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The environment that a child grows up in has a profound effect on their child development. For example, key outcomes such as academic ability or behaviour, cognitive ability and the neurobiology of the brain have been found to be associated to a child's environment. However, the factors that make up a child's environment are highly complex and yet the majority of research treats SES as a single number. In addition, the environment is related to several aspects of a child's development, yet there is very little research considering how these multiple levels of development relate to each other and interact. This thesis builds on the current literature by investigating how multiple aspects of a child's environment combine to create an environmental profile that is associated with positive child development. We endeavour to address three questions: 1) Which environmental factors most strongly relate to a child's academic ability, behaviour, cognitive ability and neural development? 2) Does the wider environment mediate the relationship between standard measures of SES and child development? 3) How might the environment impact academic and behaviour outcomes? In particular, is this relationship mediated by a child's cognition or the structural and functional connectivity of their brain? 7-11 year-old children (N=97) and their caregivers took part in this study. Several environmental domains and child behaviour were assessed through caregiver and child questionnaires. Academic and cognitive ability were measured using behavioural assessments. Resting state functional connectivity was measured using a magnetoencephalography (MEG) scan and structural connectivity was measured in an optional MRI scan on a separate visit (N=87). Partial Least Squares (PLS) methods identified significant relationships between the environment and child development. Multiple environmental domains were found to be reliably related to each aspect of child development. Furthermore, the wider environmental domains mediated the association between SES measures and each aspect of child development. Finally, cognition and the structural connectivity of a child's brain mediated the association between the environment and academic outcomes. This was not found for the behavioural outcomes. This thesis provides key advances towards addressing the considerable methodological challenge presented in the investigation of the complex relationships between a child's environment and multiple aspects of their development. We believe that this work will complement the available research to date and provide important detail to enable practitioners and policymakers to better support children at risk from disadvantaged environments.

To better understand the human brain, further advancements in the capabilities of computational modelling and analysis of functional neuroimaging data are required. Electro− and magneto− encephalography (EEG and MEG) technologies provide efficient ways to study the complex neural characteristics of both healthy and diseased brains. This thesis contributes towards extending the current approaches for analysis and functional connectivity (FC) modelling of M/EEG data. It includes an extensive review of the different computational modelling and analysis methods applied for the pre-processing, feature mining, and feature learning of the M/EEG signals in pursuit of single-trial neurophysiological patterns and their applications in various fields. This is followed by a review of current frameworks for modelling large scale functional brain networks. These reviews have identified open research challenges in terms of low performance of M/EEG based single-trial detection systems due to insufficient/inappropriate pre-processing and feature extraction methods and lack of neurophysiological validation of such systems. The research undertaken towards addressing these challenges have led to three original research contributions. In the first contribution, the estimation of current source density (CSD) is introduced as an essential pre-processing step for EEG analysis. It is shown that CSD significantly improves the distinction of motor-imagery (MI) related brain responses and has performed better than other referencing schemes (i.e. common reference and common average reference) and spherical surface Laplacian (SSL) methods (i.e. finite difference method and SSL using realistic head model). In the second contribution, EEG-based single-trial FC networks are introduced for MI (hand, feet, and tongue imagery kinaesthetic movements) and cognitive imagery (CI) tasks (word generation, mathematical subtraction, and spatial navigation) by implementing 'partial' granger causality modelling (PGCM) on two publically available EEG datasets. The outcome demonstrated that EEG brain networks for mixed imagery tasks (i.e. combination of CI and MI) can provide higher classification accuracy (for both binary and multiclass approaches) as compared to the current state-of-the-art method i.e. common spatial patterns (CSP). Moreover, it is shown for the first time that the FC between spatially distributed brain regions can provide additional useful discriminant information for the classification of the brain responses evoked during imagery tasks. In the third contribution, we investigated the temporal evolution and reorganisation of resting-state MEG FC networks over the four weeks of a multi-modal EEG-driven post-stroke upper limb (UL) movement rehabilitative therapy. The findings provide reliable brain connectivity patterns for evaluating UL functional recovery during stroke neurorehabilitation.

The current thesis addresses some of the unresolved predictions of recent models of the semantic brain system, such as the hub-and-spokes model. In particular, we tackle different aspects of the hypothesis that a widespread network of interacting heteromodal (hub(s)) and unimodal (spokes) cortices underlie semantic cognition. For this purpose, we use connectivity analyses, measures of graph theory and permutation-based statistics with source reconstructed Electro-/MagnetoEncephaloGraphy (EEG/MEG) data in order to track dynamic modulations of activity and connectivity within the semantic networks while a concept unfolds in the brain. Moreover, in order to obtain more accurate connectivity estimates of the semantic networks, we propose novel methods for some of the challenges associated with EEG/MEG connectivity analysis in source space. We utilised data-driven analyses of EEG/MEG recordings of visual word recognition paradigms and found that: 1) Bilateral Anterior Temporal Lobes (ATLs) acted as potential processor hubs for higher-level abstract representation of concepts. This was reflected in modulations of activity by multiple contrasts of semantic variables; 2) ATL and Angular Gyrus (AG) acted as potential integrator hubs for integration of information produced in distributed semantic areas. This was observed using Dynamic Causal Modelling of connectivity among the main left-hemispheric candidate hubs and modulations of functional connectivity of ATL and AG to semantic spokes by word concreteness. Furthermore, examining whole-brain connectomes using measures of graph theory revealed modules in the right ATL and parietal cortex as global hubs; 3) Brain oscillations associated with perception and action in low-level cortices, in particular Alpha and Gamma rhythms, were modulated in response to words with those sensory-motor attributes in the corresponding spokes, shedding light on the mechanism of semantic representations in spokes; 4) Three types of hub-hub, hub-spoke and spoke-spoke connectivity were found to underlie dynamic semantic graphs. Importantly, these results were obtained using novel approaches proposed to address two challenges associated with EEG/MEG connectivity. Firstly, in order to find the most suitable of several connectivity metrics, we utilised principal component analysis (PCA) to find commonalities and differences of those methods when applied to a dataset and identified the most suitable metric based on the maximum explained variance. Secondly, reconstruction of EEG/MEG connectomes using anatomical or fMRI-based parcellations can be significantly contaminated by spurious leakage-induced connections in source space. We, therefore, utilised cross-talk functions in order to optimise the number, size and locations of cortical parcels, obtaining EEG/MEG-adaptive parcellations. In summary, this thesis proposes approaches for optimising EEG/MEG connectivity analyses and applies them to provide the first empirical evidence regarding some of the core predictions of the hub-and-spokes model. The key findings support the general framework of the hub(s)-and-spokes, but also suggest modifications to the model, particularly regarding the definition of semantic hub(s).

