# Presentation: 062085

## 3dDeconvolveAdvanced Features

**Just in case you weren’t**
**confused enough already**

## Other Features of 3dDeconvolve - 1

-  **-input1D** = used to process a single time series, rather than a dataset full of time series
-  e.g., test out a stimulus timing sequence on sample data
-  **-nodata** option can be used to check for collinearity
-  **-censor** = used to turn off processing for some time points
-  for time points that are “bad” (e.g., too much movement; scanner hiccup)
-  **-CENSORTR 2:37**** **= newer way to specify omissions (e.g., run #2, index #37)
-  **-sresp** = output standard deviation of HRF (********* ***) estimates
-  can then plot error bands around HRF in AFNI graph viewer
-  **-errts** = output residuals (difference between fitted model and data)
-  for statistical analysis of time series noise
-  **-TR_times dt** = calculate **-iresp** and **-sresp** HRF results with time step **dt** (instead of input dataset TR)
-  Can be used to make HRF graphs look better
-  **-jobs*** ****N*** = run with independent threads — ***N*** of them
-  extra speed, if you have a dual-CPU system (or more)!

##  http://afni.nimh.nih.gov/pub/dist/doc/misc/Decon/DeconSummer2004.html
 http://afni.nimh.nih.gov/pub/dist/doc/misc/Decon/DeconSpring2007.html
 Equation solver: Program computes condition number for X matrix (measures of how sensitive regression results are to changes in X)
 If the condition number is “bad” (too big), then the program will not actually proceed to compute the results
 You can use the -GOFORIT option on the command line to force the program to run despite X matrix warnings
 But you should strive to understand why you are getting these warnings!!
 Other matrix checks:
 Duplicate stimulus filenames, duplicate regression matrix columns, all zero matrix columns
  Check the screen output for WARNINGs and ERRORs
 Such messages also saved into file 3dDeconvolve.err

** ****http://afni.nimh.nih.gov/pub/dist/doc/misc/Decon/DeconSpring2007.html**
-  Equation solver: Program computes **condition number** for **X** matrix (measures of how sensitive regression results are to changes in **X**)
-  If the condition number is “bad” (too big), then the program will not actually proceed to compute the results
-  You can use the **-GOFORIT** option on the command line to force the program to run despite **X** matrix warnings
-  But you should strive to understand why you are getting these warnings!!
-  Other matrix checks:
-  Duplicate stimulus filenames, duplicate regression matrix columns, all zero matrix columns
-   Check the screen output for **WARNING**s and **ERROR**s
-  Such messages also saved into file **3dDeconvolve.err**

- _Other Features - 2_

## Other Features - 3

-  All-zero regressors *are* allowed (via **-allzero_OK** or **-GOFORIT**)
-  Will get zero weight in the solution
-  Example: task where subject makes a choice for each stimulus (e.g., male or female face?)
-  You want to analyze correct and incorrect trials as separate cases
-  What if some subject makes no mistakes?  Hmmm...
-  Can keep the all-zero regressor (e.g., all  **-stim_times** = *****)
-  Input files and output datasets for error-making and perfect-performing subjects will be organized the same way
-  **3dDeconvolve_f**** **program can be used to compute linear regression results in single precision (7 decimal places) rather than double precision (16 places)
-  For better speed, but with lower numerical accuracy
-  Best to do at least one run ***both*** ways to check if results differ significantly  (Equation solver *should* be safe, but ...)

##  Default output format is 16-bit short integers, with a scaling factor for each sub-brick to convert it to floating point values
 -float option can be used to get 32-bit floating point format output — more precision, and more disk space
 3dDeconvolve recommends a -polort value, and prints that out as well as the value you chose (or defaulted to)
 -polort A can be used to let the program set the detrending (AKA “high pass filtering”, since detrending removes low frequency content from data) level automatically
 -stim_file is used to input a column directly into X matrix
 Motion parameters (as in previous examples)
 If you create a stimulus+response model outside 3dDeconvolve (e.g., using program waver)

-  **-float** option can be used to get 32-bit floating point format output — more precision, and more disk space
-  **3dDeconvolve**** **recommends a **-polort** value, and prints that out as well as the value you chose (or defaulted to)
-  **-polort A** can be used to let the program set the detrending (AKA “high pass filtering”, since detrending removes low frequency content from data) level automatically
-  **-stim_file** is used to input a column directly into **X** matrix
-  Motion parameters (as in previous examples)
-  If you create a stimulus+response model outside **3dDeconvolve **(e.g., using program **waver**)

