I'm looking to assess a very particular contrast involving a categorical x categorical x quantitative triple interaction.
Quick background
My project assesses whether a particular TMEM gene allele offers a protective effect against gray matter atrophy in individuals with genetic frontotemporal dementia.
In R formula syntax the model I am looking at is as follows:
pGM ~ FTDGene_CarrierStatus * TMEM_IsProtective * TimeFromBaseline + AgeAtBaseline + Sex + TIV + (1  Site / Family / Subj)
(spaces added for readability)
Where:
Variable Detected_Type Details
Subj Subjects Num Subjects=304
ID Categorical Unique levels=1013
TIV Quantitative Min=1037.11  Max=1917.92  Num outliers=9
TMEM_IsProtective Categorical Counts: NonProtective=380  Protective=633
FTDGene_CarrierStatus Categorical Unique levels=5
TimeFromBaseline Quantitative Min=0  Max=7.200546
AgeAtBaseline Quantitative Min=19.35387  Max=85.67283
Sex Categorical Counts: Female=592  Male=421
Site Categorical Unique levels=20
Family Categorical Unique levels=132
To clarify, FTDGene_CarrierStatus is 5 levels (Control and GRN/C9orf72 Presymptomatic/Symptomatic)
A picture is 1000 words, so I'd summarize my hypothesis as follows:
My Questions
 After doing a trial run with a more parsimonious model (no covariates), the results look promising, but I'm secondguessing myself at the formulation of my gltCode and would like to request a clarification:
gltCode DeltaSlope_C9Sympint 'TMEM_IsProtective : 1*Protective 1*NonProtective FTDGene_CarrierStatus : 1*C9orf72_Symptomatic 1*Control TimeFromBaseline :' \
gltCode DeltaSlope_GRNSympint 'TMEM_IsProtective : 1*Protective 1*NonProtective FTDGene_CarrierStatus : 1*GRN_Symptomatic 1*Control TimeFromBaseline :' \
gltCode DeltaSlope_C9PreSymp 'TMEM_IsProtective : 1*Protective 1*NonProtective FTDGene_CarrierStatus : 1*C9orf72_Presymptomatic 1*Control TimeFromBaseline :' \
gltCode DeltaSlope_GRNPreSymp 'TMEM_IsProtective : 1*Protective 1*NonProtective FTDGene_CarrierStatus : 1*GRN_Presymptomatic 1*Control TimeFromBaseline :' \
These test the interaction effect in differenes of slope changes as per diagram, yes?

I noticed that there appear to be two volumes output for each of the gltCode above. I assume that each pair of volumes corresponds to one of the onetailed directions, as there aren't any negative values therein, right? Anecdotally, one of the two volumes is nearzero and the other has clusters, so I'm 99.9% sure this is the case, but I would just appreciate a sanity check. Forgive my ignorance, as I was more used to working with FSL/SPM's outputs where tcontrasts give both positive and negative direction outcomes in the same volume.

I noticed that specifying
qVars
results in each specified variable being automatically centered. Since I'm looking at slopes, this is irrelevant. For future studies however, I would like the interpretation of the intercept to refer to the xaxis variable being zero, not the mean. Is there a way to specify that a variable is quantitative but have it NOT be centered automatically?
Thanks in advance and I will be more than pleased to give reference to AFNI on anything that comes of this! Fingers crossed!
Best,
Maurice