The dementias are persistent or progressive disorders affecting more than one cognitive domain that interfere with an individual’s ability to function at work or home, and represent a decline from a previous level of function. In this thesis I consider the neurophysiology of dementia at a number of levels. I investigate the ways in which the connectivity and function of the brain predisposes to the specific focal patterns of neurodegeneration seen in the various dementias. I aim to identify the mesoscopic changes that occur in individuals with neurodegeneration and how these relate to their cognitive difficulties. I show how, by assessing patients in whom there is focal disruption of brain networks and observing the outcomes in comparison to controls, I can gain insight into the mechanisms by which the normal brain makes predictions and processes language. In Chapter 1, I set the scene for the focussed experimental investigations of model diseases by beginning with an introductory, clinically-focussed review that sets out the features, aetiology, management, epidemiology and prognosis of the dementias. This places these model diseases in the context of the broader clinical challenge posed by the dementias. In Chapter 2, I turn to ‘prototypical’ model diseases that represent neurodegenerative tauopathies with predominantly cortical (Alzheimer’s disease, AD) and subcortical (Progressive Supranuclear Palsy, PSP) disease burdens. I investigate the neurophysiological causes and consequences of Tau accumulation by combining graph theoretical analyses of resting state functional MR imaging and in vivo ‘Tau’ PET imaging using the ligand AV-1451. By relating Tau distribution to the functional connectome I provide in vivo evidence consistent with ‘prion-like’ trans-neuronal spread of Tau in AD but not PSP. This provides important validation of disease modification strategies that aim to halt or slow down the progression of AD by sequestration of pathological Tau in the synapse. In contrast, I demonstrate associations consistent with regional vulnerability to Tau accumulation due to metabolic demand and a lack of trophic support in PSP but not AD. With a cross-sectional approach, using Tau burden as a surrogate marker of disease severity, I then go on to show how the changes in functional connectivity that occur as disease progresses account for the contrasting cognitive phenotypes in AD and PSP. In advancing AD, functional connectivity across the whole brain becomes increasingly random and disorganised, accounting for symptomatology across multiple cognitive domains. In advancing PSP, by contrast, disrupted cortico-subcortical and cortico-brainstem interactions meant that information transfer passed through a larger number of cortical nodes, reducing closeness centrality and eigenvector centrality, while increasing weighted degree, clustering, betweenness centrality and local efficiency. Together, this resulted in increasingly modular processing with inter-network communication taking less direct paths, accounting for the bradyphrenia characteristic of the ‘subcortical dementias’. From chapter 3 onwards, I turn to the in-depth study of a model disease called non-fluent variant Primary Progressive Aphasia (nfvPPA). This disease has a clear clinical phenotype of speech apraxia and agrammatism, associated with a focal pattern of mild atrophy in frontal lobes. Importantly, general cognition is usually well preserved until late disease. In chapter 3 itself, I relate an experiment in which patients with nfvPPA and matched controls performed a receptive language task while having their brain activity recorded with magnetoencephalography. I manipulated expectations and sensory detail to explore the role of top-down frontal contributions to predictive processes in speech perception. I demonstrate that frontal neurodegeneration led to inflexible and excessively precise predictions, and that fronto-temporal interactions play a causal role in reconciling prior predictions with degraded sensory signals. The discussion here concentrates on the insights provided by neurodegenerative disease into the normal function of the brain in processing language. Overall, I demonstrate that higher level frontal mechanisms for cognitive and behavioural flexibility make a critical functional contribution to the hierarchical generative models underlying speech perception In chapter 4, I precisely define the sequence processing and statistical learning abilities of patients with nfvPPA in comparison to patients with non-fluent aphasia due to stroke and neurological controls. I do this by exposing participants to a novel, mixed-complexity artificial grammar designed to assess processing of increasingly complex sequencing relationships, and then assessing the degree of implicit rule learning. I demonstrate that agrammatic aphasics of two different aetiologies are not disproportionately impaired on complex sequencing relationships, and that the learning of phonological and non-linguistic sequences occurs independently in health and disease. In chapter 5, I summarise the synergies between the experimental chapters, and explain how I have applied a systems identification framework to a diverse set of experimental methods, with the common goal of defining the physiology of dementia. I then return to the results of chapter 3 with a clinical focus to explain how inflexible predictions can account for subjective speech comprehension difficulties, auditory processing abnormalities and (in synthesis with chapter 4) receptive agrammatism in nfvPPA. Overall, this body of work has contributed to knowledge in several ways. It has achieved its tripartite aims by: 1) Providing in vivo evidence consistent with theoretical models of trans-neuronal Tau spread (chapter 2), and a comprehensive clinical account of the previously poorly-understood receptive symptomatology of nfvPPA (chapter 5), thus demonstrating that systems neuroscience can provide a translational bridge between the molecular biology of dementia and clinical trials of therapies and medications. In this way, I begin to disentangle the network-level causes of neurodegeneration from its consequences. 2) Providing evidence for a causal role for fronto-temporal interactions in language processing (chapter 3), and demonstrating domain separation of statistical learning between linguistic and non-linguistic sequences (chapter 4), thus demonstrating that studies of patients with neurodegenerative disease can further our understanding of normative brain function. 3) Successfully integrating neuropsychology, behavioural psychophysics, functional MRI, structural MRI, magnetoencephalography and computational modelling to provide comprehensive research training, as the platform for a future research programme in the physiology of dementia.

This thesis provides a novel dynamic large-scale network perspective on brain activity of human sleep based on the analysis of unique human neuroimaging data. Specifically, I provide new information based on integrating spatial and temporal aspects of brain activity both in the transitions between and during wakefulness and various stages of non-rapid-eye movement (NREM) sleep. This is achieved through investigations of inter-regional interactions, functional connectivity (FC), between activity timecourses throughout the brain. Overall, the presented findings provide new important whole-brain insights for our current understanding of sleep, and potentially also of sleep disorders and consciousness in general. In Chapter 2 I present a robust global increase in similarity between the structural connectivity (SC) and the FC in slow-wave sleep (SWS) in almost all of the participants of two independent fMRI datasets. This could point to a decreased state repertoire and more rigid brain dynamics during SWS. Chapter 2 further identifies the changes in FC strengths between wakefulness and individual stages of NREM sleep across the whole-brain fMRI network. I report connectivity in posterior parts of the brain as particularly strong during wakefulness, while connections between temporal and frontal cortices are increased in strength during N1 and N2 sleep. SWS is characterised by a global drop in FC. In Chapter 3 I take advantage of rare MEG recordings of NREM sleep to show, for the first time, the feasibility of constructing source-space FC networks of sleep using power envelope correlations. The increased temporal information of MEG signals allows me to identify the specific frequencies underlying the FC differences identified in Chapter 2 with fMRI. The beta band (16 – 30 Hz) thus stands out as important for the strong posterior connectivity of wakefulness, while a range of frequency bands from delta (0.25 – 4 Hz) to sigma (13 – 16 Hz) all appear to contribute to N2-specific FC increases. Consistent with the fMRI results, slow-wave sleep shows the lowest level of FC. Interestingly, however, the MEG signals suggest a fronto-temporal network of high connectivity in the alpha band, possibly reflecting memory processes. In Chapter 4 I expand the within-frequency FC analysis of Chapter 3 to explore potential cross-frequency interactions in the MEG FC networks. It is shown that N2 sleep involves an abundance of frequency cross-talk, while SWS includes very little. A multi-layer network approach shows that the gamma band (30 – 48 Hz) is particularly integrated in wakefulness. Chapter 5 addresses the identified MEG FC findings from the perspective of traditional spectral sleep staging. By correlating temporal changes in spectral power at the sensor level to fluctuations in average FC, a specific type of transient events is found to underlie the strong N2-specific coupling in static FC values. Lastly, in Chapter 6 I make the leap out of the constraints of traditional low-resolution sleep staging, and extract dynamic states of FC from fMRI timecourses in a completely unsupervised fashion. This provides a novel representation of whole-brain states of sleep and the dynamics governing them. I argue that data-driven approaches like this are necessary to fully characterise the spatiotemporal principles underlying wakefulness and sleep in the human brain.

Human motor control is a complex process involving both central and peripheral components of the nervous system. Type Ia afferent input contributes to both motor unit recruitment and firing frequency, however, whether maximal force production is dependent on this input is unclear. Therefore, chapter 2 examined maximal and explosive force production of the knee extensors following prolonged infrapatellar tendon vibration; designed to attenuate the efficacy of the homonymous Ia afferent-α-motoneuron pathway. Despite a marked decrease in H-reflex amplitude, indicating an attenuated efficacy of the Ia afferent-α-motoneuron pathway, both maximal and explosive force production were unaffected after vibration. This suggested that maximal and explosive isometric quadriceps force production was not dependent upon Ia afferent input to the homonymous motor unit pool. Voluntary movements are linked with various modulations in ongoing neural oscillations within the supraspinal sensorimotor system. Despite considerable interest in the oscillatory responses to movements per se, the influence of the motor parameters that define these movements is poorly understood. Subsequently, chapters 3 and 4 investigated how the motor parameters of voluntary contractions modulated the oscillatory amplitude. Chapter 3 recorded electroencephalography from the leg area of the primary sensorimotor cortex in order to investigate the oscillatory responses to isometric unilateral contractions of the knee-extensors at four torque levels (15, 30, 45 and 60% max.). An increase in movement-related gamma (30-50 Hz) activity was observed with increments in knee-extension torque, whereas oscillatory power within the delta (0.5-3 Hz), theta (3-7 Hz), alpha (7-13 Hz) and beta (13-30 Hz) bands were unaffected. Chapter 4 examined the link between the motor parameters of voluntary contraction and modulations in beta (15-30 Hz) oscillations; specifically, movement-related beta decrease (MRBD) and post-movement beta rebound (PMBR). Magnetoencephalography (MEG) was recorded during isometric ramp and constant-force wrist-flexor contractions at distinct rates of force development (10.4, 28.9 and 86.7% max./s) and force output (5, 15, 35 and 60%max.), respectively. MRBD was unaffected by RFD or force output, whereas systematic modulation of PMBR by both contraction force and RFD was identified for the first time. Specifically, increments in isometric contraction force increased PMBR amplitude, and increments in RFD increased PMBR amplitude but decreased PMBR duration. Physical fatigue arises not only from peripheral processes within the active skeletal muscles but also from supraspinal mechanisms within the brain. However, exactly how cortical activity is modulated during fatigue has received a paucity of attention. Chapter 5 investigated whether oscillatory activity within the primary sensorimotor cortex was modulated when contractions were performed in a state of physical fatigue. MEG was recorded during submaximal isometric contractions of the wrist-flexors performed both before and after a fatiguing series of isometric wrist-flexions or a time matched control intervention. Physical fatigue offset the attenuation in MRBD observed during the control trial, whereas PMBR was increased when submaximal contractions were performed in a fatigued state.