- _Other Features - 4_

##  -stim_times has some other basis function options for the HRF model besides BLOCK and TENT
 CSPLIN = cubic spline instead of TENT = linear spline
 Same parameters: (start,stop,number of regressors)  
 Can be used as a “drop in” replacement for TENT

-  **CSPLIN** = cubic spline instead of **TENT** = linear spline
-  Same parameters: **(start,stop,number of regressors) **
-  Can be used as a “drop in” replacement for **TENT**

- _Other Features - 5_

**Red**    = **CSPLIN**
**Black** = **TENT**
**Differences are not significant**
**(But looks nicer)**

**-iresp** plotted using **-TR_times**** ****0.1**

## Other Features - 6

-  **-fitts** option is used to create a synthetic dataset
-  each voxel time series is full (signal+baseline) model as fitted to the data time series in the corresponding voxel location
-  **3dSynthesize** program can be used to create synthetic datasets from *subsets* of the full model
-  Uses **-x1D** and **-cbucket** outputs from **3dDeconvolve**
-  **-cbucket** stores ****** coefficients for each **X** matrix column into dataset
-  **-x1D** stores the matrix columns (and **-stim_label**s)
-  Potential uses:
-  Baseline only dataset
-  **3dSynthesize -cbucket fred+orig -matrix fred.x1D -select baseline -prefix fred_base**
-  Could subtract this dataset from original data (via **3dcalc**) to get signal+noise dataset that has no baseline component left
-  Just one stimulus class model (+ baseline) dataset
** ****3dSynthesize -cbucket fred+orig -matrix fred.x1D -select baseline Faces -prefix fred_Faces**

## Other Recent Small Changes

-  Defaults are changed:
-  **-nobout** & **-full_first** & **-bucket** & **-x1D**  are always implied
-  Names of statistics sub-bricks are slightly altered (to be more consistent)
-  Checks if **-stim_times** inputs are out of range (AKA: the PSFB syndrome)
-  Prints **WARNING** message, but continues analysis
-  When using **-nodata** with **-stim_times**, it is important to give the number of time points and the TR, as in  **-nodata 250 2.3**
** **With **-input1D**, use **-TR_1D 2.3** to specify TR

##  IM = Individual Modulation
Compute separate amplitude of response for each stimulus
Instead of computing average amplitude of responses to multiple stimuli in the same class
Response amplitudes (s) for each individual block/event will be highly noisy
Can’t use individual activation map for much
Must pool the computed s in some further statistical analysis (e.g., a t-test via 3dttest?)
Usage: -stim_times_IM k tname model
Like -stim_times, but creates a separate regression matrix column for each time given

- Compute separate amplitude of response for each stimulus
- Instead of computing average amplitude of responses to multiple stimuli in the same class
- Response amplitudes (**s) for each individual block/event will be highly noisy
- Can’t use individual activation map for much
- Must pool the computed **s in some further statistical analysis (e.g., a *t*-test via **3dttest**?)
- Usage:** ****-stim_times_IM**** ****k tname model**
- Like **-stim_times**, but creates a separate regression matrix column for each time given

**IM**** Regression - 1**

##  Only application of IM thus far has been in checking some data we received from another institution
 Experiment: 64 blocks of sensorimotor task (8 runs each with 8 blocks)

-  Experiment: 64 blocks of sensorimotor task (8 runs each with 8 blocks)

_**IM**__** Regression - 2**_

- Plot of 64 **BLOCK** **s from **-cbucket** output

- N.B.: sign reversal in run #4 = stimulus timing error!

## IM works naturally with blocks, which only have 1 amplitude parameter per stimulus
With event-related experiment and deconvolution, have multiple amplitude parameters per stimulus
Difficulty: each event in same class won’t get the same shaped HRF this way
Desideratum: allow response shape to vary (that’s deconvolution), but only allow amplitude to vary between responses in the same stimulus class
Problem: get unknowns that multiply each other (shape parameters  amplitude parameters) — and we step outside the realm of linear analysis
Possible solution: Total Least Squares mathematical methodology — am investigating now

- With event-related experiment and deconvolution, have multiple amplitude parameters per stimulus
- Difficulty: each event in same class won’t get the same shaped HRF this way
- Desideratum: allow response shape to vary (that’s deconvolution), but only allow amplitude to vary between responses in the same stimulus class
- Problem: get unknowns that multiply each other (shape parameters **** amplitude parameters) **—** and we step outside the realm of ***linear*** analysis
- Possible solution: **Total Least Squares** mathematical methodology **—** am investigating now

