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Magnetoencephalography (MEG) is a functional neuroimaging technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers. Arrays of SQUIDs are currently the most common magnetometer, while the SERF magnetometer is being investigated for future machines. Applications of MEG include basic research into perceptual and cognitive brain processes, localizing regions affected by pathology before surgical removal, determining the function of various parts of the brain, and neurofeedback. This can be applied in a clinical setting to find locations of abnormalities as well as in an experimental setting to simply measure brain activity.
Functional neuroimaging is the use of neuroimaging technology to measure an aspect of brain function, often with a view to understanding the relationship between activity in certain brain areas and specific mental functions. It is primarily used as a research tool in cognitive neuroscience, cognitive psychology, neuropsychology, and social neuroscience.
A brain-computer interface (BCI), sometimes called a neural-control interface (NCI), mind-machine interface (MMI), direct neural interface (DNI), or brain-machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCI differs from neuromodulation in that it allows for bidirectional information flow. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.
A gamma wave is a pattern of neural oscillation in humans with a frequency between 25 and 140 Hz, the 40-Hz point being of particular interest. Gamma rhythms are correlated with large scale brain network activity and cognitive phenomena such as working memory, attention, and perceptual grouping, and can be increased in amplitude via meditation or neurostimulation. Altered gamma activity has been observed in many mood and cognitive disorders such as Alzheimer's disease, epilepsy, and schizophrenia.
Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity.
Electrocorticography (ECoG), or intracranial electroencephalography (iEEG), is a type of electrophysiological monitoring that uses electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. In contrast, conventional electroencephalography (EEG) electrodes monitor this activity from outside the skull. ECoG may be performed either in the operating room during surgery or outside of surgery. Because a craniotomy is required to implant the electrode grid, ECoG is an invasive procedure.
Brain mapping is a set of neuroscience techniques predicated on the mapping of (biological) quantities or properties onto spatial representations of the brain resulting in maps.
A connectome is a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram". More broadly, a connectome would include the mapping of all neural connections within an organism's nervous system.
Marcel Just is D. O. Hebb Professor of Psychology at Carnegie Mellon University. His research uses brain imaging (fMRI) in high-level cognitive tasks to study the neuroarchitecture of cognition. Just's areas of expertise include psycholinguistics, object recognition, and autism, with particular attention to cognitive and neural substrates. Just co-directs the Brain Imaging Research Center and is a member of the Center for the Neural Basis of Cognition at CMU.
Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain. It is typically noninvasive, with the electrodes placed along the scalp, although invasive electrodes are sometimes used, as in electrocorticography, sometimes called intracranial EEG.
Brain connectivity estimators represent patterns of links in the brain. Connectivity can be considered at different levels of the brain's organisation: from neurons, to neural assemblies and brain structures. Brain connectivity involves different concepts such as: neuroanatomical or structural connectivity, functional connectivity and effective connectivity.
Resting state fMRI is a method of functional magnetic resonance imaging (fMRI) that is used in brain mapping to evaluate regional interactions that occur in a resting or task-negative state, when an explicit task is not being performed. A number of resting-state conditions are identified in the brain, one of which is the default mode network. These resting brain state conditions are observed through changes in blood flow in the brain which creates what is referred to as a blood-oxygen-level dependent (BOLD) signal that can be measured using fMRI.
The biological basis of personality is the collection of brain systems and mechanisms that underlie human personality. Human neurobiology, especially as it relates to complex traits and behaviors, is not well understood, but research into the neuroanatomical and functional underpinnings of personality are an active field of research. Animal models of behavior, molecular biology, and brain imaging techniques have provided some insight into human personality, especially trait theories.
Dynamic functional connectivity (DFC) refers to the observed phenomenon that functional connectivity changes over a short time. Dynamic functional connectivity is a recent expansion on traditional functional connectivity analysis which typically assumes that functional networks are static in time. DFC is related to a variety of different neurological disorders, and has been suggested to be a more accurate representation of functional brain networks. The primary tool for analyzing DFC is fMRI, but DFC has also been observed with several other mediums. DFC is a recent development within the field of functional neuroimaging whose discovery was motivated by the observation of temporal variability in the rising field of steady state connectivity research.
Andreas Karl Engel is a German neuroscientist. He is the director of the Department of Neurophysiology and Pathophysiology at the University Medical Center Hamburg-Eppendorf (UKE).