The experiments described in this thesis aimed to investigate the neurophysiological consequences, at the cortical level, of the neurodegenerative condition, amyotrophic lateral sclerosis (ALS). A principle tenet of this study was that ALS is, first and foremost, a disorder of the cortical motor system, the precise pathological mechanisms of which remain incompletely understood. Furthermore, the degree to which neurodegeneration can be evidenced before the onset of symptoms is thus far uncertain, and the optimal means by which to measure therapeutic response has yet to be determined. Chapter 1 introduces relevant key concepts in ALS and briefly summarises three studies completed in the early phases of pursuit for the above degree. These studies respectively considered presmyptomatic cellular ALS pathology, quantitation of disease progression and eyetracking assessment of cognitive dysfunction. Chapter 2 describes magnetoencephalography, the investigative technology utilised in the subsequent experimental chapter. In chapter 3, the effects of ALS on movement related modulation of neuronal oscillations are determined. An excessive peri-movement desynchronisation and delayed post-movement rebound was described. Functional connectivity between cortical regions at rest is appraised in chapter 4. ALS appeared to result in quite striking increases in functional connectivity, in keeping with the fMRI literature and in support of diminished intracortical inhibitory influences. The functional communication from the motor cortices is directly considered during active motor performance in chapter 5. ALS related reductions in beta-band coherence were noted in both corticospinal and inter- hemispheric communication. In conclusion, the results demonstrated considerable support for proposed excitotoxic disease mechanisms and were in alignment with reported findings in other neurodegenerative diseases. Finally, a pilot study by which the neural mechanisms for cognitive impairment in ALS are explored via antisaccade performance is described. While underpowered, the experimental design showed promise for future application.

This thesis is concerned with the age-related changes in working memory (WM), and the inter-individual differences in cognitive and neural mechanisms that correspond to healthy versus poor ageing of WM function. The first half of this thesis focusses on the age-related decline in WM and whether preserved top-down attentional control could mitigate such deficits. In Chapter 2, I present a functional MRI study showing that older adults reliably recruit brain networks that subserve cognitive control, which work in concert with the task relevant sensory areas during effective selective WM. In Chapter 3, I show that older adults retain flexible control over WM representations, and this ability corresponded to the reliable recruitment of neural signals of orienting attention qualitatively similar to those observed in younger adults. Magnetoencephalographic recordings showed that the neural dynamics during orienting attention within WM was predictive of good performance, demonstrating that the more efficient the process of orienting within WM to select the target item, the better the memory representation can be preserved for upcoming behaviour. In the second half of this thesis, I explored whether WM for affective content has a special status in healthy ageing. In Chapter 4, I developed an emotional WM precision task to measure WM abilities for emotional content appropriate for elderly adults. In Chapter 5, I tested a group of young and older adults on WM and perceptual-matching abilities for emotional faces. The results suggest that older adults show a general impairment in task performance, but possibly with some preservation in the ability to maintain emotional content in WM. There were marked differences in how the emotional information was processed between age groups, in which older adults have a tendency to represent negative stimuli as less negative than younger adults in perception and WM, and tended to show a positive interpretation of the valence of more ambiguous emotional stimuli. In Chapter 6, I summarise the findings presented in this thesis, discuss the implications of the key findings, and consider some suggestions for future studies that aim to elucidate the mechanisms of WM in healthy ageing.

Visual working memory (WM) is the ability to hold information in mind for a short time before acting on it. The capacity of WM is strikingly limited. To make the most of this precious resource, humans exhibit a high degree of cognitive flexibility: We can prioritize information that is relevant to behavior, and inhibit unnecessary distractions. This thesis examines some behavioral and neural correlates of flexibility in WM. When information is of particular importance, anticipatory attention can be directed to where it will likely appear. Oscillations in visual cortex, in the 10-Hz range, play an important role in regulating excitability of such prioritized locations. Chapter 4 describes how even spontaneous fluctuations in 10-Hz synchronization (measured by electroencephalography, EEG) before encoding influence WM. Chapters 2 and 3 describe how attention can be directed retrospectively to items even if they are already stored in WM. Chapter 3 discusses how retrospective cues change neural synchronization similarly to anticipatory cues. Behavioral and neural measures additionally indicate that the boosting of an item through retrospective cues does not require prolonged deployment of attention: rather, it may be a transient process. The second half of this thesis additionally examines how items are represented in visual WM. Chapter 5 summarizes a study using pattern analysis of magnetoencephalographic (MEG) and EEG data to decode features of visual templates stored in WM. Decoding appears transiently around the time when potential target stimuli are expected, in line with a flexible reactivation mechanism. Chapter 6 further examines separate cortical networks involved in protecting vs. updating items in WM, and tests whether task relevance changes how well WM contents can be decoded. Finally, Chapter 7 summarizes the thesis and discusses how attentional flexibility can merge WM with a wider range of sources of behavioral control.

Explorations into the organisation of spontaneous activity within the brain have demonstrated the existence of networks of temporally correlated activity, consisting of brain areas that share similar cognitive or sensory functions. These so-called resting state networks (RSNs) emerge spontaneously during rest and disappear in response to overt stimuli or cognitive demands. In recent years, the study of RSNs has emerged as a valuable tool for probing brain function, both in the healthy brain and in disorders such as schizophrenia, Alzheimer’s disease and Parkinson’s disease. However, analyses of these networks have so far been limited, in part due to assumptions that the patterns of neuronal activity that underlie these networks remain constant over time. Moreover, the majority of RSN studies have used functional magnetic resonance imaging (fMRI), in which slow fluctuations in the level of oxygen in the blood are used as a proxy for the activity within a given brain region. In this thesis we develop the use of magnetoencephalography (MEG) to study resting state functional connectivity. Unlike fMRI, MEG provides a direct measure of neuronal activity and can provide novel insights into the temporal dynamics that underlie resting state activity. In particular, we focus on the application of non- stationary analysis methods, which are able to capture fast temporal changes in activity. We first develop a framework for preprocessing MEG data and measuring interactions within different RSNs (Chapter 3). We then extend this framework to assess temporal variability in resting state functional connectivity by applying time- varying measures of interactions and show that within-network functional connectivity is underpinned by non-stationary temporal dynamics (Chapter 4). Finally we develop a data driven approach based on a hidden Markov model for inferring short lived connectivity states from resting state and task data (Chapter 5). By applying this approach to data from multiple subjects we reveal transient states that capture short lived patterns of neuronal activity (Chapter 6).

Catechol-O-methyltransferase (COMT) metabolizes catechols, notably dopamine. The COMT Val158Met polymorphism influences its enzyme activity, and multiple neural correlates of this genotype on dopaminergic phenotypes have been reported, particularly with regards to working memory. COMT activity can also be regulated pharmacologically by COMT inhibitors. The ‘inverted-U’ relationship between dopamine signalling and cognitive performance predicts that the effects of COMT inhibition will differ according to COMT genotype. The goal of this thesis was to better understand COMT’s impact on brain function and behaviour. Here, 33 subjects homozygous for COMT Val158 (‘Val homozygotes’) and 34 homozygous for COMT Met158 (‘Met homozygotes’) were randomly assigned, double-blind, to a single dose of the brain-penetrant COMT inhibitor tolcapone (200mg) or placebo. They completed the N-back task of working memory, an emotional face processing task, and a gambling task, in a magnetoencephalography (MEG) scanner, allowing both behavioural performance and neural activity to be investigated. The data presented in this thesis confirm that COMT activity influences performance on, and neural activity during, the N-back task, in a way consistent with the inverted-U model of dopamine function. The effect on risky decision making is novel, and indicates that COMT plays roles in domains beyond working memory, and that such domains may also follow an inverted-U. Neural activity during the faces task and the gambling task also show COMT-modulated differences. The behavioural results show that the direction of effect of a drug can be influenced by sequence variation in its target gene. They are of translational relevance, since COMT inhibitors are used in the adjunctive treatment of Parkinson’s disease and are under evaluation in schizophrenia and other disorders. The MEG data show that for the three tasks, there are effects of Val158Met genotype, of tolcapone, and their interaction, on neural activity (for example, the P300 during N-back), revealing a complex temporal and spatial pattern which sheds some light on the neural processing underlying these tasks and their previously reported fMRI correlates.