_**IM**__** Regression - 3**_

##  AM = Amplitude Modulated (or Modulation)
 Have some extra data measured about each response to a stimulus, and maybe the BOLD response amplitude is modulated by this
 Reaction time; Galvanic skin response; Pain level perception; Emotional valence (happy or sad or angry face?)
Want to see if some brain activations vary proportionally to this ABI (Auxiliary Behaviorial Information)
Discrete levels (2 or maybe 3) of ABI:
Separate the stimuli into sub-classes that are determined by the ABI (“on” and “off”, maybe?)
Use a GLT to test if there is a difference between the FMRI responses in the sub-classes
3dDeconvolve ...                                                 \
 -stim_times 1 regressor_on.1D  'BLOCK(2,1)' -stim_label 1 'On'  \
 -stim_times 2 regressor_off.1D 'BLOCK(2,1)' -stim_label 2 'Off' \
 -gltsym 'SYM: +On | +Off' -glt_label 1 'On+Off'                 \
 -gltsym 'SYM: +On -Off'   -glt_label 2 'On-Off' ...
“On+Off” tests for any activation in either the “on” or “off” conditions
“On-Off” tests for differences in activation between “on” and “off” conditions
Can use 3dcalc to threshold on both statistics at once to find a conjunction

-  Have some extra data measured about each response to a stimulus, and *maybe* the BOLD response amplitude is modulated by this
-  Reaction time; Galvanic skin response; Pain level perception; Emotional valence (happy or sad or angry face?)
- Want to see if some brain activations vary proportionally to this **ABI** (**A**uxiliary **B**ehaviorial **I**nformation)
- Discrete levels (2 or maybe 3) of ABI:
- Separate the stimuli into sub-classes that are determined by the ABI (“on” and “off”, maybe?)
- Use a GLT to test if there is a difference between the FMRI responses in the sub-classes
**3dDeconvolve ****...****                                                 \**
** ****-stim_times 1 regressor_on.1D  'BLOCK(2,1)' -stim_label 1 'On'  \**
** ****-stim_times 2 regressor_off.1D 'BLOCK(2,1)' -stim_label 2 'Off' \**
** ****-gltsym 'SYM: +On | +Off' -glt_label 1 'On+Off'                 \**
** ****-gltsym 'SYM: +On -Off'   -glt_label 2 'On-Off' ****...**
**“****On+Off****” **tests for any activation in *either* the “on” or “off” conditions
**“****On-Off****” **tests for differences in activation *between* “on” and “off” conditions
- Can use **3dcalc** to threshold on ***both***** **statistics at once to find a **conjunction**

**AM**** Regression - 1**

## Continuous (or several finely graded) ABI levels
 Want to find active voxels whose activation level also depends on ABI
 3dDeconvolve is a linear program, so must make the assumption that the change in FMRI signal as ABI changes is linearly proportional to the changes in the ABI values
Need to make 2 separate regressors
One to find the mean FMRI response (the usual -stim_times analysis)
One to find the variations in the FMRI response as the ABI data varies
The second regressor should have the form

Where ak = value of k th ABI value, and a is the average ABI value
Response ( ) for first regressor is standard activation map
Statistics and  for second regressor make activation map of places whose BOLD response changes with changes in ABI
Using 2 regressors allows separation of voxels that are active but are not detectably modulated by the ABI from voxels that are ABI-sensitive

-  Want to find active voxels whose activation level also depends on ABI
-  **3dDeconvolve** is a linear program, so must make the assumption that the change in FMRI signal as ABI changes is linearly proportional to the changes in the ABI values
- Need to make 2 separate regressors
- One to find the mean FMRI response (the usual **-stim_times** analysis)
- One to find the variations in the FMRI response as the ABI data varies
- The second regressor should have the form
- Where *a**k** *= value of *k** *th ABI value, and *a** *is the average ABI value
- Response (*** *) for first regressor is standard activation map
- Statistics and* *** for second regressor make activation map of places whose BOLD response changes with changes in ABI
- Using 2 regressors allows separation of voxels that are active but are *not* detectably modulated by the ABI from voxels that *are* ABI-sensitive

_**AM Regression - 2**_

## New feature of 3dDeconvolve: -stim_times_AM2
Use is very similar to standard -stim_times
 -stim_times_AM2 1 times_ABI.1D 'BLOCK(2,1)' 
The times_ABI.1D file has time entries that are “married” to ABI values:

