Poster No:
1352
Submission Type:
Late-Breaking Abstract Submission
Authors:
Rebekah Brueckner1, Jale Oezyurt1, Christoph Herrmann1, Christiane Thiel1, Florian Kasten2
Institutions:
1University of Oldenburg, Oldenburg, Lower Saxony, 2University of Trier, Trier, Rhineland-Palatinate
First Author:
Co-Author(s):
Jale Oezyurt
University of Oldenburg
Oldenburg, Lower Saxony
Late Breaking Reviewer(s):
Introduction:
Frontal Midline Theta (FMΘ) activity originating from the anterior cingulate cortex (ACC) has been shown to increase during more cognitively challenging executive functioning tasks in in younger individuals and is linked to performance improvement (Ishii et al., 2014). Older participants, however, have been shown to have a reduction in FMΘ activity compared to their younger counterparts (Kardos et a., 2014, Cummins et al. 2007). This reduction, in turn, has been linked to decreased performance in more cognitively challenging tasks, and when upregulated, has been shown to enhance executive functioning skills that transferred to other tasks (Anguera et al., 2013).
Transcranial Alternating Current Stimulation (tACS) can be used to increase frequency-specific activity through neural entrainment (Herrmann et al., 2013). Previous tACS E-field simulations targeting FMΘ activity have used a standardized brain or have only been applied to younger healthy adults (Ishii et al., 2014). However, one-size-fits-all models tend to sub optimally stimulate due to high inter-individual variability (Kasten et al., 2019), and this effect may be more pronounced in older subjects given structural and functional brain changes associated with aging.
This project proposes an analysis pipeline for targeting FMΘ activity in older healthy adults using structural MRI and task-related MEG data. Source localization of functional FMΘ was combined with E-field modeling via SimNIBS to obtain custom electrode locations and stimulation amplitudes to target FMΘ activity.
Methods:
Structural T1 and T2 MRI (3T) data and visual Go/NoGo MEG (306 channel Neuromag TRIUX) data was recorded from 13 healthy older (50+) volunteers. Individual participant data was bandpass filtered, downsampled, ICA cleaned, epoched into correct Go (button pressed) and correct NoGo (button not pressed) conditions, and the same number of trials were selected randomly from the Go condition to match the corresponding NoGo trials.
Based on inspection of time-frequency representations (TFRs) of the data, we identified a time of interest (TOI) of 250-500msec post-stimulus onset and a frequency range of 3-6 Hz for FMΘ. An MEG time series of each trial was projected into source space using an LCMV beamformer. TFRs were computed from these time series and grand averaged. A dependent sample cluster permutation t-test was used to compare activity in the TOI and FMΘ between the Go and NoGo conditions.
We identified a significant cluster (p < 0.05) as the source of FMΘ activity for an older healthy cohort. This was then used as a region of interest for targeting using SimNIBS, 32-electrode montage matching the StarStim32 high-definition tACS system.
This group-level functional t-map was combined with an MNI152 structural brain to determine if stimulation could be optimized for larger source activity as compared to direct targeting of the ACC.
Results:
On a group level, we were able to identify significantly upregulated FMΘ activity near the ACC. Due to the clusters slight lateralization, optimization varied significantly as compared to direct ACC targeting with the MNI152 brain.
Individual peak FMΘ activity was highly variable in location and amplitude in older adults, so on an individual level, localization was not possible in our sample. However, there was a trend towards slightly higher activation in attended NoGo trials compared to attended Go trials on an individual level.
Conclusions:
Optimization of montages for tACS in older participants is especially important due to the inconsistency between individual FMΘ activity during cognitively challenging tasks and the currently sub-optimal targeting of the ACC. Targeting functionally relevant FMΘ activity at group level, rather than relying solely on anatomical landmarks, may enhance theta upregulation in older adults and improve executive functioning.
Brain Stimulation:
Non-invasive Electrical/tDCS/tACS/tRNS
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Lifespan Development:
Aging
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Keywords:
Aging
Data analysis
MEG
Modeling
MRI
Other - Transcranial Alternating Current Stimulation (tACS)
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
MEG
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
Free Surfer
Other, Please list
-
Fieldtrip
Provide references using APA citation style.
Anguera, J. A., Boccanfuso, J., Rintoul, J. L., Al-Hashimi, O., Faraji, F., Janowich, J., Kong, E., Larraburo, Y., Rolle, C., Johnston, E., & Gazzaley, A. (2013). Video game training enhances cognitive control in older adults. Nature, 501(7465), 97–101. https://doi.org/10.1038/nature12486
Cummins, T. D. R., & Finnigan, S. (2007). Theta power is reduced in healthy cognitive aging. International Journal of Psychophysiology, 66(1), 10–17. https://doi.org/10.1016/j.ijpsycho.2007.05.008
Herrmann, C. S., Rach, S., Neuling, T., & Strüber, D. (2013). Transcranial alternating current stimulation: A review of the underlying mechanisms and modulation of cognitive processes. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00279
Ishii, R., Canuet, L., Ishihara, T., Aoki, Y., Ikeda, S., Hata, M., Katsimichas, T., Gunji, A., Takahashi, H., Nakahachi, T., Iwase, M., & Takeda, M. (2014). Frontal midline theta rhythm and gamma power changes during focused attention on mental calculation: An MEG beamformer analysis. Frontiers in Human Neuroscience, 8. https://doi.org/10.3389/fnhum.2014.00406
Kardos, Z., Tóth, B., Boha, R., File, B., & Molnár, M. (2014). Age-related changes of frontal-midline theta is predictive of efficient memory maintenance. Neuroscience, 273, 152–162. https://doi.org/10.1016/j.neuroscience.2014.04.071
Kasten, F.H., Duecker, K., Maack, M.C. et al. Integrating electric field modeling and neuroimaging to explain inter-individual variability of tACS effects. Nat Commun 10, 5427 (2019). https://doi.org/10.1038/s41467-019-13417-6
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