Autism spectrum disorders (ASD) are neurodevelopmental disorders with multiple neurobiological aetiologies, which could be genetic, structural, metabolic or immune-mediated. ASDs are diagnosed with deficits in social communication and restricted and repetitive behaviours, and are associated with sensorial atypicalities. 30% of cases have co-existing epilepsy. A series of in vitro, in vivo and post-mortem investigations were undertaken to examine sensory atypicalities in ASD. In vitro characterisation of hippocampal neuronal cultures using immunofluorescence demonstrated the presence of multiple cell types including neurons, astrocytes and microglia. The distribution of ion channels of the Shaker family and tumour necrosis factor α receptors in astrocytes and neurons were identified but not explored further. Neuroanatomical and neuropathological investigations of primary olfactory cortex, using post-mortem stereology, demonstrated a specific increase in glial cell densities in layer II, which was negatively associated with age in ASD. Increases in glia were also associated with symptom severity and often co-localised with the presence of corpora amylacea in layer I. Qualitative analysis of the olfactory tubercle demonstrated that corpora amylacea did not extend to this neighbouring region of the primary olfactory cortex in ASD. These changes were independent of co-existing epilepsy and not observed in epilepsy without ASD. Preliminary pilot studies of the hippocampus provided a stereological sampling strategy to quantify cell densities in future investigations of this area in ASD. Neurophysiological investigations using collected magnetoencephalography data demonstrated diminished occipital gamma oscillatory synchrony in ASD in a visual time perception task. This did not always predict behavioural outcome but was specific to ASD and could not be explained simply in terms of changes in task performance. Moreover, changes in oscillatory synchrony were associated with symptom severity. These observations in primary sensory domains in post-mortem tissue and in patients suggest possible novel mechanisms in ASD and extend knowledge of the neurobiological bases of these disorders.

Alzheimer's disease (AD) is developing into the single greatest healthcare challenge in the coming decades. The development of early and effective treatments that can prevent the pathological damage responsible for AD-related dementia is of utmost priority for healthcare authorities. The role of the APOE-ε4 genotype, which has been shown to increase an individual's risk of developing AD, is of central interest to this goal. Understanding the mechanism by which possession of this gene modulates brain function, leading to a predisposition towards AD is an active area of research. Functional connectivity (FC) is an excellent candidate for linking APOE-related differences in brain function to sites of AD pathology. Magnetoencephalography (MEG) is a neuroimaging tool that can provide a unique insight into the electrophysiology underpinning resting-state networks (RSNs) - whose dysfunction is postulated to lead to a predisposition to AD. This thesis presents a range of methods for measuring functional connectivity in MEG data. We first develop a set of novel adaptations for preprocessing MEG data and performing source reconstruction using a beamformer (chapter 3). We then develop a range of analyses for measuring FC through correlations in the slow envelope oscillations of band-limited source-space MEG data (chapter 4). We investigate the optimum time scales for detecting FC. We then develop methods for extracting single networks (using seed-based correlation) and multiple networks (using ICA). We proceed to develop a group-statistical framework for detecting spatial differences in RSNs and present a preliminary finding for APOE-genotype-dependent differences in RSNs (chapter 5). We also develop a statistical framework for quantifying task-locked temporal differences in functional networks during task-positive experiments (chapter 6). Finally, we demonstrate a data-driven parcellation and network analysis pipeline that includes a novel correction for signal leakage between parcels. We use this framework to show evidence of stationary cross-frequency FC (chapter 7).

Selective attention is a mechanism used to allocate resources to information processing. Both mood states and emotionally salient stimuli can influence which information is selectively attended. This information is subsequently processed in a more elaborative manner and affects task performance. The experiments presented in this thesis explore the influence of mood and emotional stimuli on selective attention and consequently task performance. Mood induction procedures were used to induce transient neutral, sad, and happy mood states in healthy volunteers. A systematic review and meta-analysis of 41 studies using sad mood induction procedures showed cognitive impairments in performance in the context of task neutral stimuli. In contrast biases in attention towards mood-congruent negative stimuli led to improved task performance. A series of three behavioural experiments with 197 participants demonstrated that participants made decisions on the basis of less information when that information was preceded by emotional but not neutral stimuli. Induced mood state did not affect performance. The behavioural and neural correlates of visual attentional processing to emotional stimuli were explored using magnetoencephalography in 24 healthy participants following sad, happy, and neutral mood induction procedures. The M300, a component associated with selective attention, had greater amplitude following presentation of negative compared with positive stimuli, which was associated with improved task performance. Reduced M300 amplitude and impairments in performance occurred following sad mood induction procedures. The experiments presented in this thesis demonstrate prioritized processing of emotional information and provide some evidence for impaired performance following sad mood induction procedures.

A fundamental aspect of goal‐directed behaviour concerns the closure of motion‐gaps in a timely fashion. An influential theory about how this can be achieved is provided by the tautheory (Lee, 1998). Tau is defined as the ratio of the current distance‐to‐goal gap over the current instantaneous speed towards the goal. In this work we investigated the neural representation of tau in two sets of experiments. In one study we recorded neuromagnetic fluxes (using magnetoencephalography, MEG) from the whole brain of human subjects performing discrete hand movements aimed to targets in space, whereas the other study involved recordings of single cell activity from prefrontal and posterior parietal areas of a behaving monkey during geometrical shape‐copying tasks. These two studies provided complementary information, for the former covered the whole brain (at the cost of weak localization), whereas the latter used the finest neural grain (at the expense of limited brain regions). However, the two studies together yielded valuable information concerning the dynamic, time‐varying neural representation of tau, with respect to both integrated synaptic events in neuronal ensembles (recorded by MEG) and neural spike outputs (recorded by microelectrodes). The relations between neural signals and tau were analyzed using a linear regression model where the time‐varying neural signal (magnetic field strength in fT or spike density function) was the dependent variable and the corresponding value of movement tau and speed were the independent variables. In addition, the model included an autoregressive term to account for the expected correlated errors, given the time series nature of the data. The neurophysiological study revealed a statistically significant (p < 0.05) relation of spike density function to tau (in the presence or absence of a significant speed effect) in 17% of cells in the posterior parietal cortex (N = 399) and 8% of cells in the prefrontal cortex (N = 163). These results are in accord with previous findings in an interception task. The MEG study revealed that a mean of 21.98 (± 6.08) % of sensor signals had a statistically significant (p < 0.05) relation to tau across all subjects. These effects were distributed predominantly over the left parietal‐temporo‐occipital sensor space, with additional foci over the frontal sensorimotor regions. Altogether, these findings demonstrate a specific involvement of neurons and neuronal ensembles with the tau variable and pave the way for further studies on predictive tau control.

The research presented here is divided into two parts. The first part addresses questions relating to the localisation capability of magnetoencephalography (MEG), with emphasise on testing the accuracy of typical MEG systems before any source reconstruction is applied. The second and main part is concerned with localisation and time courses of neuronal generators contributing to both the slow and fast neuromagnedc field changes associated with the contingent negative variation (CNV) in the normal human brain. We use multi-channel and full head MEG systems to study the magnetic counterpart of the CNV, the contingent magnetic variation (CMV). MEG analysis of such near DC-like signals requires advanced source reconstruction that is able to identify widely distributed as well as focal sources which often fire simultaneously from cortical as well as from deeper brain structures. We use magnetic field tomography (MFT) to extract time courses of regional brain activity. Results are presented from a multi-subject CMV study performed using the BTi MAGNES II system (Experiment 1) and a single subject experiment, using the CTF whole cortex system (Experiment 2). From Experiment 1 we identified four different CMV generators (auditory cortex, sensorimotor cortex, inferior prefrontal cortex, posterior inferior parietal area) and observed priming of the auditory cortex as part of the early CMV complex and the priming of the sensorimotor cortex as part of the late CMV complex. These results were confirmed by Experiment 2, where the full head coverage also revealed two additional areas, the supplementary motor area and the posterior cingulate cortex, which were dramatically reduced when identical runs were repeated. The SMA activity has been notoriously difficult to identify non-invasively from electrophysiological data, especially from MEG, so our success in identifying them clearly and showing how they change with repetition can he considered as the highlight of our project.

Anhedonia is a complex symptom of Major Depressive Disorder (MDD) that presents itself through multiple behavioral phenotypes of diminished reward processing. In order to better understand this deficit, we aim to look at a marker of reward that is sensitive to information content and valence, the Reward Positivity (RewP). The source of this signal is, however, up for debate. This study used concurrent EEG and MEG to establish the source of the RewP as a distributed network involving ventromedial prefrontal cortex (vmPFC), anterior midcingulate cortex (aMCC), and insulae. Additionally, only vmPFC showed a deficit in MDD. fMRI resting state functional connectivity analysis of these regions showed that these areas are all highly correlated with each other and with the nucleus accumbens (NAcc), a known, subcortical center of reward processing. Group comparisons of functional connectivity showed no differences between the MEG-derived regions and NAcc. Functional connectivity between vmPFC and aMCC was found to anticorrelate with MASQ – General Depression scores within the MDD+ group, but not MASQ – Anhedonia scores. These findings suggest that while vmPFC activation in response to reward may be diminished in MDD, the resting state network involved remains largely intact, perhaps with the exception of those with extreme symptom severity.

