High frequency noise related to MB and temporal ICA

Michael Stevens

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Mar 10, 2026, 12:08:21 PM (6 days ago) Mar 10

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Our group has been getting some push-back about using higher MB factors with HCP pulse sequences lately... at several levels -- our collaborators raising concerns, at peer review of manuscripts, and even new grant application reviews. There seems to be an increasing concern about the presence of uncorrected high frequency noise that specifically reduces signal detectability in subcortical "center of brain" regions. These concerns have been expressed frequently enough to our group over the past 6 months that we realize it's now "out there" as a real thing we have to carefully address. Now that we're looking closely, we do see some worrisome differences in our newer studies particularly in basic caudate/VS signal detection using identical fMRI paradigms to our older work.

Some colleagues we've spoken with simply advocate dropping the use of MB altogether when the project focus is on subcortical regions. But that essentially makes it difficult or impossible to achieve the spatial resolution needed to optimize HCP processing approaches on our own data. I began to wonder if the temporal ICA denoising approach that the HCP group has been working on for the past few years addresses this issue at all? Or if other, additional denoising steps that are compatible with HCP pipeline-driving processing might be introduced? If tICA is intended to remove or even just mitigate this specific problem, is the automatic application of tICA currently implemented in the most recent HCP pipeline release? And if the answer to that is yes, can I be pointed to an information resource that details implementation... or even a knowledgeable contact who'd be willing to advise? Switching will be non-trivial for us given lots of planned changes in our local cluster configuration... our institution is basically doing a $10M upgrade to a new set of VM- and cloud-based resources... So we have to re-map everything. But the collective worries about this problem has become enough that I'm willing to put the effort into figuring out a whole new approach to data prep at our institution if necessary.

Thanks in advance for any insights or direction folks can offer.

Mike

Glasser, Matthew

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Mar 10, 2026, 2:13:53 PM (6 days ago) Mar 10

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Hi Mike,

What I currently recommend for fMRI at 3T for new projects (and in progress clinically) is a little different from what the HCP acquired in the past:

*2.4mm isotropic

*TR=1s

*60 angled slices

A Prisma can achieve the above with MB=4. A high-end XT Vida can achieve it with MB=5. A low-end XQ Vida/Skyra requires MB=6. All of these are producing very nice images (better than 3T HCP).

For 7T fMRI, I would stick with what we did with HCP-YA (1.6mm isotropic, TR=1s, 85 angled slices, MB=5, IPAT=2).

The gains of going substantially faster than 1s are not that high in simulations and so in so far as simulations don’t adequately account for all the losses, one might not be gaining any more. Up to 1s is a lot steeper gain. The HCP-Style approach recommends voxel size less than mean cortical thickness, which is 2.6mm, so stepping back from 2mm is also reasonable. 7T has much more CNR available, so pushing to minimum cortical thickness at 1.6mm makes sense there.

Temporal ICA is for removing global respiratory artifact. It is available in the HCP Pipelines GitHub, and we have been working on getting the automated classifier hosted as well.

Wishart Filtering is a better approach to reduce random thermal noise (which is what it sounds like you are talking about). How to use it best is a little complicated, though, and we plan to put a paper out on that. That paper is pending having some NHP connectivity data comparisons that will produce strong evidence for the benefits though.

Overall, the main potentially available technical advances would be a head only scanner capable of much shorter echo spacing (makes multi-echo viable) and some of the TIDY fMRI stuff where k-space info is shared across timepoints (amongst other benefits). I don’t have any TIDY fMRI data yet myself though, so I cannot comment on any limitations that technology might have.

Matt.

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Michael Stevens

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Mar 11, 2026, 7:16:08 PM (5 days ago) Mar 11

to HCP-Users, Glasser, Matthew

Thanks, Matt — incredibly helpful as always.

