Glossary
Date: June 23, 2024 11:15 PM
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Diffusion tensor imaging (DTI)
= model for parsing diffusion MRI data, the measurements acquired for the different directions are put into a 3x3 matrix called a tensor, which can then be defined by eigenvectors and eigenvalues.
a priori approach
have them do a task, that area that lights up = the area related to that task
- not always translatable to differrent modalities
field map
uses a image at two separate echo times to compute the difference in the magnetic field, and tehn apply that to the area with dropout, to estimate what the og reading should have looked like
independent component analysis (ICA)
“finds a set of components that are independent of one another” ex: boundaries identified in the brain through the analysis of functionally specialized areas
validated parcellation scheme
research-based mapping of functional sections of the brain
Diffusion MRI
= measures the diffusion (movement direction & magnitude) of water molecules in the brain; primary method of studying macroscale brain activity
works because axons = water barriers, so the water must flow along the axon
Voxel
A point in space and time in a 3D region
hemodynamic response (HR)
hemodynamic fluctuation in the BOLD response, need to control it out to get real measures of activation
Region of Interest (ROI)
Define specific brain regions or regions of interest for connectivity analysis.
Seed-Based Analysis (SCA)
Examine connectivity patterns from a seed region.
Independent Component Analysis (ICA)
Identify independent components representing functional networks.
nuisance model
method to remove unwanted activation-based correlation. basically fitting a model that explains the variation from the task and the activation. then control based on that model, and analyze other regions for their residuals
beta series model
response to each event estimated w/ a separate regressor for each trial
correlation between these regressors shows correlations between-trials of diff regions
*need 8-10secs between trials to control for HR
Psychophysiological Interaction (PPI)
models how activity in seed region is modulated by some other factor (task)
General Linear Model (GLM)
estimates the coefficient of the task interaction over the timecourse
deconvolution
most likely neuronal signal that would have given rise to that fMRI signal
- principal components analysis (PCA)
- PCA is a method for reexpressing a dataset in terms of a set of components that are uncorrelated,
matrix factorization analysis (MFA)
decomposing matrix into separate compontents → find active networks
orthogonal
uncorrelated
- Principal Components Analysis
- assumption: groups gaussian & orthogonal
- first group: direction thru data w/ most amt variance. 2nd group = second amt variace
- pros: simple + easy to implement
- cons: looking for orthogonal groups & will find even if they’re not there, only sensitive to gaussian distribution signals
- may be used for data reduction (only use first few principal components)
- method: format data into 2 dimensional matrix (voxels in one, timepoints in other), run
large-scale dynamical circuit model:
computational framework that aims to simulate and understand the complex interactions and dynamics of neural circuits on a large scale (ie many circuits and neurons)
dynamic
time dynamics, patterns of activity over time
dynamic connectivity
signal correlation between 2 regions over time
stochastic
random
neuronal dynamics
changes in electrical and chemical activity of neurons and network as a whole
synchrony:
simultaneous action / occurrence
spontaneous brain activity aka resting state analysis
analyzing which areas are firing simultaneously/connectedly while the body is at rest in the fMRI scanner
local circuit properties
average properties of the neural circuitry in different regions of the brain. ie synaptic strength, firing rate, and other anatomical and functional properties
index of
serve as a representative, proportional or indicative measure of another property
principal resting-state functional connectivity (FC) gradient
the gradual change or variation in functional connectivity across the brain
strength observed between different brain regions
parcellation
division of something into subsections
temporal resolution
how accurate it is to real-time— for neuroimaging, measuring every millisecond = high temporal res (EEG is this), measuring every 2 seconds = low (fMRI is this)
Pearson correlation
Common correlation metric to measure how correlated two data series are
sliding window correlation (SWC) analysis
approach for evaluating dynamic functional connectivity
computes a succession of pairwise correlation matrices using the time series from a given parcellation of brain regions
method:
- define window size (aka time points within the whole scan, aka temporal resolution)
- for each time point:
- create correlation matrix for each brain regions to all of the others (correlation measure like Pearson correlation
- stack onto all other matrices
- → creates set of matrices = connectivity over time (aka dynamic connectivity)
- compute statistics for this set of matrices
tensor
multilayer matrix— matrices tend to have only 2 dimensions, X and Y, but a tensor can have infinite dimensions, to build more and more metadata and higher-order analysis into the nodes and links characterized by the connections matrix
functional connectivity vs diffusion MRI
Diffusion MRI (dMRI) and fcMRI have recently emerged as promising tools for mapping the connectivity of the human brain, each with distinct strengths and weaknesses. dMRI measures the diffusion of water, thus allowing direct noninvasive mapping of white matter pathways (Basser et al. 1994). However, dMRI is presently limited to resolving major fiber tracts. By contrast, fcMRI measures intrinsic functional correlations between brain regions (Biswal et al. 1995) and is sensitive to coupling between distributed as well as adjacent brain areas (e.g., see Sepulcre et al. 2010 for discussion). Although not a direct measure of anatomical connectivity, the functional couplings detected by fcMRI are sufficiently constrained by anatomy to provide insights into properties of circuit organization (for reviews, see Fox and Raichle 2007; Van Dijk et al. 2010). When describing these correlations, we use the term functional connectivity as coined by Karl Friston (1994) to denote “temporal correlations between remote neurophysiological events” for which the causal relation is undetermined.
