Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship

Development of quality standards for multi-center, longitudinal magnetic resonance imaging studies in clinical neuroscience

Magnetic resonance imaging (MRI) data is generated by a complex procedure. Many possible sources of error exist which can lead to a worse signal. For example, hidden defective components of a MRI-scanner, changes in the static magnetic field caused by a person simply moving in the MRI scanner room as well as changes in the measurement sequences can negatively affect the signal-to-noise ratio (SNR). A comprehensive, reproducible, quality assurance (QA) procedure is necessary, to ensure reproducible results both from the MRI equipment and the human operator of the equipment. To examine the quality of the MRI data, there are two possibilities. On the one hand, water or gel-filled objects, so-called "phantoms", are regularly measured. Based on this signal, which in the best case should always be stable, the general performance of the MRI scanner can be tested. On the other hand, the actually interesting data, mostly human data, are checked directly for certain signal parameters (e.g., SNR, motion parameters). This thesis consists of two parts. In the first part a study-specific QA-protocol was developed for a large multicenter MRI-study, FOR2107. The aim of FOR2107 is to investigate the causes and course of affective disorders, unipolar depression and bipolar disorders, taking clinical and neurobiological effects into account. The main aspect of FOR2107 is the MRI-measurement of more than 2000 subjects in a longitudinal design (currently repeated measurements after 2 years, further measurements planned after 5 years). To bring MRI-data and disease history together, MRI-data must provide stable results over the course of the study. Ensuring this stability is dealt with in this part of the work. An extensive QA, based on phantom measurements, human data analysis, protocol compliance testing, etc., was set up. In addition to the development of parameters for the characterization of MRI-data, the used QA-protocols were improved during the study. The differences between sites and the impact of these differences on human data analysis were analyzed. The comprehensive quality assurance for the FOR2107 study showed significant differences in MRI-signal (for human and phantom data) between the centers. Occurring problems could easily be recognized in time and be corrected, and must be included for current and future analyses of human data. For the second part of this thesis, a QA-protocol (and the freely available associated software "LAB-QA2GO") has been developed and tested, and can be used for individual studies or to control the quality of an MRI-scanner. This routine was developed because at many sites and in many studies, no explicit QA is performed nevertheless suitable, freely available QA-software for MRI-measurements is available. With LAB-QA2GO, it is possible to set up a QA-protocol for an MRI-scanner or a study without much effort and IT knowledge. Both parts of the thesis deal with the implementation of QA-procedures. High quality data and study results can be achieved only by the usage of appropriate QA-procedures, as presented in this work. Therefore, QA-measures should be implemented at all levels of a project and should be implemented permanently in project and evaluation routines

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Mind over chatter: plastic up-regulation of the fMRI alertness network by EEG neurofeedback

EEG neurofeedback (NFB) is a brain-computer interface (BCI) approach used to shape brain oscillations by means of real-time feedback from the electroencephalogram (EEG), which is known to reflect neural activity across cortical networks. Although NFB is being evaluated as a novel tool for treating brain disorders, evidence is scarce on the mechanism of its impact on brain function. In this study with 34 healthy participants, we examined whether, during the performance of an attentional auditory oddball task, the functional connectivity strength of distinct fMRI networks would be plastically altered after a 30-min NFB session of alpha-band reduction (n=17) versus a sham-feedback condition (n=17). Our results reveal that compared to sham, NFB induced a specific increase of functional connectivity within the alertness/salience network (dorsal anterior and mid cingulate), which was detectable 30 minutes after termination of training. Crucially, these effects were significantly correlated with reduced mind-wandering 'on-task' and were coupled to NFB-mediated resting state reductions in the alpha-band (8-12 Hz). No such relationships were evident for the sham condition. Although group default-mode network (DMN) connectivity was not significantly altered following NFB, we observed a positive association between modulations of resting alpha amplitude and precuneal connectivity, both correlating positively with frequency of mind-wandering. Our findings demonstrate a temporally direct, plastic impact of NFB on large-scale brain functional networks, and provide promising neurobehavioral evidence supporting its use as a noninvasive tool to modulate brain function in health and disease

Predictive decoding of neural data

In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder\u27s sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose modeling assumptions, it removes potential bias in the analysis of neural data due to otherwise possible forward model misspecification. The proposed methodology has been formalized into a free and open source Python framework for statistical learning based data analysis. This framework, PyMVPA, is described in Chapter 5