Locked-in syndrome (LIS) is a condition in which patients are in full-body paralysis but retain cognitive function. Improving the ability of these patients to communicate with others may significantly increase their quality of life. Since motor control is almost completely gone for LIS patients, a brain-computer interface (BCI) using neural speech decoding may be the only method to restore communication ability. As a non-invasive neuroimaging modality, magnetoencephalography (MEG) offers several strong attributes for potential speech-BCI usage. Its high spatial and temporal resolutions make it suitable for potential real-time speech decoding, and its non-invasive quality is crucial for accessibility. Neural speech decoding using MEG is a relatively new field, and there is little research on how different regions of the brain contribute to speech decoding. Additionally, the contributions of different brainwaves from individual brain regions towards MEG speech decoding hasn’t been studied. Such information may be helpful for future MEG speech-BCI studies regarding feature selection and sensor placement. This dissertation investigates the degree in which MEG signals from the eight individual brain regions corresponding to the lobes of the brain (left- and right- frontal, parietal, temporal, and occipital lobes) contribute towards neural speech decoding, as well as how individual brainwaves from these areas influence decoding performance. Both imagined and overt speech phrases were decoded using MEG signals collected from seven healthy participants. Results indicated that almost all regions provided neuromagnetic signals helpful for speech decoding. Additionally, both temporal regions offered relatively high individual decoding performances, and when combined, speech decoding performance was almost on-par with whole-brain performance. Lastly, frequency-specific analysis showed signs that helpful MEG signals for speech decoding may be encoded similarly across brain regions, with the delta band (0.3-4Hz) consistently providing the best information for speech decoding. Possessing flexibility in sensor count and placements, optically pumped magnetometer (OPM) MEG systems may find the results of this dissertation useful in determining sensor placements and reducing overall sensor count, which would lower the cost of the system.

Speech is central to human communication. One of the features of speech we control to make distinguishable speech is formants, which are the spectral frequencies in our speech that we vary to make different vowel sounds. It is well-understood that the control of speech features, including formants, depends heavily on feedback information. However, the exact mechanisms of feedback control of formants are not fully understood. The body of this dissertation investigates the feedback control of formants in different scenarios to explore its relationship with other speech control mechanisms. We first investigated the relationship of feedback control and the adaptation of feedforward control of formants and found that they have distinct mechanisms. We then explored the neural substrate of feedback processing for formants as compared to pitch using MEG, which allowed us to investigate network activity changes with a high temporal resolution. We found similar neural regions involved in feedback processing for both pitch and formants, though we observed opposite lateralization for pitch and formants across different frequency bands. Finally, we explored the interaction between auditory and somatosensory feedback information in the control of formants by attenuating oral somatosensory feedback using lidocaine while perturbing auditory feedback. We found a direction-dependent interaction between the two feedback modalities, specifically where we observed an increase in compensation responses to downward auditory feedback perturbations but a decrease in compensation responses to upward auditory feedback perturbations when the oral somatosensory feedback was attenuated.

Prenatal alcohol exposure (PAE), the leading cause of childhood developmental disability, has long-lasting effects extending throughout the lifespan. The teratogenic effects of PAE, not always physically discernable, may corrupt brain structure resulting in neural dysfunction. These aberrant effects often result in executive functioning deficits, associated with social and academic difficulties, which hinder typical development. As maternal alcohol use during pregnancy is heavily stigmatized, children without physical dysmorphology often go unreported and untreated; resulting in increased deficiencies compared to those more severely affected who receive early life interventions. This phenomenon requires the discovery of neurophysiological makers of PAE in order to facilitate early identification and in turn early life interventions. Structural, functional, and behavioral deficits in individuals with PAE are well known. However, little research has been conducted assessing how these deficits in structure and function are associated together and with behavior. In this dissertation, we intend to show how white matter integrity as assessed by fractional anisotropy is associated with neural functioning, assed via magnetoencephalography, and how this association relates to behavior. It is well documented that children prenatally exposed to alcohol have difficulties inhibiting behavior and sustaining attention. Thus, the Sustained Attention to Response Task, a Go/No-go paradigm, is especially well suited to assess the behavioral and neural functioning characteristics of PAE children. This dissertation shows that children 8-12 years old with PAE have decreased associations between brain structure and functions, and that these deficits are associated with poorer performance on neuropsychological functioning.

Computational modeling is a tool that allows for biological systems involving large networks to be studied, such as in studying the correlations between structural connectivity and functional connectivity in the human brain. Raj et al. proposed the spectral graph model in 2019 as a linear, low-dimensional alternative to conventional neural field and mass models that are more computationally expensive, especially when optimizing parameters, which is necessary in order to obtain quantitative and qualitative information about functional neural activity. The initial method used for inferring the spectral graph model parameters was Markov chain Monte Carlo (MCMC) sampling, which provided a robust way to estimate what the target parameter distributions were most likely to be. However, MCMC methods are still slow and computationally expensive. In this study, we trained a fully connected neural network on MCMC-simulated magnetoencephalography (MEG) data to perform parameter estimation for the spectral graph model in an accelerated manner. We found that the neural network was able to predict most parameters of interest without much loss in precision while generating the parameters in less than a second. This approach puts us closer to obtaining real time neurophysiological information from functional neuroimaging data for applications in diagnosis, prognosis, and characterization of various neurological diseases.

Sensory feedback plays a crucial role in speech production in both healthy individuals and in individuals with production-limited speech. However, the vast majority of research on the sensory consequences of speech production has focused on auditory feedback while relatively little is known about the role of vocal tract somatosensory feedback. The body of this dissertation investigates speech and nonspeech production in the absence of vocal tract somatosensory feedback by training subjects to use a touchscreen-based speech production platform. Contact with the touchscreen results in instant playback of a vowel or complex tone dependent on the location selected. Because the axes of the touchscreen are associated with continuous F2 and F1 frequencies, every possible vowel within a wide formant range can be produced. Participants with no initial knowledge of the mapping of screen areas to playback sounds were asked to reproduce auditory vowel or complex tonal targets. Their responses were evaluated for accuracy and consistency, and in some cases participants underwent functional neuroimaging via MEG during training. Following training, participants were capable of using the touchscreen to produce speech and nonspeech sounds in the absence of the vocal tract. Their increased accuracy and consistency as they learned to produce speech and nonspeech sounds indicates the development of new audiomotor maps, as does significant changes in their task-based functional neuroimaging over the course of training. While participants demonstrated learning in both speech and nonspeech production, the neural and behavioral differences indicated different learning processes. We hypothesize that these differences can at least partially be attributed to the presence of an existing audiomotor network for producing speech sounds. This would account for more rapid learning rates in the speech variants of the task, the presence of generalization in touchscreen-based speech production, and the neural similarities to vocal speech production in the touchscreen-produced speech sounds that presented differently in touchscreen-produced nonspeech sounds.

Chronic neuropathic pain is a debilitating disorder of the central or peripheral nervous system and estimated to affect 100 million Americans. Pain is not just physiological, but a social issue because it leads to severe disability and affects quality of life. According to pain “neuromatrix” theory, pain has significant affective and cognitive components that enhances pain experience and hence disability. The current project aims to characterize this affect component using magnetoencephalography (MEG) in patients with post-stroke thalamic pain (PSTP). For this purpose, specific experimental paradigms to evoke pain anticipation were designed along with MEG data analysis methodology. In Experiment-1, normal healthy subjects were studied to establish a standard against which patient population could be studied. Pain was conditioned using visual countdown cues. In Experiment-2, conditioning cues from visual, tactile and auditory modalities were employed to study the effect of multi-modal conditioning on pain affect in normal healthy population. In Experiment-3, PSTP patients who had stroke induced lesion in the thalamic nuclei and consequently unrelenting chronic pain on the side of the body contralateral to the lesion, were studied. Pain affect was characterized at baseline and after deep brain stimulation (DBS) treatment. Departing from conventional approach, in this project DBS of the ventral capsule was employed to modulate the affective (or anticipatory) component of pain and thereby reduce pain related disability in PSTP patients. MEG correlates of pain anticipation at both sensor and source level from each experiment is consolidated and summarized. Findings support MEG’s role as a functional biomarker to characterize neural oscillatory activity related to pain affect and anticipation.

Background: Post-traumatic stress disorder (PTSD) is a mental health disorder characterized by symptoms such as insomnia, irritability, issues with memory, difficulty concentrating, and poor decision-making abilities. With symptoms that closely resemble those of other anxiety disorders, it is very difficult to accurately diagnose. More research is needed to identify structural and functional imaging biomarkers to aid in diagnosis. Methods: Ten right-handed male subjects (5 combat-exposed veterans, 5 healthy civilian controls) underwent magnetoencephalographic recording for this study. MEG data were acquired with a 275-channel whole-head CTF Omega 2000 system. Resting-state and tasked-based (Stroop Color-Naming Task) data were acquired. Voxel-based time-frequency analysis was subsequently performed using NUTMEG and SPM8.Results: Significant differences were found between the two groups at rest (in delta, theta, gamma, and high-gamma neural oscillatory frequency bands) and during the Stroop Color-Naming task (in alpha, beta, and gamma, and high-gamma frequency bands). Conclusions: Despite the small sample size, we were able to replicate some aspects of previous MEG research in veterans with PTSD. Not only does this result substantiate the use of MEG for population studies, but it also shows that PTSD is a mental disorder that is physical in nature and can be characterized through passively observing electromagnetic neuronal activity.

