 | This article needs additional citations for verification .(January 2021) |
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.
Functional neuroimaging is one type of 'brain scanning'. It involves the measurement of brain activity. The measurement technique depends on the imaging technology (e.g., fMRI and PET). The scanner produces a 'map' of the area that is represented as voxels. Each voxel represents the activity of a specific volume in three-dimensional space. The exact size of a voxel varies depending on the technology. fMRI voxels typically represent a volume of 27 mm3 in an equilateral cuboid.
Researchers examine brain activity linked to a specific mental process or processes. One approach involves asking 'which areas of the brain are significantly more active when doing task A compared to task B?'. Although the tasks might be designed to be identical, except for the behaviour under investigation, the brain is still likely to show changes in activity between tasks due to factors other than task differences (as the brain coordinates many parallel functions unrelated to the task). Further, the signal may contain noise from the imaging process itself.
To filter out these random effects, and to highlight the areas of activity linked specifically to the process under investigation, statistics look for the most significant differences. This involves a multi-stage process to prepare the data, and to analyse it using a general linear model.
Images from the scanner may be pre-processed to remove noise or correct for sampling errors.
A study usually scans a subject several times. To account for the motion of the head between scans, the images are typically adjusted so voxels in each image correspond (approximately) to the same site in the brain. This is referred to as realignment or motion correction, see image realignment.
Functional neuroimaging studies usually involve multiple participants, each of whom have differently shaped brains. All are likely to have the same gross anatomy, saving minor differences in overall brain size, individual variation in topography of the gyri and sulci of the cerebral cortex, and morphological differences in deep structures such as the corpus callosum. To aid comparisons, the 3D image of each brain is transformed so that superficial structures line up, via spatial normalization . Such normalization typically involves translation, rotation and scaling and nonlinear warping of the brain surface to match a standard template. Standard brain maps such as the Talairach-Tournoux or templates from the Montréal Neurological Institute (MNI) allow researchers from across the world to compare their results.
Images can be smoothed to make the data less noisy (similar to the 'blur' effect used in some image-editing software) by which voxels are averaged with their neighbours, typically using a Gaussian filter or by wavelet transformation.
Parametric statistical models are assumed at each voxel, using the general linear model to describe the data variability in terms of experimental and confounding effects, with residual variability. Hypotheses expressed in terms of the model parameters are assessed at each voxel with univariate statistics.
Analyses may examine differences over time (i.e. correlations between a task variable and brain activity in a certain area) using linear convolution models of how the measured signal is caused by underlying changes in neural activity.
Because many statistical tests are conducted, adjustments have to be made to control for type I errors (false positives) potentially caused by the comparison of levels of activity over many voxels. A type I error would result in falsely assessing background brain activity as related to the task. Adjustments are made based on the number of resels in the image and the theory of continuous random fields in order to set a new criterion for statistical significance that adjusts for the problem of multiple comparisons.
Differences in measured brain activity can be represented in various ways.
They can be presented as a table, displaying coordinates that show the most significant differences in activity between tasks. Alternatively, differences in brain activity can be shown as patches of colour on a brain 'slice', with the colours representing the location of voxels with statistically significant differences between conditions. The color gradient is mapped to statistical values, such as t-values or z-scores. This creates an intuitive and visually appealing map of the relative statistical strength of a given area.
Differences in activity can be represented as a 'glass brain', a representation of three outline views of the brain as if it were transparent. Only the patches of activation are visible as areas of shading. This is useful as a means of summarizing the total area of significant change in a given statistical comparison.
SPM is software written by the Wellcome Department of Imaging Neuroscience at University College London to aid in the analysis of functional neuroimaging data. It is written using MATLAB and is distributed as free software. [1]
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.
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.
Analysis of Functional NeuroImages (AFNI) is an open-source environment for processing and displaying functional MRI data—a technique for mapping human brain activity.
Neurophilosophy or the philosophy of neuroscience is the interdisciplinary study of neuroscience and philosophy that explores the relevance of neuroscientific studies to the arguments traditionally categorized as philosophy of mind. The philosophy of neuroscience attempts to clarify neuroscientific methods and results using the conceptual rigor and methods of philosophy of science.
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 research studies of brain disease and psychiatric illness. Neuroimaging is highly multidisciplinary involving neuroscience, computer science, psychology and statistics, and is not a medical specialty. Neuroimaging is sometimes confused with neuroradiology.
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.
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.
Event-related functional magnetic resonance imaging (efMRI) is a technique used in magnetic resonance imaging of medical patients.
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-analyzing studies on differences in brain activity or structure which used 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.
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.
The following outline is provided as an overview of and topical guide to brain mapping:
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".
CONN is a Matlab-based cross-platform imaging software for the computation, display, and analysis of functional connectivity in fMRI in the resting state and during task.
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.