Longitudinal FBA: small absolute / large standard effect

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Hi Michelle,

I’m rather sceptical of the numbers you’re reporting, so would encourage some very critical assessment of the data as a whole.

What those absolute effect statistics are claiming is that, in that region, the rate of change of log(FC) with respect to time in weeks is ~ 2e-08. So over the course of a year, log(FC) is predicted to change by ~ 1e-06. That’s a tiny number, well below the precision of image registration that the FC metric is derived from. Yet this is supposedly the region with a statistically significant effect. So something foul is afoot.

  1. You need to confirm that your usage of the fixelcfestats command is faithful to your intended hypothesis test. There are a lot of ways in which a GLM other than the correct one can be invoked that won’t result in an error, but produce all sorts of weird outputs.

  2. Check the raw fixel data input, make sure that they make sense and are within the expected numerical ranges.

  3. Make sure that you are performing fixel data smoothing. In previous software versions, this was performed internally within the fixelcfestats command; as of 3.0.0, this is instead done prior to fixelcfestats using command fixelfilter. Failing to do so can result in very small statistically significant regions, as there are greater opportunities to obtain very large test statistics due to large effect / small variance by chance alone.

    Indeed based on your data I would suggest that it is an exceptionally small standard deviation in those fixels that has led to the significant result. You will need to try to determine whether that is due to chance, or maybe some other factor in your processing has resulted in your subjects containing equivalent or almost-equivalent values in those fixels. You could address whatever caused such, or you could omit those fixels from your statistical inference.

  4. Look at the template streamlines tractogram in the region of the significant result, and compare it to the fixel analysis mask. Fixels that possess very little fixel-fixel connectivity can be problematic due to intrinsic non-stationarity correction (essay on forum). Deriving a fixel mask for statistical inference that necessitates adequate fixel-fixel connectivity can be beneficial; indeed I’ll likely explicitly recommend this when I get around to revising the pipeline documentation.

If we calculate the percentage effect as described we get the following result:
volume mean median std min max count
[ 0 ] 0 0 0 0 0 3

I can only hypothesize that this is a floating-point precision issue. That calculation for log(FC) only depends on the absolute effect, nothing else. Those calculations should all be being done using double-precision, in which case I’d have expected a non-zero result; but if the numbers bottle-neck to single-precision at any point, then e^(2.0 x 10^-8) will be so close to 1 that the result of the total computation is zero.

Rob