The ability to explore novel environments and make decisions is a fundamental component of human and animal behavior. Even though significant progress has been made in recent years in understanding the mechanisms of exploration and decision-making, little is known on how the brain extracts, encodes and processes information from the environment to make decisions. The primary goal of this thesis is to understand the behavioral and neural mechanisms underlying the processing of spatial information, acquired during exploration of realistic environments to make spatial decisions. We designed a novel task, in which subjects had to explore maps from various U.S. cities to decide where to build a City Hall, while neuromagnetic fluxes were recorded from their heads using a whole-head MEG device. We found that ongoing neuronal activity in a network of cortical regions was associated with particular spatial parameters of the city maps. This network involved predominantly the right frontal and prefrontal areas of the brain, suggesting that these areas have an important role in processing spatial information for making decisions. Additionally, we found other brain areas that were also involved in the processing of spatial information, such as right temporal areas and the cerebellum. These results indicate that processing spatial information for making a decision is a complex process that requires the involvement of more than one regions. Finally, we found that the associations between changes in the ongoing neural activity and spatial parameters were modulated by the street network type. This suggests that, depending on the type of street network, people may use different spatial information to explore the map and make a spatial decision.We also studied how people make spatial decisions in realistic environments when they were forced to select between a limited set of choices. In this experiment, individuals had to explore maps from various U.S. cities, but now to select between two locations to build a hypothetical Post Office. We recorded subjects' eye positions and analyzed the gaze behavior to characterize how people explored maps to select between these options. We found that subjects were continuously exploring the areas around the two options and the center of the map, by looking back and forth between them before making a decision. Unlike economic choices, in which people follow similar strategies by looking repeatedly at the available options, in our experiment individuals were also exploring the area around the center of the map. These findings suggest that the subjects might have mentally placed themselves at the center of the map and evaluated the alternative options with respect to their current location. We also found other similarities with economic choice paradigms, such as people spent more time exploring the area around the option ultimately chosen. Finally, subjects showed a strong bias to select the option they initially explored.

Functional neuroimaging involves the study of cognitive scientific questions by measuring and modelling brain activity, using techniques such as Functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). These non-invasive methods give indirect views into brain functioning: fMRI measures changes in relative blood oxygenation as a response to neural activation, and MEG externally samples the weak magnetic fields generated by neural activity. Additionally the data are corrupted by noise. So analysing the data from these modalities presents many challenges. This thesis presents four new statistical signal processing methods for improving the analysis of functional neuroimaging data. The thesis opens with a motivating chapter. Then chapter 2 makes the case for the significance of the topics tackled in the later chapters by reviewing pertinent literature. Chapter 3 develops a suite of formal statistical diagnostic tests to critique the model construction process, when analysing fMRI data from task-based experiments. The methods are developed in the Lagrange Multiplier testing framework (long popular in econometrics) as an approximation to likelihood ratio tests. In particular we develop three tests that examine the adequacy of assuming non-linearity, non-stationarity and the validity of the common Double Gamma specification for hemodynamic response in fMRI. Chapter 4 of this thesis develops a new system identification method, using T2* fMRI to construct a map of the direct effects of transcranial direct current stimulation (tDCS) on the brain. The method is developed in the framework of so-called time-series Intervention Analysis to quantitatively determine whether a single stimulus to a system has significant effect on its operation. In Chapter 5 we develop a new method for solving the MEG inverse problem. The method calculates maximally sparse estimates of brain activity via spatial L0 regularisation, while constraining the solution to be smooth throughout time. In Chapter 6 we develop a new approach to describe the fMRI based functional connectivity of the brain using a network model. The method measures Mutual Information between brain regions, and jointly accounts for spurious spatial correlations, temporal correlations, and sparsity of connections between brain regions, to construct a single estimate of activity interactions. We demonstrate the utility of all these methods by analysing simulated data and real experimental data.

The task of speaking is the translation of thoughts into an acoustic wave through the coordination of hundreds of muscles. Sensory-motor transformations are continuously occurring during ongoing speech as feedback is monitored and errors in production are rapidly recognized and corrected. While it is known that auditory feedback is used to monitor and maintain proper pitch production, the neural control of this important aspect of speech production remains poorly understood. Modulation of the timing and frequency of vocal pitch conveys important semantic and affective information during speaking. The goal of this thesis was to study sensory-motor control of speech production by examining the neural circuits involved in the auditory feedback control of pitch. In order to achieve this aim we used a combination of magnetoencephalography (MEG) and real-time pitch altered auditory feedback. To this end, we conducted a series of experiments to understand the real-time cortical monitoring of one's own speech production, the recognition of errors in auditory feedback, the cortical processing of these errors, and the motor (and subsequently acoustic) compensatory change. In this series of studies we have described the cortical networks and their mechanisms in monitoring auditory feedback during speech production. We have demonstrated that the cortical monitoring of the onset of a vocalization occurs with the suppression of auditory, cerebellar and frontal regions in both the left and right hemisphere. We showed this suppression is released when the perceived auditory feedback does not match the expected auditory feedback. We investigated the cortical networks involved in monitoring ongoing productions for errors in feedback, and the behavioral and cortical response when an error is perceived. We have shown that while the cortical networks monitoring the onset of speech and mid-utterance are overlapping, that these networks have distinct timing, patterns of response, and anatomical locations. Importantly, both feedback monitoring and motor control of speech at onset and mid-utterance show changes in cortical dynamics in both hemispheres. Feedback control of pitch during a mid-utterance error has corresponding increased inter-hemispheric communication. The work in this thesis provides evidence that inter-hemispheric communication is important for the feedback control of vocal pitch production.

With modern digital technology, it is now possible to capture, store and describe the brain's response to musical stimuli with some degree of confidence. Increasing financial and materiel resources are being made available to music-brain researchers and, as a result, the number of music perception and cognition publications is expanding exponentially (Levitin & Tirovolas, 2011). Many neuro-musicologists have access to at least one of the two most popular brain-imaging technologies, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). EEG measures the electric brain signal passing through soft tissues, which becomes 'smeared' and difficult to separate from signals measured elsewhere on the scalp. fMRI measure the blood-oxygen level dependent (BOLD) signal, however this signal develops too slowly (2-5 seconds) to accurately capture the brain's swift processing of individual melodic notes. In contrast, magneto-encephalography (MEG) affords high temporal resolution (1 ms) and high fidelity (i.e. the clean, direct measurement of undistorted electromagnetic fluctuations in neural populations) and is therefore the most suitable method for matching the brain's dynamic, interacting sub-networks to the processing of melodies played at normal tempos. To explore my idea that this evolving process is both observable and quantifiable, I have performed a series of MEG experiments involving human subjects listening to melodic stimuli. This dissertation details my examination of the brain's response to melodic pitch, contour, interval distance and next-note probability.

While most people take identification with their bodies for granted, conditions like phantom limb pain, alien hand syndrome, and Xenomelia demonstrate that feelings of bodily congruence or incongruence are tied to neural construction of body image. Individuals with Xenomelia, for example, show reduced right parietal representation of body parts they find over-present and aversive. Similarly, transsexual individuals often find their untreated sexed body parts incongruent and aversive. Could differences in representation of these body parts in the right parietal lobe underlie this sense of bodily incongruity? Moreover, could a similar mechanism be involved in the counterfactual feelings of fatness in patients with anorexia nervosa (AN)? In this dissertation I investigate sensory processing and neural body representation of congruent and incongruent-feeling body parts in presurgical female-to-male (FTM) transsexual individuals, and dysphoric and neutral-feeling body parts in people with AN. Experiment 1 demonstrates heightened skin conductance response to tapping of the breast in presurgical FTM individuals, suggesting that aversion to body parts that feel incongruent extends to the level of automatic sensory processing. Utilizing magnetoencephalography (MEG), Experiment 2 demonstrates different integration of sensation from a body part that feels incongruent with one's gender. Experiment 3 reports diminished visual-tactile integration in individuals with AN, suggesting either greater reliance on somatosensory information or deficits in multisensory integration. Experiment 4 discusses differences in the somatosensory response (measured through MEG) to sensation from the abdomen between individuals with and without AN. Experiment 5 reports preliminary research on the use of allocentric mirror viewing strategies to correct body image distortion. I also report within-subject effects of body satisfaction on visual and tactile estimates of body size, suggesting rapid top-down modulation of sensory body representation. In summary, these studies demonstrate the presence of low-level differences in somatosensory processing for body parts that feel incongruent or dysphoric, suggesting strong connections between lower- level sensory representations of the body and higher-level, explicit body image and body dysphoria. This work advances our understanding of the involvement of sensory processing and sensory integration in the construction of experiences of body and self

