fNIRS Datablitz 2020 Q&A: Software and Hardware

Wednesday October 14 2020, 10:00-11:00 AM EDT

The video recording of the session is below.

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We8 Theodore Huppert
University of Pittsburgh, USA

Recent additions to the NIRS-Toolbox
Q&A
5
Question to Dr Huppertx
We9 Javier Andreu-Perez
University of Essex, UK
Computational intelligence in fNIRS data analysis
Q&A
1
Question to Dr Andreu-Perezx
We10 Luca Pollonini
University of Houston, USA
Assessment of fNIRS data quality at individual and group levels
Q&A
2
Question to Dr Polloninix
We11 Qianqian Fang
Northeastern University, Boston, USA
Wearable and modular fNIRS probe
Q&A
1
Question to Dr Fangx
We12 Joe Culver
Washington University, St. Louis, MO, USA
Wearable high density diffuse optical tomography
Q&A
4
Question to Dr Culverx
We13 Adam Eggebrecht
Washington University, St. Louis, USA
Illumination childhood development with high density diffuse optical tomography
Q&A
1
Question to Dr Eggebrechtx
We14 Stefan Carp
Massachusetts General Hospital, USA
Optimization of experimental and computational approaches for cerebral blood flow monitoring
Q&A
1
Question to Dr Carpx
WeP2 Panel Discussion Software and Hardware Moderators Rickson Mesquita & Mari Franceschini
Q&A
0
Question to the Software and Hardware Panelx

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BoasDavid
BoasDavid (@boasdavid)
12 days ago
Q&A Theodore Huppert" Read more »

Ted, do your brain analyzer object classes integrate well with the SNIRF format? Seems that this will be super powerful if we can load the SNIRF file into your objects.

HuppertTed
HuppertTed (@huppertted)
Reply to  BoasDavid
12 days ago

Yes, we allow import and export of SNIRF using the commands nirs.io.loadSNIRF and .saveSNIRF. In particular, importing via SNIRF will preserve all the 3D registration, any loaded subject demographics and metadata, and stimulus timing information

Andrew Lapointe
Andrew Lapointe
12 days ago
Q&A Theodore Huppert" Read more »

Love the multimodal integration of your package. What level of EEG analyses can be performed? (E.g., microstates, FFT)

HuppertTed
HuppertTed (@huppertted)
Reply to  Andrew Lapointe
12 days ago

So far, we have some basic methods including filtering, PCA /ICA for artifact removal, spectral analysis, ERP estimation, and image reconstruction. We have been focusing on multimodal integration methods, for example using canonical correlation of mixed modality signals and joint image reconstruction.

EmbersonLauren
EmbersonLauren (@embersonlauren)
12 days ago
Q&A Theodore Huppert" Read more »

Hi Ted– Lots of great improvements. I’d love to hear more about the short channel integration with FC connectivity. How does it operate differently than task-based GLM analyses?

HuppertTed
HuppertTed (@huppertted)
Reply to  EmbersonLauren
12 days ago

This is work in progress that we are working with Pradyumna Lanka and Heather Bortfeld to compare methods. What we are comparing is preprocessing methods (e.g. using SS data as a filter) verses partial correlation methods, grangers causality, etc. It looks like partial correlation is going to win, but we are still working on how to deal with motion artifacts and noisy SS data through robust statistical models.

Alicia
Alicia
12 days ago
Q&A Theodore Huppert" Read more »

Amazing talk Dr. Huppert. What would happen if the short channels included as regressors are noisy?, Would that affect the outcome?

Do you recommend to add also 1st and 2nd principal components as regressors? How adding this will affect the data?

HuppertTed
HuppertTed (@huppertted)
Reply to  Alicia
12 days ago

For the GLM model, we found that using a PCA decomposition of the SS data and using the components as the regressors is better than using the data directly. We still use all the components, but using the PCA redistributes the noise. We also use robust statistical methods (iterative outlier rejection methods) which is essential for the issue of noisy SS data.

LukeRobert
LukeRobert (@lukerobert)
12 days ago
Q&A Luca Pollonini" Read more »

Very intuitive figures. Would it be possible to save the data quality metric back in to the SNIRF file so it could be easily used by all toolboxes? Perhaps using the custom metadata tags that SNIRF supports? BIDS has the concept of a bad channel, but that might be too binary for your rich metric.

Luca Pollonini
Luca Pollonini (@lpolloni)
Editor
Reply to  LukeRobert
12 days ago

Thanks, Rob! It is definitely possible to save a simpler, binary output of the assessment into SNIRF/BIDS to the extent that the formats allow (Sam has all details on the code, and he will be involved in our BIDS effort)

Anna Blasi Ribera
Anna Blasi Ribera (@a-blasiucl-ac-uk)
12 days ago
Q&A Luca Pollonini" Read more »

Thanks for your fantastic ongoing work, Lucca, looking forward to having the tools fully available to the community. Can you elaborate a bit on the difference between motion artefact and poor optical coupling (the red and black bands) on the chart mapping data quality?

