, (c) overfitting is avoided by using the Group Lasso penalty. This approach allows to extract the appropriate level cross-talk between the extracted sources and in this manner we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure

Characterising population variability in brain structure through models of whole-brain structural connectivity

Models of whole-brain connectivity are valuable for understanding neurological function. This thesis seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically acquired diffusion data. We propose new approaches for studying these models. The aim is to develop techniques which can take models of brain connectivity and use them to identify biomarkers or phenotypes of disease. The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections are traced between 77 regions of interest, automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data. These are compared in subsequent studies. To date, most whole-brain connectivity studies have characterised population differences using graph theory techniques. However these can be limited in their ability to pinpoint the locations of differences in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include a spectral clustering approach for comparing population differences in the clustering properties of weighted brain networks. In addition, machine learning approaches are suggested for the first time. These are particularly advantageous as they allow classification of subjects and extraction of features which best represent the differences between groups. One limitation of the proposed approach is that errors propagate from segmentation and registration steps prior to tractography. This can cumulate in the assignment of false positive connections, where the contribution of these factors may vary across populations, causing the appearance of population differences where there are none. The final contribution of this thesis is therefore to develop a common co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject into a single probabilistic model of diffusion for the population. This allows tractography to be performed only once, ensuring that there is one model of connectivity. Cross-subject differences can then be identified by mapping individual subjects’ anisotropy data to this model. The approach is used to compare populations separated by age and gender

The smoothness constraint in spatially informed minimum norm approaches for the reconstruction of neuroelectromagnetic sources

Neuronal processes in the brain give rise to electromagnetic signals that can be measured by means of EEG/MEG. However, the ambiguity of the bioelectromagnetic inverse problem limits the localizability of the underlying generators. The solution of the inverse problem requires additional assumptions. A very common method is to model brain activity using distributed sources. In that case, a large number of equivalent current dipoles covers the volume in which activity is expected (usually the cortex). Reconstruction methods on the basis of distributed sources allow the incorporation of additional information on the functional similarity between sources (i.e. information on the spatial structure of brain activity). This kind of information can be derived from prior knowledge, for instance from the subdivision of the cortex into distinct functional areas (i.e. parcellations) or from fMRI. The work presented here is based on a previously published method that combines a general smoothness constraint with priori knowledge on the (binary) similarity between neighboring sources by means of a 2nd order spatial derivative operator (PatchLORETA). The first part of this work addressed the systematic evaluation on how the integration of prior knowledge into the derivative operator affects the estimation of a priori assumed source covariances. It turned out that the method introduced incorrect prior assumptions. Consequently, some extensions were proposed to generalize the approach. These are an additional normalization operator and an additional parameter to encode arbitrary mutual similarity between neighbors. Moreover, a technique was developed to adjust the correlation structure according to a desired smoothness level. The final method (called informed LORETA) is particularly suited for the use of functio-anatomical boundaries. The second part addressed the systematic evaluation of the question whether the use of prior knowledge (derived from parcellations) can improve source localization. This was done using Monte-Carlo simulations. A main focus was the evaluation on how potential errors / uncertainties in the prior knowledge influence the reconstruction performance. Finally, informed LORETA was used for the localization of auditory evoked potentials from experimental data. It turned out that spatially informed methods provide very plausible reconstruction results.EEG/MEG ermöglicht die Messung elektrischer Gehirnaktivität, die durch neuronale Prozesse im Gehirn hervorgerufen wird. Die Lokalisierbarkeit der Aktivität ist aufgrund der fehlenden Eindeutigkeit des bioelektromagnetischen inversen Problems allerdings eingeschränkt. Zur Lösung sind Zusatzannahmen erforderlich. Eine Klasse von Lösungsverfahren basiert auf der Verwendung verteilter Quellenmodelle. Dabei werden im gesamten wahrscheinlichen Quellraum (typischerweise im Cortex) Stromdipole modelliert, um schließlich eine räumliche Verteilung der Dipolstärken zu bestimmen. Dieser Ansatz erlaubt es, Zusatzannahmen über die funktionelle Ähnlichkeit zwischen den Dipolen (d.h. über die räumliche Strukturierung von Gehirnaktivität) zu formulieren. Derartiges Wissen kann zum Beispiel aus der Unterteilung des Cortex in funktional unterschiedliche Areale (Parzellierungen) oder mittels fMRI gewonnen werden. Diese Arbeit befasst sich mit einer bereits zuvor publizierten Technik, bei der Zusatzwissen über die funktionelle Ähnlichkeit benachbarter Quellen in einen Differentialoperator integriert und mit einer allgemeinen Glattheitsannahme kombiniert wird (PatchLORETA). Im ersten Teil dieser Arbeit wurde systematisch untersucht, wie sich eine derartige Integration auf die tatsächliche Korrelationsstruktur auswirkt. Dabei wurden verschiedene Probleme identifiziert, die zu fehlerhaften a priori Annahmen führen. Aus diesem Grund wurde die Methode um einen Normalisierungsoperator, lokale Ähnlichkeitsparameter, und ein Verfahren zur Einstellung einer definierten Glattheitsannahme erweitert. Im Ergebnis liegt ein als informed LORETA bezeichnetes Verfahren vor, in das grundsätzlich beliebige Ähnlichkeitsinformation eingebunden werden kann. Es ist besonders zur Integration funktio-anatomischer Grenzen geeignet. Im zweiten Teil dieser Arbeit wurde die Nutzbarkeit informierter linearer inverser Verfahren mithilfe von Monte-Carlo-Simulationen und unter Verwendung von Parzellierungen systematisch untersucht. Im Fokus stand dabei vor allem der Einfluss möglicher Fehler im Zusatzwissen auf die Rekonstruktionsqualität. Abschließend wurde informed LORETA zur Lokalisierung auditorisch evozierter Aktivität aus EEG/MEG-Daten eingesetzt. Dabei konnte gezeigt werden, dass die Plausibilität der rekonstruierten Quellenverteilung durch die Integration von Zusatzwissen deutlich gesteigert werden kann

