Welcome to the forum - please feel free to add any comments or questions or suggestions here. This is intended as a forum for users to share experiences and advice, so if you are able to answer any of the questions on the forum please do so!
Jonas Larsson, 2023/02/21 12:35, 2023/02/21 12:36
I am interested in procuring a high-resolution transcranial brain stimulation (tDCS & tACS) system which is compatible with the MRI environment, and looking to gauge interest among CUBIC members for such a device. Systems with 32 channels and integrated EEG that can also be used as a wearable device, are available for <£50k. Please contact me jonas.larsson@rhul.ac.uk if you are interested or want to know more about what such a system can do and whether it might be useful for your research.
Joe Bathelt, 2023/05/03 09:32
To follow-up on our discussion at the CUBIC meeting in May 2023, I wanted to share some resources related to precision functional mapping:
Definition: “precision mapping refers to a data acquisition approach aimed at overcoming the low SNR of fMRI, in which the same individual is repeatedly sampled and the data are then averaged within an individual instead of across different individuals” (Fair 2018 Neuron)
Rationale
Elliott et al. 2021 TiCS: Acquiring more data per participant can help to uncover stable individual differences
Fair & Yeo 2020 Neuron: Dense sampling of individuals can uncover brain plasticity in vivo. This bridges the gap between observation and experimentation in humans.
Poldrack 2015 Neuron: “An essential question for neuroimaging researchers is whether our approach of averaging small amounts of data from larger groups of individuals may have led us to mischaracterize some aspects of the functional organization of the brain.”
Applications
Gordon et al. 2017 Neuron: Precision mapping of 10 individual reveals individual-specific functional organisation that is absent from the group-averaged networks
Poldrack et al. 2015 Nature Communications: Dense sampling of one individual indicates that much longer acquisitions are needed to identify stable individual features of functional connectivity
Kraus et al. 2021 NeuroImage: precision-mapping shows that individual network topographies are stable during task and rest acquisitions
Methods
Monitoring movement to improve acquisitions:
FIRMM is a software for real-time monitoring of motion during fMRI acquisition that helps to collect sufficient data from all participants (Dosenbach et al. 2017 NeuroImage)
Badke D'Andrea et al. 2022 Developmental Cognitive Neuroscience used this in infants
Removing respiration artefacts (Gratton et al. 2020 NeuroImage, Fair et al. 2020 NeuroImage)
mostly affects very young, very old, or less fit participants
in typical participants, respiration does not affect rsfMRI connectivity estimates
References:
Kraus, B. T., Perez, D., Ladwig, Z., Seitzman, B. A., Dworetsky, A., Petersen, S. E., & Gratton, C. (2021). Network variants are similar between task and rest states. NeuroImage, 229(117743), 117743. https://doi.org/10.1016/j.neuroimage.2021.117743
Badke D’Andrea, C., Kenley, J. K., Montez, D. F., Mirro, A. E., Miller, R. L., Earl, E. A., Koller, J. M., Sung, S., Yacoub, E., Elison, J. T., Fair, D. A., Dosenbach, N. U. F., Rogers, C. E., Smyser, C. D., & Greene, D. J. (2022). Real-time motion monitoring improves functional MRI data quality in infants. Developmental Cognitive Neuroscience, 55(101116), 101116. https://doi.org/10.1016/j.dcn.2022.101116
Fair, D. A., & Yeo, B. T. T. (2020). Precision Neuroimaging Opens a New Chapter of Neuroplasticity Experimentation [Review of Precision Neuroimaging Opens a New Chapter of Neuroplasticity Experimentation]. Neuron, 107(3), 401–403. https://doi.org/10.1016/j.neuron.2020.07.017
Dosenbach, N. U. F., Koller, J. M., Earl, E. A., Miranda-Dominguez, O., Klein, R. L., Van, A. N., Snyder, A. Z., Nagel, B. J., Nigg, J. T., Nguyen, A. L., Wesevich, V., Greene, D. J., & Fair, D. A. (2017). Real-time motion analytics during brain MRI improve data quality and reduce costs. NeuroImage, 161, 80–93. https://doi.org/10.1016/j.neuroimage.2017.08.025
Poldrack, R. A., Laumann, T. O., Koyejo, O., Gregory, B., Hover, A., Chen, M.-Y., Gorgolewski, K. J., Luci, J., Joo, S. J., Boyd, R. L., Hunicke-Smith, S., Simpson, Z. B., Caven, T., Sochat, V., Shine, J. M., Gordon, E., Snyder, A. Z., Adeyemo, B., Petersen, S. E., … Mumford, J. A. (2015). Long-term neural and physiological phenotyping of a single human. Nature Communications, 6, 8885. https://doi.org/10.1038/ncomms9885
Elliott, M. L., Knodt, A. R., & Hariri, A. R. (2021). Striving toward translation: strategies for reliable fMRI measurement. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2021.05.008 PMID - 34134933
Jonas Larsson, 2023/05/03 14:09, 2023/05/03 14:09
Here is a link to the cumulative head motion estimates for long scan durations I showed at the last users' meeting. x-axis is total cumulative scan time (excluding breaks) in units of TR (here, 2.5s), y-axis represents translations along the cardinal axes (X, Y, Z) in mm. The total session length for this data was 2h. Head movement was estimated using mcflirt. Take-home message is that head motion is qualitatively worse towards the end of long scanning sessions (presumably due to subject fatigue), implying that data quality is likely to be better for 2 1h scanning sessions than 1 2h session.
