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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 magnetic resonance imaging or functional MRI (fMRI) measures brain activity by detecting changes associated with blood flow. This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases.
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.
Statistical parametric mapping (SPM) is a statistical technique for examining differences in brain activity recorded during functional neuroimaging experiments. It was created by Karl Friston. It may alternatively refer to software created by the Wellcome Department of Imaging Neuroscience at University College London to carry out such analyses.
David J. Heeger is an American neuroscientist, psychologist, computer scientist, data scientist, and entrepreneur. He is a professor at New York University, Chief Scientific Officer of Statespace Labs, and Chief Scientific Officer and co-founder of Epistemic AI.
Neuroimaging is the use of quantitative (computational) techniques to study the structure and function of the central nervous system, developed as an objective way of scientifically studying the healthy human brain in a non-invasive manner. Increasingly it is also being used for quantitative studies of brain disease and psychiatric illness. Neuroimaging is a highly multidisciplinary research field and is not a medical specialty.
Event-related functional magnetic resonance imaging (efMRI) is a technique used in magnetic resonance imaging of medical patients.
Neurophysics is the branch of biophysics dealing with the development and use of physical methods to gain information about the nervous system. Neurophysics is an interdisciplinary science using physics and combining it with other neurosciences to better understand neural processes. The methods used include the techniques of experimental biophysics and other physical measurements such as EEG mostly to study electrical, mechanical or fluidic properties, as well as theoretical and computational approaches. The term "neurophysics" is a portmanteau of "neuron" and "physics".
Signal enhancement by extravascular water protons, or SEEP, is a contrast mechanism for functional magnetic resonance imaging (fMRI), which is an alternative to the more commonly employed BOLD contrast. This mechanism for image contrast changes corresponding to changes in neuronal activity was first proposed by Dr. Patrick Stroman in 2001. SEEP contrast is based on changes in tissue water content which arise from the increased production of extracellular fluid and swelling of neurons and glial cells at sites of neuronal activity. Because the dominant sources of MRI signal in biological tissues are water and lipids, an increase in tissue water content is reflected by a local increase in MR signal intensity. A correspondence between BOLD and SEEP signal changes, and sites of activity, has been observed in the brain and appears to arise from the common dependence on changes in local blood flow to cause a change in blood oxygenation or to produce extracellular fluid. The advantage of SEEP contrast is that it can be detected with MR imaging methods which are relatively insensitive to magnetic susceptibility differences between air, tissues, blood, and bone. Such susceptibility differences can give rise to spatial image distortions and areas of low signal, and magnetic susceptibility changes in blood give rise to the BOLD contrast for fMRI. The primary application of SEEP to date has been fMRI of the spinal cord because the bone/tissue interfaces around the spinal cord cause poor image quality with conventional fMRI methods. The disadvantages of SEEP compared to BOLD contrast are that it reveals more localized areas of activity, and in the brain the signal intensity changes are typically lower, and it can therefore be more difficult to detect.
Functional magnetic resonance imaging (fMRI) of the spinal cord is an adaptation of the fMRI method that has been developed for use in the brain (1). Although the basic principles underlying the methods are the same, spinal fMRI requires a number of specific adaptations to accommodate the periodic motion of the spinal cord, the small cross-sectional dimensions, the length, and the fact that the magnetic field that is used for MRI varies with position in the spinal cord because of magnetic susceptibility differences between bone and tissues. Spinal fMRI has been used to produce maps of neuronal activity at most levels of the spinal cord in response to various stimuli, such as touch, vibration, and thermal changes, and with motor tasks. Research applications of spinal fMRI to date include studies of normal sensory and motor function, and studies of the effects of trauma to the spinal cord (1-3) and multiple sclerosis (4). Two different data acquisition methods have been applied, one based on the established BOLD fMRI methods used in the brain, and the other based on SEEP contrast with essentially proton-density weighted spin-echo imaging. The majority of the studies published to date are based on the SEEP contrast method. Methods demonstrated to overcome the challenges listed above include using a recording of the heart-beat to account for the related time course of spinal cord motion, acquiring image data with relatively high spatial resolution to detect fine structural details, and acquiring images in thin contiguous sagittal slices to span a large extent of the spinal cord. Methods based on BOLD contrast have employed parallel imaging techniques to accelerate data acquisition, and imaging slices transverse to the spinal cord, in order to reduce the effects of spatial magnetic field distortions (5). Methods based on SEEP contrast have been developed specifically because they have low sensitivity to magnetic field distortions while maintaining sensitivity to changes in neuronal activity.
Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected spanning the nervous system including the various areas of cortex, cerebellum, the retina, the peripheral nervous system and neuromuscular junctions.
Nikos K. Logothetis is a Greek biologist and neuroscientist. Logothetis studies visual perception and object recognition; he is well-known for his work demonstrating that BOLD fMRI data is related to neuronal activity. Logothetis directed the department of Physiology of Cognitive Processes at the Max Planck Institute for Biological Cybernetics in Tübingen from 1996 to 2020. He will co-direct the International Center for Primate Brain Research in Shanghai beginning in late 2020 or early 2021.
Robert Turner is a British neuroscientist, physicist, and social anthropologist. He has been a director and professor at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, and is an internationally recognized expert in brain physics and magnetic resonance imaging (MRI). Coils inside every MRI scanner owe their shape to his ideas.
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 brain networks have been identified, one of which is the default mode network. These brain networks 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.
Functional magnetic resonance spectroscopy of the brain (fMRS) uses magnetic resonance imaging (MRI) to study brain metabolism during brain activation. The data generated by fMRS usually shows spectra of resonances, instead of a brain image, as with MRI. The area under peaks in the spectrum represents relative concentrations of metabolites.
The following outline is provided as an overview of and topical guide to brain mapping:
An MRI sequence in magnetic resonance imaging (MRI) is a particular setting of pulse sequences and pulsed field gradients, resulting in a particular image appearance.
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.
Victoria L. Morgan is an American biomedical engineer who is a professor of neurology and radiology at Vanderbilt University. She makes use of functional magnetic resonance imaging to understand neural activation and function. Her research looks to quantify and understand the impact of epilepsy in the brain.
Functional MRI imaging methods have allowed researchers to combine neurocognitive testing with structural neuroanatomical measures, take into consideration both cognitive and affective paradigms, and subsequently create computer-aided diagnosis techniques and algorithms. Functional MRI has several benefits, such as its non-invasive quality, relatively high spatial resolution, and decent temporal resolution. One particular method used in recent research is resting-state functional magnetic resonance imaging, rs-fMRI. fMRI imaging has been applied to numerous behavioral studies for schizophrenia, the findings of which have hinted toward potential brain regions that govern key characteristics in cognition and affect.
References
- ↑ Heeger, D.J. & Ress, D. (February 2002). What does fMRI tell us about Neuronal Activity?, Nature Reviews, Volume 3, 142-151.
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