A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

Using multi-modal neuroimaging to characterise social brain specialisation in infants

The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate, and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4- to 7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing, coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high-frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high-frequency brain activity. The development of simultaneous multimodal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain

Integration of EEG-FMRI in an Auditory Oddball Paradigm Using Joint Independent Component Analysis

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. The overall objective of this dissertation is to determine the sensitivity and limitations of joint independent component analysis (jICA) within-subject for integration of ERP and fMRI data collected simultaneously in a parametric auditory oddball paradigm. The main experimental finding in this work is that jICA revealed significantly stronger and more extensive activity in brain regions associated with the auditory P300 ERP than a P300 linear regression analysis, both at the group level and within-subject. The results suggest that, with the incorporation of spatial and temporal information from both imaging modalities, jICA is more sensitive to neural sources commonly observed with ERP and fMRI compared to a linear regression analysis. Furthermore, computational simulations suggest that jICA can extract linear and nonlinear relationships between ERP and fMRI signals, as well as uncoupled sources (i.e., sources with a signal in only one imaging modality). These features of jICA can be important for assessing disease states in which the relationship between the ERP and fMRI signals is unknown, as well as pathological conditions causing neurovascular uncoupling, such as stroke

Linking brain structure, activity and cognitive function through computation

Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits

Disentangling neuronal pre- and post-response activation in the acquisition of goal-directed behavior through the means of co-registered EEG-fMRI