user-forum.txt · Last modified: 2023/02/20 16:46 by jonas
CUBIC users discussion forum
Welcome to the forum - please feel free to add any comments or questions or suggestions here. This is intended as a forum for users to share experiences and advice, so if you are able to answer any of the questions on the forum please do so!
I am interested in procuring a high-resolution transcranial brain stimulation (tDCS & tACS) system which is compatible with the MRI environment, and looking to gauge interest among CUBIC members for such a device. Systems with 32 channels and integrated EEG that can also be used as a wearable device, are available for <£50k. Please contact me jonas.larsson@rhul.ac.uk if you are interested or want to know more about what such a system can do and whether it might be useful for your research.
To follow-up on our discussion at the CUBIC meeting in May 2023, I wanted to share some resources related to precision functional mapping:
Definition: “precision mapping refers to a data acquisition approach aimed at overcoming the low SNR of fMRI, in which the same individual is repeatedly sampled and the data are then averaged within an individual instead of across different individuals” (Fair 2018 Neuron)
Rationale
Applications
Methods Monitoring movement to improve acquisitions:
Removing respiration artefacts (Gratton et al. 2020 NeuroImage, Fair et al. 2020 NeuroImage)
References:
Kraus, B. T., Perez, D., Ladwig, Z., Seitzman, B. A., Dworetsky, A., Petersen, S. E., & Gratton, C. (2021). Network variants are similar between task and rest states. NeuroImage, 229(117743), 117743. https://doi.org/10.1016/j.neuroimage.2021.117743
Badke D’Andrea, C., Kenley, J. K., Montez, D. F., Mirro, A. E., Miller, R. L., Earl, E. A., Koller, J. M., Sung, S., Yacoub, E., Elison, J. T., Fair, D. A., Dosenbach, N. U. F., Rogers, C. E., Smyser, C. D., & Greene, D. J. (2022). Real-time motion monitoring improves functional MRI data quality in infants. Developmental Cognitive Neuroscience, 55(101116), 101116. https://doi.org/10.1016/j.dcn.2022.101116
Fair, D. A., & Yeo, B. T. T. (2020). Precision Neuroimaging Opens a New Chapter of Neuroplasticity Experimentation [Review of Precision Neuroimaging Opens a New Chapter of Neuroplasticity Experimentation]. Neuron, 107(3), 401–403. https://doi.org/10.1016/j.neuron.2020.07.017
Dosenbach, N. U. F., Koller, J. M., Earl, E. A., Miranda-Dominguez, O., Klein, R. L., Van, A. N., Snyder, A. Z., Nagel, B. J., Nigg, J. T., Nguyen, A. L., Wesevich, V., Greene, D. J., & Fair, D. A. (2017). Real-time motion analytics during brain MRI improve data quality and reduce costs. NeuroImage, 161, 80–93. https://doi.org/10.1016/j.neuroimage.2017.08.025
Poldrack, R. A., Laumann, T. O., Koyejo, O., Gregory, B., Hover, A., Chen, M.-Y., Gorgolewski, K. J., Luci, J., Joo, S. J., Boyd, R. L., Hunicke-Smith, S., Simpson, Z. B., Caven, T., Sochat, V., Shine, J. M., Gordon, E., Snyder, A. Z., Adeyemo, B., Petersen, S. E., … Mumford, J. A. (2015). Long-term neural and physiological phenotyping of a single human. Nature Communications, 6, 8885. https://doi.org/10.1038/ncomms9885
Elliott, M. L., Knodt, A. R., & Hariri, A. R. (2021). Striving toward translation: strategies for reliable fMRI measurement. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2021.05.008 PMID - 34134933
Here is a link to the cumulative head motion estimates for long scan durations I showed at the last users' meeting. x-axis is total cumulative scan time (excluding breaks) in units of TR (here, 2.5s), y-axis represents translations along the cardinal axes (X, Y, Z) in mm. The total session length for this data was 2h. Head movement was estimated using mcflirt. Take-home message is that head motion is qualitatively worse towards the end of long scanning sessions (presumably due to subject fatigue), implying that data quality is likely to be better for 2 1h scanning sessions than 1 2h session.