The nature of the integration of auditory and motor processes in the brain has been a subject of long-standing interest in the cognitive sciences and neuroscience(Price, 2012). Dysfunction in auditory-motor integration underlies pathology in a wide range of disease processes including stroke, stuttering, dyslexia, and neuropsychiatric disorders such as schizophrenia. Understanding the functional integration of the neural circuits that subserve the different stages of auditory-motor transformation and coordination remains a major challenge in both basic and clinical human systems neuroscience.Here, we report on the neural oscillatory correlates of speech auditory encoding, working memory and verbal reproduction (the "phonological loop") in healthy human subjects, and schizophrenic patients both before and after computerized cognitive training. We present a high-resolution spatiotemporal description of the brain network that supports the functions of the phonological loop in the healthy subjects, show the aberrant behavior of this network in schizophrenia, reveal novel compensatory mechanisms deployed in the cortex in schizophrenics, and show how some of the significant abnormalities are normalized through cognitive training. In the healthy subjects, we demonstrate that Area Spt and Broca's Area act as dissociable input and output buffers in a reverberating bi-directional dorsal stream for phoneme perception and production. These buffers interact in time along a tightly controlled schedule as part of a speech-motor feedback loop that operates before speech onset. In the schizophrenic patients, we find that the normal dorsal stream pathway is disrupted, and that schizophrenics activate the ventral visual stream in compensation, and in particular the visual word form area (VWFA), in a manner mediated by an overall suppressed dorsal lateral prefrontal cortex, the activity level of which in turn correlated with overall patient functional status. Both the posterior dorsal stream deficits and the VWFA compensation are greater in patients with severe auditory hallucinations, reflecting the significant underlying lesion to the auditory-motor system in these patients. Finally, we demonstrate that computerized cognitive training acts to normalize the oscillatory deficits of the dorsal stream and that this normalization correlates with improvements in both task performance and long-term verbal learning and memory scores.

One of the most challenging questions in linguistics is why the ability to acquire language declines with age. The critical period hypothesis, which claims that language acquisition is driven by brain maturation, is widely accepted despite a lack of evidence to support it. Because most children experience language from birth, the relationship between brain growth and early language experience is poorly understood. This dissertation describes the language acquisition and the neural language processing in three deaf individuals (cases) who were cut off from nearly all language until adolescence; they could not hear spoken language and, due to anomalies in their upbringing, did not experience sign language until adolescence when they became immersed in American Sign Language (ASL). These developmental circumstances allow us to investigate the effects of first language acquisition begun in adolescence, and test the critical period hypothesis from a unique perspective. The first part of the dissertation focuses on the cases' language following one to two years of ASL use. Their language is remarkably similar to that of young children: their lexicons are biased towards nouns, and their utterances are short and simple. The second part of the dissertation explores the link between the age onset of language acquisition and the neural representation of sign meaning using anatomically constrained magnetoencephalography. Chapter 3 demonstrates that under ideal developmental circumstances, when language is available from birth, the neural processing of sign in deaf participants is highly similar to the processing of speech in hearing participants. However, in subsequent studies with the cases (Chapter 4), we observe atypical neural activation patterns, which diverge significantly from those associated with native sign or spoken language learning. These results indicate that early language experience is crucial in establishing canonical neural language processing patterns. The atypical neural activation patterns we find in the cases may be associated with the slowed rate of language development we observe in follow-up language studies. Our findings provide some of the initial direct evidence in support the critical period hypothesis and have important theoretical and practical implications

Coherence estimation is one of the methods for understanding functional connectivity deficits and frequency specific deficits in schizophrenia. Coherence between different lobes of the brain from task related data at different frequency bands was investigated in patients with Schizophrenia (SP) and Healthy Normal Volunteers (HNV). The task was aimed to study the neural mechanisms underlying auditory and visual integration in patients with schizophrenia relative to healthy controls, which requires intact connectivity between the lobes of the brain in order to recombine the sensory information into a complete percept of the external world. Coherence was calculated from the processed magneto-encephalography (MEG) data for each pair of lobes of left temporal and parietal, left temporal and occipital, right temporal and parietal, right temporal and occipital, parietal and occipital at the frequency bands of delta (0 to 4 Hz), theta (4 -8 Hz), alpha (8-13 Hz), beta (13 -30 Hz) and gamma (30-100 Hz). Analysis Of Variance (ANOVA) was performed on the coherence data of 30 subjects comprised of 15 patients and 15 controls. There was a significant interaction between frequency and diagnosis with age. Significant differences were found between patients and controls at the Delta frequency band, which was confirmed with Bonferroni-corrected -t-tests at the delta frequency range in each pair of regions. It was found that patients had higher coherence than controls in the delta frequency band and it was significant across lobes which suggest an abnormal MEG coherence during evoked activity in schizophrenic patients.

Synesthesia is a perceptual experience in which stimuli presented through one perceptual stream will spontaneously evoke unrelated sensory experiences. While synesthesia can occur in response to drugs, sensory deprivation, or brain damage, research has largely focused on heritable variant comprising roughly 4% of the general population. This condition is by definition involuntary, automatic, and stable over time, and evidence suggests that the condition occurs from increased connectivity between the senses. However, the precise mechanisms that give rise to these sensations remain a matter of active debate. Across five studies using electroencephalography, magnetoencephalography, and behavioral measures, we present evidence that early and quick communication between low-level perceptual centers mediates the initial processing stage in synesthesia. Furthermore, this research also suggests early synesthetic mechanisms partially overlap with those that support multisensory processes in the general population. This work clarifies the mechanisms that underlie this interesting condition, and provides a roadmap to using synesthesia as a tool to better understand perceptual and conceptual processes present in all individuals

A growing majority of the world's population is able to speak and understand more than one language, yet most of our knowledge of how the brain processes words comes from monolingual speakers of a small number of languages. Furthermore, most studies of both monolingual and bilingual individuals have utilized brain imaging methods that provide either excellent spatial or temporal resolution, but not both. In aggregate, these studies have demonstrated that information is encoded in both local and regional brain networks across time, so it is therefore necessary to understand the specific spatiotemporal dynamics of these processes. This dissertation seeks to advance our knowledge of the various stages of word processing across languages in bilinguals and across the various modalities in which language occurs. Using a multimodal imaging approach that combines the temporal resolution of magnetoencephalography (MEG) with the spatial resolution of magnetic resonance imaging (MRI), we focus on an early word form encoding stage and a later lexico-semantic stage in both the visual and auditory modalities. In two studies with bilingual participants, we show that the less proficient language (regardless of whether it was learned first or second) recruits an extended network of brain regions beginning during the earliest stages of word encoding (~150 ms for written words and ~100 ms for auditory words) and continuing through lexico-semantic processing (~400 ms). In a third study, we investigate the properties of a newly discovered neural response that occurs at ~100 ms to auditory words. This response represents the encoding of acoustic information into a phonemic code, which can then be passed onto the classical lexico-semantic brain areas (and the extended network in less proficient languages) for integrating the word into the surrounding semantic context. These studies help clarify the importance of proficiency and experience in evaluating how bilinguals process each of their languages, which provides a unique perspective on the general process of language acquisition. Furthermore, understanding the spatiotemporal dynamics of word encoding in both the visual and auditory modalities allows us to identify fundamental neural mechanisms that are modality- and stimulus- independent.

In the last few decades there have been major advances in the technology of function brain imaging, allowing for insight into the functions of the human brain previously elusive to neuroscientists. These advances have been primarily on the hardware end and developing effective software to interpret the data collected by neuroimaging machines is a current challenge to the use of the technology. Magnetoencephalography (MEG), in particular, requires solving an ill-posed inverse problem in order to uncover the brain areas active during a task. While the solution to this inverse problem is not unique, there are many methods to estimate its solution and this is a field of active research. In Chapter Two of this thesis, we derive an algorithm that solves the inverse problem for MEG, and the related imaging method, electroencephalography (EEG). Our method improves upon existing algorithms in that it incorporates noise suppression into the estimation procedure and is theoretically and empirically robust to correlated sources. In Chapter Three, we show the results from extensive testing of our algorithm using simulated and real M/EEG data and we show our algorithm's results in comparison to the benchmark algorithms. Chapter Four explores variants of the algorithm, including its application to data sets without pre-stimulus data. In Chapter Five, we present methods to statistically threshold the inverse solution results using nonparametric statistics. Finally, in Chapter Six, we provide some concluding remarks and ideas for future research directions. As a whole, the work presented in this thesis improves the interpretation and analysis of M/EEG data.