Large-scale brain networks are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD signal or other recording methods such as EEG, PET and MEG. An emerging paradigm in neuroscience is that cognitive tasks are performed not by individual brain regions working in isolation but by networks consisting of several discrete brain regions that are said to be "functionally connected". Functional connectivity networks may be found using algorithms such as clustering, spatial independent component analysis (ICA), seed based, and others. Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.
Corticomuscular Coherence relates to the synchrony in the neural activity of brain's cortical areas and muscle. The neural activities are picked up by electrophysiological recordings from the brain and muscle (EMG). It is a method to study the neural control of movement.
Intermuscular Coherence is a measure to quantify correlations between the activity of two muscles, which is often assessed using electromyography. The correlations in muscle activity are quantified in frequency domain, and therefore referred to as intermuscular coherence.
Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. It uses nonlinear state-space models in continuous time, specified using stochastic or ordinary differential equations. DCM was initially developed for testing hypotheses about neural dynamics. In this setting, differential equations describe the interaction of neural populations, which directly or indirectly give rise to functional neuroimaging data e.g., functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) or electroencephalography (EEG). Parameters in these models quantify the directed influences or effective connectivity among neuronal populations, which are estimated from the data using Bayesian statistical methods.
Brain-body interactions are patterns of neural activity in the central nervous system to coordinate the activity between the brain and body. The nervous system consists of central and peripheral nervous systems and coordinates the actions of an animal by transmitting signals to and from different parts of its body. The brain and spinal cord are interwoven with the body and interact with other organ systems through the somatic, autonomic and enteric nervous systems. Neural pathways regulate brain-body interactions and allow to sense and control its body and interact with the environment.
References
- ↑ Walter, D. O. (1963-08-01). "Spectral analysis for electroencephalograms: mathematical determination of neurophysiological relationships from records of limited duration". Experimental Neurology. 8 (2): 155–181. doi:10.1016/0014-4886(63)90042-6. ISSN 0014-4886. PMID 20191690.
- ↑ Sklar, B.; Hanley, J.; Simmons, W. W. (1972-12-15). "An EEG experiment aimed toward identifying dyslexic children". Nature. 240 (5381): 414–416. doi:10.1038/240414a0. ISSN 0028-0836. PMID 4564321.
- ↑ Lei, Xu; Wu, Taoyu; Valdes-Sosa, Pedro (2015-01-01). "Incorporating priors for EEG source imaging and connectivity analysis". Frontiers in Neuroscience. 9: 284. doi:10.3389/fnins.2015.00284. ISSN 1662-453X. PMC 4539512 . PMID 26347599.
- ↑ Ramírez, Rey R.; Wipf, David; Baillet, Sylvain (2010-01-01). Chaovalitwongse, Wanpracha; Pardalos, Panos M.; Xanthopoulos, Petros (eds.). Computational Neuroscience . Springer Optimization and Its Applications. Springer New York. pp. 127–155. doi:10.1007/978-0-387-88630-5_8. ISBN 9780387886299.
- ↑ He, B.; Liu, Z. (2008-01-01). "Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG". IEEE Reviews in Biomedical Engineering. 1: 23–40. doi:10.1109/RBME.2008.2008233. ISSN 1937-3333. PMC 2903760 . PMID 20634915.
- ↑ Delorme, Arnaud; Palmer, Jason; Onton, Julie; Oostenveld, Robert; Makeig, Scott (2012-02-15). "Independent EEG Sources Are Dipolar". PLOS ONE. 7 (2): e30135. doi:10.1371/journal.pone.0030135. ISSN 1932-6203. PMC 3280242 . PMID 22355308.
- ↑ Mehrkanoon, Saeid; Breakspear, Michael; Britz, Juliane; Boonstra, Tjeerd W. (2014-09-17). "Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography". Brain Connectivity. 4 (10): 812–825. doi:10.1089/brain.2014.0280. ISSN 2158-0014. PMC 4268557 . PMID 25230358.
- ↑ Halliday, D. M., Rosenberg, J. R., Amjad, A. M., Breeze, P., Conway, B. A., & Farmer, S. F. (1995). A framework for the analysis of mixed time series/point process data—Theory and application to the study of physiological tremor, single motor unit discharges and electromyograms. Progress in Biophysics and Molecular Biology, 64(2–3), 237–278. http://doi.org/10.1016/S0079-6107(96)00009-0
- ↑ Halliday, D. M., & Rosenberg, J. R. (1999). Time and frequency domain analysis of spike train and time series data. In Modern techniques in neuroscience research (pp. 503–543). Springer. Retrieved from http://doi.org/10.1007/978-3-642-58552-4_18