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

Development of Pharmacological Magnetic Resonance Imaging Methods and their Application to the Investigation of Antipsychotic Drugs: a Dissertation

Pharmacological magnetic resonance imaging (phMRI) is the use of functional MRI techniques to elucidate the effects that psychotropic drugs have on neural activity within the brain; it is an emerging field of research that holds great potential for the investigation of drugs that act on the central nervous system by revealing the changes in neural activity that mediate observable changes in behavior, cognition, and perception. However, the realization of this potential is hampered by several unanswered questions: Are the MRI measurements reliable surrogates of changing neural activity in the presence of pharmacological agents? Is it relevant to investigate psychiatric phenomena such as reward or anxiolysis in anesthetized, rather than conscious animals? What are the methods that yield reproducible and meaningful results from phMRI experiments, and are they consistent in the investigations of different drugs? The research presented herein addresses many of these questions with the specific aims of 1) Developing pharmacological MRI methodologies that can be used in the conscious animal, 2) Validating these methodologies with the investigation of a non-stimulant, psychoactive compound, and 3) Applying these methodologies to the investigation of typical and atypical antipsychotic drugs, classes of compounds with unknown mechanisms of therapeutic action Building on recent developments in the field of functional MRI research, we developed new techniques that enable the investigator to measure localized changes in metabolism commensurate with changing neural activity. We tested the hypothesis that metabolic changes are a more reliable surrogate of changes in neural activity in response to a cocaine challenge, than changes observed in the blood-oxygen-level-dependent (BOLD) signal alone. We developed a system capable of multi-modal imaging in the conscious rat, and we tested the hypothesis that the conscious brain exhibits a markedly different response to systemic morphine challenge than the anesthetized brain. We identified and elucidated several fundamental limitations of the imaging and analysis protocols used in phMRI investigations, and developed new tools that enable the investigator to avoid common pitfalls. Finally, we applied these phMRI techniques to the investigation of neuroleptic compounds by asking the question: does treatment with typical or atypical antipsychotic drugs modulate the systems in the brain which are direct or indirect (i.e. downstream) substrates for a dopaminergic agonist? The execution of this research has generated several new tools for the neuroscience and drug discovery communities that can be used in neuropsychiatric investigations into the action of psychotropic drugs, while the results of this research provide evidence that supports several answers to the questions that currently limit the utility of phMRI investigations. Specifically, we observed that metabolic change can be measured to resolve discrepancies between anomalous BOLD signal changes and underlying changes in neural activity in the case of systemically administered cocaine. We found clear differences in the response to systemically administered morphine between conscious and anesthetized rats, and observed that only conscious animals exhibit a phMRI response that can be explained by the pharmacodynamics of morphine and corroborated by behavioral observations. We identified fundamental and drug-dependent limitations in the protocols used to perform phMRI investigations, and designed tools and alternate methods to facilitate protocol development. By applying these techniques to the investigation of neuroleptic compounds, we have gained a new perspective of the alterations in dopaminergic signaling induced by treatment with antipsychotic medications, and have found effects in many nuclei outside of the pathways that act as direct substrates for dopamine. A clearer picture of how neuroleptics alter the intercommunication of brain nuclei would be an invaluable resource for the classification of investigational antipsychotic drugs, and would provide the basis for future studies that examine the neuroplastic changes that confer therapeutic efficacy following chronic treatment with antipsychotic medications

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