There are important limitations of fcMRI, including sensitivity to indirect anatomical connectivity and functional coupling that changes in response to recent experience and the current task being engaged (Buckner 2010). For these reasons, some discussions of fcMRI have emphasized that intrinsic activity measured by fcMRI reflects the prior history of activity through brain systems and not simply static anatomical connectivity (Power et al. 2010). fcMRI also does not presently provide information about whether connections are feedforward (ascending) or feedback (descending). These limitations constrain how analyses are conducted and results can be interpreted.
FreeSurfer
FreeSurfer is software that constitutes a suite of automated algorithms for reconstructing accurate surface mesh representations of the cortex from individual subjects’ T1 images (Fig. 1, B and C) and the overlay of fMRI on the surfaces for group analysis
silhouette
the silhouette of a data point measures the similarity of the data point to other data points of the same cluster compared with data points belonging to the next closest cluster
large-scale dynamical circuit model:
computational framework that aims to simulate and understand the complex interactions and dynamics of neural circuits on a large scale (ie many circuits and neurons)
dynamic
time dynamics, patterns of activity over time
dynamic connectivity
signal correlation between 2 regions over time
stochastic
random
neuronal dynamics
changes in electrical and chemical activity of neurons and network as a whole
synchrony:
simultaneous action / occurrence
spontaneous brain activity aka resting state analysis
analyzing which areas are firing simultaneously/connectedly while the body is at rest in the fMRI scanner
local circuit properties
average properties of the neural circuitry in different regions of the brain. ie synaptic strength, firing rate, and other anatomical and functional properties
index of
serve as a representative, proportional or indicative measure of another property
principal resting-state functional connectivity (FC) gradient
the gradual change or variation in functional connectivity across the brain
strength observed between different brain regions
parcellation
division of something into subsections
temporal resolution
how accurate it is to real-time— for neuroimaging, measuring every millisecond = high temporal res (EEG is this), measuring every 2 seconds = low (fMRI is this)
Pearson correlation
Common correlation metric to measure how correlated two data series are
sliding window correlation (SWC) analysis
approach for evaluating dynamic functional connectivity
computes a succession of pairwise correlation matrices using the time series from a given parcellation of brain regions
method:
- define window size (aka time points within the whole scan, aka temporal resolution)
- for each time point:
- create correlation matrix for each brain regions to all of the others (correlation measure like Pearson correlation
- stack onto all other matrices
- → creates set of matrices = connectivity over time (aka dynamic connectivity)
- compute statistics for this set of matrices
tensor
multilayer matrix— matrices tend to have only 2 dimensions, X and Y, but a tensor can have infinite dimensions, to build more and more metadata and higher-order analysis into the nodes and links characterized by the connections matrix
tractography
involves defining white matter connections at the macroanatomical scale, which permits the measurement of connectivity strengths by counting the streamlines that link each pair of regions. Streamlines are constructed to pass through multiple adjacent voxels in DTI data, when the principal diffusion tensor per voxel aligns well with some of its direct neighbors (9). Tractography therefore produces subject-specific measures of regional interconnectivity that are ideally suited for brain network-level analysis.
- Diffusion tensor imaging (DTI)
- enables in vivo noninvasive study of white matter in the brain (6). This technique characterizes the diffusion of water molecules, which occurs preferentially in parallel to nerve fibers due to constraints imposed by axonal membranes and myelin sheaths (7). Metrics commonly derived from DTI, such as fractional anisotropy or mean diffusivity, reflect white matter microstructure and can index its integrity (7, 8).
MNI Space
defines the boundaries around the brain, expressed in millimeters, from a set origin
White Matter
White matter structures are largely composed of axonal fibres and myelin sheaths. Communication of neurons with far-away other neurons
Grey Matter (Gray)
Pyramidal and other types of neuronal cell bodies, and their dendrites. Communication of neurons and close-by neighbors
Diffusion tensor imaging (DTI)
= model for parsing diffusion MRI data, the measurements acquired for the different directions are put into a 3x3 matrix called a tensor, which can then be defined by eigenvectors and eigenvalues.
a priori approach
have them do a task, that area that lights up = the area related to that task
- not always translatable to differrent modalities
field map
uses a image at two separate echo times to compute the difference in the magnetic field, and tehn apply that to the area with dropout, to estimate what the og reading should have looked like