Imaging and Unix Glossary

Imaging and Unix Glossary

Index:1 2 3 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

1D files Files generated by the Afni Waver program.

2D Anatomicals See Anatomical files

3dAFNItoANALYZE an image conversion utility that comes with the afni package

3dcalc An afni tool that allows you to perform algebraic operations on images (e.g., multiplying images together to create a mask. See example). See also imcalc.

3dDeconvolve Afni tool for deconvolving data, generally used to identify the HRF of individual voxels using the data and the stimulus information. See Deconvolution Models, Sample Data Analysis with 3DDeconvolve, Irregular Stimulus Timing: Analysis with 3dDeconvolve,

3dinfo An afni utility that shows you information about the history of your afni BRIK.

>3dinfo fred+orig

>3dinfo -verb fred+orig

(for even more information.)

3dIntracranial An afni command used for skull stripping.

3dMINCtoAFNI an image conversion utility that comes with the afni package

3D Anatomicals See Anatomical files

AAL Atlas A free atlas of anatomical regoins in the brain. Used by WFU Pickatlas, MRIcro and Marsar. Official AAL Site

AC-PC The line from the Anterior Commisure to the Posterior Commisure defines a plane of section used in the Talairach atlas and thus frequently desirable as the plane of section for MRIs. A simple graphical representation of how to find this line in the sagittal plane is provided (borrowed from Chris Rorden's Mricro page).

Activ 2000 fMRI analysis package for MS Windows. Activ 2000 Home page

Activation When a voxel responds positively to a condition, that is, the intensity of the signal in the voxel rises over time in response to the condition. InAFNI, activation and deactivation are represented with different color scales. In SPM, there is no simple way to identify the difference between deactivations and activations.
SPM Archives -- 2000 (#1330)
"You should also be aware that an "activation" or a "deactivation" is always relative to some baseline which may be more or less well defined. If you are using rapid stimulus presentation (short SOA) without null events it will be less well defined, and it will be very difficult to
determine between activations and deactivations. In that case the interpretation of a positive finding in the contrast [1 -1] can be larger activation in A than in B, or less deactivation in A than in B, or anything in betweeen.
http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind00&L=spm&P=R114824&I=-1

ADC See Diffusion.

Adjusted Data In SPM, data adjusted for confounds (e.g., global flow) and high and low pass filtering.

Affine Transformations Mathematical transformations that effectively change the view of an image by transformations such as rotation, translation etc. Affine transformations are considered linear. Nonlinear transformations that alter the relative size of different parts of the brain are non-affine.

From Mathworld: An affine transformation is any transformation that preserves collinearity (i.e., all points lying on a line initially still lie on a line after transformation) and ratios of distances (e.g., the midpoint of a line segment remains the midpoint after transformation). While an affine transformation preserves proportions on lines, it does not necessarily preserve angles or lengths. Any triangle can be transformed into any other by an affine transformation, so all triangles are affine and, in this sense, affine is a generalization of congruent and similar.

If you have a partial brain or a very abnormal brain, you may minimize non-affine transformations:

spm99: These options are under Defaults->Spatial Normalization->Defaults for Parameter Estimation:
Nonlinear Basis functions can be set to None.
Nonlinear Iterations can be set to One (there isn't a None option)
Nonlinear regularization can be set to "Very Light"

spm2: These options are under "Defaults" Defaults->Spatial Normalization->Defaults for Parameter Estimation: Nonlinear Regularization: Very light regularization
# Nonlinear Iterations? One nonlinear iteration

Afni (Automated Functional Neuro-Imaging), a free unix based fmri image processing program, available as source code or binaries for several unix platforms. Start afni by typing >afni at any command prompt (installed on merlin). Afni uses the BRIK/HEAD file format, but now supports the MINC format. Using to3d, one can create an appropriate HEAD file for any uncompressed image data, including SPM (Analyze), DICOM etc. See Conversion. http://afni.nimh.nih.gov/afni/ . See also nifti, subbrick, multibucket image, fim, fico, to3d, afni preprocessing scripts.

The standard citation for the Afni software is:

Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers & Biomedical Research, 29, 162-173.

afnireg2bshort An image conversion program that comes with the mgh package. Very similar to grecon2bshort.