Luca Pollonini
Luca Pollonini (@lpolloni)
Editor
Reply to  Anna Blasi Ribera
12 days ago

Thanks a lot, Anna! I will pass along the slides to you for better visual reference, but in essence we use a combo of SCI and PSP measures to separate movement artifacts (red pixels, high SCI + low PSP) from poor coupling (black pixels, low SCI + low PSP), and we do so on very short signal windows to keep our temporal resolution high. Does this sufficiently answer your question?

MesquitaRickson
MesquitaRickson (@mesquitarickson)
12 days ago
Q&A Javier Andreu-Perez" Read more »

Great talk, Javier. Do you think that using computational intelligence to both preprocess and analyze activation may lead to overfitting?

Javier Andreu-Perez
Javier Andreu-Perez (@jvrandreugmail-com)
Reply to  MesquitaRickson
12 days ago

Thanks Rickson, when using CI to model neural data, there is always the risk of including residual variations (i.e. noise), but a good strategy is to include a lot of strong learning constraints to penalize the complexity of the model (e.g. regularization) and also in the intermediate learning steps (e.g dropouts). If the model is well controlled in this respect, it should converge to the desired modeling phenomena and ignore the redundant effects. However, I would say that to leverage the automated learning process, it’s suggested some pre-filtering of the fNIRS signal before the AI modelling.

Adam Noah
Adam Noah
12 days ago
Q&A Theodore Huppert" Read more »

I am interseted in your thoughts on full head probe arrangements on different size heads. We find that 3.0cm separation is optimal for large males, but not for average size females. See http://fmri.org/wp-content/uploads/2020/10/nirscap-scaled.jpg for a reference.

HuppertTed
HuppertTed (@huppertted)
Reply to  Adam Noah
12 days ago

This is a good question and something we are still trying to figure out.
In fiber based systems and NIRx, the optodes are fixed to 10-20 coordinates such that optode distance scales with head size. The advantage of this is that the channels are over roughly the same anatomical areas in all subjects. This allows you to just stack and average the data across subjects. However, the disadvantage of this is that SNR scales logarithmically with optode spacing. Thus, if you wanted to test the hypothesis of male vs female brain activation (or its even worse for something like child development) this is difficult statistically because your noise is unbalanced across the groups. This is not a trivial statistical problem. On the other hand, if you use fixed distances (say 3cm irregardless of the head size), then you need to adjust for the activity moving across different channels. We are working on analysis to account for this later problem. Since noise is logarithmic with distance, it looks like keeping source-detector distance constant across subjects is probably the better solution from a statistical point of view

Piotr Sawosz
Piotr Sawosz
12 days ago
Q&A Joe Culver" Read more »

Hi, What detector do you use? If these are APDs, arent you worried about high voltage? do you use any galvanic isolation?

CulverJoseph
CulverJoseph (@culverjoseph)
Reply to  Piotr Sawosz
12 days ago

We use hamamatsu Photodiodes. I’ll look up partno.

CulverJoseph
CulverJoseph (@culverjoseph)
Reply to  CulverJoseph
12 days ago

The PD that we use is from Hamamatsu, S12158-01CT. I think this is the same as used by Gowerlabs (Rob?) Seems like this PD might play the same role as the Hamamatsu 5460 APD has done for many of the fiber systems. It’s a great ultralow noise PD, with 4 mmx4mm area. 

Ippeita Dan
Ippeita Dan
12 days ago
Q&A Joe Culver" Read more »

Great development! How robust against hair is your new wearable system? It seems your probes are placed on the occipital hairy region.

CulverJoseph
CulverJoseph (@culverjoseph)
Reply to  Ippeita Dan
12 days ago

Yes. Over the hair. That was a design constraint, has to work everywhere. Seems to work OK. Pretty similar to fibers.

Giovanni Hering
Giovanni Hering (@giohering)
12 days ago
Q&A Qianqian Fang" Read more »

Thank you for the presentation Qianqian, really impressive work.

I was curious about which kind of microcontroller are you using for the modules, and wether each module has its own controller.

Thank you!

Martin Wolf
Martin Wolf (@mwolf)
12 days ago
Q&A Adam Eggebrecht" Read more »

Overt language production changes the pCO2 in the blood, which strongly affects brain hemodynamics. Did you measure pCO2?

Luca Pollonini
Luca Pollonini (@lpolloni)
Editor
12 days ago
Q&A Joe Culver" Read more »

Very exciting, Joe! I am interested in the power line issues that you mentioned: is it mostly 60 Hz component (and harmonics) that you see? I imagine that Faraday cage around the probe helps!

CulverJoseph
CulverJoseph (@culverjoseph)
Reply to  Luca Pollonini
12 days ago

Well there are true EM interference stuff, and so that’s why we have the foil around. We are looking to reduce the wait size of that, but that helps. But also we found we can get rid of what looks like EM, but fixing the power supply and ground planes.
I think the 60 Hz is gone. One source remaining is the digital signal from the A/D readout.

Luca Pollonini
Luca Pollonini (@lpolloni)
Editor
Reply to  CulverJoseph
12 days ago

Excellent, congrats!

Cooper
Cooper (@cooper)
12 days ago
Q&A Joe Culver" Read more »

Great stuff Joe – really interesting system design. Is there a paper on the way?

mari
mari (@mari)
Admin
12 days ago
Q&A Stefan Carp" Read more »

should w all move to heterodyne DCS?