Gray matter is a major component of the central nervous system, consisting of neuronal cell bodies, neuropil, glial cells, synapses, and capillaries. Grey matter is distinguished from white matter in that it contains numerous cell bodies and relatively few myelinated axons, while white matter contains relatively few cell bodies and is composed chiefly of long-range myelinated axons. The colour difference arises mainly from the whiteness of myelin. In living tissue, grey matter actually has a very light grey colour with yellowish or pinkish hues, which come from capillary blood vessels and neuronal cell bodies.
The caudate nucleus is one of the structures that make up the corpus striatum, which is a component of the basal ganglia. While the caudate nucleus has long been associated with motor processes due to its role in Parkinson's disease, it plays important roles in various other nonmotor functions as well, including procedural learning, associative learning and inhibitory control of action, among other functions. The caudate is also one of the brain structures which compose the reward system and functions as part of the cortico–basal ganglia–thalamic loop.
The precuneus is the portion of the superior parietal lobule on the medial surface of each brain hemisphere. It is located in front of the cuneus. The precuneus is bounded in front by the marginal branch of the cingulate sulcus, at the rear by the parietooccipital sulcus, and underneath by the subparietal sulcus. It is involved with episodic memory, visuospatial processing, reflections upon self, and aspects of consciousness.
The planum temporale is the cortical area just posterior to the auditory cortex within the Sylvian fissure. It is a triangular region which forms the heart of Wernicke's area, one of the most important functional areas for language. Original studies on this area found that the planum temporale was one of the most asymmetric regions in the brain, with this area being up to ten times larger in the left cerebral hemisphere than the right.
Neuroscience and intelligence refers to the various neurological factors that are partly responsible for the variation of intelligence within species or between different species. A large amount of research in this area has been focused on the neural basis of human intelligence. Historic approaches to study the neuroscience of intelligence consisted of correlating external head parameters, for example head circumference, to intelligence. Post-mortem measures of brain weight and brain volume have also been used. More recent methodologies focus on examining correlates of intelligence within the living brain using techniques such as magnetic resonance imaging (MRI), functional MRI (fMRI), electroencephalography (EEG), positron emission tomography and other non-invasive measures of brain structure and activity.
Functional integration is the study of how brain regions work together to process information and effect responses. Though functional integration frequently relies on anatomic knowledge of the connections between brain areas, the emphasis is on how large clusters of neurons – numbering in the thousands or millions – fire together under various stimuli. The large datasets required for such a whole-scale picture of brain function have motivated the development of several novel and general methods for the statistical analysis of interdependence, such as dynamic causal modelling and statistical linear parametric mapping. These datasets are typically gathered in human subjects by non-invasive methods such as EEG/MEG, fMRI, or PET. The results can be of clinical value by helping to identify the regions responsible for psychiatric disorders, as well as to assess how different activities or lifestyles affect the functioning of the brain.
FreeSurfer is a brain imaging software package originally developed by Bruce Fischl, Anders Dale, Martin Sereno, and Doug Greve. Development and maintenance of FreeSurfer is now the primary responsibility of the Laboratory for Computational Neuroimaging at the Athinoula A. Martinos Center for Biomedical Imaging. FreeSurfer contains a set of programs with a common focus of analyzing magnetic resonance imaging (MRI) scans of brain tissue. It is an important tool in functional brain mapping and contains tools to conduct both volume based and surface based analysis. FreeSurfer includes tools for the reconstruction of topologically correct and geometrically accurate models of both the gray/white and pial surfaces, for measuring cortical thickness, surface area and folding, and for computing inter-subject registration based on the pattern of cortical folds.
Talairach coordinates, also known as Talairach space, is a 3-dimensional coordinate system of the human brain, which is used to map the location of brain structures independent from individual differences in the size and overall shape of the brain. It is still common to use Talairach coordinates in functional brain imaging studies and to target transcranial stimulation of brain regions. However, alternative methods such as the MNI Coordinate System have largely replaced Talairach for stereotaxy and other procedures.
NeuroImage is a peer-reviewed scientific journal covering research on neuroimaging, including functional neuroimaging and functional human brain mapping. The current Editor in Chief is Michael Breakspear. Abstracts from the annual meeting of the Organization for Human Brain Mapping have been published as supplements to the journal. Members of the Organization for Human Brain Mapping are eligible for reduced subscription rates. In 2012, Elsevier launched an online-only, open access sister journal to NeuroImage, entitled NeuroImage: Clinical.
Statistical shape analysis is an analysis of the geometrical properties of some given set of shapes by statistical methods. For instance, it could be used to quantify differences between male and female gorilla skull shapes, normal and pathological bone shapes, leaf outlines with and without herbivory by insects, etc. Important aspects of shape analysis are to obtain a measure of distance between shapes, to estimate mean shapes from samples, to estimate shape variability within samples, to perform clustering and to test for differences between shapes. One of the main methods used is principal component analysis (PCA). Statistical shape analysis has applications in various fields, including medical imaging, computer vision, computational anatomy, sensor measurement, and geographical profiling.
Psychophysiological interaction (PPI) is a brain connectivity analysis method for functional brain imaging data, mainly functional magnetic resonance imaging (fMRI). It estimates context-dependent changes in effective connectivity (coupling) between brain regions. Thus, PPI analysis identifies brain regions whose activity depends on an interaction between psychological context and physiological state of the seed region.
Brain morphometry is a subfield of both morphometry and the brain sciences, concerned with the measurement of brain structures and changes thereof during development, aging, learning, disease and evolution. Since autopsy-like dissection is generally impossible on living brains, brain morphometry starts with noninvasive neuroimaging data, typically obtained from magnetic resonance imaging (MRI). These data are born digital, which allows researchers to analyze the brain images further by using advanced mathematical and statistical methods such as shape quantification or multivariate analysis. This allows researchers to quantify anatomical features of the brain in terms of shape, mass, volume, and to derive more specific information, such as the encephalization quotient, grey matter density and white matter connectivity, gyrification, cortical thickness, or the amount of cerebrospinal fluid. These variables can then be mapped within the brain volume or on the brain surface, providing a convenient way to assess their pattern and extent over time, across individuals or even between different biological species. The field is rapidly evolving along with neuroimaging techniques—which deliver the underlying data—but also develops in part independently from them, as part of the emerging field of neuroinformatics, which is concerned with developing and adapting algorithms to analyze those data.
Sophia Frangou is Professor of Psychiatry at the Icahn School of Medicine at Mount Sinai where she heads the Psychosis Research Program. She is a Fellow of the Royal College of Psychiatrists and Vice-Chair of the RCPsych Panamerican Division. She is a Fellow of the European Psychiatric Association (EPA) and of the American Psychiatric Association (APA). She served as vice-president for Research of the International Society for Bipolar Disorders from 2010–2014. She has also served on the Council of the British Association for Psychopharmacology. She is founding member of the EPA NeuroImaging section and founding chair of the Brain Imaging Network of the European College of Neuropsychopharmacology. She is one of the two Editors of European Psychiatry, the official Journal of the European Psychiatric Association.
Karl John Friston FRS, FMedSci, FRSB, is a British neuroscientist at University College London and an authority on brain imaging. He gained reputation as the main proponent of the free energy principle, active inference and predictive coding theory.
Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care.
Eleanor Anne Maguire is an Irish neuroscientist. Since 2007, she has been Professor of Cognitive Neuroscience at University College London where she is also a Wellcome Trust Principal Research Fellow.
The neuroscience of sex differences is the study of characteristics that separate the male and female brain. Psychological sex differences are thought by some to reflect the interaction of genes, hormones, and social learning on brain development throughout the lifespan.
The delayed-maturation theory of obsessive-compulsive disorder suggests that obsessive-compulsive disorder (OCD) can be caused by delayed maturation of the frontal striatal circuitry or parts of the brain that make up the frontal cortex, striatum, or integrating circuits. Some researchers suspect that variations in the volume of specific brain structures can be observed in children that have OCD. It has not been determined if delayed-maturation of this frontal circuitry contributes to the development of OCD or if OCD is the ailment that inhibits normal growth of structures in the frontal striatal, frontal cortex, or striatum. However, the use of neuroimaging has equipped researchers with evidence of some brain structures that are consistently less adequate and less matured in patients diagnosed with OCD in comparison to brains without OCD. More specifically, structures such as the caudate nucleus, volumes of gray matter, white matter, and the cingulate have been identified as being less developed in people with OCD in comparison to individuals that do not have OCD. However, the cortex volume of the operculum (brain) is larger and OCD patients are also reported to have larger temporal lobe volumes; which has been identified in some women patients with OCD. Further research is needed to determine the effect of these structural size differences on the onset and degree of OCD and the maturation of specific brain structures.
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.
An estimated 90% of the world's human population consider themselves to be right-handed. The human brain's control of motor function is a mirror image in terms of connectivity; the left hemisphere controls the right hand and vice versa. This theoretically means that the hemisphere contralateral to the dominant hand tends to be more dominant than the ipsilateral hemisphere, however this is not always the case and there are numerous other factors which contribute in complex ways to physical hand preference.