AIR (Automated Image Registration) Developed by Roger Woods, this program does an excellent job of realigning functional images that might otherwise be useless because of movement artifacts. AIR uses a file format compatible with Analyze. http://bishopw.loni.ucla.edu/AIR

Analyze A medical image processing program from the Mayo Clinic. With slight modifications, the image file header structure has been used for SPM. Visit the Analyze Home Page. See also Analyze File Format Specs, Conversion and Image.

analyze2genesis One of a suite of imaging tools from UCLA. This one converts an img/hdr pair back into a series of *.MR files. See UCLA Brain Imaging Center.

Anatomical files Usually 256x256 grayscale images of the structures in the brain. Our axial images are in radiological orientation by default. Each image represents a slice through the brain and thus has thickness (which means it consistes of voxels rather than pixels). These can be overlaid with the much lower resolution (64x64) functional images, so that the regions of activation in the functional image can be localized in the brain's anatomy. The anatomical images may also be used for morphometry. Our images are in Genesis format (the native format of the GE scanner).
We create 2 different series of these images, the "2D" series which is usually 17-19 images in axial orientation and the 3D series which is usually ~124 images, taken sagittally from left to right.
Our files normally have the 2 bytes of image depth (16 bits) or 65,536 levels of gray (i.e., 2^16). Many image processing programs use 8 bit depth or 256 levels of gray (i.e., 2^8).
If you want to read the image as a "raw" image in ScionImage, ImageJ etc., then you need to know the offset. See also Image. See rdgehdr (Read GE Header).

Anisotropic In diffusion weighted imaging, movement of water molecules that is impeded in some directions more than others (e.g., movement through a tube). Compare to isotropic.

Anterior Toward the face or front of the head. Compare to Posterior

AR(1) or AR(1) + w (or (AR(2), AR(3), etc.): Terms used to describe different models of autocorrelation in your fMRI data. See autocorrelation below for more info. AR stands for autoregression. AR models are used to estimate to what extent the noise at each time point in your data is influenced by the noise in the time point (or points) before it. The amount of autocorrelation of noise is estimated as a model parameter, just like a beta weight. The difference between AR(1), AR(2), AR(1) + w, etc., is in which parameters are estimated. An AR(1) model describes the autocorrelation function in your data by looking only at one time point before each moment. In other words, only the correlation of each time point to the first previous time point is considered. In an AR(2) model, the correlation of each time point to the first previous time point and the second previous time point is considered; in an AR(3) model, the three time points before each time point are considered as parameters, etc. The "w" in AR(1) + w stands for "white noise." An AR(1) + w model assumes the value of noise isn't solely a function of the previous noise; it also includes a random white noise parameter in the model. AR(1) + w models, which are used in SPM2 and other packages, seem to do a pretty good job describes the "actual" fMRI noise function. A good model can be used to remove the effects of noise correlation in your data, thus validating the assumptions of the general linear model. (From Gablab Wiki: Glossary)

Artifact Image artifacts are problems in the image created by metal, problems with the machine, poorly sheilded wires.

Autocorrelation (function, correction, etc.): One major problem in the statistical analysis of fMRI data is the shape of fMRI noise. Analysis with the general linear model assumes each timepoint is an independent observation, implying the noise at each timepoint is independent of the noise at the next timepoint. But several empirical studies have shown that in fMRI, that assumption's simply not true. Instead, the amount of noise at each timepoint is heavily correlated with the amount of noise at the timepoints before and after. fMRI noise is heavily "autocorrelated," i.e., correlated with itself. This means that each timepoint isn't an independent observation - the temporal data is essentially heavily smoothed, which means any statistical analysis that assumes temporal independence will give biased results. The way to deal with this problem is pretty well-established in other scientific domains. If you can estimate what the autocorrelation function is - in other words, what, exactly, is the degree of correlation of the noise from one timepoint to the next - than you can remove the amount of noise that is correlated from the signal, and hence render your noise "white," or random (rather than correlated). This strategy is called pre-whitening, and is referred to in some fMRI packages as autocorrelation correction. The models used to do this in fMRI are mostly AR(1) + w models, but sometimes more complicated ones are used." (From Gablab Wiki: Glossary)

averager One of a suite of imaging tools from UCLA. This program averages together a set of timepoints specified in a text file. See UCLA Brain Imaging Center.

