Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

Gamma-Range Auditory Steady-State Responses and Cognitive Performance: A Systematic Review

The auditory steady-state response (ASSR) is a result of entrainment of the brain's oscillatory activity to the frequency and phase of temporally modulated stimuli. Gamma-range ASSRs are utilized to observe the dysfunctions of brain-synchronization abilities in neuropsychiatric and developmental disorders with cognitive symptoms. However, the link between gamma-range ASSRs and cognitive functioning is not clear. We systematically reviewed existing findings on the associations between gamma-range ASSRs and cognitive functions in patients with neuropsychiatric or developmental disorders and healthy subjects. The literature search yielded 1597 articles. After excluding duplicates and assessing eligibility, 22 articles were included. In healthy participants, the gamma-range ASSR was related to cognitive flexibility and reasoning as measured by complex tasks and behavioral indicators of processing speed. In patients with schizophrenia, the studies that reported correlations found a higher ASSR to be accompanied by better performance on short-term memory tasks, long-term/semantic memory, and simple speeded tasks. The main findings indicate that individual differences in the gamma-range ASSR reflect the level of attentional control and the ability to temporary store and manipulate the information, which are necessary for a wide range of complex cognitive activities, including language, in both healthy and impaired populations

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions

Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed. Keywords: Cognition; Electrophysiology; Event-related-potentials; Neural oscillations; Neural synchronisation; Neuromodulatio

A neurocomputational account of self-other distinction: from cell to society

Human social systems are unique in the animal kingdom. Social norms, constructed at a higher level of organisation, influence individuals across vast spatiotemporal scales. Characterising the neurocomputational processes that enable the emergence of these social systems could inform holistic models of human cognition and mental illness. Social neuroscience has shown that the processing of ‘social’ information demands many of the same computations as those involved in reasoning about inanimate objects in ‘non-social’ contexts. However, for people to reason about each other’s mental states, the brain must be able to distinguish between one mind and another. This ability, to attribute a mental state to a specific agent, has long been studied by philosophers under the guise of ‘meta-representation’. Empathy research has taken strides in describing the neural correlates of representing another person’s affective or bodily state, as distinct from one’s own. However, Self-Other distinction in beliefs, and hence meta-representation, has not figured in formal models of cognitive neuroscience. Here, I introduce a novel behavioural paradigm, which acts as a computational assay for Self-Other distinction in a cognitive domain. The experiments in this thesis combine computational modelling with magnetoencephalography and functional magnetic resonance imaging to explore how basic units of computation, predictions and prediction errors, are selectively attributed to Self and Other, when subjects have to simulate another agent’s learning process. I find that these low-level learning signals encode information about agent identity. Furthermore, the fidelity of this encoding is susceptible to experience-dependent plasticity, and predicts the presence of subclinical psychopathological traits. The results suggest that the neural signals generating an internal model of the world contain information, not only about ‘what’ is out there, but also about ‘who’ the model belongs to. That this agent-specificity is learnable highlights potential computational failure modes in mental illnesses with an altered sense of Self

NMDA Receptor Dysfunction and Development of Translational Biomarkers for Autism and Schizophrenia

Autism and schizophrenia are neurodevelopmental disorders which both have highly disabling negative and cognitive symptoms with few effective treatments. A challenge to developing effective therapeutics is a dearth of pre-clinical models. Part of the difficulty in developing predictive models is that the symptoms being treated are complex, and difficult to reduce to a simple behavioral task. Therefore, the use of endophenotypes from methods such as EEG presents a new promising avenue for a model of complex human behaviors pre-clinically. New evidence suggests that autism and schizophrenia have reliable electrophysiological endophenotypes, some of which have been correlated to negative and cognitive symptoms. These endophenotypes therefore represent a possible new pathway for understanding the disrupted circuits in both diseases and developing treatments. Evidence has been accumulating for glutamate disruption in both schizophrenia and autism; accordingly, pre-clinical models are being developed around NMDA receptor (NMDAR) disruption to examine both diseases. NMDA disruption models have been used for many behavioral tasks, but only a few possible electrophysiological endophenotypes such as ERP amplitudes have been investigated. Investigating pre-clinical models of established clinical endophenotypes could lead to better translational biomarkers of disease symptoms. This thesis\u27s unifying theme is the study of how glutamate disruption can recreate the electrophysiological endophenotypes present in autism and schizophrenia and develop their use as translational biomarkers in both diseases. The primary models of focus are acute NMDA antagonist administration and NMDAR knockdown of PV interneurons. I used these models to examine the relationship between dose and EEG changes, along with the perturbations present with NMDAR disruption in PV interneurons. I investigated the degree to which NMDAR antagonists recreate signal-to-noise ratio (SNR) and timing perturbations in schizophrenia, and found a dose-dependent decrease in SNR and timing consistency. I assessed the extent to which low dose NMDAR antagonism recreates latency and gamma synchrony perturbations present in autism and found latency was increased and gamma synchrony was decreased dose-dependently. I examined the extent to which Parvalbumin (PV) containing interneurons cell type selective NR1 KO mice recreate the clinical EEG profiles of autism and found selective deficits in social behavior and increases in N1 latency

Análisis de conectividad funcional de la dinámica neuroenergética del TDAH = Functional Connectivity Analysis of Neuroenergetic Dynamics for ADHD

A fast and economic pilot study for measuring the neuroenergetic dynamics in an ADHD-diagnosed sample is performed. Based in a simplified connectome version, a graph theory application for neural connectivity, the performance and subjective states are linked through brain activity analysis during a behavioral attention test. ADHD is a neurobehavioral disorder related to a deficient filtering of stimuli, inefficacy performing in sustained activities and difficulties responding to unpredictable situations. There are two main strategies to evaluate this disorder: (1) behavioral tests and (2) neural biomarkers. Behavioral tests provide a criterion for classifying responses in a collection of tasks, looking for unstructured and inconsistent responses to given instructions or rules. Hyperactivity, inattention and impulsivity are some criteria analyzed. By the other hand, neural biomarkers are measurable indicators for particular states or diseases set up from EEG data. Since 2013, the theta/beta ratio was accepted as the ADHD biomarker, suggesting a misbalance of electrical brain activity. In this study, brain connectivity on sustained attention task performed by children between 7 to 13 years old from a public school. Ten participants were ADHD-diagnosed and five were selected for the control group to compare EEG signals collected with low-cost neuroheadset. Graphs show different connectivity dynamics in both groups for Theta (4-8 Hz), SMR (12-15 Hz) and Beta (15-20 Hz), indicating connectivity variations in brain regions according to the neuroenergetics theory. The connectivity in the ADHD group is reduced in lower frequencies first (Theta), then SMR and finally Beta. In contrast, the control graphs for Theta and SMR brainwaves are closer to the small-world networks and it can be noticed by comparing the measurements of the different graphs among themselves. The decay process corresponds to the bottom-up approach, where random stimuli trigger transitions from one state to the other, which is in this case the transition from attention to inattention. The declining of resources placed for disposal at the randomized SART stage might imply a limitation regulating the production of the required resources for the tasks fulfillment, as it has been reported in previous studies where other techniques are implemented