Tuesday October 13 2020, 7:00-8:30 AM EDT
The video recording of the session is below.
Use the commenting tool below to discuss the presentations given in this session. Please register as a member to be able to ask and answer questions https://fnirs.org/account/. You can register for free for this web content.
| Tu13pd1 | Keum-Shik Hong Pusan National University, South Korea |
Detection of mild cognitive impairment: Spatiotemporal feature maps of fNIRS | Q&A |
| Tu13pd2 | Yasuyo Minagawa Keio University, Japan |
Typical and atypical neurocognitive development: language and social interaction | Q&A |
| Tu13pd3 | Robert Luke Macquarie University, Australia |
Introduction to the Brain Imaging Data Structure (BIDS) for fNIRS, and fNIRS data processing with the MNE toolbox | Q&A |
| Tu13pd4 | Ippeita Dan Chuo University, Japan |
Empowering fNIRS data with AI | Q&A |
| Tu13pd5 | Haijing Niu Beijing Normal University, China |
Development of functional network and hemispheric lateralization in children revealed by resting-state fNIRS imaging | Q&A |
| Tu13pd6 | Noman Naseer Air University, Pakistan |
fNIRS for BCI and Neurorehabilitation | Q&A |
| Tu13pd7 | Hasan Ayaz Drexel University, USA |
Observing the brain-on-task using fNIRS: Recent neuroergonomic applications | Q&A |
| Tu13pd8 | Stacey Gorniak University of Houston, USA |
Functional neuroimaging in postmenopausal women with Type II Diabetes | Q&A |
| Tu13pd9 | Christophe Grova Concordia University Montreal, Canada |
Accuracy of fNIRS local 3D reconstructions using Maximum Entropy on the Mean: fNIRS response to excitability variations induced by TMS | Q&A |
| TuPpd | Panel Discussion | Post-Deadline Session Moderators Sungho Tak & Felipe Orihuela-Espina | Q&A |
Return to main chat page https://fnirs.org/conferences/fnirs-datablitz-2020-chat/
Good morning! Can you share more details on the clinical characteristics of your MCI patients?
Great work! What happens when you optimize your AI to increase sensitivity tuned for screening purpose? You may not have to stick to overall accuracy.
This is a great work. I like it.
I have a question regarding the duration of task presentation in each block (Block-time period). In your work you used 90s why not for example 30s? or 40s or 20s.
Does MNE-NIRS also perform preprocessing or should these steps be done in another software?
MNE-NIRS does pre processing. You can import your raw data from vendor machine and do preprocessing in MNE. Here are two examples of pre processing in MNE…
Here is an example epoching analysis https://mne.tools/stable/auto_tutorials/preprocessing/plot_70_fnirs_processing.html
And here is an example GLM Analysis https://mne.tools/mne-nirs/auto_examples/plot_11_hrf_measured.html
Also a note… MNE can read SNIRF files, so this should make it accessible to a wide range of commercial devices.
Can you put the link to the BIDS proposal here? Also, what can we do now to more coordinate between this initiative and SNIRF? Luca Pollonini and I have talked a little about this. I assume you and Luca are talking about this as well.
Good idea! Here is the link.
bids.neuroimaging.io/bep030
Yes we have briefly discussed, but should do more to coordinate between BIDS and SNIRF. Perhaps a brief meet for people interested in both initiatives? Or a review of each specification? I will keep thinking what else we can do.
There is validators for each initiative, perhaps passing the same data through both would highlight potential end user pain points? (now im just thinking on the fly ;))
it would be good to have a semi-regular meeting to make sure everything is coordinated as this all moves forward
Fantastic. I will email you and Luca to coordinate and loop in any interested parties (first thing in the morning, its late here!).
Which of fNIRS image (eg PFC activation) or behavioral parameters is more sensitive to TMS (or tDCS) treatment? I expect fNIRS image is often more sensitive.
well, we have seen so far SDST task (symbol digit substitution task) to be very effective, however, we have also looked at before just the target stimulation area under the TMS coil (or tdcs target) without any task, and showed active stimulation impacts the activation in the area.\
Interesting results! How many channels do you need to detect global efficiency in the case of ADHD? It seems not so many channels are necessary guessing from your slide.
Thanks. Eighty channels for our study. I think, it will obtain much more details from the brain network if many more measurement channels are used.
Interesting results! R&L classification seems very robust. Then how long do you need to successfully classify them? Say, is one trial OK?
It was not single trial. Several trials in both cases were used. As you know, in ML problems, we need to have large amount of data. The advantage is the good decoding accuracy but disadvantage is the long training time and time delays.
Great work. I used this software for our work.
In your own work, did you check other connectivity measures for ADHD classification.?
Thanks. We didn’t try the other connecitivty measures for our ADHD classsification.
Nice talk. The results of vector phase analysis based features has already published or not? Is there a preprint available. Thank you.
https://iopscience.iop.org/article/10.1088/1741-2552/abb417/meta
Interesting application! What is happening in the brains of Diabetes patients? Does the hemodynamic change reflect physiological change or cognitive one?
Very interesting and relevant work. About the wheelchair control, beyond congitive load, do you think that cognitive decline and fatigue could be also a relevant issue for brain-controlled wheelchairs?
thanks, good points, yes, we should account for those for extended use or different subjects groups, And, for brain computer interface, I guess mental discipline is critical for effective use.
Excellent talk! Were different head motion corrections used for the typically developing children compared to the ADHD children?
Thanks. We used the ICA algorithm for both the ADHD children and the healthy controls.
Thank you for the great talk. I was wondering how you estimated synchronization of brain activity between persons, and how the different delayed onset of infant data was accommodated in the estimates.
Thank you for the interesting talk. Was there any challenge when applying the classification method to fNIRS data?
Thank you for your presentation. Is it possible to implement the maximum entropy method for real-time processing?