Axial (Same as "transverse" and "horizontal")

B-spline, B-spline interpolation: A type of spline which is the generalization of the Bezier curve. MathWorld has this to say about them: B-Spline. Essentially, though, a B-spline is a type of easily describable and computable function which can take many locally smooth but globally arbitrary shapes. This makes them very nice for interpolation. SPM2 has ditched sinc interpolation in all of its resampling/interpolation functions (like normalization or coregistration - anything involving resampling and/or reslicing). Instead, it's now using B-spline interpolation, improving both computational speed and accuracy." (From Gablab Wiki: Glossary)

Backward Font See Font
Baseline: A) The point from which deviations are measured. In a signal measure like % signal change, the baseline value is the answer to, "Percent signal change from what?" It's the zero point on a % signal change plot. B) A condition in your experiment that's intended to contain all of the cognitive tasks of your experimental condition - except the task of interest. In fMRI, you generally can only measure differences between two conditions (not anything absolute about one condition). So an fMRI baseline task is one where the person is doing everything you're not interested in, and not doing the thing you're interested in. This way you can look at signal during the baseline, subtract it from signal during the experimental condition, and be left with only the signal from the task of interest. Designing a good baseline is crucially important to your experiment. Resting with the eyes open is a common baseline for certain types of experiment, but inappropriate for others, where cognitive activity during rest may corrupt your results. In order to get good estimates of the shape of your HRF, you need to have a baseline condition (as opposed to several experimental conditions.) (From Gablab Wiki: Glossary)

Basis Function (SPM) The hemodynamic response to each stimulus or epoch type is modeled in SPM as one or more basis functions. These are functions that extend over a relatively short (event-related) or long (epoch-related) period of time, and are convolved with the stimulus pulse functions to arrive at the linear regressor(s) representing what the brain response should look like at each voxel. If you ask SPM for "time derivatives", you get one extra basis function for each of the original basis functions. If there are multiple functions comprising the basis set, SPM adjusts them so that they are orthogonal.

In event-related designs, one possible basis function is a single "hrf" function. In SPM99, this is a pre-canned function equal to the sum of two beta functions and extending for 32 seconds, which is used by many investigators as a model of the Hemodynamic Response Function. The "hrf" basis function is fixed in shape, though you can add time and dispersion derivative functions to it to create a basis set that may be a more accurate model.

Definition taken from: The Stanford Gablab: StimBasisPlotting.html

Batch file (See script below)

Bayesian Analysis Opening page for International Society for Bayesian Analysis website.
"Scientific inquiry is an iterative process of integrating accumulating information. Investigators assess the current state of knowledge regarding the issue of interest, gather new data to address remaining questions, and then update and refine their understanding to incorporate both new and old data. Bayesian inference provides a logical, quantitative framework for this process. It has been applied in a multitude of scientific, technological, and policy settings."
http://www.bayesian.org/openpage.html

beta image Also called a parameter image. It's a voxel-by-voxel summary of the beta weight for a given condition. Usually it's written as an actual image file or sub-dataset, so you could look at it just like a regular brain image, exploring the beta weight at each voxel. In SPM, you get one of these written out for every column in your design matrix - one for each experimental effect for which you're estimating parameter values. (From Gablab Wiki: Glossary)

beta weights Also called parameter weights, parameter values, etc. This is the value of the parameter estimated for a given effect / column in your design matrix. If you think of the general linear model as a multiple regression, the beta weight is the slope of the regression line for this effect. The parameter gets its name as a "beta" weight from the standard regression equation: Y = BX + E. Y is the signal, X is the design matrix, E is error, and B is a vector of beta weights, which estimate how much each column of the design matrix contributes to the signal. Beta weights can be examined, summed, and contrasted at the voxel-wise level for a standard analysis of fMRI results. They can also be aggregated across regions or correlated between subjects for a more region-of-interest-based analysis. (From Gablab Wiki: Glossary)

BIC (Brain Imaging Center) at MNI. http://www.bic.mni.mcgill.ca/software/

Big Endian Describes a computer architecture (hardware) in which, within a given multi-byte numeric representation, the most significant byte has the lowest address (the word is stored 'big-end-first'). This is used on Suns, SGI's and MACs. (See also Byte Swapping and Little Endian)

Bitmap Image An image composesd of pixels. Check out the Beginner's Guide to Bitmaps. See also Image, voxel and image depth.

Blackman filter see filter

Block Design (also called "Boxcar" design) An experimental design in which stimuli are presented for fixed periods of time, regardless of subject response. Because a block is treated as a single indivisible unit for analysis, trials within a block should all belong to a single condition. Block design thus reduces the opportunity to randomize and mix trials or to use differences in speed or accuracy of subject response to analyze the data later on (Compare to Event-Related Design, See also Afni Block Design). Blocks are also called epochs.

