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This chapter presents various strategies of combining separately recorded electroencephalography/magnetoencephalography (EEG/MEG) and functional magnetic resonance imaging (fMRI) data sets. To help the experimenter decide in the first place whether to use concurrent recordings of EEG and fMRI or separate recordings, it attempts to weigh the relative merits of combined versus separate EEG/MEG and fMRI measurements, and puts them in perspective with respect to various experimental goals. The principle of MEG recording and its advantages, as compared to EEG, are also described; these particular advantages of MEG recordings are important to consider because, at present, they are only available when data are recorded separately, due to the current incompatibility of MRI and MEG measurement equipment.

Developments in M/EEG analysis allows for models that are sophisticated enough to capture the full richness of the data. This chapter focuses on dynamic causal modeling (DCM) for M/EEG, which entails the inversion of informed spatiotemporal models of observed responses. The idea is to model condition-specific responses over channels and peristimulus time with the same model, where the differences among conditions are explained by changes in only a few key parameters. The face and predictive validity of DCM have been established, which makes it a potentially useful tool for group studies.

As long as valid assumptions can be made about a focal source, MEG can transform the challenge of EEG based 2D inference of lateralization or regional localization to 3D sublobar indication of epilepsy-related spike generators. As such MEG spike source imaging provides a unique tool for targeting epileptogenic tissue for the surgical treatment of epilepsy. This information can be particularly valuable for patients with neocortical epilepsy in whom intracranial EEG (icEEG) investigations are commonly necessary. MEG localization of spikes may provide more accurate electrode sampling of the cortex responsible for seizures, and as a result, increase epilepsy localization and surgical resection accuracy. Combined with mapping of eloquent cortical function, MEG can play a role in multiple aspects of the preoperative (non-invasive) decision-making—potential to improve (1) patient selection, (2) ICEEG yield, and (3) increase the net number of seizure-free outcomes. Work remains to determine the validity of various analysis methods (stratified on different types of spike sources), and the cost effectiveness of MEG in epilepsy surgery, but it can be concluded that any patients able to proced to surgery that otherwise would not without MEG would contribute evidence to added clinical utility even if the cure rate is unchanged.

As with most complex behaviors, visual word recognition is thought to result from the dynamic interplay between the elements of a distributed cortical and subcortical network. To understand fully how visual word recognition is achieved therefore, and how it may fail in developmental dyslexia, not only the necessary and sufficient complement of nodes that comprise this network—its functional anatomy—need to be identified, but also how information flows through this network with time needs to be understood, and indeed how the structure of the network itself may adapt in both the short and long term. This chapter takes a historical approach to reviewing recent magnetoencephalography (MEG) research that elucidates these temporal dynamics, focusing particularly on events with the first 300 milliseconds (ms) of a visually presented word, and which should set crucial constraints on models of visual word recognition and reading.

This chapter focuses on the basic physiological and biophysical aspects of how magnetic signals are generated in the brain. It begins with a brief description of the main features of magnetoencephalography as a method to study brain functions in man. It then discusses the main features of MEG, some basic notions of cellular neurophysiology and biophysics, neuronal models, and the transfer of magnetic signals from the brain to the skull. Answers to frequently asked questions about MEG are provided.

This chapter reviews the methods and technology required for magnetoencephalographic measurements. The key concepts discussed include sensor components, noise reduction methods, co-registration of MEG with anatomical images, stimulators and their MEG compatibility, and filtering and averaging.

Running successful MEG measurements requires not only an understanding of the principles of MEG but also the mastering of various practical points that impact the quality of the acquired data. Investing time and effort in the MEG recordings most often pays off; nothing facilitates data analysis more than a well-planned experiment and good-quality data. This chapter addresses the practical issues encountered when conducting MEG measurements. The key concepts covered are measurement procedure and identifying and avoiding artefacts.

This chapter focuses on the fusion between electromagnetic and hemodynamic signals for brain mapping. Since measurement techniques have their own strengths and limitations, combining different experimental approaches to probe more effectively a scientific question is a commonplace idea. In the human brain imaging domain, two or more imaging techniques are often combined to investigate a cognitive process or a disease, in addition to other measures such as behavioral testing. Recently, the combination of electro- or magnetoencephalography with fMRI or positron emission tomography has become more and more popular amongst neuroscientists.