This paper analyzes adaptive array-gain spatial filtering as a way of interpreting magnetoencephalography (MEG) data to locate centers of neuronal activity within the brain. When neurons in the brain communicate they generate an electric current which, in turn, generates a magnetic field. MEG can record this magnetic signal when it is generated simultaneously in multiple neurons. The features of MEG include good spatial resolution, very good temporal resolution, and no radiation. Currently, inverse modeling is used in the clinical setting to interpret the MEG signal. Since there is no unique solution to the inverse method, the results rely on the skilled interpretation of a neurologist to evaluate their concordance with the patient’s clinical symptoms. Spatial filtering is a way of locating sources of power within the brain that avoids having to use inverse methods and, therefore, avoids many of the localization errors and artifacts. Spatial filtering accomplishes this by using the relationship between the signals from the individual detectors, in the form of a covariance matrix, along with the known detector sensitivity to magnetic dipoles, known as the lead field. In this study, the head is modeled as a homogenous conducting sphere and the neuronal activity is modeled as an electrical current dipole. Both patient data and simulated data are analyzed in this study. For the simulated data, dipoles at various positions and orientations are analyzed with and without various levels of Gaussian white noise added in. The results of this study show that adaptive array-gain spatial filtering has the potential to become a standard method in the localization of neuronal activity centers in the brain. With its propensity for reduced error and speed in interpreting the data, spatial filtering may one day help to make MEG a standard procedure in pre-surgical brain mapping, thereby reducing patient morbidity.

Motor behavior involves creating and executing appropriate action plans based on goals and relevant information. This information characterizes the state of environment, the task and the state of actions performed. The perceptual system gathers this information from different sources: touch, vision, audition, scent and taste. Despite the richness of environment and the sophistication of our sensory system, it is not possible to extract a complete and accurate representation of the required states for motor behavior because of noise and ambiguity. Consequently, people effectively have “limited information” and therefore may not be certain about the outcomes of specific actions. For motor behavior to be robust to uncertainty, the brain needs to represent both relevant states and their uncertainties, and it needs to build compensation for uncertainty into its motor strategy. Generating motor behavior requires the brain to convert goals and information into action sequences, and the flexibility of human motor behavior suggests that brain implements a complex control model. The primary goal of this work is to improve the characterization of this control model by studying motor compensation for uncertainty and determining the neural mechanisms underlying information processing and the control model. Part of this thesis focuses on studying human compensation strategies in natural tasks like grasping. We experimentally tested the hypothesis that people compensate for object position uncertainty by adopting strategies that minimize the impact of uncertainty in grasp success. As we hypothesized, we found that people compensate for object position uncertainty by approaching the object along the direction of maximal position uncertainty. Additionally, we modeled the grasping task within the optimal control framework and found that human strategies share many characteristics with optimal strategies for grasping objects with position uncertainty. We are also interested to understand how the brain encodes and processes information relevant to movements. To accomplish this, we studied the spatial and temporal interactions of cortical regions underlying continuous and sequential movements using magnetoencephalography (MEG). Particularly, we took data from a previous study, in which subjects continuously copied a pentagon shape for 45 s using an XY joystick. Using Box-Jenkins time series analysis techniques, we found that neural interactions and variability of movement direction are integrated in a feedforward-feedback scheme. MEG sensors related to feedforward scheme were distributed around the left motor cortex and the cerebellum, whereas sensors related to feedback scheme had a strong focus around the parietal and the temporal cortices.

Darwin proposed that communication of information concerning psychological state was an evolved mechanism. Much emotional signaling in humans occurs via facial expression. Neuroimaging techniques have identified regions involved in facial emotion perception and, although successively more sophisticated models have sought to explain this processing, results suggest that further development is needed. Neuroimaging research in facial emotion perception is currently focused on characterizing major sources of activation in development of these models. Although facial processing is a visual task, decoding of expression has been theorized to take place not in the primary visual regions of the occipital cortex but later within specialized portions of temporal cortex. Portions of the fusiform gyrus respond preferentially to facial stimuli and, although current models hypothesize that processing of facial expression takes place elsewhere, some evidence suggests that facial expression modulates the fusiform response. Lewis et al. (2003) found differential activation of an equivalent current magnetic dipole in the right fusiform when viewing happy versus disgusted versus neutral faces. The current work sought to replicate and extend these findings by, 1) expanding the stimulus set to all six basic emotions and a non-face control condition, 2) investigating the primary visual response of the calcarine fissure for an emotion-dependent component, and 3) investigating effects of gender and age. Contrary to expectations, findings here included emotion-related differences in peak amplitude and latency in responses of both the calcarine and fusiform. In the calcarine happy faces were seen to elicit greater amplitude than neutral, swirled, and sad faces, while in the fusiform fearful and surprised faces resulted in greater amplitude than disgusted faces and non-face objects. In the fusiform swirled faces elicited longer response latencies than recognizable faces regardless of emotion with fewer significant comparisons making a similar suggestion in the calcarine. Surprised faces required greater latencies in the right fusiform than happy and neutral faces. Partial support for the findings of Lewis and colleagues is suggested. A main effect was found for gender, with women displaying greater amplitude, shorter-latency responses. The amplitude response of the fusiform was greater than the calcarine and this differential increased with age.

The main goal of this study was to investigate, using MEG, the dynamic neural mechanisms underlying facial tactile stimulation in two groups of subjects, namely a control group (without pain) and a TMD pain group (arthromyalgia), by stimulating the facial skin with a non-painful air-driven plastic membrane. Our first specific aim was to investigate and compare the spatial and temporal features of the ECDs following innocuous tactile stimuli in both groups. And t he second specific aim was to investigate the differences in dynamic brain function between these two groups using a time-frequency analysis of the MEG data. In summary, innocuous tactile stimulation proved to be a successful way to measure brain spatio-temporal dynamics in two group population. We were able to demonstrate very clear the differences in brain organization and dynamics between these two groups using an innocuous stimulus and without causing an unpleasant feeling. The results obtained allow for a paradigm shift in future research of brain mechanisms in pain by the use of non-painful tactile stimuli to evaluate brain function in various orofacial (or other) pain conditions, including neurovascular and neuropathic pains and other complex orofacial pain disorders.

Introduction: Task-specific focal hand dystonia (FhDtsp) is a debilitating, recalcitrant involuntary movement disorder characterized by end-range postures resulting from involuntary co-contractions of agonists and antagonists when performing a common repetitive task. Purpose: This study used magnetoencephalography (MEG) to elaborate the differences in contralateral and ipsilateral temporal and spatial processing in the primary somatosensory cortex (S1), secondary somatosensory cortex and parietal ventral area (S2/PV), primary motor cortex (MI) and premotor motor cortex (PMC) between healthy controls and patients with FhDtsp. Subjects: Thirteen with FhDtsp and 13 age- and sex-matched healthy controls. Procedures: Whole head MEG was used to investigate the spatiotemporal integration of novel, low and high rate sensory stimulation in S1 and S2/PV and self-paced finger movement responses in M1 and PMC. Clinical measures of function, sensation, motor speed, strength, and motor control were integrated into regression equations to predict aberrant neurophysiological processing. Results: Compared to controls, subjects with FhDtsp had: 1) increased peak amplitude in contralateral and ipsilateral S2/PV in response to high rate and novel stimuli; 2) increased latency in contralateral S1 in response to novel stimuli and in ipsilateral S1 and ipsilateral S2/PV in response to high rate stimuli; 3) early activation with maintenance of firing in contralateral M1 and ipsilateral PMC associated with finger movement and 4) impaired sensation, motor speed and intrinsic muscle strength with sensation predictive of aberrant somatosensory processing (latency). Conclusions: Given the simultaneous increases in amplitude in contralateral and ipsilateral S2/PV related to novel and high rate stimulation in patients with FhDtsp, bilateral sensory retraining targeting higher levels of sensory discrimination may be needed to reduce the heightened sensory response. Additionally, bilateral, simultaneous biofeedback technology may be able to increase patient awareness as well as assist in retraining to better balance ipsilateral firing to aid inhibition while decreasing excessive contralateral MI excitation.

The field of brain imaging has exploded in the past two decades due to new technological developments mostly on the hardware/acquistion end. Consequently, neuroscientists have been excited to study all aspects of brain function and the general public have been excited to hear what results come from such studies. Magnetoencephalography (MEG) is a brain imaging method which passively detects the naturally occuring magnetic fields outside the head resulting from neural cells' activity in the brain. The MEG sensors record direct neural activity with millisecond resolution. However, there is inherently no unique solution to determining where exactly in the brain the neural activity was located that produced the brainwaves measured by the sensors. With certain general assumptions, a reasonable estimate of the location can be made. However, measurement noise and other artifacts, including heartbeat and eyeblinks, swamp the signals of interest, making localization nearly impossible. In this dissertation, two novel methods are proposed which improve neural source estimate by removing the effects of noise and interference. These new methods are tested against standard methods for both simulated and real data and show improved performance. These methods are further tested on real data obtained from primates with the ultimate goal of using electrophysiological data from these primates to compare the MEG localization with the true location. Finally, an example is shown of one way to combine neural activity measured by MEG with a method for measuring white-matter anatomical connections obtained by diffusion tensor imaging.