Behavior is considered goal-directed when the actor integrates information about the subsequent outcome of an action (Balleine & O'Doherty, 2010; Dickinson & Balleine, 1994; Kiesel & Koch, 2012), potentially enabling the anticipation of consequences of an action. Thus, it requires prior acquisition of knowledge about the current contingencies between behavioral responses and their outcomes under certain stimulus conditions (J. Hoffmann & Engelkamp, 2013). This association chain enables events lying in the future to be mentally represented and assessed in terms of value and achievability. However, while neural correlates of instructed goal-directed action integration processes have already been examined in a functional magnetic resonance imaging (fMRI) study using this paradigm (Ruge & Wolfensteller, 2015), there has been no information if those processes are also reflected in Electroencephalography (EEG) and if so which specific EEG parameters are modulated by them. This dissertation set out to investigate neurocognitive mechanisms of instructed outcome response learning utilizing two different imaging methods, namely EEG and fMRI. Study 1 was an exploratory study to answer the question what kinds of learning-related EEG correlates were to expect. The O-R outcome integration specific EEG correlates identified in Study 1 served as regressors in a unified general linear model (EEG-informed fMRI analysis) in the co-registered EEG-fMRI study (Study 2). One of the key questions in this study was if the EEG signal could help to differentiate between BOLD pre-response activation associated with processes related to response preparation or initiation and activation associated with post-response outcome integration processes. The foundation to both studies of this work was an experimental paradigm of instructed S-R-O learning, which included a learning and a test phase. Stimuli were four abstract visual patterns that differed in each block. Each visual stimulus required a distinct manual response and was predictably followed by a distinct auditory outcome. Instructions were delivered via a “guided implementation” procedure in which the instruction was embedded within the first three successful behavioral implementation trials. In these first three trials, the visual stimulus was followed by an imperative stimulus highlighting the correct response. The guided implementation phase was followed by an unguided implementation phase where the correct response now had to be retrieved from memory. Behaviorally, the strength of acquired O-R associations can be analyzed via O-R compatibility effects measured in a subsequent outcome-priming test phase (Greenwald, 1970). In this test phase a previously learned outcome becomes an imperative stimulus that requires either the response, which produced that outcome in the preceding learning phase (O-R compatible), or a response, which produced a different outcome (O-R incompatible). The experimental design was embedded into an EEG recording setup in study 1 while study 2 comprised a simultaneous EEG-fMRI recording setup in which EEG scalp potentials were continuously recorded during the experimental session inside the MR scanner bore. Study 1 revealed various ERP markers correlated with outcome response learning. An ERP post-response anterior negativity following auditory outcomes was increasingly attenuated as a function of the acquired association strength. This suggests that previously reported action-induced sensory attenuation effects under extensively trained free choice conditions can be established within few repetitions of specific R-O pairings under forced choice conditions. Furthermore, an even more rapid development of a post-response but pre-outcome fronto-central positivity, which was reduced for high R-O learners, might indicate the rapid deployment of preparatory attention towards predictable outcomes. Finally, the study identified a learning-related stimulus-locked activity modulation within the visual P1-N1 latency range, which was thought to reflect the multi-sensory integration of the perceived antecedent visual stimulus with the anticipated auditory outcome. In general, study 2 was only partially able to replicate the EEG activity dynamics related to the formation of bidirectional R-O associations that were observed in study 1. Primarily, it was able to confirm the modulation in EEG negativity in the visual P1-N1 latency range over the learning course. The EEG-informed analysis revealed that learning-related modulations of the P1-N1 complex are functionally coupled to activation in the orbitofrontal cortex (OFC). More specifically, growing attenuation of the EEG negativity increase from early to late SRO repetition levels in high R-O learners was associated with an increase in activation in the OFC. An additional exploratory EEG analysis identified a recurring post outcome effect at central electrode sites expressed in a stronger negativity in late compared to early learning stages. This effect was present in both studies and showed no correlation with any of the behavioral markers of learning. The EEG-informed fMRI analysis resulted in a pattern of distinct functional couplings of this parameter with different brain regions, each correlated with different behavioral markers of S-R-O learning. First of all, increased coupling between the late EEG negativity and activation in the supplementary motor area (SMA) was positively correlated with the O-R compatibility effect. Thus, high R-O learners exhibited a stronger coupling than low R-O learners. Secondly, increased couplings between the late EEG negativity and activation in the somatosensory cortex as well as the dorsal caudate, on the other hand, were positively correlated with individual reaction time differences between early and late stages of learning. Regarding activation patterns prior to the behavioral response the results indicate that the OFC could serve as a (multimodal) hub for integrating stimulus information and information about its associated outcome in an early pre-stage of action selection and initiation. Learnt S-O contingencies would facilitate initiating the motor program of the action of choice. Hence, the earlier an outcome is anticipated (based on stimulus outcome associations), the better it will be associated with its response, eventually leading to stronger O-R compatibility effects later on. Thus, one could speculate that increased activation in response to S-R-O mappings possibly embodies a marker for the ongoing transition from mere stimulus-based behavior to a goal-directed behavior throughout the learning course. Post-response brain activation revealed a seemingly two-fold feedback integration stream of O-R contingencies. On one hand the SMA seems to be engaged in bidirectional encoding processes of O-R associations. The results promote the general idea that the SMA is involved in the acquisition of goal-directed behavior (Elsner et al., 2002; Melcher, Weidema, Eenshuistra, Hommel, & Gruber, 2008; Melcher et al., 2013). Together with prior research (Frimmel, Wolfensteller, Mohr, & Ruge, 2016) this notion can be generalized not only to extensive learning phases but also to learning tasks in which goal-directed behavior is acquired in only few practice trials. However, there is an ongoing debate on whether SMA activation can be clearly linked to sub-processes prior or subsequent to an agent’s action (Nachev, Kennard, & Husain, 2008). The results of this work provide additional evidence favoring an involvement of the SMA only following a performed action in response to an imperative stimulus and even more, subsequent to the perception of its ensuing effect. This may give rise to the interpretation that the SMA is associated with linking the motor program of the performed action to the sensory program of the perceived effect, hence establishing and strengthening O-R contingencies. Furthermore, the analysis identified an increased coupling of a late negativity in the EEG signal and activation in the dorsal parts of the caudate as well as the somatosensory cortex. The dorsal caudate has not particularly been brought into connection with O-R learning so far. I speculate that the coupling effect in this part of the caudate reflects an ongoing process of an early automatization of the acquired behavior. It has already be shown in a similar paradigm that behavior can be automatized within only few repetitions of novel instructed S-R mappings (Mohr et al., 2016).:Table of contents Table of contents II List of Figures IV List of Tables VI List of Abbreviations VII 1 Summary 1 1.1 Introduction 1 1.2 Study Objectives 2 1.3 Methods 3 1.4 Results 4 1.5 Discussion 4 2 Theoretical Background 7 2.1 Introduction 7 2.2 Theories of acquiring goal-directed behavior 9 2.2.1 Instrumental learning 9 2.2.1.1 Behavioral aspects 9 2.2.1.2 Neurophysiological correlates 14 2.2.2 Acquisition of goal-directed behavior according to ideomotor theory 16 2.2.2.1 Behavioral aspects 16 2.2.2.2 Neurophysiological correlates 22 2.3 Summary 25 2.4 Methodological background 26 2.4.1 Electroencephalography (EEG) 26 2.4.2 Functional magnetic resonance imaging (fMRI) 28 2.4.3 Co-registered EEG-fMRI 29 3 General objectives and research questions 34 4 Study 1 – Learning-related brain-electrical activity dynamics associated with the subsequent impact of learnt action-outcome associations 36 4.1 Introduction 36 4.2 Methods 39 4.3 Results 47 4.4 Discussion 60 5 Study 2 - Within trial distinction of O-R learning-related BOLD activity with the means of co-registered EEG information 64 5.1 Introduction 64 5.2 Methods 66 5.3 Results 86 5.4 Discussion 101 6 Concluding general discussion 109 6.1 Brief assessment of study objectives 109 6.2 Novel insights into rapid instruction based S-R-O learning? 109 6.2.1 Early stimulus outcome information retrieval indicates the transition from stimulus based behavior to goal-directed action 110 6.2.2 Post-response encoding and consolidation of O-R contingencies enables goal-directedness of behavior 112 6.3 Critical reflection of the methodology and outlook 116 6.3.1 Strengths and limitations of this work 116 6.3.2 Data quality assessment 117 6.3.3 A common neural foundation for EEG and fMRI? 119 6.3.4 How can co-registered EEG-fMRI contribute to a better understanding of the human brain? 121 6.4 General Conclusion 123 7 References 124 Danksagung Erklärun

The present and future use of functional near‐infrared spectroscopy (fNIRS) for cognitive neuroscience

The past few decades have seen a rapid increase in the use of functional near‐infrared spectroscopy (fNIRS) in cognitive neuroscience. This fast growth is due to the several advances that fNIRS offers over the other neuroimaging modalities such as functional magnetic resonance imaging and electroencephalography/magnetoencephalography. In particular, fNIRS is harmless, tolerant to bodily movements, and highly portable, being suitable for all possible participant populations, from newborns to the elderly and experimental settings, both inside and outside the laboratory. In this review we aim to provide a comprehensive and state‐of‐the‐art review of fNIRS basics, technical developments, and applications. In particular, we discuss some of the open challenges and the potential of fNIRS for cognitive neuroscience research, with a particular focus on neuroimaging in naturalistic environments and social cognitive neuroscience

Using multi-modal neuroimaging to characterise social brain specialisation in infants

The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4-to-7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high frequency brain activity. The development of simultaneous multi-modal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain