Voxel-based morphometry is a computational approach to neuroanatomy that measures differences in local concentrations of brain tissue, through a voxel-wise comparison of multiple brain images. [1] [2] In traditional morphometry, volume of the whole brain or its subparts is measured by drawing regions of interest (ROIs) on images from brain scanning and calculating the volume enclosed. However, this is time consuming and can only provide measures of rather large areas. Smaller differences in volume may be overlooked. The value of VBM is that it allows for comprehensive measurement of differences, not just in specific structures, but throughout the entire brain. VBM registers every brain to a template, which gets rid of most of the large differences in brain anatomy among people. Then the brain images are smoothed so that each voxel represents the average of itself and its neighbors. Finally, the image volume is compared across brains at every voxel.
However, VBM can be sensitive to various artifacts, which include misalignment of brain structures, misclassification of tissue types, differences in folding patterns and in cortical thickness. [3] All these may confound the statistical analysis and either decrease the sensitivity to true volumetric effects, or increase the chance of false positives. For the cerebral cortex, it has been shown that volume differences identified with VBM may reflect mostly differences in surface area of the cortex, rather than differences in cortical thickness. [4] [5]
Over the past two decades, hundreds of studies have shed light on the neuroanatomical structural correlates of neurological and psychiatric disorders. Many of these studies were performed using voxel-based morphometry (VBM), a whole-brain technique for characterizing between groups' regional volume and tissue concentration differences from structural magnetic resonance imaging (MRI) scans. [6]
One of the first VBM studies and one that came to attention in mainstream media was a study on the hippocampus brain structure of London taxicab drivers. [7] The VBM analysis showed the back part of the posterior hippocampus was on average larger in the taxi drivers compared to control subjects while the anterior hippocampus was smaller. London taxi drivers need good spatial navigational skills and scientists have usually associated hippocampus with this particular skill.
Another famous VBM paper was a study on the effect of age on gray and white matter and CSF of 465 normal adults. [8] The VBM analysis showed global gray matter was decreased linearly with age, especially for men, whereas global white matter did not decline with age.
A key description of the methodology of voxel-based morphometry is Voxel-Based Morphometry—The Methods [9] —one of the most cited articles in the journal NeuroImage . [10] The usual approach for statistical analysis is mass-univariate (analysis of each voxel separately), but pattern recognition may also be used, e.g., for classifying patients from healthy. [11]
Usually VBM is performed for examining differences across subjects, but it may also be used to examine neuroanatomical differences between hemispheres detecting brain asymmetry. [12] [13] A technical procedure for such an investigation may use the following steps: [14] [15]
The outcome of these steps is a statistical parametric map, highlighting all voxels of the brain where intensities (volume or GM concentration depending on whether the modulation step has been applied or not) in a group images are significantly lower/higher than those in the other group under investigation.
Before the advent of VBM, the manual delineation of region of interest was the gold standard for measuring the volume of brain structures. However, compared to the region of interest approach, VBM presents a large number of advantages that explain its wide popularity within the neuroimaging community. Indeed, it is an automated and relatively easy-to–use, time-efficient, whole-brain tool that could detect the focal microstructural differences in brain anatomy in vivo between groups of individuals without requiring any a priori decision concerning which structure to evaluate. Moreover, VBM exhibits comparable accuracy to manual volumetry. Indeed, several studies have shown good correspondence between the two techniques, providing confidence in the biological validity of the VBM approach. [16]
Grey matter, or brain matter in American English, 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.
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.
In neuroanatomy, 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 parieto-occipital sulcus, and underneath by the subparietal sulcus. It is involved with episodic memory, visuospatial processing, reflections upon self, and aspects of consciousness.
Brodmann area 9, or BA9, refers to a cytoarchitecturally defined portion of the frontal cortex in the brain of humans and other primates. Its cytoarchitecture is referred to as granular due to the concentration of granule cells in layer IV. It contributes to the dorsolateral and medial prefrontal cortex.
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 studying 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 brain imaging software 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.
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.
Seed-based d mapping or SDM is a statistical technique created by Joaquim Radua for meta-analysis studies assessing differences in brain activity or structure via neuroimaging techniques such as fMRI, VBM, DTI or PET. It may also refer to a specific piece of software created by the SDM Project to carry out such meta-analyses.
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.
Karl John Friston FRS FMedSci FRSB is a British neuroscientist and theoretician at University College London. He is an authority on brain imaging and theoretical neuroscience, especially the use of physics-inspired statistical methods to model neuroimaging data and other random dynamical systems. Friston is a key architect of the free energy principle and active inference. In imaging neuroscience he is best known for statistical parametric mapping and dynamic causal modelling. Friston also acts as a scientific advisor to numerous groups in industry.
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 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.
The parieto-frontal integration theory (P-FIT) considers intelligence to relate to how well different brain regions integrate to form intelligent behaviors. The theory proposes that large scale brain networks connect brain regions, including regions within frontal, parietal, temporal, and cingulate cortices, underlie the biological basis of human intelligence. These regions, which overlap significantly with the task-positive network, allow the brain to communicate and exchange information efficiently with one another. Support for this theory is primarily based on neuroimaging evidence, with support from lesion studies. The P-FIT is influential in that it explains the majority of current neuroimaging findings, as well as increasing empirical support for cognition being the result of large-scale brain networks, rather than numerous domain-specific processes or modules. A 2010 review of the neuroscience of intelligence described P-FIT as "the best available answer to the question of where in the brain intelligence resides".
Joaquim Radua is a Spanish psychiatrist and developer of methods for meta-analysis of neuroimaging studies. He has been named as one of the most cited researchers in Psychiatry / Psychology.
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.
CAT is a free and open source software package used for the analysis of structural brain imaging data, in particular magnetic resonance imaging (MRI). Developed by Christian Gaser and Robert Dahnke of the Structural Brain Mapping Group at the University of Jena, CAT is an extension of the SPM software.
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