Such files can be created from 2 standard ASCII .1D files using the new 1dMarry program
The -divorce option can be used to split them up
 3dDeconvolve automatically creates the two regressors (unmodulated and amplitude modulated)
Use -fout option to get statistics for activation of the pair of regressors (i.e., testing null hypothesis that both  weights are zero: that there is no ABI-independent or ABI-proportional signal change)
Use -tout option to test each  weight separately
Can 1dplot X matrix columns to see each regressor

- Use is very similar to standard **-stim_times**
** ****-stim_times_AM2 1 times_ABI.1D 'BLOCK(2,1)'**** **
- The **times_ABI.1D** file has time entries that are “married” to ABI values:
- Such files can be created from 2 standard ASCII .1D files using the new **1dMarry** program
- The **-divorce** option can be used to split them up
-  **3dDeconvolve** automatically creates the two regressors (unmodulated and amplitude modulated)
- Use **-fout** option to get statistics for activation of the pair of regressors (i.e., testing null hypothesis that *both* ** weights are zero: that there is no ABI-independent *or* ABI-proportional signal change)
- Use **-tout** option to test each ** weight separately
- Can **1dplot** **X** matrix columns to see each regressor

_**AM Regression - 3**_

**10*5 23*4 27*2 39*5**
**17*2 32*5**
*****
**16*2 24*3 37*5 41*4**

## The AM feature is new, and so needs some practical user experiences before it can be considered “standard practice”
In particular: don’t know how much data or how many events are needed to get good ABI-dependent statistics
If you want, -stim_times_AM1 is also available
It only builds the regressor proportional to ABI data directly, with no mean removed:

Can’t imagine what value this option has, but you never know ... (if you can think of a good use, let me know)
Future directions:
Allow more than one amplitude to be married to each stimulus time (insert obligatory polygamy/polyandry joke here) 
How many ABI types at once is too many?  I don’t know.
How to deal with unknown nonlinearities in the BOLD response to ABI values?  I don’t know.  (Regress each event separately, then compute MI?)
Deconvolution with amplitude modulation?  Requires more thought.

- In particular: don’t know how much data or how many events are needed to get good ABI-dependent statistics
- If you want, **-stim_times_AM1** is also available
- It only builds the regressor proportional to ABI data directly, with no mean removed:
- Can’t imagine what value this option has, but you never know ... (if you can think of a good use, let me know)
- Future directions:
- Allow more than one amplitude to be married to each stimulus time (insert obligatory polygamy/polyandry joke here)
- How many ABI types at once is too many?  I don’t know.
- How to deal with unknown nonlinearities in the BOLD response to ABI values?  I don’t know.  (Regress each event separately, then compute MI?)
- Deconvolution with amplitude modulation?  Requires more thought.

_**AM Regression - 4**_

## Timing: AM.1D = 10*1 30*2 50*3 70*1 90*2 110*3 130*2 150*1 170*2 190*3 210*2 230*1
3dDeconvolve -nodata 300 1.0 -num_stimts 1 \
    -stim_times_AM1 1 AM.1D 'BLOCK(10,1)' -x1D AM1.x1D
1dplot AM1.x1D'[2]'

3dDeconvolve -nodata 300 1.0 \
-num_stimts 1     \
-stim_times_AM2 1  \ 
AM.1D 'BLOCK(10,1)' \  
-x1D AM2.x1D
1dplot -sepscl \
  AM2.x1D'[2,3]'

- Timing:** AM.1D** = **10*1 30*2 50*3 70*1 90*2 110*3 130*2 150*1 170*2 190*3 210*2 230*1**
**3dDeconvolve -nodata 300 1.0 -num_stimts 1 \**
**    ****-stim_times_AM1 1 AM.1D 'BLOCK(10,1)' -x1D AM1.x1D**
**1dplot AM1.x1D'[2]'**
**3dDeconvolve -nodata 300 1.0 \**
**-num_stimts 1     \**
**-stim_times_AM2 1  \ **
**AM.1D 'BLOCK(10,1)' \  **
**-x1D AM2.x1D**
**1dplot -sepscl \**
**  ****AM2.x1D'[2,3]'**

_**AM Regression - 5**_

**AM1** model of signal (modulation = ABI)

**AM2** model of signal:
- is 2D sub-space spanned by these 2 time series

## First actual user: Whitney Postman (formerly NIDCD; PI=Al Braun)
Picture naming task in aphasic stroke patient
ABI data = number of alternative names for each image (e.g., “balcony” & “porch” & “veranda”, vs. “strawberry”), from 1 to 18
8 imaging runs, 144 stimulus events
2 slices showing activation map for BOLD responses proportional to ABI (AM2)
What does this mean?  Don’t ask me!