Boxcar Design (see Block Design).

bfloat The bfloat and accompanying hdr file of the same name (e.g., fred.bfloat and fred.hdr) are an image format used for functional data in the MGH-fsfast software. bfloat files contain blocks of floating point numbers representing the image data, and have a small, very short, header that specifies the number of pixels in each of (only)three dimensions, usually interpreted as Y, X and time. Mutiple slice locations are generally represented by increasing the y dimension to make a vertical stack format. (UCLA Brain Mapping Center Image Format Page)

Brain Voyager A commercial package from the Netherlands for the analysis and visualization of functional and structural magnetic resonance imaging data sets. BrainVoyager can do both standard and surface based analyses and runs on Windows and Unix machines. Brain Voyager uses *.VMR files but appears to be able to import files in Analyze format. Visit the Brain Voyager Home.

Brede Database http://hendrix.imm.dtu.dk/services/jerne/brede/brede.html A database where you input Talairach coordinates and it outputs studies that got activation there. See also xbrain.org

bshort The bshort and accompanying hdr file of the same name (e.g., fred.bshort and fred.hdr) are a format sometimes used for structural/anatomical files by the MGH-fsfast software. "bshort files contain blocks of unsigned short integers representing the image data, and have a small, very short, header that specifies the number of pixels in each of (only)three dimensions, usually interpreted as Y, X and time. Mutiple slice locations are generally represented by increasing the y dimension to make a vertical stack format." (UCLA Brain Mapping Center Image Format Page)

BRIK A BRIK is the afni file that holds images. A BRIK is accompanied by a HEAD file which contains header information about the such things as the size and number of images in the BRIK. Typically a BRIK holds either a small set of anatomical slices (e.g., 17-124) corresponding to a T1 or spgr image set, OR a set of thousands of functional images from a single functional run. See also afni and image, to3d. Bucket See Multibucket Image

Burn See CD Burning.

Byte Swapping The process of converting a file that uses little endian byte order to big endian byte order or vice-versa. The string 'UNIX' might look like 'NUXI' on a machine with a different 'byte order'. We sometimes need to worry about this for our images when we move them from the PC to sun or sgi (or vice-versa). See little endian and big endian. Also see the new afni preprocessing scripts (that help you deal with some of the byte swapping issues).

C
C-Programming A couple of useful sites: http://www.eskimo.com/~scs/cclass/notes/top.html and http://www.cs.cf.ac.uk/Dave/C/node3.html#SECTION00310000000000000000

Canonical HRF A model of an "average" HRF. Intended to describe the shape of a generic HRF; given this shape and the design matrix, an analysis package will look for signals in the fMRI data whose shape matches the canonical HRF. The different analysis packages (SPM, AFNI, BrainVoyager, etc.) use slightly different canonical HRFs, but they all share the same basic features - a gradual rise up to a peak around six seconds, followed by a more gradual fall back to baseline. Some progams model a slight undershoot; some don't. (From Gablab Wiki: Glossary) (From Gablab Wiki: Glossary) Cantata Part of the Khoros package.

Capture Images See Screenshots

Caret (Computerized Anatomical Reconstruction and Editing Toolkit) is designed for interactively viewing, manipulating (flattening), and analyzing surface reconstructions of the cerebral cortex. You can access it on Merlin by typing >caret at the prompt. Caret is distributed as free standing binaries available for sgis, sun and linux systems. Caret's companion program, Surefit, is used to generate the anatomical files that Caret requires, and any endeavour to make flat maps should likely begin with SureFit and then move on to Caret. Several tutorial data sets are available. Caret is one of several cortical cartography programs available from the Van Essen labs. See also the Caret homepage.

cat A unix command (short for concatenate) which can be used to paste one file to the bottom of another file. In the example below, we concatenate file1 and file2 into file3. See also cut and paste (for side by side concatenation):

>cat file1 file2 >file3

Cavity A topological error in which an island of gray matter voxels is stranded in a sea of white matter. Such errors are important in the reconstruction of the gray matter surface. See also topolgy and handle.

CD Burning How to burn CDs on unix and linux machines.

Cell Array A cell array is a useful Matlab structure to know about if you want to work in SPM. A cell array can hold different sized vectors in each cell. In SPM, you can use the cell array to hold a vector of stimulus onsets for each of several conditions (e.g., the vector for the first condition is in cell 1. The vector for the second condition is in cell 2 etc.)
To create a cell array:
>>a{1} = [1 2 4 6 8]
>>a{2} = [ 5 77 89]
>>a{3} = [3 4 5 6 2 1 7 8 9 334]
You now have a cell array, a, that contains 3 row vectors (this is perfect for SPM stimulus onset times) in 3 cells.
To view a description of the cell array:
>>a
To see the contents of cell 1:
>>a{1}
To alter the 4th value in cell 1 from 6 to 99:
>>a{1}(4)=99

See also pg 13 of the SPM99WorkbookStudy1.doc.

Client CNL_FMRI See listserv, and imaging-analysis listserv

CNR Contrast to Noise Ratio. Determines the differences between distinct types of tissues in medical images. Compare to SNR.

con image, contrast image A voxel-by-voxel summary of the value of some contrast you've defined. This is often created as a voxel-by-voxel weighted sum of beta images, with the weights given by the value of the contrast vector. In SPM, it's actually written out as a separate image file; in other programs, it's usually written as a separate sub-bucket or the equivalent. It shouldn't be confused with the statistic image, which is a voxel-by-voxel of the test statistic associated with each contrast value. (In SPM, those statistic images are labeled spmT or spmF images.) Only the contrast images - not the statistic images - are suitable for input to a second-level group analysis. (From Gablab Wiki: Glossary) Contact Phonelist

Contrast The actual signal in fMRI data is unfortunately kind of arbitrary. The numbers at each voxel in your functional images don't have a whole lot of connection to any physiological parameter, and so it's hard to look at a single functional image (or set of images) and know the state of the brain. On the other hand, you can easily look at two functional images and see what's different between them. If those functional images are taken during different experimental conditions, and the difference between them is big enough, then you know something about what's happening in the brain during those conditions, or at least you can probably write a paper claiming you do. Which is good! So the fundamental test in fMRI experiments is not done on individual signal values or beta weights, but rather on differences of those things. A contrast is a way of specifying which images you want to include in that difference. A given contrast is specified as a vector of weights, one for each experimental condition / column in your design matrix. The contrast values are then created by taking a weighted sum of beta weights at each voxel, where the weights are specified by the contrast vector. Those contrast values are then tested for statistical significance in a variety of ways. (From Gablab Wiki: Glossary) See also activation and F-contrast.

Coregistration The process of bringing two brain images into alignment Ideally, you'd like them lined up so that their edges line up and the point represented by a given voxel in one image represents the same point in the other image. Coregistration generally refers specifically to the problem of aligning two images of different modalities - say, T1 fMRI images and PET images, or anatomical MRI scans and functional MRI scans. It goes for some of the same goals as realignment, but it generally uses different algorithms to make it more robust.(From Gablab Wiki: Glossary)

Conversion (between Image formats) Afni allows several conversions...more all the time... between BRIK, IMA, img and mnc files. See Bob Cox's What's New page for the newest updates on these capabilities. MRIcro and ezDicom are also very useful for converting between image formats and displaying different formats (mostly raw, dicom and img). See also UCLA Brain Imaging Center and imconvert. See mri_convert. In this glossary, see, nifti, image and format.

Conversion Test Image The Left Lesion Test Data (This data has a big hole in the left front, so you can test your understanding of what is happening to right and left given a particular program or manipulation. There is a single functional image, and "2D" and "3D" structural images in spm format).

Package	From	To	Sample Command	Notes
afni	BRIK/HEAD	img/hdr	>3dAFNItoANALYZE fred test+orig	Converts an afni pair test+orig.BRIK/HEAD into the appropriate number of spm readable *.img/hdr pairs: one for an anatomical image and one for each time point for a BRIK built from a P-file.
afni	mnc	BRIK/HEAD	>3dMINCtoAFNI test.mnc	Converts test.mnc to an afni BRIK/HEAD pair
afni	BRIK/HEAD	reconstructed P-files	>from3d -input test+orig -prefix fred	deconstructs brik
afni	BRIK/HEAD	mnc	>3dAFNItoMINC brain+orig.*	Creates a single output file, brain.mnc rather than a pair.
afni	BRIK/HEAD	nifti *.nii	>3dAFNItoNIFTI brain+orig	Creates a single output file, brain.nii rather than a pair.
afni	bfloat	BRIK/HEAD	>to3d -epan -prefix name -time:tz 120 17 2000 seqplus 3Df:0:0:64:64:120:'test.bfloat'
afni	bshort	BRIK/HEAD	>to3d -anat -prefix fred test*.bshort	Structurals:This will create fred+orig.BRIK and HEAD files from a series of test bshort files (structural slices). The to3d interface may require additional info. P-file brik (a brik with a time dimension) start with deconstruction to make a series of bshorts, and then format them into bshorts.
afni	img/hdr	BRIK/HEAD	>to3d test.hdr	to3d interface may require additional info
MGH	the output of from3d (for afnireg2bshort) greconed P-files (for grecon2bshort)	bshort	>afnireg2bshort -i study1_3dreg -fgs 1 -nas 17 -nfs 80 >grecon2bshort -i P15872 -fgs 1 -nas 17 -nfs 80	-i=input, -fgs=first good slice, -nas number of anatomical slices, -nfs number of functional slices. A bshort and header file will be produces for each anatomical slice (e.g., 17 files will be produced for these sample commands)
MGH	*.MR	bshort	>MR2bshort -i E22078 -s 2 -fs 1 -ns 17 -o fred -slice3w Post Sept, 2002 default data format (e.g., 42.4.1.001 etc.): >MR2bshort2 -i 42 -s 4 -fs 1 -ns 25 -o out -slice3w	-i <exam#> -s <series#> -fs <first slice> -ns <# of slices> -o <output prefix> -slice3w (this tells the program the numbering should be 3 characters wide). A bshort and hdr file will be produced for each MR file.

Convolution To add waveforms together. A convolution is an integral that expresses the amount of overlap of one function g as it is shifted over another function f. It therefore "blends" one function with another. http://mathworld.wolfram.com/Convolution.html.
Example: convolve Vector A "1 2" with Vector B "2 3 4".
Row 1: Multiply the first element in A by each element in B.
Row 2: Shift right, multiply the second element in A by each element in B.
Row 3/Result: Add values in columns:

>>conv ([1 2] ,[2 3 4])

Row 1	1*2	1*3+	1*4 +
Row 2	shift	2*2	2*3	2*4
Row 3	2	7	10	8

Copy copy files copy directories

COR The native file format used by freesurfer to store 3D structural image data. COR volumes always have 3 dimensions (no time dimension). Each dimension is 256 voxels, voxels are always 1 mm and isotropic. Voxel values are stored as unsigned bytes in coronal slices, one slice to eachfile, labelled COR-001 through COR-256. See also nifti.

Coregistration In SPM, "coregistration" refers specifically to the process of aligning the functional image with a higher resolution anatomical image. See also Realignment.

Coronal

Cut and Paste Check the link to see how to do it in a Unix shell window. See also cat.

D
Databases Databases of brain areas and their apparent functions are becoming more common: See xbrain.org and the Brede database http://hendrix.imm.dtu.dk/services/jerne/brede/brede.html

Deactivation The inverse of activation. When a voxel deactivates, its intensities dip in response to a condition rather than rising in response to a condition. See Activation.

Deconvolution To take waveforms apart. See Convolution and 3dDeconvolve.

Deformation Field A "map" of what stretching, squishing, moving and resizing operations need to be done to each voxel so that the individual brain you are normalizing can be fitted to the template brain. This deformation field is generated as an *sn.mat file by normalization in spm and can be used for vbm. Here we see a representation of a simple deformation field (left) and then we see the field applied to an image of a cross to warp it (Image from: Ashburner, J and Friston, K.J. "Spatial Normalization using Basis Functions" Chapter 3, Human Brain Function)

Depth See Image Depth

Design matrix A model of your experiment and what you expect the neuronal response to it to be. In general represented as a matrix (funnily enough), where each row represents a time point / TR / functional image and each column represents a different experimental effect. It becomes the model in a multiple regression, following the vector equation: Y = BX + E. Y is a vector of length a (equal to nframes from the scanner), usually representing the signal from a single voxel. B is a vector of b, representing the effect sizes for each of b experimental conditions. E is an error vector the same length as Y. X is your design matrix, of size a x b. (From Gablab Wiki: Glossary)

DICOM (Digital Imaging and Communications in Medicine) The DICOM image format is commonly used for transfer and storage of medical images. Visit Chris Rorden's Dicom page for information about the format and free software to view and manipulate it. See also Image, ezDicom and MRIcro.

Diffusion Weighted Imaging (DWI) MRI sequences weighted by the diffusion of water. Diffusion Weighted Imaging (DWI) measures the molecular mobility of water in tissue. Less attenuation of signal is expected in regions of less restriction (i.e., less compartmentalization of the water). Diffusion in biological systems is complex, but directly