A quick follow‑up: Do you have guidance (here or offline) on the best way to implement Wishart filtering for HCP time series? I dug into it today, and I think I’ve pieced together the typical workflow:

concatenate the “clean” output from DedriftandResample,
load the dense data into MATLAB and de‑mean,
run SVD,
apply the Wishart filter, and
reconstruct the denoised data.

Even if that general flow is correct, having the steps isn’t the same as knowing I’m doing it right — e.g., selecting thresholds, handling surface vs. more problematic subcortical/cerebellar/brainstem voxelwise data, etc. And since I’m not using any HCP code for this yet (I assembled Matlab code from scratch for everything), there’s plenty of room for error. Could you offer some technical guidance on how you recommend implementing this? And the challenges you alluded to in your prior post?

We’re trying to see how much thermal noise-related problem we can mitigate across several datasets collected over the past 5+ years. We’re also choosing a sequence for new projects, including one starting in two weeks. So implementing Wishart filtering soon could help inform that decision.

Thanks again for your help,
Mike

Glasser, Matthew

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Mar 11, 2026, 7:23:48 PM (5 days ago) Mar 11

to Michael Stevens, HCP-Users

Hi Mike,

It is a bit complex because some analyses benefit and others don’t and when you apply it also matters. I would prefer not to unleash the technique onto the world without the detailed guidance. Sorry for the delay on that. We have a mostly drafted manuscript, but I think it really needs the comparison to gold-standard connectivity to convince the skeptical reviewer (we have done it we preliminary data before, but we were hoping to get a final dataset together for this).

I would recommend the sequences below regardless of Wishart Filtering. They represent our best current approach. If you need it, I could provide a .exar1 (assuming you are on XA60 that is).

Also, Wishart Filtering is mainly important for dense analyses. Parcellation remains the best approach for reducing unstructured noise if you care about the effects at the level of brain areas (and Wishart Filtering doesn’t actually make much of a difference in the context of parcellated analyses because the parcellation effectively kills the unstructured noise already).

Michael Stevens

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Mar 12, 2026, 4:25:06 PM (4 days ago) Mar 12

to HCP-Users, Glasser, Matthew, Michael Stevens

Thanks Matt... Again, incredibly helpful. I spent a bit more time yesterday looking into what folks seem to know about Wishart filtering and can begin to appreciate the complexities. No problem waiting until the approach is ready for release. I'll be on the lookout for a preprint; or keep me in mind when it's finally accepted?

And yes, if you could share a sequence we can load up into our Skyra, that'd be great. We're still running VE11C.2024 release however. Happy to grab it at an FTP site or any Github repository that's easiest.

Mike

Glasser, Matthew

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Mar 12, 2026, 4:38:31 PM (4 days ago) Mar 12

to Michael Stevens, HCP-Users

I can’t build things for old software, just what I have on our MRPM server. I could send you a PDF if you want.

Harms, Michael

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Mar 12, 2026, 5:47:00 PM (4 days ago) Mar 12

to hcp-...@humanconnectome.org, Michael Stevens

If you want, we have an E11C protocol for a Skyra for the AMP SCZ program that you could import as a starting point:

https://zenodo.org/records/14530974

cheers,

-MH

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Michael Harms, Ph.D.

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Professor of Psychiatry

Washington University School of Medicine

Department of Psychiatry, Box 8134

660 South Euclid Ave.

St. Louis, MO 63110

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Glasser, Matthew

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Mar 12, 2026, 6:08:13 PM (4 days ago) Mar 12

to hcp-...@humanconnectome.org, Michael Stevens

Is that what I suggested below or a different protocol?

Matt.

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Harms, Michael

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Mar 12, 2026, 6:37:00 PM (4 days ago) Mar 12

to hcp-...@humanconnectome.org, Michael Stevens

It's 2.4 mm isotropic for the BOLD, with 60 slices, and TR=900 ms. Easy from there to modify the TR and MB factor (if allowed, for the chosen TR), if Michael wants to make changes

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