- Picture naming task in aphasic stroke patient
- ABI data = number of alternative names for each image (e.g., “balcony” & “porch” & “veranda”, vs. “strawberry”), from 1 to 18
- 8 imaging runs, 144 stimulus events
- 2 slices showing activation map for BOLD responses proportional to ABI (**AM2)
- What does this mean?  Don’t ask me!

_**AM Regression - 6**_

## Alternative: use IM to get individual s for each block/event and then do external regression statistics on those values
Could do nonlinear fitting via 3dNLfim, or inter-class contrasts via 3dttest, 3dLME, 3dANOVA, etc.
What is better: AM or IM+ ?
We don’t know – experience with these options is limited thus far – you can always try both!
If AM doesn’t fit your models/ideas, then IM is clearly the way to go
Probably need to consult with SSCC to get some hints/advice

- Could do nonlinear fitting via **3dNLfim**, or inter-class contrasts via **3dttest**, **3dLME**, **3dANOVA**, etc.
- What is better: **AM** or **IM+** ?
- We don’t know – experience with these options is limited thus far – you can always try both!
- If **AM** doesn’t fit your models/ideas, then **IM** is clearly the way to go
- Probably need to consult with SSCC to get some hints/advice

_**AM Regression - 7**_

##  Can have activations with multiple phases that are not always in the same time relationship to each other; e.g.:
 subject gets cue #1
 variable waiting time (“hold”)
 subject gets cue #2, emits response
 which depends on both cue #1 and #2
 Cannot treat this as one event with one HRF, since the different waiting times will result in different overlaps in separate responses from cue #1 and cue #2
 Solution is multiple HRFs: separate HRF (fixed shape or deconvolution) for cue #1 times and for cue #2 times
 Must have significant variability in inter-cue waiting times, or will get a nearly-collinear model
impossible to tell tail end of HRF #1 from the start of HRF #2, if always locked together in same temporal relationship
 How much variability is “significant”?  Good question.

-  subject gets cue #1
-  variable waiting time (“hold”)
-  subject gets cue #2, emits response
-  which depends on both cue #1 and #2
-  Cannot treat this as one event with one HRF, since the different waiting times will result in different overlaps in separate responses from cue #1 and cue #2
-  Solution is multiple HRFs: separate HRF (fixed shape or deconvolution) for cue #1 times and for cue #2 times
-  Must have significant variability in inter-cue waiting times, or will get a nearly-collinear model
- impossible to tell tail end of HRF #1 from the start of HRF #2, if always locked together in same temporal relationship
-  How much variability is “significant”?  Good question.

**Other Advanced Topics in Regression**

##  Solving a visually presented puzzle:
 subject sees puzzle
 subject cogitates a while
 subject responds with solution
 The problem is that we expect some voxels to be significant in phase (b) as well as phases (a) and/or (c)
 Variable length of phase (b) means that shape for its response varies between trials
Which is contrary to the whole idea of averaging trials together to get decent statistics (which is basically what linear regression for the  weights does, in an elaborate sort of way)
 Could assume response amplitude in phase (b) is constant across trials, and response duration varies directly with time between phases (a) and (c)
Need three HRFs
Can’t generate (b) HRF in 3dDeconvolve

-  subject sees puzzle
-  subject cogitates a while
-  subject responds with solution
-  The problem is that we expect some voxels to be significant in phase (b) as well as phases (a) and/or (c)
-  Variable length of phase (b) means that shape for its response varies between trials
- Which is contrary to the whole idea of averaging trials together to get decent statistics (which is basically what linear regression for the ** weights does, in an elaborate sort of way)
-  Could assume response ***amplitude*** in phase (b) is constant across trials, and response ***duration*** varies directly with time between phases (a) and (c)
- Need three HRFs
- Can’t generate (b) HRF in **3dDeconvolve**