In the twenty years since the averaged MEG values following somatosensory stimulation, i.e. the somatosensory evoked magnetic field (SEF), were first reported, many studies have been conducted and their number continues to increase. This chapter introduces basic methods; that is, how to record a clear SEF. It also presents basic information and findings related to SEF, particularly unique and interesting aspects.

This chapter focuses on the use of MEG in studying neural processes of fluent and impaired reading. It first tracks the cortical sequence of activation when reading familiar words, and then considers the case of unfamiliar words. After contrasting the sequence of activation in reading with that in speech perception, it focuses on cortical correlates of dyslexia. A comparison of reports on neurophysiological and hemodynamic signatures of reading then follows. The chapter concludes with a glimpse into recent advances and possible future directions.

Deep brain stimulation (DBS) is a powerful clinical tool that has provided remarkable therapeutic benefits for otherwise treatment-resistant movement and affective disorders. The precise mechanisms of action for DBS remain uncertain but are likely to result through causal manipulation of both local and distributed brain networks. Recently, non-invasive neuroimaging methods such as magnetoencephalography have started to be used in conjunction with DBS in order to map the fundamental mechanisms of normal and abnormal oscillatory synchronization underlying human brain function. This chapter begins with an introductory overview of the current state-of-art of DBS and the previous use of neuroimaging techniques with DBS. It then describes the methods and results of using MEG to measure both low and high frequency stimulation. It discusses the importance of the findings, as well as potential confounds and future possibilities of combining MEG and DBS.

Most brain-computer interfaces (BCIs) determine their user's wishes by recording electromagnetic signals noninvasively from sensors on or above the scalp. The two principal noninvasive extracranial methods used for BCIs are electroencephalography (EEG) and magnetoencephalography (MEG). This chapter addresses the methods for recording EEG and MEG signals. The first part considers practical aspects of EEG and MEG recordings. It then reviews the critical physical issues associated with the use of these methods. These issues are addressed using both real EEG data and simulations in idealized physical models of the head to clarify the key features of EEG and MEG signals. Physical models of EEG and MEG recordings underlie source analysis methods and are constructed based on the fundamental properties of the electric and magnetic fields generated by current sources in the brain. The chapter looks at the two main practical issues in the acquisition of EEG signals: reference location and number of electrodes. It concludes by considering alternative data acquisition strategies in the specific context of BCI applications.

In electroencephalography the head is modeled with concentric spheres. This model is valid and important also in magnetoencephalography. Because of the low signal level of MEG, the measurements are usually made as close to the head as possible. Since sensitivity decreases rapidly with distance, and since the detector coil radius is small compared to the dimensions of the head, the head can be successfully modeled as a half-space. This holds also for multichannel MEG-detectors, because the detectors are usually placed on a spherical surface, concentric with the head.

This chapter discusses the relative importance of imaging technologies and the need to integrate information from multiple methods. It deals with several classes of computational techniques that allow the integration of data from magnetoencephalography (MEG) and magnetic resonance imaging (MRI) to improve the accuracy and reliability of functional neuroimaging by MEG. The chapter analyses the integrated application of two powerful paradigms for functional neuroimaging, exploiting the strengths and minimizing the weaknesses of each. Furthermore, it illustrates that because of the ambiguity associated with the neural electromagnetic inverse problem, a number of investigators have pursued the strategy of using fMRI to define the locations of activation while using MEG or EEG to estimate time-courses. Finally, the chapter mentions the need to expand the arsenal of methods available to the neuroscientist or physician for understanding the architecture and dynamic function of the human brain.

The presence of recognizable electrical rhythms of the brain has excited the curiosity and imagination of both professionals and laypeople alike. Psychophysiologists, neurologists, and science fiction writers have been intrigued by the presence of brain activity and the possibility of having an objective noninvasive marker that reflects underlying psychological processes. Some have taken these ideas to the extreme by suggesting that these measures can tell us what someone is thinking or feeling or even if they are telling the truth. Although it is not that simple, some researchers have begun to use brain activity to help physically disabled individuals to communicate, to move paralyzed limbs, or even to reduce seizure disorders. Understanding cortical processes through various forms of brain imaging is a complex task. Scientists have come to appreciate the complicated relationship that exists between electrocortical measures and cognitive, emotional, and behavioral processes. This chapter discusses spontaneous electroencephalography, event-related potentials, electrodes and electrode placement, and brain imaging techniques such as magnetoencephalography, positron emission tomography, and magnetic resonance imaging.