On the potential of a new generation of magnetometers for MEG: a beamformer simulation study

Magnetoencephalography (MEG) is a sophisticated tool which yields rich information on the spatial, spectral and temporal signatures of human brain function. Despite unique potential, MEG is limited by a low signal-to-noise ratio (SNR) which is caused by both the inherently small magnetic fields generated by the brain, and the scalp-to-sensor distance. The latter is limited in current systems due to a requirement for pickup coils to be cryogenically cooled. Recent work suggests that optically-pumped magnetometers (OPMs) might be a viable alternative to superconducting detectors for MEG measurement. They have the advantage that sensors can be brought to within ~4 mm of the scalp, thus offering increased sensitivity. Here, using simulations, we quantify the advantages of hypothetical OPM systems in terms of sensitivity, reconstruction accuracy and spatial resolution. Our results show that a multi-channel whole-head OPM system offers (on average) a fivefold improvement in sensitivity for an adult brain, as well as clear improvements in reconstruction accuracy and spatial resolution. However, we also show that such improvements depend critically on accurate forward models; indeed, the reconstruction accuracy of our simulated OPM system only outperformed that of a simulated superconducting system in cases where forward field error was less than 5%. Overall, our results imply that the realisation of a viable whole-head multi-channel OPM system could generate a step change in the utility of MEG as a means to assess brain electrophysiological activity in health and disease. However in practice, this will require both improved hardware and modelling algorithms

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Localization of cortico-peripheral coherence with electroencephalography.

Background The analysis of coherent networks from continuous recordings of neural activity with functional MRI or magnetoencephalography has provided important new insights into brain physiology and pathology. Here we assess whether valid localizations of coherent cortical networks can also be obtained from high-resolution electroencephalography (EEG) recordings. Methods EEG was recorded from healthy subjects and from patients with ischemic brain lesions during a tonic hand muscle contraction task and during continuous visual stimulation with an alternating checkerboard. These tasks induce oscillations in the primary hand motor area or in the primary visual cortex, respectively, which are coherent with extracerebral signals (hand muscle electromyogram or visual stimulation frequency). Cortical oscillations were reconstructed with different inverse solutions and the coherence between oscillations at each cortical voxel and the extracerebral signals was calculated. Moreover, simulations of coherent point sources were performed. Results Cortico-muscular coherence was correctly localized to the primary hand motor area and the steady-state visual evoked potentials to the primary visual cortex in all subjects and patients. Sophisticated head models tended to yield better localization accuracy than a single sphere model. A Minimum Variance Beamformer (MVBF) provided more accurate and focal localizations of simulated point sources than an L2 Minimum Norm (MN) inverse solution. In the real datasets, the MN maps had less localization error but were less focal than MVBF maps. Conclusions EEG can localize coherent cortical networks with sufficient accuracy

Lead-DBS v3.0: Mapping Deep Brain Stimulation Effects to Local Anatomy and Global Networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics

Energy landscape of resting magnetoencephalography reveals frontoparietal network impairments in epilepsy

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy. It is yet unclear to what extent JME leads to abnormal network activation patterns. Here, we characterised statistical regularities in MEG resting-state networks and their differences between JME patients and controls, by combining a pairwise maximum entropy model (pMEM) and novel energy landscape analyses for MEG. First, we fitted the pMEM to the MEG oscillatory power in the frontoparietal network (FPN) and other resting-state networks, which provided a good estimation of the occurrence probability of network states. Then, we used energy values derived from the pMEM to depict an energy landscape, with a higher energy state corresponding to a lower occurrence probability. JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta and gamma-bands. Furthermore, simulations of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of energy minima was shortened in JME patients. These network alterations were highlighted by significant classification of individual participants employing energy values as multivariate features. Our findings suggested that JME patients had altered multi-stability in selective functional networks and frequency bands in the frontoparietal cortices