Multimodal Integration: fMRI, MRI, EEG, MEG

This chapter provides a comprehensive survey of the motivations, assumptions and pitfalls associated with combining signals such as fMRI with EEG or MEG. Our initial focus in the chapter concerns mathematical approaches for solving the localization problem in EEG and MEG. Next we document the most recent and promising ways in which these signals can be combined with fMRI. Specically, we look at correlative analysis, decomposition techniques, equivalent dipole tting, distributed sources modeling, beamforming, and Bayesian methods. Due to difculties in assessing ground truth of a combined signal in any realistic experiment difculty further confounded by lack of accurate biophysical models of BOLD signal we are cautious to be optimistic about multimodal integration. Nonetheless, as we highlight and explore the technical and methodological difculties of fusing heterogeneous signals, it seems likely that correct fusion of multimodal data will allow previously inaccessible spatiotemporal structures to be visualized and formalized and thus eventually become a useful tool in brain imaging research

Functional Neuroanatomy of Dynamic Visuo-Spatial Imagery

The aim of this thesis was the examination of the neural bases of dynamic visuo-spatial imagery. In addition to the assessment of brain activity during dy-namic visuo-spatial imagery using single-trial functional magnetic resonance im-aging (fMRI) and slow cortical potentials (SCPs), several methodological issues have been investigated. The theoretical part of this thesis consists of a selective overview of fMRI and SCPs, and of the advantages of their combination for functional neuroimaging (chapter 2). The methodological and empirical chapters include: Ø the presentation of a new, highly accurate and practicable method for the co-registration of MRI- and EEG-data (chapter 3), Ø the description of the increase in the accuracy of SCP mapping resulting from the use of individual electrode coordinates and realistic head models (chapter 4), Ø the description of regional differences in the consistency of brain activity across several executions of the same task type, as assessed by a new analysis con-cept based on single-trial fMRI data (chapter 5), Ø the demonstration of the involvement of premotor regions in dynamic visuo-spatial imagery, as assessed via a combination of single-trial fMRI and SCPs (chapter 6), Ø the description of a combined fMRI-SCP investigation in which earlier findings concerning individual differences in neural efficiency during dynamic imagery could not be replicated (chapter 7)

Biomarkers and neuromodulation techniques in substance use disorders

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders