In the usual GLM parlance, an "__event__" is a stimulus or a series of stimuli of some specified shape that becomes a single regressor within the model. An event may be specific to one run or span a series of runs. When fitting data, a single scaling factor is applied to each event, and the magnitude of the event (or scaling factor) can be tested for statistical significance. Event shapes correspond to an expected model for the neural response (e.g, rapid on/off, or slow wash-in and wash-out of drug). __Conditions__ are statistical tests on the magnitude of a single event or the net magnitude of sums or differences of events.

Event identifiers

To keep i/o simple on the display user interface, *all events are denoted by a single numeric or alphabetic character (1-9, a-z, A-Z)*. WARNING: The Mac OS X file system does not distinguish between upper and lower case, so do not use a small and capital event (e.g., a & A) in the same GLM.

Event shapes

For sensorimotor or cognitive stimuli, the standard event type is "square", meaning that neural function increase and decreases very rapidly (instantaneously) relative to the response of the blood supply (with which the square shape can be convolved). "ramp-up" and "ramp-down" events are similar to the "square" event are generally are used only to test assumptions about the neural response. Of course, the neural response can be represented as a summation of a square and ramp event. "gamma" events take the form t/tau*exp(-t/tau) and generally represent drug stimuli. Alternatively, one might use a "sigmoidal" event for a response that does not resolve toward baseline at later times. A "table" event points to a column in a table file that is associated with each run. This event allows non-parametric data (e.g., blood pressure, behavioral indices, motion-correction parameters, ...) with one value per time point. Events that have been tested less well (by me) include sine and cosine events, and interaction events, which are products of other events.

Conditions

A "condition" (or “contrast”) refers to a specific statistical test within the GLM. For instance, if there are two events defined in the GLM with names L and R for stimulation of a left or right sensorimotor area, then one might want to create statistical maps corresponding to the individual events, the sum of events, and the difference of events. The sum of events is specified by just concatenating the individual events to be "LR". The difference of the two events could be either "L-R" or "R-L". If there were 4 events labeled as 1, 2, 3, and 4, then condition 12-34 means "+1+2-3-4", so that everything that occurs after the "-" sign is negative. If one want to put values other than 1 or -1 in the contrast vector, such as "event 1 - 0.5 * event 2", then the only way to do this to scale the event within the stimulus to have a magnitude different from 1. This is an uncommon need, but it does occur. For instance, one might want to combine two injections of different doses of one drug into a single functional map using a single regressor/event under the hypothesis of a specific dose response.

One can create F tests as well as T tests. An F test, which asks if either of one or more specific tests reached significance, is created by inserting a comma. The condition "L,R" is an F test for either L or R, whereas "LR" is a T test for the sum of L and R.

Multiple conditions are listed together with one or more spaces separating them in the GLM control file: "conditions L R L-R"

Visualizing regressors

After convolution with a hemodynamic response function, event regressors in the GLM can be visualized in two ways:

- a file named "basis.dat" is generated by running the GLM executable; this file contains all the basis functions (regressors) used the GLM, and
- the time graph in the display executable will show the overall fit as well as individual fits; note that individual events can be displayed separately or omitted from the time series in this display when using the fMRI GLM. Use the "include" or "exclude" fields below the graph window.