All traditional neuropsychological assessment techniques emerged in an era prior to modern neuroimaging. In fact, question-answer/paper-and-pencil test origins that gained traction with Alfred Binet in 1905 remain the same core techniques today. Indeed, Binet’s efforts began the era of standardized human metrics designed to assess a broad spectrum of cognitive, emotional, and behavioral functions and abilities. During the early part of the 20th century, the concept of an intellectual quotient expressed as a standard score with a mean of 100 and a standard deviation of 15 also initiated the era of quantitative descriptions of mental and emotional functioning (Anastasi, 1968; Stern, 1912). Other descriptive statistical metrics were applied to human measurement, including scaled, percentile, T-score, and z-score statistics. Statistical measures became part of the assessment lexicon and each possessed strength as well as weakness for descriptive purposes, but together proved to be immensely effective for communicating test findings and inferring average and above or below the norm performances. In turn, descriptive statistical methods became the cornerstone for describing neuropsychological findings, typically reported by domain of functioning (memory, excutive, language, etc.; Cipolotti & Warrington, 1995; Lezak, Howieson, Bigler, & Tranel, 2012). As much as psychology and medicine have incorporated descriptive statistics into research and clinical application, a major focus of both disciplines also has been binary classification—normal versus abnormal. This dichotomization recognizes some variability and individual differences within a test score or laboratory procedure, but at some point the clinician makes the binary decision of normal or abnormal. In the beginnings of neuroimaging, which are discussed more thoroughly below, interpretation of computed tomographic (CT) or magnetic resonance imaging (MRI) scans mostly was approached in this manner. Although lots of information was available from CT and MRI images, if nothing obviously abnormal was seen, the radiological conclusion merely stated in the Impression section, “Normal CT (or MRI) of the brain,” with no other qualification (or quantification) of why the findings were deemed normal other than the image appeared that way. Until recently, quantification of information in an image required hand editing and was excruciatingly time consuming.

The literature supports the idea that attention is not a unified concept, but involves separate mechanisms that support its varied functions (Petersen & Posner, 2012). One common taxonomy involves three such functions: obtaining and maintaining the alert state, orienting to sensory stimuli, and resolving conflict among competing responses. Each of the functions has a long history and has spawned tests designed to measure individual differences in attention. Many individual tests and batteries of tests are designed to measure attention. Tests of vigilance usually involve maintaining attention over long periods of time, originally simulating the job of scanning radar returns for low-probability targets (Mackworth, 1969; Parasuraman, 1985). Another approach is to require responses to infrequent events, as in the continuous performance test (Rosvold et al., 1956) or the serial response test (Manly et al., 1999). Vigilance varies with the diurnal rhythm and vigilance can be reduced by sleep deprivation. Collectively, the tests of performance during continuous tasks are often called measures of tonic alertness, which is thought to change rather slowly. It is also possible to cause phasic shifts of the level of alertness by the use of warning signals (Nickerson, 1967). A warning signal can bring a person from a relatively relaxed state to one fostering the very best performance within less than half a second. Recent fMRI studies have defined a default state in which a person is off task (Raichle, 2009). It seems likely that scalp electrodes recording direct current shifts following warning signals called the contingent negative variation (CNV) capture the shift from the default to the alert state. The most frequently studied area in attention research involves orienting to a sensory source that contains a target. For example, in a visual search, a target may be defined as a red triangle. If it appears in a field that contains other colored triangles and red forms other than triangles, one can ensure that the field is carefully searched until the target is found.

Visual processing is divided into two pathways: the magnocellular pathway and the parvocellular pathway. One is concerned with motion and spatial analysis, the other with form and color perception. This chapter discusses the impairment in motion direction discrimination, particularly in the said pathways, of patients with unilateral cerebral hemispheric lesions. Such a condition causes impaired perception as well.