_**Even More Complicated Case**_

## Noise Issues

-  “Noise” in FMRI is caused by several factors, not completely characterized
-  MR thermal noise (well understood, unremovable)
-  Cardiac and respiratory cycles (partly understood)
-  In principle, could measure these sources of noise separately and then try to regress them out
-  RETROICOR program underway (Rasmus Birn of FIM / NIMH)
-  Scanner fluctuations (e.g., thermal drift of hardware)
-  Small subject head movements (10-100 m)
-  Very low frequency fluctuations (periods longer than 100 s)
-  Data analysis should try to remove what can be removed and allow for the statistical effects of what can’t be removed
-  “Serial correlation” in the noise time series affects the *t*- and *F*-statistics calculated by **3dDeconvolve**
-  At present, nothing is done to correct for this effect (by us)

## Nonlinear Regression

-  Linear models aren’t the only possibility
-  e.g., could try to fit HRF of the form
-  Unknowns ***b*** and ***c*** appear nonlinearly in this formula
-  Program **3dNLfim** can do nonlinear regression (including nonlinear deconvolution)
-  User must provide a C function that computes the model time series, given a set of parameters (e.g., ***a***, ***b***, ***c***)
-  We could help you develop this C model function
-  Several sample model functions in the AFNI source code distribution
-  Program then drives this C function repeatedly, searching for the set of parameters that best fit each voxel
-  Has been used to fit pharmacological wash-in/wash-out models (difference of two exponentials) to FMRI data acquired during pharmacological challenges
-  e.g., injection of nicotine, cocaine, ethanol, etc.
-  these are difficult experiments to do ***and*** to analyze

## Smooth data in space before analysis
Average data across anatomically-selected regions of interest ROI (before or after analysis)
Labor intensive (i.e., hire more students)
Reject isolated small clusters of above-threshold voxels after analysis

- Average data across anatomically-selected regions of interest ROI (before or after analysis)
- Labor intensive (*i.e.*, hire more students)
- Reject isolated small clusters of above-threshold voxels after analysis

_**Spatial Models of Activation**_

## Spatial Smoothing of Data

-  Reduces number of comparisons
-  Reduces noise (by averaging)
-  Reduces spatial resolution
- Blur it enough: Can make FMRI results look like low resolution PET data
-  Smart smoothing: average ***only*** over nearby brain or gray matter voxels
- Uses resolution of FMRI cleverly
- New AFNI program: **3dBlurToFWHM**
- _Or_: average over selected ROIs
- _Or_: cortical surface based smoothing

## Spatial Clustering

- Analyze data, create statistical map  (*e.g.*, ***t*** statistic in each voxel)
- Threshold map at a low ***t*** value, in each voxel separately
- Will have many false positives
- Threshold map by rejecting clusters of voxels below a given size
- Can control false-positive rate by adjusting ***t*** threshold and cluster-size thresholds together

## Cluster-Based Detection

_**Cluster-Based Detection**_

## What the World Needs Now

- Unified HRF/Deconvolution ****** **Blob analysis
- Time****Space patterns computed all at once, _instead of_ arbitrary spatial smoothing
- Increase statistical power by bringing data from multiple voxels together cleverly
- _Instead of_ time analysis followed by spatial analysis (described earlier)
- _Instead of_ component-style analyses (*e.g.*, ICA) that do not use stimulus timing
- Difficulty: models for spatial blobs
- Little information *à priori* **** must be adaptive

## 3dBlurToFWHM

-  New program to smooth FMRI time series datasets to a specified smoothness (as estimated by FWHM of noise spatial correlation function)
-  Don’t just add smoothness (à la **3dmerge**) but control it (locally and globally)
-  Goal: use datasets from diverse scanners
-  Why blur FMRI time series?
-  Averaging neighbors will reduce noise
-  Activations are (usually) blob-ish (several voxels across)
-  Diminishes the multiple comparisons problem
-  **3dBlurToFWHM** blurs only inside a mask
-  To avoid mixing air (noise-only) and brain voxels
-  Partial Differential Equation (PDE) based blurring method
-  2D (intra-slice) or 3D blurring

## In the Pondering Stages

-  “Area under curve” addition to **-gltsym** to allow testing of pieces of HRF models from **-stim_times**
-  Slice- and/or voxel-dependent regressors
-  For physiological noise cancellation, etc.
-  To save memory?  (Could process each slice separately)
-  One slice-at-a-time regression can be done in a Unix script, using 3dZcutup and 3dZcat programs
-  Extend AM regression to allow for more than 1 piece of auxiliary information at each stimulus time
-  Interactive tool to examine **-x1D** matrix for problems
-  and **3dDeconvolve** testing of GLT submatrices